From 0841900f9db21509aa469f6f8504f7892b4c6726 Mon Sep 17 00:00:00 2001 From: Bennet Meyers Date: Tue, 26 Aug 2025 10:36:07 -0700 Subject: [PATCH 1/2] update to dc-opf notebook --- dc-opf/network_flow_example_v4.py | 287 ++++++++++++++++++------------ 1 file changed, 178 insertions(+), 109 deletions(-) diff --git a/dc-opf/network_flow_example_v4.py b/dc-opf/network_flow_example_v4.py index e0fa765a..3cfe8fc7 100644 --- a/dc-opf/network_flow_example_v4.py +++ b/dc-opf/network_flow_example_v4.py @@ -1,9 +1,129 @@ import marimo -__generated_with = "0.14.12" +__generated_with = "0.14.17" app = marimo.App(width="medium") +@app.cell +def _(cp, np): + def make_problem(incidence_mat, generator_dict, load_dict, flow_dict): + def indices_not_in_list(array_length, given_indices): + all_indices = set(range(array_length)) + given_indices_set = set(given_indices) + not_in_list_indices = all_indices - given_indices_set + return list(not_in_list_indices) + _B = incidence_mat + num_buses = _B.shape[0] + num_edges = _B.shape[1] + gen_ixs = np.array(list(generator_dict.keys())) - 1 + nogen_ixs = indices_not_in_list(num_buses, gen_ixs) + p_flows = cp.Variable((num_edges), name='line flows') + p_gen = cp.Variable((num_buses), nonneg=True, name='p_gen') + p_load = cp.Variable((num_buses), nonneg=True, name='p_load') + l_min = cp.Parameter( + shape=num_buses, + value=[_v['l_min'] for _k, _v in load_dict.items()], + name='l_min' + ) + l_upper = cp.Parameter( + shape=num_buses, + value=[_v['l_upper'] for _k, _v in load_dict.items()], + name='l_upper' + ) + # l_cost = cp.Parameter( + # shape=num_buses, + # value=[_v['cost'] for _k, _v in load_dict.items()], + # name='l_cost' + # ) + l_cost = [_v['cost'] for _k, _v in load_dict.items()] + g_min = cp.Parameter( + shape=len(gen_ixs), + value=np.array([_v['p_min'] for _k, _v in generator_dict.items()]), + name='g_min' + ) + g_max = cp.Parameter( + shape=len(gen_ixs), + value=np.array([_v['p_max'] for _k, _v in generator_dict.items()]), + name='g_max' + ) + c0 = cp.Parameter( + shape=len(gen_ixs), + value=np.array([_v['c0'] for _k, _v in generator_dict.items()]), + name='c0' + ) + c1 = cp.Parameter( + shape=len(gen_ixs), + value=np.array([_v['c1'] for _k, _v in generator_dict.items()]), + name='c1' + ) + # c2 = cp.Parameter( + # shape=len(gen_ixs), + # value=np.array([_v['c2'] for _k, _v in generator_dict.items()]), + # name='c2' + # ) + c2 = np.array([_v['c2'] for _k, _v in generator_dict.items()]) + r = cp.Parameter( + shape=num_edges, + value=np.array([_v['f_resistance'] for _k, _v in flow_dict.items()]), + name='line resistance' + ) + f_max = cp.Parameter( + shape=num_edges, + value=np.array([_v['f_max'] for _k, _v in flow_dict.items()]), + name='line capacities' + ) + # generator costs + cost = cp.sum(c0 + cp.multiply(c1, p_gen[gen_ixs]) + cp.multiply(c2, cp.square(p_gen[gen_ixs]))) + # load curtailment costs + cost += cp.sum( + cp.multiply( + cp.neg(p_load - l_upper), + l_cost + ) + ) + # line penalties + cost += cp.sum_squares(cp.multiply(r, p_flows)) + constraints = [ + _B @ p_flows + p_gen - p_load == 0, + p_load >= l_min, + cp.abs(p_flows) <= f_max, + p_gen[nogen_ixs] == 0, + p_gen[gen_ixs] <= g_max, + p_gen[gen_ixs] >= g_min + ] + problem = cp.Problem(cp.Minimize(cost), constraints) + return problem + return (make_problem,) + + +@app.cell +def _(mo): + mo.md( + """ + # DC Optimal Power Flow + + This notebook demonstrates the optimal flow problem, over 6 test networks provided by [pypower](https://github.com/rwl/PYPOWER). + + - network with $n$ nodes, $m$ edges, given by $n \\times m$ incidence + matrix $A$ + - edge power vector $p \\in \\mathbf{R}^m$, + with capacity limits $|p_j| \\leq C_j$ + - node generator power $g_i$, load $l_i$, + each with lower and upper limits + - flow conservation $Af + g = l$ + - generator cost function is + $\\phi_i(g_i) = a_i g_i + b_i g_i^2$, total $G= \\sum_i \\phi_i (g_i)$ + - load shortfall cost is $\\psi_i(l_i) = c_i(l^\\text{tar}_i-l_i)_+$, + total is $S = \\sum_i \\psi_i(l_i)$ + - total line loss is $L = \\sum_j r_j p_j^2$ + - objective is $G + \\lambda L + \\mu S$ + + The model automatically populates parameters for the generators, loads, and lines, with the sliders set to the default values for the test case. Users may try adjusting these values with the sliders to see how it changes the optimal flow. + """ + ) + return + + @app.cell(hide_code=True) def _(mo, os, re): # model loading widget @@ -69,7 +189,7 @@ def _(lines, np, num_buses, sns): for _ix, _l in enumerate(lines): B[_l[0]-1, _ix] = -1 B[_l[1]-1, _ix] = 1 - fig_heatmap = sns.heatmap(B, cmap='seismic', center=0) + fig_heatmap = sns.heatmap(B, cmap='bwr', center=0) return B, fig_heatmap @@ -116,7 +236,7 @@ def _(B, flow_dict, generator_dict, load_dict, make_problem): @app.cell(hide_code=True) -def _(flow_limits, gen_limits, load_limits, mo, problem): +def _(flow_limits, gen_limits, load_limits, mo, np, problem): # solve with updated parameters am_solving = True problem.param_dict['g_min'].value = [_v[0] for _v in gen_limits.value] @@ -125,8 +245,9 @@ def _(flow_limits, gen_limits, load_limits, mo, problem): problem.param_dict['l_upper'].value = [_v[1] for _v in load_limits.value] problem.param_dict['line capacities'].value = flow_limits.value obj_val = problem.solve(verbose=False, solver='CLARABEL') + constrained_lines = np.where(~np.isclose(problem.constraints[2].dual_value, 0, atol=1e-2))[0] mo.md(f'objective value: {obj_val:.2f}') - return (am_solving,) + return am_solving, constrained_lines @app.cell @@ -147,7 +268,8 @@ def _(generator_dict, mo, np): stop=_max, label=f'gen {_ix}', value=[_gen['p_min'], _gen['p_max']], - debounce=True) + debounce=True, + show_value=True) for _ix, _gen in generator_dict.items()], label='generator limits') return (gen_limits,) @@ -160,7 +282,8 @@ def _(load_dict, mo, np): stop=_max, label=f'load {_ix}', value=[_load['l_min'], _load['l_upper']], - debounce=True) + debounce=True, + show_value=True,) for _ix, _load in load_dict.items()], label='load limits') return (load_limits,) @@ -173,7 +296,8 @@ def _(flow_dict, mo, np): stop=_max, label=f'line {_ix+1}', value=_line['f_max'], - debounce=True) + debounce=True, + show_value=True,) for _ix, _line in flow_dict.items()], label='flow limits') return (flow_limits,) @@ -218,7 +342,7 @@ def _( @app.cell(hide_code=True) -def _(model_data, model_ui): +def _(model_data, model_ui, np): # construct dictionaries for problem formulation def merge_nested_dicts(dict1, dict2): result = {} @@ -233,12 +357,22 @@ def merge_nested_dicts(dict1, dict2): result[key] = merged_inner_dict return result - generator_dict1 = {int(_g[0]): {'p_min': _g[9], 'p_max': _g[8]} for _g in model_data['gen']} + # minimum generation value occaisonally negative + generator_dict1 = {int(_g[0]): {'p_min': np.clip(_g[9], 0, np.inf), 'p_max': _g[8]*2} for _g in model_data['gen']} generator_dict2 = {int(model_data['gen'][_ix, 0]): {'c0': _g[-1], 'c1': _g[-2], 'c2': _g[-3]} for _ix, _g in enumerate(model_data['gencost'])} generator_dict = merge_nested_dicts(generator_dict1, generator_dict2) - load_dict = {int(_l[0]): {'l_min': _l[2]*0.5, 'l_upper': _l[2], 'cost': 250} for _l in model_data['bus']} + # power demand is occaisonally negative + # load_dict = {int(_l[0]): {'l_min': 0, 'l_upper': np.abs(_l[2]), 'cost': 250} for _l in model_data['bus']} + load_dict = {int(_l[0]): {'l_min': np.abs(_l[2]*0.5), 'l_upper': np.abs(_l[2]), 'cost': 1e4} for _l in model_data['bus']} + _fmax = 10 * np.max([np.max(_b[5:7]) for _b in model_data['branch']]) if model_ui.value != 'case14.py': - flow_dict = {int(_ix): {'f_max': _b[5], 'f_resistance': _b[2]} for _ix, _b in enumerate(model_data['branch'])} + flow_dict = {} + for _ix, _b in enumerate(model_data['branch']): + _fx = np.max(_b[5:7]) + if _fx > 0: + flow_dict[_ix] = {'f_max': _fx, 'f_resistance': _b[2]} + else: + flow_dict[_ix] = {'f_max': _fmax, 'f_resistance': _b[2]} else: flow_dict = {int(_ix): {'f_max': 175, 'f_resistance': _b[2]} for _ix, _b in enumerate(model_data['branch'])} line_resistance = model_data['branch'][:, 2] @@ -246,99 +380,15 @@ def merge_nested_dicts(dict1, dict2): @app.cell(hide_code=True) -def _(cp, np): - def make_problem(incidence_mat, generator_dict, load_dict, flow_dict): - def indices_not_in_list(array_length, given_indices): - all_indices = set(range(array_length)) - given_indices_set = set(given_indices) - not_in_list_indices = all_indices - given_indices_set - return list(not_in_list_indices) - _B = incidence_mat - num_buses = _B.shape[0] - num_edges = _B.shape[1] - gen_ixs = np.array(list(generator_dict.keys())) - 1 - nogen_ixs = indices_not_in_list(num_buses, gen_ixs) - p_flows = cp.Variable((num_edges), name='line flows') - p_gen = cp.Variable((num_buses), nonneg=True, name='p_gen') - p_load = cp.Variable((num_buses), nonneg=True, name='p_load') - l_min = cp.Parameter( - shape=num_buses, - value=[_v['l_min'] for _k, _v in load_dict.items()], - name='l_min' - ) - l_upper = cp.Parameter( - shape=num_buses, - value=[_v['l_upper'] for _k, _v in load_dict.items()], - name='l_upper' - ) - # l_cost = cp.Parameter( - # shape=num_buses, - # value=[_v['cost'] for _k, _v in load_dict.items()], - # name='l_cost' - # ) - l_cost = [_v['cost'] for _k, _v in load_dict.items()] - g_min = cp.Parameter( - shape=len(gen_ixs), - value=np.array([_v['p_min'] for _k, _v in generator_dict.items()]), - name='g_min' - ) - g_max = cp.Parameter( - shape=len(gen_ixs), - value=np.array([_v['p_max'] for _k, _v in generator_dict.items()]), - name='g_max' - ) - c0 = cp.Parameter( - shape=len(gen_ixs), - value=np.array([_v['c0'] for _k, _v in generator_dict.items()]), - name='c0' - ) - c1 = cp.Parameter( - shape=len(gen_ixs), - value=np.array([_v['c1'] for _k, _v in generator_dict.items()]), - name='c1' - ) - # c2 = cp.Parameter( - # shape=len(gen_ixs), - # value=np.array([_v['c2'] for _k, _v in generator_dict.items()]), - # name='c2' - # ) - c2 = np.array([_v['c2'] for _k, _v in generator_dict.items()]) - r = cp.Parameter( - shape=num_edges, - value=np.array([_v['f_resistance'] for _k, _v in flow_dict.items()]), - name='line resistance' - ) - f_max = cp.Parameter( - shape=num_edges, - value=np.array([_v['f_max'] for _k, _v in flow_dict.items()]), - name='line capacities' - ) - # generator costs - cost = cp.sum(c0 + c1 @ p_gen[gen_ixs] + c2 @ cp.square(p_gen[gen_ixs])) - # load curtailment costs - cost += cp.sum( - cp.multiply( - cp.neg(p_load - l_upper), - l_cost - ) - ) - # line penalties - cost += cp.sum_squares(cp.multiply(r, p_flows)) - constraints = [ - _B @ p_flows + p_gen - p_load == 0, - p_load >= l_min, - cp.abs(p_flows) <= f_max, - p_gen[nogen_ixs] == 0, - p_gen[gen_ixs] <= g_max, - p_gen[gen_ixs] >= g_min - ] - problem = cp.Problem(cp.Minimize(cost), constraints) - return problem - return (make_problem,) - - -@app.cell(hide_code=True) -def _(am_solving, both, gen_only, lines, load_only, problem): +def _( + am_solving, + both, + constrained_lines, + gen_only, + lines, + load_only, + problem, +): # power flow graph view 1 am_solving solution2 = ["flowchart LR"] @@ -365,11 +415,28 @@ def _(am_solving, both, gen_only, lines, load_only, problem): if len(both) > 0: _str = ",".join([str(int(_i)) for _i in both]) solution2 += "\n class "+_str+" genload;" + if len(constrained_lines) > 0: + _str = ",".join([str(int(_i)) for _i in constrained_lines]) + solution2 += "\n linkStyle "+_str+" color:red;" return (solution2,) +@app.cell +def _(solution): + solution + return + + @app.cell(hide_code=True) -def _(am_solving, both, gen_only, lines, load_only, problem): +def _( + am_solving, + both, + constrained_lines, + gen_only, + lines, + load_only, + problem, +): # power flow graph view 2 am_solving solution = ["flowchart LR"] @@ -383,9 +450,9 @@ def _(am_solving, both, gen_only, lines, load_only, problem): # else: # solution.append(f" {_t:.0f}(({_t})) == {-_lf:.2f} ==> {_f:.0f}(({_f}))") solution = "\n".join(solution) - solution += '''\n - classDef gen stroke-dasharray: 5 5;\n - classDef load fill:#f9f;\n + solution += ''' + classDef gen stroke-dasharray: 5 5; + classDef load fill:#f9f; classDef genload stroke-dasharray: 5 5,fill:#f9f;''' if len(load_only) > 0: _str = ",".join([str(int(_i)) for _i in load_only]) @@ -396,6 +463,9 @@ def _(am_solving, both, gen_only, lines, load_only, problem): if len(both) > 0: _str = ",".join([str(int(_i)) for _i in both]) solution += "\n class "+_str+" genload;" + if len(constrained_lines) > 0: + _str = ",".join([str(int(_i)) for _i in constrained_lines]) + solution += "\n linkStyle "+_str+" color:red;" return (solution,) @@ -407,7 +477,6 @@ def _(): import cvxpy as cp import matplotlib.pyplot as plt import seaborn as sns - from model import Model return cp, mo, np, os, plt, re, sns From 24a36287b9b157c324e0e7421f2fefcf8e13a6e3 Mon Sep 17 00:00:00 2001 From: Bennet Meyers Date: Tue, 2 Sep 2025 09:16:13 -0700 Subject: [PATCH 2/2] opf work --- dc-opf/SD_relax_test.py | 552 ++++++++++ dc-opf/SD_relax_test_real.py | 585 +++++++++++ dc-opf/SD_relax_test_v2.py | 559 +++++++++++ dc-opf/SOC_relax_test.py | 635 ++++++++++++ dc-opf/case240.py | 939 ++++++++++++++++++ .../session/real_battery_objs.py.json | 338 +++++++ taylor/__marimo__/session/chapter_3.py.json | 549 ++++++++++ 7 files changed, 4157 insertions(+) create mode 100644 dc-opf/SD_relax_test.py create mode 100644 dc-opf/SD_relax_test_real.py create mode 100644 dc-opf/SD_relax_test_v2.py create mode 100644 dc-opf/SOC_relax_test.py create mode 100644 dc-opf/case240.py create mode 100644 mpc_dev/__marimo__/session/real_battery_objs.py.json create mode 100644 taylor/__marimo__/session/chapter_3.py.json diff --git a/dc-opf/SD_relax_test.py b/dc-opf/SD_relax_test.py new file mode 100644 index 00000000..8b4b8f0c --- /dev/null +++ b/dc-opf/SD_relax_test.py @@ -0,0 +1,552 @@ +import marimo + +__generated_with = "0.15.0" +app = marimo.App(width="medium") + + +@app.cell +def _(cp, np): + def make_problem(incidence_mat, generator_dict, load_dict, flow_dict): + def indices_not_in_list(array_length, given_indices): + all_indices = set(range(array_length)) + given_indices_set = set(given_indices) + not_in_list_indices = all_indices - given_indices_set + return list(not_in_list_indices) + _B = incidence_mat + _A = np.abs(_B) @ np.abs(_B.T) - np.diag(np.diag(np.abs(_B) @ np.abs(_B.T))) + _yij = 1/(np.array([_v['r'] for _k, _v in flow_dict.items()]) + 1j * np.array([_v['x'] for _k, _v in flow_dict.items()])) + num_buses = _B.shape[0] + num_edges = _B.shape[1] + _Y = np.zeros((num_buses, num_buses), dtype=complex) + for _idx, _edge in enumerate(_B.T): + _i, _j = np.where(_edge==-1)[0], np.where(_edge==1)[0] + _Y[_i, _j] = _yij[_idx] + _Y[_j, _i] = _yij[_idx] + _YH = _Y.conjugate().T + gen_ixs = np.array(list(generator_dict.keys())) - 1 + nogen_ixs = indices_not_in_list(num_buses, gen_ixs) + # p_flows = cp.Variable((num_edges), name='line flows') + p_gen = cp.Variable((num_buses), nonneg=True, name='p_gen') + p_load = cp.Variable((num_buses), nonneg=True, name='p_load') + q_bus = cp.Variable((num_buses), name='q_bus') + V = cp.Variable((num_buses, num_buses), complex=True, name='V', PSD=True) + P = cp.Variable((num_buses, num_buses), name='P') + Q = cp.Variable((num_buses, num_buses), name='P') + l_min = cp.Parameter( + shape=num_buses, + value=[_v['l_min'] for _k, _v in load_dict.items()], + name='l_min' + ) + l_upper = cp.Parameter( + shape=num_buses, + value=[_v['l_upper'] for _k, _v in load_dict.items()], + name='l_upper' + ) + # l_cost = cp.Parameter( + # shape=num_buses, + # value=[_v['cost'] for _k, _v in load_dict.items()], + # name='l_cost' + # ) + l_cost = [_v['cost'] for _k, _v in load_dict.items()] + g_min = cp.Parameter( + shape=len(gen_ixs), + value=np.array([_v['p_min'] for _k, _v in generator_dict.items()]), + name='g_min' + ) + g_max = cp.Parameter( + shape=len(gen_ixs), + value=np.array([_v['p_max'] for _k, _v in generator_dict.items()]), + name='g_max' + ) + c0 = cp.Parameter( + shape=len(gen_ixs), + value=np.array([_v['c0'] for _k, _v in generator_dict.items()]), + name='c0' + ) + c1 = cp.Parameter( + shape=len(gen_ixs), + value=np.array([_v['c1'] for _k, _v in generator_dict.items()]), + name='c1' + ) + # c2 = cp.Parameter( + # shape=len(gen_ixs), + # value=np.array([_v['c2'] for _k, _v in generator_dict.items()]), + # name='c2' + # ) + c2 = np.array([_v['c2'] for _k, _v in generator_dict.items()]) + r = cp.Parameter( + shape=num_edges, + value=np.array([_v['r'] for _k, _v in flow_dict.items()]), + name='line resistance' + ) + f_max = cp.Parameter( + shape=num_edges, + value=np.array([_v['f_max'] for _k, _v in flow_dict.items()]), + name='line capacities' + ) + # generator costs + cost = cp.sum(c0 + cp.multiply(c1, p_gen[gen_ixs]) + cp.multiply(c2, cp.square(p_gen[gen_ixs]))) + # load curtailment costs + cost += cp.sum( + cp.multiply( + cp.neg(p_load - l_upper), + l_cost + ) + ) + # line penalties + cost += cp.sum_squares(cp.multiply(r, P)) + constraints = [ + (p_gen - p_load) + 1j * q_bus == cp.diag(V @ _YH), + cp.sum(P, axis=1) == p_gen - p_load, + cp.sum(Q, axis=1) == q_bus, + cp.multiply(P, 1 - _A) == 0, + cp.multiply(Q, 1 - _A) == 0, + p_load >= l_min, + cp.abs(P) <= f_max, + p_gen[nogen_ixs] == 0, + p_gen[gen_ixs] <= g_max, + p_gen[gen_ixs] >= g_min, + cp.real(cp.diag(V)) >= 0.9, + cp.real(cp.diag(V)) <= 1.1, + cp.imag(cp.diag(V)) == 0, + cp.real(V) == cp.real(V.T), + cp.imag(V) == -cp.imag(V.T) + ] + problem = cp.Problem(cp.Minimize(cost), constraints) + return problem + return (make_problem,) + + +@app.cell +def _(mo): + mo.md( + """ + # DC Optimal Power Flow + + This notebook demonstrates the optimal flow problem, over 6 test networks provided by [pypower](https://github.com/rwl/PYPOWER). + + - network with $n$ nodes, $m$ edges, given by $n \\times m$ incidence + matrix $A$ + - edge power vector $p \\in \\mathbf{R}^m$, + with capacity limits $|p_j| \\leq C_j$ + - node generator power $g_i$, load $l_i$, + each with lower and upper limits + - flow conservation $Af + g = l$ + - generator cost function is + $\\phi_i(g_i) = a_i g_i + b_i g_i^2$, total $G= \\sum_i \\phi_i (g_i)$ + - load shortfall cost is $\\psi_i(l_i) = c_i(l^\\text{tar}_i-l_i)_+$, + total is $S = \\sum_i \\psi_i(l_i)$ + - total line loss is $L = \\sum_j r_j p_j^2$ + - objective is $G + \\lambda L + \\mu S$ + + The model automatically populates parameters for the generators, loads, and lines, with the sliders set to the default values for the test case. Users may try adjusting these values with the sliders to see how it changes the optimal flow. + """ + ) + return + + +@app.cell(hide_code=True) +def _(mo, os, re): + # model loading widget + _list = sorted( + [x for x in os.listdir(".") if re.match("case.+.py",x)], # get only "case*.py" + key=lambda x: int(re.sub("[^0-9]*([0-9]+)[^0-9]*", r"\1", x, 1)), # sort by numerical order not lexical + ) + _options = {os.path.splitext(x)[0]: x for x in _list} + model_ui = mo.ui.dropdown( + options=_options, value=os.path.splitext(_list[0])[0], label='select model:' + ) + return (model_ui,) + + +@app.cell(hide_code=True) +def _(model_ui, os): + # import model from file + from importlib.machinery import SourceFileLoader + _n = model_ui.value.split('.')[0] + model_loader = SourceFileLoader(_n, os.path.join(".",model_ui.value)).load_module() + load_model = getattr(model_loader, _n) + model_data = load_model() + return (model_data,) + + +@app.cell(hide_code=True) +def _(model_data): + # define network + num_buses = model_data['bus'].shape[0] + lines = [(int(_b[0]), int(_b[1])) for _b in model_data['branch']] + return lines, num_buses + + +@app.cell(hide_code=True) +def _(both, gen_only, lines, load_only): + # build graph + graph = ["flowchart LR"] + for _ix, _l in enumerate(lines): + _f = _l[0] + _t = _l[1] + graph.append(f" {_f:.0f}(({_f})) == {_ix+1} === {_t:.0f}(({_t}))") + graph = "\n".join(graph) + graph += '''\n + classDef gen stroke-dasharray: 5 5;\n + classDef load fill:#f9f;\n + classDef genload stroke-dasharray: 5 5,fill:#f9f;''' + if len(load_only) > 0: + _str = ",".join([str(int(_i)) for _i in load_only]) + graph += "\n class "+_str+" gen;" + if len(gen_only) > 0: + _str = ",".join([str(int(_i)) for _i in gen_only]) + graph += "\n class "+_str+" load;" + if len(both) > 0: + _str = ",".join([str(int(_i)) for _i in both]) + graph += "\n class "+_str+" genload;" + return (graph,) + + +@app.cell(hide_code=True) +def _(lines, np, num_buses, sns): + # define incidence matrix + B = np.zeros((num_buses, len(lines))) + for _ix, _l in enumerate(lines): + B[_l[0]-1, _ix] = -1 + B[_l[1]-1, _ix] = 1 + fig_heatmap = sns.heatmap(B, cmap='bwr', center=0) + return B, fig_heatmap + + +@app.cell +def _(model_data, model_ui, np): + # construct dictionaries for problem formulation + def merge_nested_dicts(dict1, dict2): + result = {} + + for key in dict1.keys() | dict2.keys(): + inner_dict1 = dict1.get(key, {}) + inner_dict2 = dict2.get(key, {}) + + # Merge the inner dictionaries + merged_inner_dict = {**inner_dict1, **inner_dict2} + + result[key] = merged_inner_dict + + return result + # minimum generation value occaisonally negative + generator_dict1 = {int(_g[0]): {'p_min': np.clip(_g[9], 0, np.inf), 'p_max': _g[8]*2} for _g in model_data['gen']} + generator_dict2 = {int(model_data['gen'][_ix, 0]): {'c0': _g[-1], 'c1': _g[-2], 'c2': _g[-3]} for _ix, _g in enumerate(model_data['gencost'])} + generator_dict = merge_nested_dicts(generator_dict1, generator_dict2) + # power demand is occaisonally negative + # load_dict = {int(_l[0]): {'l_min': 0, 'l_upper': np.abs(_l[2]), 'cost': 250} for _l in model_data['bus']} + load_dict = {int(_l[0]): {'l_min': np.abs(_l[2]*0.5), 'l_upper': np.abs(_l[2]), 'cost': 1e4} for _l in model_data['bus']} + _fmax = 20 * np.max([np.max(_b[5:7]) for _b in model_data['branch']]) + + flow_dict = {} + for _ix, _b in enumerate(model_data['branch']): + _fx = np.max(_b[5:7]) + flow_dict[_ix] = {'r': _b[2], 'x': _b[3], 'b': _b[4], 'g': _b[2] / (_b[2]**2 + _b[3]**2)} + if model_ui.value == 'case14.py': + flow_dict[_ix]['f_max'] = 175 + elif _fx > 0: + flow_dict[_ix]['f_max'] = _fx + else: + flow_dict[_ix]['f_max'] = _fmax + line_resistance = model_data['branch'][:, 2] + return flow_dict, generator_dict, load_dict + + +@app.cell(hide_code=True) +def _(model_data, np): + # label nodes + generator_nodes = model_data['gen'][:, 0] + load_nodes = np.where(model_data['bus'][:, 2] != 0)[0] + 1 + set_gen_nodes = set(generator_nodes) + set_load_nodes = set(load_nodes) + gen_only = np.asarray(list(set_gen_nodes.difference(set_load_nodes))) + load_only = np.asarray(list(set_load_nodes.difference(set_gen_nodes))) + both = np.asarray(list(set_gen_nodes.intersection(set_load_nodes))) + return both, gen_only, load_only + + +@app.cell +def _(model_ui): + model_ui + return + + +@app.cell(hide_code=True) +def _(fig_heatmap, generator_dict, graph, load_dict, mo): + mo.ui.tabs({ + 'graph': mo.mermaid(graph), + 'incidence matrix': fig_heatmap, + 'generators': mo.accordion({str(_k): _v for _k, _v in generator_dict.items()}), + 'loads': mo.accordion({str(_k): _v for _k, _v in load_dict.items()}) + }) + return + + +@app.cell +def _(flow_limits, gen_limits, load_limits, mo): + mo.hstack([load_limits, gen_limits, flow_limits]) + return + + +@app.cell +def _(B, flow_dict, generator_dict, load_dict, make_problem): + problem = make_problem(B, generator_dict, load_dict, flow_dict) + return (problem,) + + +@app.cell(hide_code=True) +def _(flow_limits, gen_limits, load_limits, mo, np, problem): + # solve with updated parameters + am_solving = True + problem.param_dict['g_min'].value = [_v[0] for _v in gen_limits.value] + problem.param_dict['g_max'].value = [_v[1] for _v in gen_limits.value] + problem.param_dict['l_min'].value = [_v[0] for _v in load_limits.value] + problem.param_dict['l_upper'].value = [_v[1] for _v in load_limits.value] + problem.param_dict['line capacities'].value = flow_limits.value + obj_val = problem.solve(verbose=True, solver='CLARABEL') + constrained_lines = np.where(~np.isclose(problem.constraints[2].dual_value, 0, atol=1e-2))[0] + mo.md(f'objective value: {obj_val:.2f}') + return am_solving, constrained_lines + + +@app.cell +def _(am_solving, np, problem): + am_solving + evals, evecs = np.linalg.eig(problem.var_dict['V'].value) + return (evals,) + + +@app.cell +def _(am_solving, evals): + am_solving + evals + return + + +@app.cell +def _(am_solving, problem): + am_solving + problem.var_dict['V'].value + return + + +@app.cell +def _(np, problem, sns): + sns.heatmap(np.imag(problem.var_dict['V'].value), cmap='seismic', center=0) + return + + +@app.cell +def _(np, problem): + Vnew = np.imag(problem.var_dict['V'].value) + 1j * np.real(problem.var_dict['V'].value) + np.allclose(Vnew, Vnew.conjugate().T) + return (Vnew,) + + +@app.cell +def _(Vnew, np): + np.isclose(Vnew, Vnew.conjugate().T) + return + + +@app.cell +def _(problem): + problem.var_dict['V'].value - (problem.var_dict['V'].value).conjugate().T + return + + +@app.cell +def _(bus_power_fig, mo, solution, solution2): + mo.ui.tabs({ + 'bus power': bus_power_fig, + 'line flows': mo.mermaid(solution), + 'line flows ordered': mo.mermaid(solution2) + }) + return + + +@app.cell(hide_code=True) +def _(generator_dict, mo, np): + # generator ui + _max = np.ceil(1.5*np.max([_v['p_max'] for _k, _v in generator_dict.items()])) + gen_limits = mo.ui.array([mo.ui.range_slider(start=0, + stop=_max, + label=f'gen {_ix}', + value=[_gen['p_min'], _gen['p_max']], + debounce=True, + show_value=True) + for _ix, _gen in generator_dict.items()], label='generator limits') + return (gen_limits,) + + +@app.cell(hide_code=True) +def _(load_dict, mo, np): + # load ui + _max = np.ceil(1.5*np.max([_v['l_upper'] for _k, _v in load_dict.items()])) + load_limits = mo.ui.array([mo.ui.range_slider(start=0, + stop=_max, + label=f'load {_ix}', + value=[_load['l_min'], _load['l_upper']], + debounce=True, + show_value=True,) + for _ix, _load in load_dict.items()], label='load limits') + return (load_limits,) + + +@app.cell(hide_code=True) +def _(flow_dict, mo, np): + # flow ui + _max = np.ceil(1.5*np.max([_v['f_max'] for _k, _v in flow_dict.items()])) + flow_limits = mo.ui.array([mo.ui.slider(start=0, + stop=_max, + label=f'line {_ix+1}', + value=_line['f_max'], + debounce=True, + show_value=True,) + for _ix, _line in flow_dict.items()], label='flow limits') + return (flow_limits,) + + +@app.cell(hide_code=True) +def _( + am_solving, + gen_limits, + generator_dict, + load_limits, + np, + num_buses, + plt, + problem, +): + # plot bus generators and loads + am_solving + _fig, _ax = plt.subplots(nrows=3, sharex=True, figsize=(9, 5.5)) + _ax[0].stem(np.arange(num_buses)+1, problem.var_dict['p_gen'].value, label='gen') + _ax[0].plot((np.arange(num_buses)+1)[np.array(list(generator_dict.keys()))-1], [_v[1] for _v in gen_limits.value], + ls='none', marker=7, color='orange', label='max') + _ax[0].plot((np.arange(num_buses)+1)[np.array(list(generator_dict.keys()))-1], [_v[0] for _v in gen_limits.value], + ls='none', marker=6, color='red', label='min') + _ax[0].legend() + _ax[0].set_title('generator production') + _ax[0].set_ylabel('power [MW]') + _ax[1].stem(np.arange(num_buses)+1, problem.var_dict['p_load'].value, label='served') + _ax[1].plot(np.arange(num_buses)+1, [_v[1] for _v in load_limits.value], + ls='none', marker=7, color='orange', label='desired') + _ax[1].plot(np.arange(num_buses)+1, [_v[0] for _v in load_limits.value], + ls='none', marker=6, color='red', label='min') + _ax[1].legend() + _ax[1].set_title('load served') + _ax[1].set_ylabel('power [MW]') + _ax[2].scatter(np.arange(num_buses)+1, -1 * problem.constraints[0].dual_value) + _ax[2].set_title('node prices') + _ax[2].set_ylabel('dual value (price)') + _ax[2].set_xlabel('bus number') + plt.tight_layout() + bus_power_fig = _fig + return (bus_power_fig,) + + +@app.cell(hide_code=True) +def _( + am_solving, + both, + constrained_lines, + gen_only, + lines, + load_only, + problem, +): + # power flow graph view 1 + am_solving + solution2 = ["flowchart LR"] + for _ix, _l in enumerate(lines): + _f = _l[0] + _t = _l[1] + _lf = problem.var_dict['line flows'][_ix].value + if _lf >= 0: + solution2.append(f" {_f:.0f}(({_f})) == {_lf:.2f} ==> {_t:.0f}(({_t}))") + else: + # solution2.append(f" {_f:.0f}(({_f})) ~~~ {_t:.0f}(({_t}))") + solution2.append(f" {_t:.0f}(({_t})) == {-_lf:.2f} ==> {_f:.0f}(({_f}))") + solution2 = "\n".join(solution2) + solution2 += '''\n + classDef gen stroke-dasharray: 5 5;\n + classDef load fill:#f9f;\n + classDef genload stroke-dasharray: 5 5,fill:#f9f;''' + if len(load_only) > 0: + _str = ",".join([str(int(_i)) for _i in load_only]) + solution2 += "\n class "+_str+" gen;" + if len(gen_only) > 0: + _str = ",".join([str(int(_i)) for _i in gen_only]) + solution2 += "\n class "+_str+" load;" + if len(both) > 0: + _str = ",".join([str(int(_i)) for _i in both]) + solution2 += "\n class "+_str+" genload;" + if len(constrained_lines) > 0: + _str = ",".join([str(int(_i)) for _i in constrained_lines]) + solution2 += "\n linkStyle "+_str+" color:red;" + return (solution2,) + + +@app.cell +def _(solution): + solution + return + + +@app.cell(hide_code=True) +def _( + am_solving, + both, + constrained_lines, + gen_only, + lines, + load_only, + problem, +): + # power flow graph view 2 + am_solving + solution = ["flowchart LR"] + for _ix, _l in enumerate(lines): + _f = _l[0] + _t = _l[1] + _lf = problem.var_dict['line flows'][_ix].value + solution.append(f" {_f:.0f}(({_f})) == {_lf:.2f} ==> {_t:.0f}(({_t}))") + # if _lf >= 0: + # solution.append(f" {_f:.0f}(({_f})) == {_lf:.2f} ==> {_t:.0f}(({_t}))") + # else: + # solution.append(f" {_t:.0f}(({_t})) == {-_lf:.2f} ==> {_f:.0f}(({_f}))") + solution = "\n".join(solution) + solution += ''' + classDef gen stroke-dasharray: 5 5; + classDef load fill:#f9f; + classDef genload stroke-dasharray: 5 5,fill:#f9f;''' + if len(load_only) > 0: + _str = ",".join([str(int(_i)) for _i in load_only]) + solution += "\n class "+_str+" gen;" + if len(gen_only) > 0: + _str = ",".join([str(int(_i)) for _i in gen_only]) + solution += "\n class "+_str+" load;" + if len(both) > 0: + _str = ",".join([str(int(_i)) for _i in both]) + solution += "\n class "+_str+" genload;" + if len(constrained_lines) > 0: + _str = ",".join([str(int(_i)) for _i in constrained_lines]) + solution += "\n linkStyle "+_str+" color:red;" + return (solution,) + + +@app.cell +def _(): + import marimo as mo + import os, re + import numpy as np + import cvxpy as cp + import matplotlib.pyplot as plt + import seaborn as sns + return cp, mo, np, os, plt, re, sns + + +if __name__ == "__main__": + app.run() diff --git a/dc-opf/SD_relax_test_real.py b/dc-opf/SD_relax_test_real.py new file mode 100644 index 00000000..0f412583 --- /dev/null +++ b/dc-opf/SD_relax_test_real.py @@ -0,0 +1,585 @@ +import marimo + +__generated_with = "0.15.0" +app = marimo.App(width="medium") + + +@app.cell +def _(B, Yksbar, flow_dict, np): + _yij = 1/(np.array([_v['r'] for _k, _v in flow_dict.items()]) + 1j * np.array([_v['x'] for _k, _v in flow_dict.items()])) + _num_buses = B.shape[0] + _num_edges = B.shape[1] + _Y = np.zeros((_num_buses, _num_buses), dtype=complex) + for _idx, _edge in enumerate(B.T): + _i, _j = np.where(_edge==-1)[0], np.where(_edge==1)[0] + _Y[_i, _j] = -_yij[_idx] + _Y[_j, _i] = -_yij[_idx] + np.fill_diagonal(_Y, np.sum(-_Y, axis=1)) + Ysel = [np.outer(_e, _e) @ _Y for _e in np.eye(_Y.shape[0])] + Yks = [0.5 * np.block([[np.real(_Yk + _Yk.T), np.imag(_Yk.T - _Yk)], + [np.imag(_Yk - _Yk.T), np.real(_Yk + _Yk.T)]]) for _Yk in Ysel] + Yksbars = [-0.5 * np.block([[np.imag(_Yk + _Yk.T), np.real(_Yk - _Yk.T)], + [np.real(_Yk.T - _Yk), np.imag(_Yk + _Yk.T)]]) for _Yk in Ysel] + Yksbar[0] + return + + +@app.cell +def _(P, cp, np): + def make_problem(incidence_mat, generator_dict, load_dict, flow_dict): + def indices_not_in_list(array_length, given_indices): + all_indices = set(range(array_length)) + given_indices_set = set(given_indices) + not_in_list_indices = all_indices - given_indices_set + return list(not_in_list_indices) + ### Problem Data ### + _B = incidence_mat + _A = np.abs(_B) @ np.abs(_B.T) - np.diag(np.diag(np.abs(_B) @ np.abs(_B.T))) + _yij = 1/(np.array([_v['r'] for _k, _v in flow_dict.items()]) + 1j * np.array([_v['x'] for _k, _v in flow_dict.items()])) + num_buses = _B.shape[0] + num_edges = _B.shape[1] + _Y = np.zeros((num_buses, num_buses), dtype=complex) + for _idx, _edge in enumerate(_B.T): + _i, _j = np.where(_edge==-1)[0], np.where(_edge==1)[0] + _Y[_i, _j] = -_yij[_idx] + _Y[_j, _i] = -_yij[_idx] + np.fill_diagonal(_Y, np.sum(-_Y, axis=1)) + Ysel = [np.outer(_e, _e) @ _Y for _e in np.eye(_Y.shape[0])] + Yks = [0.5 * np.block([[np.real(_Yk + _Yk.T), np.imag(_Yk.T - _Yk)], + [np.imag(_Yk - _Yk.T), np.real(_Yk + _Yk.T)]])for _Yk in Ysel] + Yksbars = [-0.5 * np.block([[np.imag(_Yk + _Yk.T), np.real(_Yk - _Yk.T)], + [np.real(_Yk.T - _Yk), np.imag(_Yk + _Yk.T)]])for _Yk in Ysel] + gen_ixs = np.array(list(generator_dict.keys())) - 1 + nogen_ixs = indices_not_in_list(num_buses, gen_ixs) + ### Variables ### + p_gen = cp.Variable((num_buses), nonneg=True, name='p_gen') + p_load = cp.Variable((num_buses), nonneg=True, name='p_load') + q_bus = cp.Variable((num_buses), name='q_bus') + W = cp.Variable((2 * num_buses, 2 * num_buses), name='V', PSD=True) + v_bus = cp.Variable(num_buses, name='v') + ### Parameters ### + l_min = cp.Parameter( + shape=num_buses, + value=[_v['l_min'] for _k, _v in load_dict.items()], + name='l_min' + ) + l_upper = cp.Parameter( + shape=num_buses, + value=[_v['l_upper'] for _k, _v in load_dict.items()], + name='l_upper' + ) + # l_cost = cp.Parameter( + # shape=num_buses, + # value=[_v['cost'] for _k, _v in load_dict.items()], + # name='l_cost' + # ) + l_cost = [_v['cost'] for _k, _v in load_dict.items()] + g_min = cp.Parameter( + shape=len(gen_ixs), + value=np.array([_v['p_min'] for _k, _v in generator_dict.items()]), + name='g_min' + ) + g_max = cp.Parameter( + shape=len(gen_ixs), + value=np.array([_v['p_max'] for _k, _v in generator_dict.items()]), + name='g_max' + ) + c0 = cp.Parameter( + shape=len(gen_ixs), + value=np.array([_v['c0'] for _k, _v in generator_dict.items()]), + name='c0' + ) + c1 = cp.Parameter( + shape=len(gen_ixs), + value=np.array([_v['c1'] for _k, _v in generator_dict.items()]), + name='c1' + ) + # c2 = cp.Parameter( + # shape=len(gen_ixs), + # value=np.array([_v['c2'] for _k, _v in generator_dict.items()]), + # name='c2' + # ) + c2 = np.array([_v['c2'] for _k, _v in generator_dict.items()]) + r = cp.Parameter( + shape=num_edges, + value=np.array([_v['r'] for _k, _v in flow_dict.items()]), + name='line resistance' + ) + f_max = cp.Parameter( + shape=num_edges, + value=np.array([_v['f_max'] for _k, _v in flow_dict.items()]), + name='line capacities' + ) + ### Problem Formulation ### + # generator costs + cost = cp.sum(c0 + cp.multiply(c1, p_gen[gen_ixs]) + cp.multiply(c2, cp.square(p_gen[gen_ixs]))) + # load curtailment costs + cost += cp.sum( + cp.multiply( + cp.neg(p_load - l_upper), + l_cost + ) + ) + # line penalties + for _idx, _edge in enumerate(_B.T): + _i, _j = np.where(_edge==-1)[0], np.where(_edge==1)[0] + cost += 0.5 * r[_idx] * (P[_i, _j] + P[_j, _i]) + constraints = [ + + ] + # constraints = [ + # (p_gen - p_load) + 1j * q_bus == cp.diag(V @ _YH), + # cp.sum(P, axis=1) == p_gen - p_load, + # cp.sum(Q, axis=1) == q_bus, + # cp.multiply(P, 1 - _A) == 0, + # cp.multiply(Q, 1 - _A) == 0, + # p_load >= l_min, + # cp.abs(P) <= f_max, + # p_gen[nogen_ixs] == 0, + # p_gen[gen_ixs] <= g_max, + # p_gen[gen_ixs] >= g_min, + # cp.real(cp.diag(V)) >= 0.9, + # cp.real(cp.diag(V)) <= 1.1, + # cp.imag(cp.diag(V)) == 0, + # cp.real(V) == cp.real(V.T), + # cp.imag(V) == -cp.imag(V.T) + # ] + problem = cp.Problem(cp.Minimize(cost), constraints) + return problem + return (make_problem,) + + +@app.cell +def _(mo): + mo.md( + """ + # DC Optimal Power Flow + + This notebook demonstrates the optimal flow problem, over 6 test networks provided by [pypower](https://github.com/rwl/PYPOWER). + + - network with $n$ nodes, $m$ edges, given by $n \\times m$ incidence + matrix $A$ + - edge power vector $p \\in \\mathbf{R}^m$, + with capacity limits $|p_j| \\leq C_j$ + - node generator power $g_i$, load $l_i$, + each with lower and upper limits + - flow conservation $Af + g = l$ + - generator cost function is + $\\phi_i(g_i) = a_i g_i + b_i g_i^2$, total $G= \\sum_i \\phi_i (g_i)$ + - load shortfall cost is $\\psi_i(l_i) = c_i(l^\\text{tar}_i-l_i)_+$, + total is $S = \\sum_i \\psi_i(l_i)$ + - total line loss is $L = \\sum_j r_j p_j^2$ + - objective is $G + \\lambda L + \\mu S$ + + The model automatically populates parameters for the generators, loads, and lines, with the sliders set to the default values for the test case. Users may try adjusting these values with the sliders to see how it changes the optimal flow. + """ + ) + return + + +@app.cell +def _(am_solving, np, plt, problem, sns): + am_solving + sns.heatmap(np.real(problem.var_dict['V'].value), cmap='seismic', center=0) + plt.title('real part') + plt.gcf() + return + + +@app.cell +def _(am_solving, np, plt, problem, sns): + am_solving + sns.heatmap(np.imag(problem.var_dict['V'].value), cmap='seismic', center=0) + plt.title('imag part') + plt.gcf() + return + + +@app.cell +def _(am_solving, np, plt, problem): + am_solving + plt.stem(np.diag(problem.var_dict['V'].value)) + plt.gcf() + return + + +@app.cell(hide_code=True) +def _(mo, os, re): + # model loading widget + _list = sorted( + [x for x in os.listdir(".") if re.match("case.+.py",x)], # get only "case*.py" + key=lambda x: int(re.sub("[^0-9]*([0-9]+)[^0-9]*", r"\1", x, 1)), # sort by numerical order not lexical + ) + _options = {os.path.splitext(x)[0]: x for x in _list} + model_ui = mo.ui.dropdown( + options=_options, value=os.path.splitext(_list[0])[0], label='select model:' + ) + return (model_ui,) + + +@app.cell(hide_code=True) +def _(model_ui, os): + # import model from file + from importlib.machinery import SourceFileLoader + _n = model_ui.value.split('.')[0] + model_loader = SourceFileLoader(_n, os.path.join(".",model_ui.value)).load_module() + load_model = getattr(model_loader, _n) + model_data = load_model() + return (model_data,) + + +@app.cell(hide_code=True) +def _(model_data): + # define network + num_buses = model_data['bus'].shape[0] + lines = [(int(_b[0]), int(_b[1])) for _b in model_data['branch']] + return lines, num_buses + + +@app.cell(hide_code=True) +def _(both, gen_only, lines, load_only): + # build graph + graph = ["flowchart LR"] + for _ix, _l in enumerate(lines): + _f = _l[0] + _t = _l[1] + graph.append(f" {_f:.0f}(({_f})) == {_ix+1} === {_t:.0f}(({_t}))") + graph = "\n".join(graph) + graph += '''\n + classDef gen stroke-dasharray: 5 5;\n + classDef load fill:#f9f;\n + classDef genload stroke-dasharray: 5 5,fill:#f9f;''' + if len(load_only) > 0: + _str = ",".join([str(int(_i)) for _i in load_only]) + graph += "\n class "+_str+" gen;" + if len(gen_only) > 0: + _str = ",".join([str(int(_i)) for _i in gen_only]) + graph += "\n class "+_str+" load;" + if len(both) > 0: + _str = ",".join([str(int(_i)) for _i in both]) + graph += "\n class "+_str+" genload;" + return (graph,) + + +@app.cell(hide_code=True) +def _(lines, np, num_buses, sns): + # define incidence matrix + B = np.zeros((num_buses, len(lines))) + for _ix, _l in enumerate(lines): + B[_l[0]-1, _ix] = -1 + B[_l[1]-1, _ix] = 1 + fig_heatmap = sns.heatmap(B, cmap='bwr', center=0) + return B, fig_heatmap + + +@app.cell +def _(model_data, model_ui, np): + # construct dictionaries for problem formulation + def merge_nested_dicts(dict1, dict2): + result = {} + + for key in dict1.keys() | dict2.keys(): + inner_dict1 = dict1.get(key, {}) + inner_dict2 = dict2.get(key, {}) + + # Merge the inner dictionaries + merged_inner_dict = {**inner_dict1, **inner_dict2} + + result[key] = merged_inner_dict + + return result + # minimum generation value occaisonally negative + generator_dict1 = {int(_g[0]): {'p_min': np.clip(_g[9], 0, np.inf), 'p_max': _g[8]*2} for _g in model_data['gen']} + generator_dict2 = {int(model_data['gen'][_ix, 0]): {'c0': _g[-1], 'c1': _g[-2], 'c2': _g[-3]} for _ix, _g in enumerate(model_data['gencost'])} + generator_dict = merge_nested_dicts(generator_dict1, generator_dict2) + # power demand is occaisonally negative + # load_dict = {int(_l[0]): {'l_min': 0, 'l_upper': np.abs(_l[2]), 'cost': 250} for _l in model_data['bus']} + load_dict = {int(_l[0]): {'l_min': np.abs(_l[2]*0.5), 'l_upper': np.abs(_l[2]), 'cost': 1e4} for _l in model_data['bus']} + _fmax = 20 * np.max([np.max(_b[5:7]) for _b in model_data['branch']]) + + flow_dict = {} + for _ix, _b in enumerate(model_data['branch']): + _fx = np.max(_b[5:7]) + flow_dict[_ix] = {'r': _b[2], 'x': _b[3], 'b': _b[4], 'g': _b[2] / (_b[2]**2 + _b[3]**2)} + if model_ui.value == 'case14.py': + flow_dict[_ix]['f_max'] = 175 + elif _fx > 0: + flow_dict[_ix]['f_max'] = _fx + else: + flow_dict[_ix]['f_max'] = _fmax + line_resistance = model_data['branch'][:, 2] + return flow_dict, generator_dict, load_dict + + +@app.cell(hide_code=True) +def _(model_data, np): + # label nodes + generator_nodes = model_data['gen'][:, 0] + load_nodes = np.where(model_data['bus'][:, 2] != 0)[0] + 1 + set_gen_nodes = set(generator_nodes) + set_load_nodes = set(load_nodes) + gen_only = np.asarray(list(set_gen_nodes.difference(set_load_nodes))) + load_only = np.asarray(list(set_load_nodes.difference(set_gen_nodes))) + both = np.asarray(list(set_gen_nodes.intersection(set_load_nodes))) + return both, gen_only, load_only + + +@app.cell +def _(model_ui): + model_ui + return + + +@app.cell(hide_code=True) +def _(fig_heatmap, generator_dict, graph, load_dict, mo): + mo.ui.tabs({ + 'graph': mo.mermaid(graph), + 'incidence matrix': fig_heatmap, + 'generators': mo.accordion({str(_k): _v for _k, _v in generator_dict.items()}), + 'loads': mo.accordion({str(_k): _v for _k, _v in load_dict.items()}) + }) + return + + +@app.cell +def _(flow_limits, gen_limits, load_limits, mo): + mo.hstack([load_limits, gen_limits, flow_limits]) + return + + +@app.cell +def _(B, flow_dict, generator_dict, load_dict, make_problem): + problem = make_problem(B, generator_dict, load_dict, flow_dict) + return (problem,) + + +@app.cell(hide_code=True) +def _(flow_limits, gen_limits, load_limits, mo, np, problem): + # solve with updated parameters + am_solving = True + problem.param_dict['g_min'].value = [_v[0] for _v in gen_limits.value] + problem.param_dict['g_max'].value = [_v[1] for _v in gen_limits.value] + problem.param_dict['l_min'].value = [_v[0] for _v in load_limits.value] + problem.param_dict['l_upper'].value = [_v[1] for _v in load_limits.value] + problem.param_dict['line capacities'].value = flow_limits.value + obj_val = problem.solve(verbose=True, solver='CLARABEL') + constrained_lines = np.where(~np.isclose(problem.constraints[2].dual_value, 0, atol=1e-2))[0] + mo.md(f'objective value: {obj_val:.2f}') + return am_solving, constrained_lines + + +@app.cell +def _(am_solving, np, problem): + am_solving + evals, evecs = np.linalg.eigh(problem.var_dict['V'].value) + return (evals,) + + +@app.cell +def _(am_solving, evals): + am_solving + evals + return + + +@app.cell +def _(am_solving, problem): + am_solving + problem.var_dict['V'].value + return + + +@app.cell +def _(bus_power_fig, mo, solution, solution2): + mo.ui.tabs({ + 'bus power': bus_power_fig, + 'line flows': mo.mermaid(solution), + 'line flows ordered': mo.mermaid(solution2) + }) + return + + +@app.cell(hide_code=True) +def _(generator_dict, mo, np): + # generator ui + _max = np.ceil(1.5*np.max([_v['p_max'] for _k, _v in generator_dict.items()])) + gen_limits = mo.ui.array([mo.ui.range_slider(start=0, + stop=_max, + label=f'gen {_ix}', + value=[_gen['p_min'], _gen['p_max']], + debounce=True, + show_value=True) + for _ix, _gen in generator_dict.items()], label='generator limits') + return (gen_limits,) + + +@app.cell(hide_code=True) +def _(load_dict, mo, np): + # load ui + _max = np.ceil(1.5*np.max([_v['l_upper'] for _k, _v in load_dict.items()])) + load_limits = mo.ui.array([mo.ui.range_slider(start=0, + stop=_max, + label=f'load {_ix}', + value=[_load['l_min'], _load['l_upper']], + debounce=True, + show_value=True,) + for _ix, _load in load_dict.items()], label='load limits') + return (load_limits,) + + +@app.cell(hide_code=True) +def _(flow_dict, mo, np): + # flow ui + _max = np.ceil(1.5*np.max([_v['f_max'] for _k, _v in flow_dict.items()])) + flow_limits = mo.ui.array([mo.ui.slider(start=0, + stop=_max, + label=f'line {_ix+1}', + value=_line['f_max'], + debounce=True, + show_value=True,) + for _ix, _line in flow_dict.items()], label='flow limits') + return (flow_limits,) + + +@app.cell(hide_code=True) +def _( + am_solving, + gen_limits, + generator_dict, + load_limits, + np, + num_buses, + plt, + problem, +): + # plot bus generators and loads + am_solving + _fig, _ax = plt.subplots(nrows=3, sharex=True, figsize=(9, 5.5)) + _ax[0].stem(np.arange(num_buses)+1, problem.var_dict['p_gen'].value, label='gen') + _ax[0].plot((np.arange(num_buses)+1)[np.array(list(generator_dict.keys()))-1], [_v[1] for _v in gen_limits.value], + ls='none', marker=7, color='orange', label='max') + _ax[0].plot((np.arange(num_buses)+1)[np.array(list(generator_dict.keys()))-1], [_v[0] for _v in gen_limits.value], + ls='none', marker=6, color='red', label='min') + _ax[0].legend() + _ax[0].set_title('generator production') + _ax[0].set_ylabel('power [MW]') + _ax[1].stem(np.arange(num_buses)+1, problem.var_dict['p_load'].value, label='served') + _ax[1].plot(np.arange(num_buses)+1, [_v[1] for _v in load_limits.value], + ls='none', marker=7, color='orange', label='desired') + _ax[1].plot(np.arange(num_buses)+1, [_v[0] for _v in load_limits.value], + ls='none', marker=6, color='red', label='min') + _ax[1].legend() + _ax[1].set_title('load served') + _ax[1].set_ylabel('power [MW]') + _ax[2].scatter(np.arange(num_buses)+1, -1 * problem.constraints[0].dual_value) + _ax[2].set_title('node prices') + _ax[2].set_ylabel('dual value (price)') + _ax[2].set_xlabel('bus number') + plt.tight_layout() + bus_power_fig = _fig + return (bus_power_fig,) + + +@app.cell(hide_code=True) +def _( + am_solving, + both, + constrained_lines, + gen_only, + lines, + load_only, + problem, +): + # power flow graph view 1 + am_solving + solution2 = ["flowchart LR"] + for _ix, _l in enumerate(lines): + _f = _l[0] + _t = _l[1] + _lf = problem.var_dict['line flows'][_ix].value + if _lf >= 0: + solution2.append(f" {_f:.0f}(({_f})) == {_lf:.2f} ==> {_t:.0f}(({_t}))") + else: + # solution2.append(f" {_f:.0f}(({_f})) ~~~ {_t:.0f}(({_t}))") + solution2.append(f" {_t:.0f}(({_t})) == {-_lf:.2f} ==> {_f:.0f}(({_f}))") + solution2 = "\n".join(solution2) + solution2 += '''\n + classDef gen stroke-dasharray: 5 5;\n + classDef load fill:#f9f;\n + classDef genload stroke-dasharray: 5 5,fill:#f9f;''' + if len(load_only) > 0: + _str = ",".join([str(int(_i)) for _i in load_only]) + solution2 += "\n class "+_str+" gen;" + if len(gen_only) > 0: + _str = ",".join([str(int(_i)) for _i in gen_only]) + solution2 += "\n class "+_str+" load;" + if len(both) > 0: + _str = ",".join([str(int(_i)) for _i in both]) + solution2 += "\n class "+_str+" genload;" + if len(constrained_lines) > 0: + _str = ",".join([str(int(_i)) for _i in constrained_lines]) + solution2 += "\n linkStyle "+_str+" color:red;" + return (solution2,) + + +@app.cell +def _(solution): + solution + return + + +@app.cell(hide_code=True) +def _( + am_solving, + both, + constrained_lines, + gen_only, + lines, + load_only, + problem, +): + # power flow graph view 2 + am_solving + solution = ["flowchart LR"] + for _ix, _l in enumerate(lines): + _f = _l[0] + _t = _l[1] + _lf = problem.var_dict['line flows'][_ix].value + solution.append(f" {_f:.0f}(({_f})) == {_lf:.2f} ==> {_t:.0f}(({_t}))") + # if _lf >= 0: + # solution.append(f" {_f:.0f}(({_f})) == {_lf:.2f} ==> {_t:.0f}(({_t}))") + # else: + # solution.append(f" {_t:.0f}(({_t})) == {-_lf:.2f} ==> {_f:.0f}(({_f}))") + solution = "\n".join(solution) + solution += ''' + classDef gen stroke-dasharray: 5 5; + classDef load fill:#f9f; + classDef genload stroke-dasharray: 5 5,fill:#f9f;''' + if len(load_only) > 0: + _str = ",".join([str(int(_i)) for _i in load_only]) + solution += "\n class "+_str+" gen;" + if len(gen_only) > 0: + _str = ",".join([str(int(_i)) for _i in gen_only]) + solution += "\n class "+_str+" load;" + if len(both) > 0: + _str = ",".join([str(int(_i)) for _i in both]) + solution += "\n class "+_str+" genload;" + if len(constrained_lines) > 0: + _str = ",".join([str(int(_i)) for _i in constrained_lines]) + solution += "\n linkStyle "+_str+" color:red;" + return (solution,) + + +@app.cell +def _(): + import marimo as mo + import os, re + import numpy as np + import cvxpy as cp + import matplotlib.pyplot as plt + import seaborn as sns + return cp, mo, np, os, plt, re, sns + + +if __name__ == "__main__": + app.run() diff --git a/dc-opf/SD_relax_test_v2.py b/dc-opf/SD_relax_test_v2.py new file mode 100644 index 00000000..0f66f37b --- /dev/null +++ b/dc-opf/SD_relax_test_v2.py @@ -0,0 +1,559 @@ +import marimo + +__generated_with = "0.15.0" +app = marimo.App(width="medium") + + +@app.cell +def _(cp, np): + def make_problem(incidence_mat, generator_dict, load_dict, flow_dict): + def indices_not_in_list(array_length, given_indices): + all_indices = set(range(array_length)) + given_indices_set = set(given_indices) + not_in_list_indices = all_indices - given_indices_set + return list(not_in_list_indices) + ### Problem Data ### + _B = incidence_mat + _A = np.abs(_B) @ np.abs(_B.T) - np.diag(np.diag(np.abs(_B) @ np.abs(_B.T))) + _yij = 1/(np.array([_v['r'] for _k, _v in flow_dict.items()]) + 1j * np.array([_v['x'] for _k, _v in flow_dict.items()])) + num_buses = _B.shape[0] + num_edges = _B.shape[1] + _Y = np.zeros((num_buses, num_buses), dtype=complex) + for _idx, _edge in enumerate(_B.T): + _i, _j = np.where(_edge==-1)[0], np.where(_edge==1)[0] + _Y[_i, _j] = -_yij[_idx] + _Y[_j, _i] = -_yij[_idx] + np.fill_diagonal(_Y, np.sum(-_Y, axis=1)) + _YH = _Y.conjugate().T + gen_ixs = np.array(list(generator_dict.keys())) - 1 + nogen_ixs = indices_not_in_list(num_buses, gen_ixs) + ### Variables ### + p_gen = cp.Variable((num_buses), nonneg=True, name='p_gen') + p_load = cp.Variable((num_buses), nonneg=True, name='p_load') + q_bus = cp.Variable((num_buses), name='q_bus') + V = cp.Variable((num_buses, num_buses), complex=True, name='V', PSD=True) + P = cp.Variable((num_buses, num_buses), name='P') + Q = cp.Variable((num_buses, num_buses), name='P') + ### Parameters ### + l_min = cp.Parameter( + shape=num_buses, + value=[_v['l_min'] for _k, _v in load_dict.items()], + name='l_min' + ) + l_upper = cp.Parameter( + shape=num_buses, + value=[_v['l_upper'] for _k, _v in load_dict.items()], + name='l_upper' + ) + # l_cost = cp.Parameter( + # shape=num_buses, + # value=[_v['cost'] for _k, _v in load_dict.items()], + # name='l_cost' + # ) + l_cost = [_v['cost'] for _k, _v in load_dict.items()] + g_min = cp.Parameter( + shape=len(gen_ixs), + value=np.array([_v['p_min'] for _k, _v in generator_dict.items()]), + name='g_min' + ) + g_max = cp.Parameter( + shape=len(gen_ixs), + value=np.array([_v['p_max'] for _k, _v in generator_dict.items()]), + name='g_max' + ) + c0 = cp.Parameter( + shape=len(gen_ixs), + value=np.array([_v['c0'] for _k, _v in generator_dict.items()]), + name='c0' + ) + c1 = cp.Parameter( + shape=len(gen_ixs), + value=np.array([_v['c1'] for _k, _v in generator_dict.items()]), + name='c1' + ) + # c2 = cp.Parameter( + # shape=len(gen_ixs), + # value=np.array([_v['c2'] for _k, _v in generator_dict.items()]), + # name='c2' + # ) + c2 = np.array([_v['c2'] for _k, _v in generator_dict.items()]) + r = cp.Parameter( + shape=num_edges, + value=np.array([_v['r'] for _k, _v in flow_dict.items()]), + name='line resistance' + ) + f_max = cp.Parameter( + shape=num_edges, + value=np.array([_v['f_max'] for _k, _v in flow_dict.items()]), + name='line capacities' + ) + ### Problem Formulation ### + # generator costs + cost = cp.sum(c0 + cp.multiply(c1, p_gen[gen_ixs]) + cp.multiply(c2, cp.square(p_gen[gen_ixs]))) + # load curtailment costs + cost += cp.sum( + cp.multiply( + cp.neg(p_load - l_upper), + l_cost + ) + ) + # line penalties + for _idx, _edge in enumerate(_B.T): + _i, _j = np.where(_edge==-1)[0], np.where(_edge==1)[0] + cost += 0.5 * r[_idx] * (P[_i, _j] + P[_j, _i]) + constraints = [ + (p_gen - p_load) + 1j * q_bus == cp.diag(V @ _YH), + cp.sum(P, axis=1) == p_gen - p_load, + cp.sum(Q, axis=1) == q_bus, + cp.multiply(P, 1 - _A) == 0, + cp.multiply(Q, 1 - _A) == 0, + p_load >= l_min, + cp.abs(P) <= f_max, + p_gen[nogen_ixs] == 0, + p_gen[gen_ixs] <= g_max, + p_gen[gen_ixs] >= g_min, + cp.real(cp.diag(V)) >= 0.9, + cp.real(cp.diag(V)) <= 1.1, + cp.imag(cp.diag(V)) == 0, + cp.real(V) == cp.real(V.T), + cp.imag(V) == -cp.imag(V.T) + ] + problem = cp.Problem(cp.Minimize(cost), constraints) + return problem + return (make_problem,) + + +@app.cell +def _(mo): + mo.md( + """ + # DC Optimal Power Flow + + This notebook demonstrates the optimal flow problem, over 6 test networks provided by [pypower](https://github.com/rwl/PYPOWER). + + - network with $n$ nodes, $m$ edges, given by $n \\times m$ incidence + matrix $A$ + - edge power vector $p \\in \\mathbf{R}^m$, + with capacity limits $|p_j| \\leq C_j$ + - node generator power $g_i$, load $l_i$, + each with lower and upper limits + - flow conservation $Af + g = l$ + - generator cost function is + $\\phi_i(g_i) = a_i g_i + b_i g_i^2$, total $G= \\sum_i \\phi_i (g_i)$ + - load shortfall cost is $\\psi_i(l_i) = c_i(l^\\text{tar}_i-l_i)_+$, + total is $S = \\sum_i \\psi_i(l_i)$ + - total line loss is $L = \\sum_j r_j p_j^2$ + - objective is $G + \\lambda L + \\mu S$ + + The model automatically populates parameters for the generators, loads, and lines, with the sliders set to the default values for the test case. Users may try adjusting these values with the sliders to see how it changes the optimal flow. + """ + ) + return + + +@app.cell +def _(am_solving, np, plt, problem, sns): + am_solving + sns.heatmap(np.real(problem.var_dict['V'].value), cmap='seismic', center=0) + plt.title('real part') + plt.gcf() + return + + +@app.cell +def _(am_solving, np, plt, problem, sns): + am_solving + sns.heatmap(np.imag(problem.var_dict['V'].value), cmap='seismic', center=0) + plt.title('imag part') + plt.gcf() + return + + +@app.cell +def _(am_solving, np, plt, problem): + am_solving + plt.stem(np.diag(problem.var_dict['V'].value)) + plt.gcf() + return + + +@app.cell(hide_code=True) +def _(mo, os, re): + # model loading widget + _list = sorted( + [x for x in os.listdir(".") if re.match("case.+.py",x)], # get only "case*.py" + key=lambda x: int(re.sub("[^0-9]*([0-9]+)[^0-9]*", r"\1", x, 1)), # sort by numerical order not lexical + ) + _options = {os.path.splitext(x)[0]: x for x in _list} + model_ui = mo.ui.dropdown( + options=_options, value=os.path.splitext(_list[0])[0], label='select model:' + ) + return (model_ui,) + + +@app.cell(hide_code=True) +def _(model_ui, os): + # import model from file + from importlib.machinery import SourceFileLoader + _n = model_ui.value.split('.')[0] + model_loader = SourceFileLoader(_n, os.path.join(".",model_ui.value)).load_module() + load_model = getattr(model_loader, _n) + model_data = load_model() + return (model_data,) + + +@app.cell(hide_code=True) +def _(model_data): + # define network + num_buses = model_data['bus'].shape[0] + lines = [(int(_b[0]), int(_b[1])) for _b in model_data['branch']] + return lines, num_buses + + +@app.cell(hide_code=True) +def _(both, gen_only, lines, load_only): + # build graph + graph = ["flowchart LR"] + for _ix, _l in enumerate(lines): + _f = _l[0] + _t = _l[1] + graph.append(f" {_f:.0f}(({_f})) == {_ix+1} === {_t:.0f}(({_t}))") + graph = "\n".join(graph) + graph += '''\n + classDef gen stroke-dasharray: 5 5;\n + classDef load fill:#f9f;\n + classDef genload stroke-dasharray: 5 5,fill:#f9f;''' + if len(load_only) > 0: + _str = ",".join([str(int(_i)) for _i in load_only]) + graph += "\n class "+_str+" gen;" + if len(gen_only) > 0: + _str = ",".join([str(int(_i)) for _i in gen_only]) + graph += "\n class "+_str+" load;" + if len(both) > 0: + _str = ",".join([str(int(_i)) for _i in both]) + graph += "\n class "+_str+" genload;" + return (graph,) + + +@app.cell(hide_code=True) +def _(lines, np, num_buses, sns): + # define incidence matrix + B = np.zeros((num_buses, len(lines))) + for _ix, _l in enumerate(lines): + B[_l[0]-1, _ix] = -1 + B[_l[1]-1, _ix] = 1 + fig_heatmap = sns.heatmap(B, cmap='bwr', center=0) + return B, fig_heatmap + + +@app.cell +def _(model_data, model_ui, np): + # construct dictionaries for problem formulation + def merge_nested_dicts(dict1, dict2): + result = {} + + for key in dict1.keys() | dict2.keys(): + inner_dict1 = dict1.get(key, {}) + inner_dict2 = dict2.get(key, {}) + + # Merge the inner dictionaries + merged_inner_dict = {**inner_dict1, **inner_dict2} + + result[key] = merged_inner_dict + + return result + # minimum generation value occaisonally negative + generator_dict1 = {int(_g[0]): {'p_min': np.clip(_g[9], 0, np.inf), 'p_max': _g[8]*2} for _g in model_data['gen']} + generator_dict2 = {int(model_data['gen'][_ix, 0]): {'c0': _g[-1], 'c1': _g[-2], 'c2': _g[-3]} for _ix, _g in enumerate(model_data['gencost'])} + generator_dict = merge_nested_dicts(generator_dict1, generator_dict2) + # power demand is occaisonally negative + # load_dict = {int(_l[0]): {'l_min': 0, 'l_upper': np.abs(_l[2]), 'cost': 250} for _l in model_data['bus']} + load_dict = {int(_l[0]): {'l_min': np.abs(_l[2]*0.5), 'l_upper': np.abs(_l[2]), 'cost': 1e4} for _l in model_data['bus']} + _fmax = 20 * np.max([np.max(_b[5:7]) for _b in model_data['branch']]) + + flow_dict = {} + for _ix, _b in enumerate(model_data['branch']): + _fx = np.max(_b[5:7]) + flow_dict[_ix] = {'r': _b[2], 'x': _b[3], 'b': _b[4], 'g': _b[2] / (_b[2]**2 + _b[3]**2)} + if model_ui.value == 'case14.py': + flow_dict[_ix]['f_max'] = 175 + elif _fx > 0: + flow_dict[_ix]['f_max'] = _fx + else: + flow_dict[_ix]['f_max'] = _fmax + line_resistance = model_data['branch'][:, 2] + return flow_dict, generator_dict, load_dict + + +@app.cell(hide_code=True) +def _(model_data, np): + # label nodes + generator_nodes = model_data['gen'][:, 0] + load_nodes = np.where(model_data['bus'][:, 2] != 0)[0] + 1 + set_gen_nodes = set(generator_nodes) + set_load_nodes = set(load_nodes) + gen_only = np.asarray(list(set_gen_nodes.difference(set_load_nodes))) + load_only = np.asarray(list(set_load_nodes.difference(set_gen_nodes))) + both = np.asarray(list(set_gen_nodes.intersection(set_load_nodes))) + return both, gen_only, load_only + + +@app.cell +def _(model_ui): + model_ui + return + + +@app.cell(hide_code=True) +def _(fig_heatmap, generator_dict, graph, load_dict, mo): + mo.ui.tabs({ + 'graph': mo.mermaid(graph), + 'incidence matrix': fig_heatmap, + 'generators': mo.accordion({str(_k): _v for _k, _v in generator_dict.items()}), + 'loads': mo.accordion({str(_k): _v for _k, _v in load_dict.items()}) + }) + return + + +@app.cell +def _(flow_limits, gen_limits, load_limits, mo): + mo.hstack([load_limits, gen_limits, flow_limits]) + return + + +@app.cell +def _(B, flow_dict, generator_dict, load_dict, make_problem): + problem = make_problem(B, generator_dict, load_dict, flow_dict) + return (problem,) + + +@app.cell(hide_code=True) +def _(flow_limits, gen_limits, load_limits, mo, np, problem): + # solve with updated parameters + am_solving = True + problem.param_dict['g_min'].value = [_v[0] for _v in gen_limits.value] + problem.param_dict['g_max'].value = [_v[1] for _v in gen_limits.value] + problem.param_dict['l_min'].value = [_v[0] for _v in load_limits.value] + problem.param_dict['l_upper'].value = [_v[1] for _v in load_limits.value] + problem.param_dict['line capacities'].value = flow_limits.value + obj_val = problem.solve(verbose=True, solver='CLARABEL') + constrained_lines = np.where(~np.isclose(problem.constraints[2].dual_value, 0, atol=1e-2))[0] + mo.md(f'objective value: {obj_val:.2f}') + return am_solving, constrained_lines + + +@app.cell +def _(am_solving, np, problem): + am_solving + evals, evecs = np.linalg.eigh(problem.var_dict['V'].value) + return (evals,) + + +@app.cell +def _(am_solving, evals): + am_solving + evals + return + + +@app.cell +def _(am_solving, problem): + am_solving + problem.var_dict['V'].value + return + + +@app.cell +def _(bus_power_fig, mo, solution, solution2): + mo.ui.tabs({ + 'bus power': bus_power_fig, + 'line flows': mo.mermaid(solution), + 'line flows ordered': mo.mermaid(solution2) + }) + return + + +@app.cell(hide_code=True) +def _(generator_dict, mo, np): + # generator ui + _max = np.ceil(1.5*np.max([_v['p_max'] for _k, _v in generator_dict.items()])) + gen_limits = mo.ui.array([mo.ui.range_slider(start=0, + stop=_max, + label=f'gen {_ix}', + value=[_gen['p_min'], _gen['p_max']], + debounce=True, + show_value=True) + for _ix, _gen in generator_dict.items()], label='generator limits') + return (gen_limits,) + + +@app.cell(hide_code=True) +def _(load_dict, mo, np): + # load ui + _max = np.ceil(1.5*np.max([_v['l_upper'] for _k, _v in load_dict.items()])) + load_limits = mo.ui.array([mo.ui.range_slider(start=0, + stop=_max, + label=f'load {_ix}', + value=[_load['l_min'], _load['l_upper']], + debounce=True, + show_value=True,) + for _ix, _load in load_dict.items()], label='load limits') + return (load_limits,) + + +@app.cell(hide_code=True) +def _(flow_dict, mo, np): + # flow ui + _max = np.ceil(1.5*np.max([_v['f_max'] for _k, _v in flow_dict.items()])) + flow_limits = mo.ui.array([mo.ui.slider(start=0, + stop=_max, + label=f'line {_ix+1}', + value=_line['f_max'], + debounce=True, + show_value=True,) + for _ix, _line in flow_dict.items()], label='flow limits') + return (flow_limits,) + + +@app.cell(hide_code=True) +def _( + am_solving, + gen_limits, + generator_dict, + load_limits, + np, + num_buses, + plt, + problem, +): + # plot bus generators and loads + am_solving + _fig, _ax = plt.subplots(nrows=3, sharex=True, figsize=(9, 5.5)) + _ax[0].stem(np.arange(num_buses)+1, problem.var_dict['p_gen'].value, label='gen') + _ax[0].plot((np.arange(num_buses)+1)[np.array(list(generator_dict.keys()))-1], [_v[1] for _v in gen_limits.value], + ls='none', marker=7, color='orange', label='max') + _ax[0].plot((np.arange(num_buses)+1)[np.array(list(generator_dict.keys()))-1], [_v[0] for _v in gen_limits.value], + ls='none', marker=6, color='red', label='min') + _ax[0].legend() + _ax[0].set_title('generator production') + _ax[0].set_ylabel('power [MW]') + _ax[1].stem(np.arange(num_buses)+1, problem.var_dict['p_load'].value, label='served') + _ax[1].plot(np.arange(num_buses)+1, [_v[1] for _v in load_limits.value], + ls='none', marker=7, color='orange', label='desired') + _ax[1].plot(np.arange(num_buses)+1, [_v[0] for _v in load_limits.value], + ls='none', marker=6, color='red', label='min') + _ax[1].legend() + _ax[1].set_title('load served') + _ax[1].set_ylabel('power [MW]') + _ax[2].scatter(np.arange(num_buses)+1, -1 * problem.constraints[0].dual_value) + _ax[2].set_title('node prices') + _ax[2].set_ylabel('dual value (price)') + _ax[2].set_xlabel('bus number') + plt.tight_layout() + bus_power_fig = _fig + return (bus_power_fig,) + + +@app.cell(hide_code=True) +def _( + am_solving, + both, + constrained_lines, + gen_only, + lines, + load_only, + problem, +): + # power flow graph view 1 + am_solving + solution2 = ["flowchart LR"] + for _ix, _l in enumerate(lines): + _f = _l[0] + _t = _l[1] + _lf = problem.var_dict['line flows'][_ix].value + if _lf >= 0: + solution2.append(f" {_f:.0f}(({_f})) == {_lf:.2f} ==> {_t:.0f}(({_t}))") + else: + # solution2.append(f" {_f:.0f}(({_f})) ~~~ {_t:.0f}(({_t}))") + solution2.append(f" {_t:.0f}(({_t})) == {-_lf:.2f} ==> {_f:.0f}(({_f}))") + solution2 = "\n".join(solution2) + solution2 += '''\n + classDef gen stroke-dasharray: 5 5;\n + classDef load fill:#f9f;\n + classDef genload stroke-dasharray: 5 5,fill:#f9f;''' + if len(load_only) > 0: + _str = ",".join([str(int(_i)) for _i in load_only]) + solution2 += "\n class "+_str+" gen;" + if len(gen_only) > 0: + _str = ",".join([str(int(_i)) for _i in gen_only]) + solution2 += "\n class "+_str+" load;" + if len(both) > 0: + _str = ",".join([str(int(_i)) for _i in both]) + solution2 += "\n class "+_str+" genload;" + if len(constrained_lines) > 0: + _str = ",".join([str(int(_i)) for _i in constrained_lines]) + solution2 += "\n linkStyle "+_str+" color:red;" + return (solution2,) + + +@app.cell +def _(solution): + solution + return + + +@app.cell(hide_code=True) +def _( + am_solving, + both, + constrained_lines, + gen_only, + lines, + load_only, + problem, +): + # power flow graph view 2 + am_solving + solution = ["flowchart LR"] + for _ix, _l in enumerate(lines): + _f = _l[0] + _t = _l[1] + _lf = problem.var_dict['line flows'][_ix].value + solution.append(f" {_f:.0f}(({_f})) == {_lf:.2f} ==> {_t:.0f}(({_t}))") + # if _lf >= 0: + # solution.append(f" {_f:.0f}(({_f})) == {_lf:.2f} ==> {_t:.0f}(({_t}))") + # else: + # solution.append(f" {_t:.0f}(({_t})) == {-_lf:.2f} ==> {_f:.0f}(({_f}))") + solution = "\n".join(solution) + solution += ''' + classDef gen stroke-dasharray: 5 5; + classDef load fill:#f9f; + classDef genload stroke-dasharray: 5 5,fill:#f9f;''' + if len(load_only) > 0: + _str = ",".join([str(int(_i)) for _i in load_only]) + solution += "\n class "+_str+" gen;" + if len(gen_only) > 0: + _str = ",".join([str(int(_i)) for _i in gen_only]) + solution += "\n class "+_str+" load;" + if len(both) > 0: + _str = ",".join([str(int(_i)) for _i in both]) + solution += "\n class "+_str+" genload;" + if len(constrained_lines) > 0: + _str = ",".join([str(int(_i)) for _i in constrained_lines]) + solution += "\n linkStyle "+_str+" color:red;" + return (solution,) + + +@app.cell +def _(): + import marimo as mo + import os, re + import numpy as np + import cvxpy as cp + import matplotlib.pyplot as plt + import seaborn as sns + return cp, mo, np, os, plt, re, sns + + +if __name__ == "__main__": + app.run() diff --git a/dc-opf/SOC_relax_test.py b/dc-opf/SOC_relax_test.py new file mode 100644 index 00000000..9519a528 --- /dev/null +++ b/dc-opf/SOC_relax_test.py @@ -0,0 +1,635 @@ +import marimo + +__generated_with = "0.15.0" +app = marimo.App(width="medium") + + +@app.cell +def _(cp, np): + def make_problem(incidence_mat, generator_dict, load_dict, flow_dict, augmented=None, rho=1): + def indices_not_in_list(array_length, given_indices): + all_indices = set(range(array_length)) + given_indices_set = set(given_indices) + not_in_list_indices = all_indices - given_indices_set + return list(not_in_list_indices) + _B = incidence_mat + _A = np.abs(_B) @ np.abs(_B.T) - np.diag(np.diag(np.abs(_B) @ np.abs(_B.T))) + _yij = 1/(np.array([_v['r'] for _k, _v in flow_dict.items()]) + 1j * np.array([_v['x'] for _k, _v in flow_dict.items()])) + num_buses = _B.shape[0] + num_edges = _B.shape[1] + _Y = np.zeros((num_buses, num_buses), dtype=complex) + for _idx, _edge in enumerate(_B.T): + _i, _j = np.where(_edge==-1)[0], np.where(_edge==1)[0] + _Y[_i, _j] = -_yij[_idx] + _Y[_j, _i] = -_yij[_idx] + np.fill_diagonal(_Y, np.sum(-_Y, axis=1)) + _YH = _Y.conjugate().T + gen_ixs = np.array(list(generator_dict.keys())) - 1 + nogen_ixs = indices_not_in_list(num_buses, gen_ixs) + # p_flows = cp.Variable((num_edges), name='line flows') + p_gen = cp.Variable((num_buses), nonneg=True, name='p_gen') + p_load = cp.Variable((num_buses), nonneg=True, name='p_load') + q_bus = cp.Variable((num_buses), name='q_bus') + V = cp.Variable((num_buses, num_buses), complex=True, name='V') + P = cp.Variable((num_buses, num_buses), name='P') + Q = cp.Variable((num_buses, num_buses), name='P') + l_min = cp.Parameter( + shape=num_buses, + value=[_v['l_min'] for _k, _v in load_dict.items()], + name='l_min' + ) + l_upper = cp.Parameter( + shape=num_buses, + value=[_v['l_upper'] for _k, _v in load_dict.items()], + name='l_upper' + ) + # l_cost = cp.Parameter( + # shape=num_buses, + # value=[_v['cost'] for _k, _v in load_dict.items()], + # name='l_cost' + # ) + l_cost = [_v['cost'] for _k, _v in load_dict.items()] + g_min = cp.Parameter( + shape=len(gen_ixs), + value=np.array([_v['p_min'] for _k, _v in generator_dict.items()]), + name='g_min' + ) + g_max = cp.Parameter( + shape=len(gen_ixs), + value=np.array([_v['p_max'] for _k, _v in generator_dict.items()]), + name='g_max' + ) + c0 = cp.Parameter( + shape=len(gen_ixs), + value=np.array([_v['c0'] for _k, _v in generator_dict.items()]), + name='c0' + ) + c1 = cp.Parameter( + shape=len(gen_ixs), + value=np.array([_v['c1'] for _k, _v in generator_dict.items()]), + name='c1' + ) + # c2 = cp.Parameter( + # shape=len(gen_ixs), + # value=np.array([_v['c2'] for _k, _v in generator_dict.items()]), + # name='c2' + # ) + c2 = np.array([_v['c2'] for _k, _v in generator_dict.items()]) + r = cp.Parameter( + shape=num_edges, + value=np.array([_v['r'] for _k, _v in flow_dict.items()]), + name='line resistance' + ) + f_max = cp.Parameter( + shape=num_edges, + value=np.array([_v['f_max'] for _k, _v in flow_dict.items()]), + name='line capacities' + ) + # generator costs + cost = cp.sum(c0 + cp.multiply(c1, p_gen[gen_ixs]) + cp.multiply(c2, cp.square(p_gen[gen_ixs]))) + # load curtailment costs + cost += cp.sum( + cp.multiply( + cp.neg(p_load - l_upper), + l_cost + ) + ) + # line penalties + cost += cp.sum_squares(cp.multiply(r, P)) + # admm + if augmented is not None: + cost += rho * 0.5 * cp.sum_squares(V - augmented) + constraints = [ + (p_gen - p_load) + 1j * q_bus == cp.diag(V @ _YH), + cp.sum(P, axis=1) == p_gen - p_load, + cp.sum(Q, axis=1) == q_bus, + cp.multiply(P, 1 - _A) == 0, + cp.multiply(Q, 1 - _A) == 0, + p_load >= l_min, + cp.abs(P) <= f_max, + p_gen[nogen_ixs] == 0, + p_gen[gen_ixs] <= g_max, + p_gen[gen_ixs] >= g_min, + cp.real(cp.diag(V)) >= 0.9, + cp.real(cp.diag(V)) <= 1.1, + cp.imag(cp.diag(V)) == 0, + cp.real(V) == cp.real(V.T), + cp.imag(V) == -cp.imag(V.T) + ] + problem = cp.Problem(cp.Minimize(cost), constraints) + return problem + return (make_problem,) + + +@app.cell +def _(np): + def proj_rank1(mat): + _evals, _evecs = np.linalg.eigh(mat) + _k = 1 + _M = _evecs[:, -_k:] + _D = np.diag(_evals[-_k:]) + return _M @ _D @ _M.conjugate().T + return (proj_rank1,) + + +@app.cell +def _( + B, + flow_dict, + generator_dict, + load_dict, + make_problem, + np, + num_buses, + proj_rank1, +): + def admm_step(zlast=None, ulast=None, rho=1): + if zlast is None: + zlast = np.zeros((num_buses, num_buses)) + if ulast is None: + ulast = np.zeros((num_buses, num_buses)) + problem = make_problem(B, generator_dict, load_dict, flow_dict, augmented=zlast - ulast, rho=rho) + problem.solve(solver='CLARABEL') + xnew = problem.var_dict['V'].value + znew = proj_rank1(xnew + ulast) + unew = ulast + xnew - znew + return xnew, znew, unew, np.linalg.norm(xnew - znew) + return (admm_step,) + + +@app.cell +def _(admm_step, mo, plt): + _zl, _ul = None, None + _rho = 1 + rs = [] + for _i in range(500): + if (_i + 1) % 10 == 0: + _rho *= 2 + xnext, znext, unext, r = admm_step(zlast=_zl, ulast=_ul, rho=_rho) + _zl, _ul = znext, unext + rs.append(r) + fig = plt.plot(rs) + mo.output.replace(fig) + plt.close() + return xnext, znext + + +@app.cell +def _(am_solving, np, plt, sns, xnext): + am_solving + sns.heatmap(np.real(xnext), cmap='seismic', center=0) + plt.title('real part') + plt.gcf() + return + + +@app.cell +def _(am_solving, np, plt, xnext): + am_solving + plt.stem(np.diag(xnext)) + plt.gcf() + return + + +@app.cell +def _(np, znext): + _eval, _evec = np.linalg.eigh(znext) + _eval + return + + +@app.cell +def _(): + return + + +@app.cell +def _(am_solving, np, plt, problem, sns): + am_solving + sns.heatmap(np.real(problem.var_dict['V'].value), cmap='seismic', center=0) + plt.title('real part') + plt.gcf() + return + + +@app.cell +def _(am_solving, np, plt, problem2, sns): + am_solving + sns.heatmap(np.imag(problem2.var_dict['V'].value), cmap='seismic', center=0) + plt.title('imag part') + plt.gcf() + return + + +@app.cell +def _(am_solving, np, plt, problem): + am_solving + plt.stem(np.diag(problem.var_dict['V'].value)) + plt.gcf() + return + + +@app.cell +def _(problem, proj_rank1): + V_proj = proj_rank1(problem.var_dict["V"].value) + error = problem.var_dict["V"].value - V_proj + return V_proj, error + + +@app.cell +def _( + B, + V_proj, + error, + flow_dict, + flow_limits, + gen_limits, + generator_dict, + load_dict, + load_limits, + make_problem, +): + problem2 = make_problem(B, generator_dict, load_dict, flow_dict, augmented=V_proj - error) + # solve with updated parameters + # am_solving = True + problem2.param_dict['g_min'].value = [_v[0] for _v in gen_limits.value] + problem2.param_dict['g_max'].value = [_v[1] for _v in gen_limits.value] + problem2.param_dict['l_min'].value = [_v[0] for _v in load_limits.value] + problem2.param_dict['l_upper'].value = [_v[1] for _v in load_limits.value] + problem2.param_dict['line capacities'].value = flow_limits.value + obj_val2 = problem2.solve(verbose=True, solver='CLARABEL') + return (problem2,) + + +@app.cell +def _(am_solving, np, problem2): + am_solving + evals, evecs = np.linalg.eigh(problem2.var_dict['V'].value) + return (evals,) + + +@app.cell +def _(evals): + evals + return + + +@app.cell(hide_code=True) +def _(mo, os, re): + # model loading widget + _list = sorted( + [x for x in os.listdir(".") if re.match("case.+.py",x)], # get only "case*.py" + key=lambda x: int(re.sub("[^0-9]*([0-9]+)[^0-9]*", r"\1", x, 1)), # sort by numerical order not lexical + ) + _options = {os.path.splitext(x)[0]: x for x in _list} + model_ui = mo.ui.dropdown( + options=_options, value=os.path.splitext(_list[0])[0], label='select model:' + ) + return (model_ui,) + + +@app.cell(hide_code=True) +def _(model_ui, os): + # import model from file + from importlib.machinery import SourceFileLoader + _n = model_ui.value.split('.')[0] + model_loader = SourceFileLoader(_n, os.path.join(".",model_ui.value)).load_module() + load_model = getattr(model_loader, _n) + model_data = load_model() + return (model_data,) + + +@app.cell(hide_code=True) +def _(model_data): + # define network + num_buses = model_data['bus'].shape[0] + lines = [(int(_b[0]), int(_b[1])) for _b in model_data['branch']] + return lines, num_buses + + +@app.cell(hide_code=True) +def _(both, gen_only, lines, load_only): + # build graph + graph = ["flowchart LR"] + for _ix, _l in enumerate(lines): + _f = _l[0] + _t = _l[1] + graph.append(f" {_f:.0f}(({_f})) == {_ix+1} === {_t:.0f}(({_t}))") + graph = "\n".join(graph) + graph += '''\n + classDef gen stroke-dasharray: 5 5;\n + classDef load fill:#f9f;\n + classDef genload stroke-dasharray: 5 5,fill:#f9f;''' + if len(load_only) > 0: + _str = ",".join([str(int(_i)) for _i in load_only]) + graph += "\n class "+_str+" gen;" + if len(gen_only) > 0: + _str = ",".join([str(int(_i)) for _i in gen_only]) + graph += "\n class "+_str+" load;" + if len(both) > 0: + _str = ",".join([str(int(_i)) for _i in both]) + graph += "\n class "+_str+" genload;" + return (graph,) + + +@app.cell(hide_code=True) +def _(lines, np, num_buses, sns): + # define incidence matrix + B = np.zeros((num_buses, len(lines))) + for _ix, _l in enumerate(lines): + B[_l[0]-1, _ix] = -1 + B[_l[1]-1, _ix] = 1 + fig_heatmap = sns.heatmap(B, cmap='bwr', center=0) + return B, fig_heatmap + + +@app.cell +def _(model_data, model_ui, np): + # construct dictionaries for problem formulation + def merge_nested_dicts(dict1, dict2): + result = {} + + for key in dict1.keys() | dict2.keys(): + inner_dict1 = dict1.get(key, {}) + inner_dict2 = dict2.get(key, {}) + + # Merge the inner dictionaries + merged_inner_dict = {**inner_dict1, **inner_dict2} + + result[key] = merged_inner_dict + + return result + # minimum generation value occaisonally negative + generator_dict1 = {int(_g[0]): {'p_min': np.clip(_g[9], 0, np.inf), 'p_max': _g[8]*2} for _g in model_data['gen']} + generator_dict2 = {int(model_data['gen'][_ix, 0]): {'c0': _g[-1], 'c1': _g[-2], 'c2': _g[-3]} for _ix, _g in enumerate(model_data['gencost'])} + generator_dict = merge_nested_dicts(generator_dict1, generator_dict2) + # power demand is occaisonally negative + # load_dict = {int(_l[0]): {'l_min': 0, 'l_upper': np.abs(_l[2]), 'cost': 250} for _l in model_data['bus']} + load_dict = {int(_l[0]): {'l_min': np.abs(_l[2]*0.5), 'l_upper': np.abs(_l[2]), 'cost': 1e4} for _l in model_data['bus']} + _fmax = 20 * np.max([np.max(_b[5:7]) for _b in model_data['branch']]) + + flow_dict = {} + for _ix, _b in enumerate(model_data['branch']): + _fx = np.max(_b[5:7]) + flow_dict[_ix] = {'r': _b[2], 'x': _b[3], 'b': _b[4], 'g': _b[2] / (_b[2]**2 + _b[3]**2)} + if model_ui.value == 'case14.py': + flow_dict[_ix]['f_max'] = 175 + elif _fx > 0: + flow_dict[_ix]['f_max'] = _fx + else: + flow_dict[_ix]['f_max'] = _fmax + line_resistance = model_data['branch'][:, 2] + return flow_dict, generator_dict, load_dict + + +@app.cell(hide_code=True) +def _(model_data, np): + # label nodes + generator_nodes = model_data['gen'][:, 0] + load_nodes = np.where(model_data['bus'][:, 2] != 0)[0] + 1 + set_gen_nodes = set(generator_nodes) + set_load_nodes = set(load_nodes) + gen_only = np.asarray(list(set_gen_nodes.difference(set_load_nodes))) + load_only = np.asarray(list(set_load_nodes.difference(set_gen_nodes))) + both = np.asarray(list(set_gen_nodes.intersection(set_load_nodes))) + return both, gen_only, load_only + + +@app.cell +def _(model_ui): + model_ui + return + + +@app.cell(hide_code=True) +def _(fig_heatmap, generator_dict, graph, load_dict, mo): + mo.ui.tabs({ + 'graph': mo.mermaid(graph), + 'incidence matrix': fig_heatmap, + 'generators': mo.accordion({str(_k): _v for _k, _v in generator_dict.items()}), + 'loads': mo.accordion({str(_k): _v for _k, _v in load_dict.items()}) + }) + return + + +@app.cell +def _(flow_limits, gen_limits, load_limits, mo): + mo.hstack([load_limits, gen_limits, flow_limits]) + return + + +@app.cell +def _(B, flow_dict, generator_dict, load_dict, make_problem): + problem = make_problem(B, generator_dict, load_dict, flow_dict) + return (problem,) + + +@app.cell(hide_code=True) +def _(flow_limits, gen_limits, load_limits, mo, np, problem): + # solve with updated parameters + am_solving = True + problem.param_dict['g_min'].value = [_v[0] for _v in gen_limits.value] + problem.param_dict['g_max'].value = [_v[1] for _v in gen_limits.value] + problem.param_dict['l_min'].value = [_v[0] for _v in load_limits.value] + problem.param_dict['l_upper'].value = [_v[1] for _v in load_limits.value] + problem.param_dict['line capacities'].value = flow_limits.value + obj_val = problem.solve(verbose=True, solver='CLARABEL') + constrained_lines = np.where(~np.isclose(problem.constraints[2].dual_value, 0, atol=1e-2))[0] + mo.md(f'objective value: {obj_val:.2f}') + return am_solving, constrained_lines + + +@app.cell +def _(bus_power_fig, mo, solution, solution2): + mo.ui.tabs({ + 'bus power': bus_power_fig, + 'line flows': mo.mermaid(solution), + 'line flows ordered': mo.mermaid(solution2) + }) + return + + +@app.cell(hide_code=True) +def _(generator_dict, mo, np): + # generator ui + _max = np.ceil(1.5*np.max([_v['p_max'] for _k, _v in generator_dict.items()])) + gen_limits = mo.ui.array([mo.ui.range_slider(start=0, + stop=_max, + label=f'gen {_ix}', + value=[_gen['p_min'], _gen['p_max']], + debounce=True, + show_value=True) + for _ix, _gen in generator_dict.items()], label='generator limits') + return (gen_limits,) + + +@app.cell(hide_code=True) +def _(load_dict, mo, np): + # load ui + _max = np.ceil(1.5*np.max([_v['l_upper'] for _k, _v in load_dict.items()])) + load_limits = mo.ui.array([mo.ui.range_slider(start=0, + stop=_max, + label=f'load {_ix}', + value=[_load['l_min'], _load['l_upper']], + debounce=True, + show_value=True,) + for _ix, _load in load_dict.items()], label='load limits') + return (load_limits,) + + +@app.cell(hide_code=True) +def _(flow_dict, mo, np): + # flow ui + _max = np.ceil(1.5*np.max([_v['f_max'] for _k, _v in flow_dict.items()])) + flow_limits = mo.ui.array([mo.ui.slider(start=0, + stop=_max, + label=f'line {_ix+1}', + value=_line['f_max'], + debounce=True, + show_value=True,) + for _ix, _line in flow_dict.items()], label='flow limits') + return (flow_limits,) + + +@app.cell(hide_code=True) +def _( + am_solving, + gen_limits, + generator_dict, + load_limits, + np, + num_buses, + plt, + problem, +): + # plot bus generators and loads + am_solving + _fig, _ax = plt.subplots(nrows=3, sharex=True, figsize=(9, 5.5)) + _ax[0].stem(np.arange(num_buses)+1, problem.var_dict['p_gen'].value, label='gen') + _ax[0].plot((np.arange(num_buses)+1)[np.array(list(generator_dict.keys()))-1], [_v[1] for _v in gen_limits.value], + ls='none', marker=7, color='orange', label='max') + _ax[0].plot((np.arange(num_buses)+1)[np.array(list(generator_dict.keys()))-1], [_v[0] for _v in gen_limits.value], + ls='none', marker=6, color='red', label='min') + _ax[0].legend() + _ax[0].set_title('generator production') + _ax[0].set_ylabel('power [MW]') + _ax[1].stem(np.arange(num_buses)+1, problem.var_dict['p_load'].value, label='served') + _ax[1].plot(np.arange(num_buses)+1, [_v[1] for _v in load_limits.value], + ls='none', marker=7, color='orange', label='desired') + _ax[1].plot(np.arange(num_buses)+1, [_v[0] for _v in load_limits.value], + ls='none', marker=6, color='red', label='min') + _ax[1].legend() + _ax[1].set_title('load served') + _ax[1].set_ylabel('power [MW]') + _ax[2].scatter(np.arange(num_buses)+1, -1 * problem.constraints[0].dual_value) + _ax[2].set_title('node prices') + _ax[2].set_ylabel('dual value (price)') + _ax[2].set_xlabel('bus number') + plt.tight_layout() + bus_power_fig = _fig + return (bus_power_fig,) + + +@app.cell(hide_code=True) +def _( + am_solving, + both, + constrained_lines, + gen_only, + lines, + load_only, + problem, +): + # power flow graph view 1 + am_solving + solution2 = ["flowchart LR"] + for _ix, _l in enumerate(lines): + _f = _l[0] + _t = _l[1] + _lf = problem.var_dict['line flows'][_ix].value + if _lf >= 0: + solution2.append(f" {_f:.0f}(({_f})) == {_lf:.2f} ==> {_t:.0f}(({_t}))") + else: + # solution2.append(f" {_f:.0f}(({_f})) ~~~ {_t:.0f}(({_t}))") + solution2.append(f" {_t:.0f}(({_t})) == {-_lf:.2f} ==> {_f:.0f}(({_f}))") + solution2 = "\n".join(solution2) + solution2 += '''\n + classDef gen stroke-dasharray: 5 5;\n + classDef load fill:#f9f;\n + classDef genload stroke-dasharray: 5 5,fill:#f9f;''' + if len(load_only) > 0: + _str = ",".join([str(int(_i)) for _i in load_only]) + solution2 += "\n class "+_str+" gen;" + if len(gen_only) > 0: + _str = ",".join([str(int(_i)) for _i in gen_only]) + solution2 += "\n class "+_str+" load;" + if len(both) > 0: + _str = ",".join([str(int(_i)) for _i in both]) + solution2 += "\n class "+_str+" genload;" + if len(constrained_lines) > 0: + _str = ",".join([str(int(_i)) for _i in constrained_lines]) + solution2 += "\n linkStyle "+_str+" color:red;" + return (solution2,) + + +@app.cell +def _(solution): + solution + return + + +@app.cell(hide_code=True) +def _( + am_solving, + both, + constrained_lines, + gen_only, + lines, + load_only, + problem, +): + # power flow graph view 2 + am_solving + solution = ["flowchart LR"] + for _ix, _l in enumerate(lines): + _f = _l[0] + _t = _l[1] + _lf = problem.var_dict['line flows'][_ix].value + solution.append(f" {_f:.0f}(({_f})) == {_lf:.2f} ==> {_t:.0f}(({_t}))") + # if _lf >= 0: + # solution.append(f" {_f:.0f}(({_f})) == {_lf:.2f} ==> {_t:.0f}(({_t}))") + # else: + # solution.append(f" {_t:.0f}(({_t})) == {-_lf:.2f} ==> {_f:.0f}(({_f}))") + solution = "\n".join(solution) + solution += ''' + classDef gen stroke-dasharray: 5 5; + classDef load fill:#f9f; + classDef genload stroke-dasharray: 5 5,fill:#f9f;''' + if len(load_only) > 0: + _str = ",".join([str(int(_i)) for _i in load_only]) + solution += "\n class "+_str+" gen;" + if len(gen_only) > 0: + _str = ",".join([str(int(_i)) for _i in gen_only]) + solution += "\n class "+_str+" load;" + if len(both) > 0: + _str = ",".join([str(int(_i)) for _i in both]) + solution += "\n class "+_str+" genload;" + if len(constrained_lines) > 0: + _str = ",".join([str(int(_i)) for _i in constrained_lines]) + solution += "\n linkStyle "+_str+" color:red;" + return (solution,) + + +@app.cell +def _(): + import marimo as mo + import os, re + import numpy as np + import cvxpy as cp + import matplotlib.pyplot as plt + import seaborn as sns + return cp, mo, np, os, plt, re, sns + + +if __name__ == "__main__": + app.run() diff --git a/dc-opf/case240.py b/dc-opf/case240.py new file mode 100644 index 00000000..b6ab5dd1 --- /dev/null +++ b/dc-opf/case240.py @@ -0,0 +1,939 @@ +from numpy import array +def case240(): + return { + "version": '2', + "baseMVA": 100.0, + "bus": array([ + [1, 1, 0.0, 0.0, 0.0, 0.0, 1, 1.03401, 5.5785, 500.0, 3, 1.1, 0.9], + [2, 1, 226.8, 600.0, 0.0, 0.0, 1, 1.014, 8.3685, 345.0, 3, 1.1, 0.9], + [3, 1, 2153.3, -366.9, 0.0, 0.0, 1, 0.97318, -0.292, 230.0, 10, 1.1, 0.9], + [4, 1, 1037.9, -5.5, 0.0, 0.0, 1, 1.03, 18.8075, 345.0, 10, 1.1, 0.9], + [5, 2, 0.0, 0.0, 0.0, 0.0, 1, 1.0139, 8.9503, 20.0, 10, 1.1, 0.9], + [6, 2, 0.0, 0.0, 0.0, 0.0, 1, 1.03341, 20.4253, 20.0, 10, 1.1, 0.9], + [7, 1, 4527.4, 137.0, 0.0, 0.0, 1, 1.01, -13.5878, 500.0, 3, 1.1, 0.9], + [8, 1, 178.9, -23.7, 0.0, 0.0, 1, 0.99465, -4.7874, 345.0, 3, 1.1, 0.9], + [9, 2, 0.0, 0.0, 0.0, 0.0, 1, 1.00946, -12.8589, 20.0, 3, 1.1, 0.9], + [10, 1, 168.1, 2.0, 0.0, 0.0, 1, 1.09136, -6.6183, 500.0, 3, 1.1, 0.9], + [11, 1, 875.6, -287.1, 0.0, 0.0, 1, 1.08, -3.8673, 500.0, 3, 1.1, 0.9], + [12, 2, 0.0, 0.0, 0.0, 0.0, 1, 1.0799, -3.0358, 20.0, 3, 1.1, 0.9], + [13, 1, 2704.8, -352.8, 0.0, 0.0, 1, 1.045, -9.8666, 500.0, 3, 1.1, 0.9], + [14, 1, 17.5, -2.3, 0.0, 0.0, 1, 1.02442, -11.6849, 500.0, 9, 1.1, 0.9], + [15, 1, 6235.1, -813.3, 0.0, 0.0, 1, 1.005, -15.2256, 345.0, 9, 1.1, 0.9], + [16, 2, 0.0, 0.0, 0.0, 0.0, 1, 1.04505, -8.7861, 20.0, 3, 1.1, 0.9], + [17, 2, 0.0, 0.0, 0.0, 0.0, 1, 1.00792, -13.7701, 20.0, 9, 1.1, 0.9], + [18, 1, 6535.5, -1524.6, 0.0, 0.0, 1, 1.035, -14.7822, 500.0, 3, 1.1, 0.9], + [19, 1, 5373.3, 601.7, 0.0, 0.0, 1, 1.05895, -20.357, 500.0, 3, 1.1, 0.9], + [20, 1, 0.0, 0.0, 0.0, 0.0, 1, 1.03527, -14.4059, 230.0, 3, 1.1, 0.9], + [21, 2, 0.0, 0.0, 0.0, 0.0, 1, 1.04004, -12.4298, 20.0, 3, 1.1, 0.9], + [22, 1, 2207.2, -287.9, 0.0, 0.0, 4, 1.0, -16.7754, 230.0, 7, 1.1, 0.9], + [23, 2, 0.0, 0.0, 0.0, 0.0, 4, 1.00135, -16.0721, 20.0, 7, 1.1, 0.9], + [24, 1, 253.0, -49.3, 0.0, 0.0, 2, 1.035, -12.4531, 230.0, 2, 1.1, 0.9], + [25, 2, 0.0, 0.0, 0.0, 0.0, 2, 1.03554, -12.0979, 20.0, 2, 1.1, 0.9], + [26, 1, 0.0, 0.0, 0.0, 0.0, 2, 0.99375, -26.5153, 500.0, 2, 1.1, 0.9], + 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bk4d/F5L1pEKj02bx7RkCFEly5pnwuuHRxn8L//aTVrOM7gr7+0cxTHtWFDosGDiV58UfsdHsd5j2sSZII14vbiJbT9zDV6sfP/aMaqrym4eA6/rhF3Ll6iI5//RAVyZ6Pgf+ZY1giqVk17LRxnwOcujjMZs0bkPE/0cOJDFEll6FXS1ojXEibQ7ffnEJ3dpJ33v/5K1KuX9jr33aedBwauETmOxdFria3pX+pBn5C2Rnya8Dpd/SqcKNz8NeLqC2/R/KTTFE5VaBy9YVmLL08eSqXrZdOOM/jxR+044jyAZfyx72sEjn+lxE+pN82nFrSBLlFBGkq/0d90PwUmJNL1z7sT9Td3jYg7fpNa0TtUgY7RA6StEQ/SX/RtwjDK2f8MURdZI9KrHeGPNcKRHaH2fZN/1oj0bEcoW9rANQJrwuDEB+gYVaD/kWZHvJ7wKd36cD7RGbEjZI04de+tEW6UmDOS8RZM4cbtBKr+2ho68V27NMmyLN0aR12a5s/Umd6ExCSq8b81FP5lm0x9HASi1+fsoT+3R1t+PvBBF8qd3X9x14V7T9OwGbsoZ7ZA2jKyIwXlzua3batsc5kkSkpxDSTRiRMBfrsGJi+NoSe6Fk+z63Dw13tp2su1bLbvn2OwfFs8dWqSL8W2/XkMrt66S41GrqdDX7RJlfEOCEyiyJP+Ow8EQbBfASTXopBZfE+Z4y2YwvaTFykpzw2q8MAhtaAC/I8AnD8W1o0x0VSoyz6ElrQHApLohx+SMtWifigmnm5mv0Yle4SpHipFgP8+AyH9sDc6LsXPWyIu+nX7G45eUP/fvJNIf+1MDgD4g3yF7lDRblgL9LrGgER695Mbfr0GVkdHplqPfvzRP+sRAnB7LsWo7Qdm4e0n0qDXzvll+8tOnlDb5vuA1uPtv2Mwc0sk3ch+jar0DacAPv76fhTuuo+Scl83dwcEQUgBrvkGg46nWJODe+ynEiWtas8FIYMijrdgChuPaYb2/QNu06pt16j4wE1U9oXV9PDgBL9sf3V4LOWrE0XTlp2j0MFbqdSzK6lFjyuUmdimO1cdH7hBYeF3KXjQZnUcOvS+lta7JvgRFDVFXtSci8ahhdT/G46c9+s+bDqWvL3pW06qffIX4WevUK5aUdTgjU1038hwKvXsKipQ95Tftg/Hd9fJS2o9+mfDZQp5ZIu6Dlve55/1aN+pOIq7cYdKNTlDx44l0chvY9QxuFHuqOnbjrt+h+bvOaW99/WXadUqopVr71Lww9pa1PF+c53euwmJ9Mv6CPX9+yNyU2RkgKpmxNcD43ZTnlpRNGndcVP3QRCElFy+fpvOlz6k1qFZ829S9VfWUdbqJ2nHyUtpvWuCYDrieAumOt7NKxSm1vXyUGjN6yqzsNUPmTYYeycuaAZdj6YFqEWrJMqa/ybtispci/rWE9qxblKuEFWrmI3atiN1HJYfPJvWuyb4kXNXb9H12wlKxbl/oxD12Pqj/nO8T12+oa7HwACi3Nmz0LFz12j/6Xi/bT/i/FX1f/VK2Whg71zqGth8XFuf/MHhs1foyq27lCd7FuraOIjatw9Q+7D28Dm/bH+dvp3mFYpQaNlAem5gfrX9nZGXlUNuJnN3Rasqh2ol81P3pgVU62W7Vtmopb4mrzP5PERlR0z8TaUtcH+9UirThtZbfP2vj3YtzNoWReeu3DJ1PwRBSCY85goh9hpaNoD69cpJnRpppbkrw2PTetcEwXTE8RYMJ/7mHdoXfVl936xCYSWv37JSEb8Z/MjwgDKFclPBPNmpfpkC6uddkdo+ZRb4/ULFGnSoWlz9v+Kg3NwyE5F6ECo4KBe1rVJUfX8o5gpdvHbbL9vfqF/ztUsXoLoh2rV4wI+O9/HzWoVH+SJ5qIF+LeyJvqyyof6Aszh1yxSgrFkCqXUl7TNYe9g/wY/t+vaxFoOQQrmpQtE8KhO/3uTKh//2xaj/+zYonWLMSiu+Hxw5Z7q2AOhWswTlyJolxe9wPOqEFKBbdxPp1w1aVlwQBPM5ek4LhlYsllf9365qMfX/qkNim2Q2rt66S+MWH6L3/9nvt3typnK8165dSz179qTg4GB1E54HdUErUH44atQoKlmyJOXKlYs6duxIR44c8ecuCgawJ+oyJSYRlS2cm0oG5bIxtMw3Nvee0hzOWqWD1P/1ymjG9q7IS5kq+HEm7qb6vlqwFk3uWE1zvLeduGh6pktIP5zUHW8EogrnzUEhhXJZsg7+dDzh6CDzCQ7G+DHjfU5zvEML51aGXt4cWVUFwOGzmvHnr/ffoKxW5t+6chFLRQpEKM0E99TdUdp6WF9fB0HbKsUsLTlmgSzytpNa1U3XmiVS/K5FxSKWyigEAMwcnwd61g5O9XvYIM+3raC+n7bppKyJguAnjsbqjndRzfFuU7moqojCmhx9STQXMlPLQY+v19EPq4/RrxtO0Opw/1SBZSrH+9q1a1SnTh367rvv7P7+008/pa+//pp+/PFH2rJlC+XJk4e6dOlCN29qDoRwbxChZ5gqF8+XytA6cCaezl81t6xvny4kVbuU5nhzlg0lrrjQM9ONrXj+HJQ/p6YgXaZwbqpULC/dTUyiNX4qcxXSnpN6fzcCYaCKfl2iBNofHNcd36ol8qkvcPCMP0vNte2XK5qXsgQGWNYDf7WeJDvemuNboWheCg7KSbfvJtKWiAumv3c4lDmyBlLVksnrcTt2vA+fM63fftmBs6qctE7poFRz41H9kC9nVrpy8y7t1aujjAZO/eXrd6hI3hzUpLyW7belU7Xi6npAK8D3q83veRcEQbPFQAU9410gd3bL+ihZ78zD4rAYS2IA/L3bf9ormcbx7tatG40ZM4Ye4LlyVuDm/+WXX9I777xDvXv3ptq1a9PUqVPp9OnTqTLjQvqGDW2UdjIwfjjbtcHkcnNWcIZxB1BuXk7fF87+ZBbHu1KxlLMFO+hZ7yV6JsgMzsTdoO0nLlocHiFtibygfQ5lC2vXQCU/O94R+vZDC+exrAEodfeHwBqyqXxj5/Wont56svOk+WsBgowQtkOVNW8XmdY2esn/SpONTG43qVUqiLJlSb7dNypXUPXbIyttVr/94v3aGtPFJtsNEACB/oeZ9wM24DtVL662Z4/AwAB6o1sV9T0yLlF6kEoQBPM4xhlv3fG2Ljc3e01kLly9RYv2naGbd/wj+CukZqdehcoVscsPnKUrNzN+5VG66fGOiIigmJgYVV7OYCZakyZNaBOGkzvg1q1ban6a9ZeQtpxgQ9vK8fZXuTkMXYg5wdCtWSp5ll69kMzV533Uzo0N9KhVUv0PgTX01hgNnLn249fQQz9uovYTVvultUBIvxlvnGMsXIX1AOcjnCBkIiF6ZTanL9+g2wmJlD1LoCXryiXX/sh4czYXWW6uPLFu+4ChYWYAgt8jO/0M+p3Z8TWj+gXGLAvYda6uvVdbWuq97utMWiP4fbGugSOQ/cexQAXC+KXhpuyLIAga12/fVTaadak5aK873qhUMbsF52jsFer17QZ6fvpO6vP9Rgm4pRE79GqwIc1CqXzRPEpvY8n+jC/+m24cbzjdoHjxlDdp/My/s8fYsWOVg85fISGaUqmQdlhKO20c75Z6uTkyHGYZmyyshuxWPitDt55exrQrk2S8j+hOla3jjWAEL3BL9YyUUdy6m0Av/bGbbtxJUKWt+IghmuHP0VGCY3E19Hhbt4Cgn87sz+aEvhYUzpOdgnJlo5zZslgyz4fOXPHbWoSgA2c9udT8uB9aT/ZEcfWN1vZi3XqTK1sWOh1301SFd67wqRuS3N/NtDGxzxvTK+DIlgzKqYIO9mil3w+Q9bhmcBDw5IVr6rPPapVZdwQqEN7qXk19P3/3adNK3wVBSK6IxD0B1YgMAsKlCuRStslGq/GTZjj+gyZtsTj/aH989c/dpm1PsM/l67ctLQf1yxa06HAsMdguTY+kG8fbW0aOHElxcXGWr6ioqLTepUwNjC2OHlqXmoNGoYVU5gnGJisNG81ei6GbMsOTnPG+RIkmifmkR9VQ9HTbGpm965SyGJlGAoEi9O4WypOdFgxrqUpZEQjJDAtpegVGxgVdvRw9/gCBF/ig6P2NNXmMEju+1tUv/hRYs7d9GHu8NpkdiIN6Oqhjsx4hAAFBIWB0AMxaYJHV4+uXTbl90FbfvhljxdbpauWocrJWM7cGwRAY2ncSkiyjD43OdqNv1DoA64iapYKoTz1tXRyz8KAECwXB5Go824Ac1omO1bRgIIsimsGMLZHqvle6YC6a90ILVR257cQlitHFaAX/sEuvPsW9GDYjWoIAqiQzevl/unG8S5TQ+sDOnk1ZZoCf+Xf2yJEjB+XPnz/Fl5AMFhN/ltGgnxF+LWbWFs2XI8XvcmXPQg1DtcyLWSXI+1jRXBdWYyDqlDNboBLzOa7P9c3Izlb0pRsp+nmt6VU32DLazSihOxiqf2zTgl6vdqpMVUrko8dblFM/f73iqBiyacT5K5rTjewqlzrD6WNH1Oxy8xN2ql9Y5OugHzLenNUIKagFHRiM9jK79QTnfLLeRMr1CLChsfSAOaV12yIuppouYQ3GiqEiBn3wRpebc/k4l5PbA4a2We1HPCOde+nd4X9dqqh7BIIAf+/KHCI/guBvOOlSoVjKxAzorrfCIViPJI7RwKGbuPa4+n5Yu4qq+omTMssOZvwS5/RYZl5Pb/2qEZyfSuTPqSomNx0zV3Q0rUk3jne5cuWUg71ixQrLY+jXhrp5s2bN0nTf7lUwNqrDhNXU7at1dMlPM3utM0z2Mh2tdEPMjL5CGLp7dEO3TkhKQxfzczkLjgxPRi/lgp+LKCK+bIETBKVhGNz/7j1jWEkrItkwXNmxf6JlOZX1RinXWun1ThPOX9MCK7bnQWVddM/skWIsrGbteFsE1vygbB6r95FD3d+a+n4YMYjgF2alZ8sSYHnP1qCnEeXvEJrjdgAjYePFWal1Bz3DtMJAozP2yk31nrD8c3uRI1rqjreR5e5Y17ZEaBn0FhWcb98aaAC82KGS+v6jfw+qigFBEMjwNhBrsU9rGoYWomL5cqgEyfqjxtuIC/eeUdlutMD0qV9aPda5RglTK4/SM9M2naCuX66ld+bts2ix+FtYrYHeBgp/ge9H0CDKyPjV8b569Srt3r1bfbGgGr6PjIxUB/3ll19WqucLFiygffv20eDBg9XM7/vvv9+fu5khQKZp6OStdO12ghI4wmgXf23XXn83066q5nijh8focpKz8bfU4gFjtnrJ1BmmZGM7gzve+mdgW+pvTa+6XG5uTGZnzo5o9X/XGiUsmVWU9A5sXEZ9/4OM6kkTLl7VAm5F8to43vpYryMmz7K2p/dQrUR+y3lqdkkZl9IXs3G8WWxsd+Rl01pPuMy8aon8qsrAFlwfjUO12d5LDxhv9G3Sxc2aOhilZS3yBgVwzL02gs3HL1o+Z3uBP9tALNZr9PoZFXxAuwsMd8xrRxbFE55sWV5VCKA9Q8YaCYLx8JSJUL31yRqsBZz1hpNsNGzvwC7JnjUwhfgjApVpEWwbOXcvtRy30lKC7y8wfWbMvwdVkPT3zZH00h+7/LbtuwmJFv0Rdryt70dQts/IVZJ+dby3b99O9erVU1/g1VdfVd+PGjVK/fz666/T8OHD6emnn6ZGjRopR33x4sWUM2dOf+5mhmBR2BnldDP/hRm/iLmb4bIGAhqINt68k2gxDI2CRXHQ14yydlvY2DYzy2Vvn/xdNuMq+AF61i6p+nyR/ffV4IXB/u8+7fx6qEFKccMnW5VT24ExjoVe8C8X9Ix34bwpHc/KxbX+unCTS82TjazkcxHZ5wK5s6nMpNnGhsXxzpcz1TqEagzMb2Y9BKPZfsK+ori9cnOjA6MQrkGlCWjmxPFGMLJg7mwUf/OuZX99ZYsbDj8Dwb2GuuG18pAxx4Cz3TDoUOnkCTDG2fhjVXZBEIzPeJcpZN8+6VFbc7yX7T+rBFuNAm11UEwHvepoVXmgfNG8KghwF5UyetDQX4SdiqOZW6NUddRzv+8wXGTSGV8uO6KE7NCGieokHBt/taUeirlC128nUL4cWVPoEDWrUFi1xZ0xWXQ0Uznebdu2VVEM268pU6ao3yPr/cEHHygV85s3b9Ly5cupcuXK/tzFDMMO3YjijCP6eY0W0LEH9xbb9lQy+Ix5XqPRGQVn/ZSAe3ngbJgxSsuWY+euqrFaAydtthij/nS8bce5WVMsf06lrAxm7/BNkBALNsZDIauKhdMa9JbW0kv8M/toMZxz/o5qn9cz3raZRx4pBvV7syLLeL8otbYWduM1gLPeyE6ayVm91Bzli9bAIWPBM7TkmMH2kxctopKuHG/sAx8ro3qs8bFWKJpHXeuOQIapfdXihpb3scPatLzj92233N2g+8HWCG37Tdzcvi0cqMjofYaC4G9gg166fifFeEtbGpQpqIKzCIquO2yczYCZ3Qj2wj60tY2a6S0p/r7mv1uVXAl4JPZqip/NBO1AbPd99EBNSzvSXzu1ykWz2aUnv6C1EqhPGwGoDOP2oxUHM27FUbrp8RaMA4b0Dv3E7t8oREWUoBzrj9I5zM0FPDPXHu31MTZGl5Ps1UeJsaNnCwxQqOhik3tNVjNG+eobc/ZaBEJG/r3Pb0qNrqoOmAGNtKAMRNF8KTNdpJeEdalRwjKyyd7YILPm9d4rgncP/bCROn2xhlaZML7JEezMFbYpNYfhgd5jVMWwAJnRcPQcGVWU/VrjD4E1XG8oOQb2nE/OyJphbF2xUhR35nhD4Azl0Kh2X2ygki+MTOv+RWd0qp7cV+freoxWH5SNI4PSuJx7ji/P70W2ydeMD9ZdjDIDTcq5zrjbo3H5QqpK58SF61KlIwgGwtV1EN7NY3NPYAKtys15HTMCLl23znYz7HgaXYXp6v7I6u3D21dU/xt5D3DGyoOx6p6DIESDsoXooQZav/vcnaf8UuK9QxdWsy4zZ1jZfoVBFVDpEXG8MyDonUQGErOUq5fMTx300jlkvc0EFyw73qUKOna8m1csrEr6kB03KgOIbe+zjO6xn/FOUW5usuONyOH2k5eUunuRvDmU4NlsvQ/abwJ3dsRLbLNt2DcYy8u9LHWFw75E70/tod8sbeEIJua3Z4ZRbvZ4++8wVV6Fe9ro+fv9FoS5oKvWF8mTMuObLUsglS+S19Q+b65+KW2n+sUisGbiSLHYeO29Yx3MnzOr3XWIHW+jz0voSOAlQwrlohJBzluleuqG4D97jBnvB+eVgzuOrknbPmuMeURbgK/rMTu9qKgokNt5fzeDsUKYMX87IdHnexSyRsiooVzRUeWTK6BRwVMxJOstCMZx8qIurFbIfrabuU8vN8fEByPulbBxuLKpm501kYOwqMAysvLIGTyrvFFoQXq6dXkVCIftftyk1idrluvZZG6rQdIErVeYSsSVo2ayQ08Msu6SNVwRi/3girWMhjjeGRCOJqGUEg4ul/+a3bMGZx99GwB93I7InT2rZaEzKvsHIx8GFxYvjLJyhD/UjJHl/nrlEfX98A6V6LEWoer7jSYHPri3E58DCC3i/OaGc6N/Iy3S+euGEz6XmTvKcOGYY1GHYJE/ZjenN+DMYDwRqgFQ8o2b2yR9pInZ8AxveyJXlUzu8+aMN5xPW7jUHH3IZkXYUU7Hwmr2JixgfYSDhmN0ONbYY7BdN/IalXWd9WUjc3PEBUMMDVQSQUMDpZzuiIsh88RBCF9H6iSXmbufbcZnw1lvZGJ8YYteZo5MCoJL3tJUv2dm5iodQTBL88Oeork19UIKUnBQTtWuZESlplbNo2V4UfVoCzLwrHvir7bArRGaDQq7KV/ObJYKHazfZoJABivGs+MNm7xdFfNnqPN9OeriDVUVxWM9rYEeCyfIMFYuIyKOdwZkJ5dx6DOzIV4Dox/OqZniCVyyCifMnoqvNe31+apGLTIcpUMmLUdWx9vmCxqiYmYZ/OidwcKCbPKQZqHURHdIkQ0yu4yHs93okcJi6opHm4aqYAVm17Kz4G2ZuSMhIzj4fAwyYwYJAiqsMTC6Z3X1/aR1x/2ioHrhqv1Sc+s+b7NmeUdd0h1vOxnvyiXyqvMOQRuzSt0dCatZn5ccLNpw1NjzksfnYTyOK1ARAEcRS4MR4/1YSBPlmvYCDvZgA8zbyhdbx9fd/m6GHW8EYn2pPmBxJF5vvKWD3veOMWtGqb0LQmYneZSY86QAys158so8AyavcAk37BRX2g4swGY2thogyaMdzXW8UXmIwCwCG9X0li/QtaZ2bBaHnTHVTt158rLF/uAJOLZ0r2mesn16QBzvDAg7oSwehIwGl92ZmfVmA9peRNEWFvSBkq4RDsjeU9rFzCWCjqgenF+VVaKcCJlHo8HrTlh6WH3/QrsKSl29VukgVe6KzBoE18zkxAX3yswZlME+qM+z9FTYw50yc4YdkIw+ys0eLCCGoFDP2sFKcwEq0r95WWXgjao5gkC28EixQyb1WSeXmqdeDxAcq6w7/hyYMGuGt62wmr3evvVHjJsZi2sco1IQ7Oyo90+7AlMGwD97T/uczVgTfs4y2s9d2OhDC46381zR1nBYb1to7GF/NYTQUBWDYIm3arYwFtnxb+JBxt0eCIQUzpNdXaf+VjoWhIwKdBPccbzB/fW0FpxVh85RnF7F5w2wL7msm51LezTzY5837k3I/iMuWl/vc+bgI0rizRRC5ioelHRbB2bxM4LR+IzMnHayU682rWenzJzpVquE5VjwfTwjIY53BgNlztynZ11myNE8nrGaVsJqDFSOobiLEQ5GlBLtjXKuaG5t8Ncold80J/DjRQeV841o3iNNy1q2ySXuPOrGLCLOX3dLWM2aZ9tUUGJCq8LPeaQ8juw1MpYwUF0JKbGIBqK8GXk+oz0OWDneiOSj/QBM3hCh5lmaBY7zRSel5tB/AEdir1hEAI2Eq2tKO+jnqxmsXav7THK8z1oy3o4d77Z6eR2yHBDAM4K/dC2HNpWLOsy229JdH++HNcmXqiRckxDMQ8WLqyBkqukDpYJU1t3b9di6v9vV/G5bsEa20rUgvK2CgqgbVPwR5KwT4l1/N4OgCSvOL96fMbMugpBW4mquSs1B1RL51agraD8s3Od9QBLrGcSFKxbLq/QkHIFSb/ihsJ+5TcksUGHILVec9cUxwT7CJl572LhAsC0chGipi94yEEBtra/B/+2LMb8it2xBp1VgdUMKqPvR4gxYbi6OdwYD2RYsVPlyZk2RaeKeO2S8zXJ8Tl1yP+MNuhlUTgIFYWcqifb6h8zo88axn6Mb3R/3qZWix5AdUzZO03KGty1QuH5UDxKMmh/m9uxMXhC71HRcZs6g+gLG7Nn4W6aVFqf3jDeqLbg6AErf0CTYowsCmgGydTA4gD1HCOsDRMfwHDjfRoI1hteDEAdCizX1INm+U/Gmiqs5G6eFvj70oGOeqRHj7jCuBsqwgJVi3QEOOq/RvqyHECMCcBqtx7R4Um7ubZ83V1N5O8bL0uftpZotZ7thsDlrN3IXrGtgcZiUmwuCr9y4nUAxevYSc7PdgdfQGVsivbZbuczcVQVQwTzZLdojZrfEodIT2CYsuPLIrD5vVDNxVZI9HY6uuk1ulro6bMu9eqC9vp3+bmu4itKI9qv0hjjeGQweYYPsmnUZScPQgpQ1MEA5PVwCajSn49zPeFur+aI00pfSGvTEINiADLqziCZTv2xyn7eR/LbxhGUcgm0AgHsOUbZoZsbXUmrugeMNXutSRVNfP3+NJq5xLfyF94D+R9BZzww5AyX3XIHBQRJ/gm1inNfXKzTRO3+ByDmycPCBuKcaAQieob7WwDmljhTNEcm2p7mA9YGDAd6W9zoC1zPmsDpSNQeckd1/Ks6Ua8IiruYk441jYHE4fexv5sAajEsENNiIchdeD+fv9m6kC3qjeRZ3p+rul5kzXBa/7sg5r5SEuZrHE2E1a1jcZ090nFcZJy4J93b7trSoUESJLp2/esvQsUaCkBnh1j6sje5OPEAbHMqfcX/yRm0bzv5qbr1xUmZu23pkthAxJ2Bgl9vTloDWhRnVcFxGj2o3BBpsge0KPwGl5maoq+NzvH03USUeXCWHuNwc1QFmVyD4G3G8M2hZK5eRMhDaqhNSwNQeFkvG28koMWugPo6ME5zmpT6Uk7BR1MNNMSHuLUEmEguzEaCPiLPdj7UoZ3ebWNBglJsV+ICxHuFFxhug3Ond+6qp779dddRSEuaIsFPxKnuNvkx3DV0ut+dSI3+Bc6vfT5vUeLfPlx3226xM60AYPg8EH5jWlYpanByzFc3tCasxNfRyb95Po4C4IIDj4khoEWWECEJgP8/EGX9j5V5lZxlv0El3vJFlQMbaF/7VSyJh5HmadYWgDIxMjJ3zJhCyO1rrz0agxVNxM75nQHAHwjueqnmjpQH7Ddyd320LPicOxrCx7F1/t2/Cagw+C64E+mV9RKZrkREEM4TVPEkKwDnkzOfvm096vM21R87RjTsJqgrTnQkP3OdtpsAabEUeo8nCagyywEG5sqkWPjNG3m7Sy8z5fdqCgAj/zowSbxbwbVC2oEtbHQF7+CxYdpf40WbzB+J4Z9SyVhvHG7AxttmkReXU5ZselZoDiE3x3GtvwLiJ1Xo/jL35jPaAcYksGHppwk4b01/6985TapQaAgkcNbUGTpfZAndwYK7cvKv6lDAX11N61QmmFhULq7Lbd+eHOTU0ObOGvkxXCvYMVwHwDEd/8dPa48qh4vPyzbl7fRJr8QR2Rnhute1sc4hwmbUvnPFGD74jeJ0w2vGO1hXN7QmrMThvIDRnVp93sqq544w3aFSukMrC4PrZHeX9uYkMBQd1eujrmicE5c5mUd2dtS3K479ful+7JttWKepVqTUMoc769lkZ3V226k4v1j97Qn6elptzNY27QKgIgUAIZ9qbDestDzcpoxxwZNvWmNh3KQiZZZSYp7bJI03LqP/n7z5NZ/SqSndhhw2BUHeSMggaIhiMfWXNIqNB4gHxXRyH4jZBYbTsYf02S92cS+jt2agMVwYs2H3a8GAjj1Br5Ma0D9BDz3r/m8EqjsTxzkDgIrFkvO1E95qVL2JanzdKE1GS56nj3adBaVWGC9E3FoXzVDgDpSvIKCKD5g5YgNk4g2qiUSPEwKDGZRwu8Kz0a1afN/d3BwflctsZtgb7/UHvmsp4hZG5YI9jQZMVeh9mBz1b6A5cVnXwzBW6ppchmw0ygKyi+cfTTal80TwqmuypY+GrsWHbAoF2DAip4Aa86fh5k2d4O3aEWGgQ64YvY5w8GSVmDWc4jVY2x5rAwnKuHG9oMUDRFSw74L2xg+sabQUFcmdzatg4o3/DEMsIHU/LvZfpEwZYFMwbMIJMe62zHgnubTaozJv3HeuPJ2J3HMyEqJo3a58jCufNoZxv8PbfYUpPRMg4oAXpvQX76YN/Dnhlfwjuc/KiZxNXmAZlC6lWPVRG/uRGGxwDXQZOELhTZg4wT7umfk8yq8+bbU5HzifbVJ4GH12BNlMoliOw4KwqCRUGXHnlTXm/I2Bf8Ai1xm5WRfH9CCPIzFR69zfieGcgUMIMpwIlzTDq7WUcMTv3dJw2wN5IuFQ0V7YsyvB0FzjpnOWYuTXS4+2yA9XNzYgmw+WIRqi8o8oApdc4tjx70tk2WdHSaLjMPLSI59luBg7isPYV1fcwRi7pzos1iDrj/eJw82fnrnIyqg2QfTZTVMwa3LwQY0K1QUih3BaxFl/HNnma+bXXfsF9/2b1vGMtAOincvZ54yaLyhEjx+vx+uIs4w1qmuR4n9ODgLgmC7rRT2iZY+2DscPCZtA8sBZW9AQ47DhmqFyZt8v9+bXox4OqN94vBxG8AfcItAdg+xv0skSPhNU8HCNmC8pBIXaHcneMEfJXf7kz/te5itonGK6j5u+XkvMMAoKyT/y2jaZsPKEmTLz25275bP2R8XZTWM2aF/VJIDO2Rro9XgprEgRGi+TN7lEVjNnzvLdZhNXs71ObSkWVc3wk9qrLlj9P4EACgt0IMDgC5eawp8EfXlReOeLouavKJsmVLYvlvu8KlJv/8HB92jSyvSrBzyiI452B4MxejVL2o/4od4biKzA6y8b93cEFcnrkAIOHm2h9dOiR9iTLg4wIG2ccGXMX7mNBz4mvirXc2w3j3dkYHRi1yO7jBuRpyZQZM7ydjRdDCTAyphiPZguXQNULKeBxWSnPrPRXnzcLZnEfL7c24CbkD8EOVnC354ByBYBZjjdHiJ0FwuAgcqWIkQJrHHBAsMMZfAOGsrmRRi8bZ0Xz5nBL3btNlaIqYImsFwewPIVHwHgSjLIF+zq0eaj6/uf1EW5XIbDTD8eTx9N4Aww+Vv9duMe9qhAE57ilwtf+atw7eC13V9BM9Xcb5PjbI0+OrDShb111bP7edUr1ewvGkJaO7rvzwpQjgCRFzmyBStRvw1HzZzhndsfbG/sEAUnYT6jCmbjWvaw3t/1AaBLXrifbMqsyFJpCu3VRX0cZb7QcNdTtJK4sNHKMmDvVWAMaaVU+C3afMqw6kYOj9csW8CgwjRZSVB5lJMTxzkCwAe9Mpj95rJixWVfuhynlorTUHq0rF1WZbzgKnowOgAAPhDOQjXBHOMOaysXyqUwg+rL3+pB9hWH8j16S3ae+8/FBMIjZ0TDjBn/Cixne9kAG9JMHa6mM9uwd0bTxaMogDZdAeVJmnqrP2w+ON0ZXrNf3vaNewgpHEMEn+DNmzqq0HalVukDq66JBGe3Gi+oBb1SkXXH5ulat4EpBlq+d/QbpHYAoyygx5+sBesxhE6FNBT26Rvd3F3UhrGZ9bfLa6E0bAjITmAgA5725zXxUT+nfKITy5ciqggCrD8d6FGByZ8KAK3rVDbYcB3eMLjaoEKzzpb+bYTEliN25U26OSi9UceHY88QKo0Fp5Ds9NPFJBCONGD2XmUHV09RNJ6jRRyvoyd+2e9RWYAThMVeUeBTOma8H1LM4Gt+tOurX/cgsILnBQeiyXmS8EZAbrlfi/b7lpKWt0dn5tWS/Z2Xm1gFxVA5hf42sAgMQgETJPOxdZ3YaT8Qwqs8btkhyf7fr+xP0oDDy7drtBMPGeW3T7xON3OzvzsiI450BM97OymrMmufNi2qpAu4ZutYgGjlI76ObvsV95UrOiEAN2NMsOzJLfCx86eWBUjaMfMxNb13Z9YLWUjfK15ugZu2tormjvqpH9EqEkX/vsziGMJA26MeLy3O9dbyN7Cm2B8qXIRQHcTHr/v/uumDHCpNmZVqXO2P7cCxLBKW+LhAwgqOCG7GRTi+DOeHAVetHdV3Z3KiMN9YVd8TVuAqnogkCa+4Kq9mKCwLM4fZ0bWQHGRUdvmScAcoABzTWer0nrY3wSMeAA0y+gGwL1hAEJd0RteEyc6PKvFEKCeMcQVV3ZprzlA60k2B6h1mgEgGtKli2Xpix06JMLHgOHFyU7cOBQnvHY79u8+usdG4paVO5qNLDebp1eeWE41xKi3GXGR0Ex+AMo7LAkzXZGnxWdUoHqTaUH1cfc/rcXZGX1LkFu4xLx90Fa4ilMtTgcnOeFgE70JnNyuMgsbYa0duM3m60g0K/x3bUrT2wb/31YNTMbZ63gNp1/E2sSrrXyLiO95XjRAc+JUq4RRQ2RnsM/18/TXTsF6IL24hOLyGKmksUd4Ao/GuiO/Epn3vrItHh74gu7SWKXkB06l+iizuJjvxEdPNcyufevU50cAJRfDjRyT+JYlYQndtIdHwK0bWolM9NTCDaP5bo2kmiiGlEseuJzq4mOvEHUfwRogOfESXcTPk3N2KIjk7S93sxUdTfRJfDiMK/IbodR7f3fKBEq4YV+4MaBgcQhX9LdHkfUdQ8olOLiC7uIDo6keoXu00vFZ+lLsLL20YT3b2h7/cRopOziGJWEp3bQHR8KtG1SKKwj4iSErV9UP9/pD2O3+N5eP7JWXTr0iF6sshcKhMUYLPfZ9V21faxH9gf7Bf27/Zly3OH5P+dCmS9TrWuTaNjhzdr7w/vE+8X7xvv3+p1b968RqGx31No9lM0sPhG7fjhOOJ44rji+OI4W+8LPgd8HvhcYlbQgOKbqHyOaCpw8hvt87N+Lj5ffM74vPG54/PHeYDzAeeF/tyrO96jvIHX6YOqKyjHtXDtfMJ5hf3GeYbzzep1W1XIR88UnUMRJ/ZRUsQMfb/XEUX8TnT1BNH+T4gS79qcs9FEx34lOr+Z6MwyosjZRHGHiA59QXTnqnoOnNgu9DMVzBJHtW/8QXRpN9GphUTR/2jfH/mB6NaFlK975wrRoS+18z/yL6IzS4nObyE6Nlnt9zuhf1GJ/DmpZ5bJ9OGC3ZQQ9gn9sngFdcu7gnqWOEKVA3YSRUwnuoprbRxR4h0719pk7TXx2pF/UbXc0fRMsQWUcDueLmwZZXWtXdD2EfuKfca+q/3+kejmeZv9vqq9dxwDHAscExwbHCMcK/25d/eOoSyUQG+Xm08B6lr7XR3r7sXCqVeBNXQqKozu7PvEtDXibGyUuh5xDLMf+jjVGhFwdiX1LXuSHiq4nA4e3W/4GlH59nKqnOME1b0+Ta0RKZ6La09fI5rnWEdt822jhPPbtWsV12yKtc2zNeL6zg+oEMXQgwVXUKm7Oy1rhPp7vA5ez+r1mwbfpYGFFtPZExucrhHafsdpax7WPidrRLmYLyhHwG3qk3OGtl0cNxdrRO9sk6lszgtU9848OnZgqbaGYy3H56X22/EacfHw39Qx/xZ6sPyFVGuEds7Ga+cPziMXawTOx+El/qbyOWOo6MW5FLF/odM14syGt6l41vP0Svn1VPLOHodrRPJ+n9euKwdrRMD+j6hvw9Lq3F2w7ZDTNQKvB+MQz21WLr+2DmA9wLoQu1b78nCNCIg/SB9VW055Aq/TnT0fuFwjAo/+pNa9V0r+6fEaoV1rd7VjimOrrxHatTaT6Moxix2B4zLm/pr0UaV5lONODM2YMZpWbVxId6P/yzB2hKM1wtaO8GWNuHv3LmU9+DEFZ4ulCQ33UKsC4RQYu4oObp7kcI1w145wd42A441ztlOVIHWcggOj6f06+6hpnr20bM0cj+wId9cIZ3aEN2uEOi9xfuI8NciOcHeN8MSOwHNQYYDjXaVYTgo4+KlXa0TAha30QZPT1DX/Btqwcx1d3vmptu0U15q2Ruzas4o65NtCL1YNp+xX9npsR3Qqe0ftr3IWPVgjXNkRkce309DCC6hdhRxO14hyt1ZT1+DT1K/AItq4/6DPa8SeQ7vpgQIraUC5k5Tr0jq31oj+Fc5Q+/w7qNjlRRRxdItPa8TFLaMp/mo8PVtsHtUveM4nX8Nfa0TytXZTO06u7AicA24SkJTB1CTi4+MpKCiI4uLiKH9+z8qP72UQmRs4abMy8je/1cHpc/v9uEkJfI3tU4sGNtaiWr4yaNJmJUbxeb86LkuuHfHC9J0qw4Ls98cP1HL63CX7Y+iZaTuUWNeGN9t7nPFmFfC241erSPfOUZ08zlTB2W06doXKrk0e2pDaVy3uVvlz3feXqWzOfy+1SjVmyluQYWw5bpUqkTrwQVevxZ3slbA+NXW7+h7lUVzZMPHRBpbRQ57y6C9bVOR3dM/qdmeeG8XjU7apclWUiD7ZqrzlcSx5OFZ4L+5+bt6AFoThM3dRo9CCNPvZ5nafM3HtMfp40SFVIjxxcENDt9/5izV0+OxVmv5kE2rhpPwZfWc1Ri9Wmbytb3VwOffaFci+9vl+I5UMykmbRjpfi8CUDRH03j8H1BiVKY81JiN4Y85emrU9il7pWJle6qgJ87jDizN3KTX/Ic3K0vu9a7r1N6gGqfvBUpWJWfRiK7sTJbzhpT92qRE6vesG01cD6rk8z//XuTINa+/+e3XG2fib1PyTlSpLtXB4S4diOOjvrvfhMvX99nc6GlJqzln8ZmNXqJGPro5py3ErVUbtt8cbq6yY2WD8H9ZEFsnEeT6keSgNaRaqKjgE15NIHpuyTemhbB7ZQZXuQ+AMiv7jHqpt+vaRCW300XIlurnlrQ6WkU6oGOswYbVaB428jgWir5YfoS+WH1YVI+P71vH6dXDvHjRpi3KIH6xfmib0q2P3Oa0/W6UEPn98pIHHpebW9jSEJnFP9Ma+tLemNvl4hWrh2/lOJzWj3BnjFh+iH1Yfox61S9J3g+r7tG1U6KBk3NP74TPTtquS/cdahNLonjW83v60zSeVpgKqD2Y+3ZQyIp74nhk3453JWKeXLjfQBZuc0bSC8aqNyaXm7o8Ss+WRplppM9R8412MbflPL4GE8IK3i2JokTxqvBSMOxZG8oTd0Ze1MvMcWZ06NtZgvi6PUjCyT5BHoaBE1Cinm8f7YNHPmyOr5TN+pnV5r51u23YHs1CjK07YH12B8wViWqwTYL6wmuOeNi77grNqdAyUVc1dqYHCWSivjzszotwcTpA7/d1MPb01BjPNjToGLJxXPL9njiCr3qPc3N3RURgjBqcbJZTVSro30tAdntKDRQjgHDtnf9QRerBZx4DLE40AzkjP2lqvNYw/f/V3MzB4O9fQAmKT1jkWU4q6eF2db2hXcqeE0gggfjT1icb0aqfKSjEZ1WOf/HeIun21lnbo43IE16M3EVCCngiPkIOQlNntRwBBKiwzaGmwnqOMe2cPXXwTTqJgHNyW4e7IV0fg3v1mt6rq+7m7ou22e0AzBU43ytq9DcTVK1NAnZsIAGJahBGwvYfzzpXTnWK0Yvg5lbDxFtxTN3N/d0XPyrxZ+wCikr7o0PD2WdQ4syOOdwYAFxb34rEirTt9xhDNMuJGh9c4c/mmZT6xt0DQAQYcegvn6krh9sAitFwXneB+XW/hPmVvRCxW6n/TukpR5VC7S6tK2vFfZyNaZoTjbW+MnK8g4rr29Xb0+xNNVPZrZHdNZMhbePGF0W6WoXU49ooaJZI7exYl4GVLuyrFLI63WUU/llFiTq6JGsFBqu8K85+NFHLBe7rshqo5w8fIiLFecIZA6ULurQWo+oCRg0ABetEM7fH20PHG2ohr6MqtuzTLzVEqmDkNYOQZkRlhkGXuWK2YysB9scy+I4CAIZR+0RNdubix1/5zbTUxo0VhZxzOON6kK+X6qmbuaLoCzzQ/clZTTbdlVbi2BsPpRnDQX2BqCEYcrX+jPX36UG1VaYZzd8DEzTR3p+N7V2YHxjvfa5Gx5MAoenGxBu6KMn/MJPfZttODr9a81KGS0uRApRf6hAVj4KkHVUv4XkVQJ6SAEmDEbfuzxeGpfs+jQiH+6m0FCq7vBnpAmNc4o5JjbP+5om7pAioAiVGfvogho+oNE2owxqtO6QIeCx+jogf3ZlSZeu3460kWcbw1xPHOACBLhVENiPC5M8oGwhG4CHExhjswaDwt3YJAlCMRKXeB0fpos7KW0hRHDhEih1iMYOzUC/Ety9FBP14w4O56KO7C4lz8Gu7SUl94t0ZcMEzNOtnxNi7jZg3KArHf7s5fdAYivnmyZ1GL+UGTBIpYQRNCg1ntVABgpAYcXji73o6Pcjfz60xgDDf4mqU0Y8RIUR9kYOGQuaNqzhF+tQ8GGJvJwmruZbzhdOOcAEYZu8niajk9Fl18oqXW/vDrhhNuCT6t1p2/tnowx0he7VRF/Q+RsQN2qhGsZ4cb6fSDKiXyqawLluGxdsYKYn3mAGibysa/99qlC1CXGtr2xy0Ot3s/YCfO0zXYKHD99msYQktfba2CwHcSkujVP/fQYi+U8TMDqGqB4CQcCp6mgAotDoSy6JlZINDLUzpaVkrteCPoxq1yE5ZK1tsI0MrEo06xphjB/7pUUVUusMF4lCB/vgv1KTM8OtRbeOyWERNosF/r9ddpWbGo2/ciBF7BsgMxPo8Rg1o77rWegGPct6Em9OluINpIxz+jIo53GgKjDj10vsLKr3C6MXPUFbj4OEOxwYCsK5fUomzL1zLnB+qVUk4ZynscKUpasvs1S7g1o9cZyJRgrBicQFZddHd82sEz8apfx1ODu0rxfMrwgHNk1DzrIyZmvI0G50hDfaTERpPmpm49ccnp6ApcJ43KaUGbVSaVm/MosVIulL3NGLF2+YY2Sgz6BbieXMHHyQi1ec7ch7h439ZgJjzYpc849QUE0C7o42a8UdDFGgQlfKxrf26Pcpndx1oFA4UDakaCPtOeutr6hKUpszsIrKBs1ugyc2ve6FpVnUMwcNGbaw3EPHGMEPDlKiqjea1zFbV9OGR/7TyVqsye7xE8fietgD7ItwPr08P6dI5XZu1R9wfBsQK+daCI1fh5LJ5ZINHATgArV9vLekMrBS0c7LQI3nP47BUVPENbBuweI0BbwEB98sMniw9ZgnIIHGO0INr/oBniC7ye4xzw1U5Hxh8JKlTgeTLysLO+ri8/gPYI7/ZhowdjxOzRr2FpZefidU7qARRP2OSD459RkaOQRrz5116q98EySx+qt+BiXKiX1tznQYSvhX4RGuF4c2bPlzJz61E6D9QvZcl62wJjk2/O3fV5r76AbChKqcHfNoadM9jgRUYV2WBPgMHRqqJx5eY4B7gUE6X69wIoYQLuzin29HhYZkbqzrU92upZOs5YGg3EVABKtfzueFuNEnMnE4reOxgFV27eVWX6voDqG9ZQcBfu894V5fsxgGENOwkxucJe9B0jizlMnxkLUSBnM4ZX62Xm9csUcNlL7y2vdKxkye7w2DCAdRCjZhBcMKu/GYG8x/UKgHfnh6lKI4azk8jgmCUqVrl4PnqlU2X1/ej5YXTU6tyEY4RKqzKFclMFXaMgLUEQ+P1eNVQpKcQzR8zZ43EVVUaHAyVoK7MGbRoIsKByy6wKJGt7B4kHR05ASKHcFtHZz5aE+6XvPCPDfdhGZbsZtHrgnoVg7X9hWkZ42ibNZoQGDdZxX0AVFlog0LLm66hLLjNHwMmTtkSUZuM9xsTf9GofEDDgYBdn8D0FlWut9OoQb7Le7PgbNW4yIyCOdxqBGzOMGO4P9Ja90XHK8UUEl8u13IHFwNA74mu5c5ReWgoDyAgebRpqKaM8E6c59dYOL5wDCPkYZWw+UE8rLVu8P8apkW27H8Cd0n5n0VRekH2dF42bAxwNI2Z4+wPur4MwlbUxbwS4HnCjgiHnrBWhXdWill5zlMMZCa4pfCagqItyZ8x+5myMETM7waXrt90uM+cAFIJIYJteLeANCIyhGgSU9WA94CwAsqjuipo5IjZey3bD6YbD6g2YrIA56yhZR8m5I9boQRsz1bQhfPegHoz8ZFFyducPfb4qSp29fZ/uGrhol8B19dG/Wsk59oF7/jpVNzfbjF5vOGrXbifQE79tV0rq4Hc9MNvJhDJ7b8F1BKXl/DmzKpEnZ+dOZgNrIvdw2xrhCFrxYytMLDdnIUJXFRrD2lVUNhWcuvE2lSaC5+0FoJoB/d3WoI2IBSjfW7Cf9kRdtiShHm+p2ZC+XsvsrK730U5z97yzJaeVXc+VrZ6w/3ScspcRQODWDm8Y2EirLpi9I9qt9isGQSsW4JT+7mTE8U4j2FDz1fHmsmuU2nmSdYD6LnqkEQDgC8NboCDJkWIjQGQUgiuI1s3cmjLCNnXTCYv6sFHGJrJVECeCqJs7AhJw0jhy7m2JIzveMM6gnOkLh2OuWgIfvkZ5/QUCBDjm6Ik0ourCmm16FQn60Z1dE8iSQfgMzuKm48buA3+myKrACHdlQOB4wJ/ytQLGeuQRKOBBFhalYGCHD/uA/m4kiGC0elJWWDIolzofcM3z5+ctCLoArG/egqzE/zpr/dU/rj5mcfZsRR45mm9Gf7c1L3esTDmyBqoRVhjvEnnhukUkqr9uFJkFRMsgIAZmbo1U/cu4b0FbBOc3RIzMBOs8JivA+Uc1BUZRoU8X7x+/G9rcdyPbSHA9v6ULUH614oil7SGzg2oNrLWo0ChvJ0DM/aysW2A02DYCve6U3WKk4sd9tHGC368+Rg//vFmNPftt4wmLeKTgGgTo1h7W1il3J794wnNtK6jJNAiQQtgQ9x4E9SFaagTsKPNa623AiW3s1pU9Pwa96mqVrPN3n/K45J3vT03KFbardeMuWOPRfgW7xrblyBkHzsSrZALa3VjHRRDHO83g0g0YLzz6xis1cz0K5kmZOUCGgDN+nlxI9ojyoqfTFYN1kTUYeiwSBXEhLCTwt1mEzQhwLFhhdcYWLYvkDPT8QCAGThv6tb01zlhUy9fgy3Z9hE2te0i4Qp1/Vcwp9d7mYIyYvX3gPrBVh4zt804W98rhVjaOsz2OdA08xRNFc9s+b1TBeNtPdlJfC+BEe5qF5OyCr33/7HhbjwryBojzQO0dCuffrjqa6vfbT1xSwTpU39hTzjcStPFwduf9f/bT8D92qe9R1mxUwNMZcFSebq1tf8TsvSrDBAY3LWvoGDFHoHph8tBG6nxGBm3Qz1vU4/fXLeWX9+8pqELA+o5qnm9Wpj53MiM8Usi2v9u2zxvBR3uBLl/BeYPrFQ6EO2OtUAmHuccssDVx7XEavWA/tfp0FXX9ci19veKIz044WsSen76Dxv53UAXTMhrQv4AOBIRMzZh8gETDZw/VVgE4JJFwWg1r7/6canftdLSBeVuJBXsENiwCwd60xMBGQUXI2fhbHo9gTe7v9i3bjAArj9r0pNyc7ZlG5QoZOub2XkeORBqBbBA7Xuv0iKCn7ImOU4saokneCEm0NcjxMbrUnEUlcIwQYftxzTF6ceYu6vntevW7LjVK+DQv3B7IGqE0GWW2rkRxLGrm1Yr5VOJoVI8xR/GbuHA00xvWTq9RI73wOhydtu0jtIfF+T/svXiJPc7pwTR3xb14XzdHXDB4hrf7+gNo3UBWFY4ri/V5ChuP3qwFzfQsFBsL3nI2zr3eend6dnlmLCptbHtPl+rVMahe8lXk0R2ebVuBQgvnVnOjUVYJAaH3etUgfzGiSxVqWLagCkRgdBa2/0I7rRfeH6Dfe+rjjS2VFNg+9+KnN3A+jOxWzVISL1lSog36dd3CwSxh9JJitCCSetzKZSRc7tu8YhG3r9eXOlaida+3o7e6V6UnW5ZT91j8KcSyPl92WDnh7cavpkd+3qI+Z0/uIYvDYqjHN+tp0b4Y+mnNcWXf+Fr95i7InO6LjlNz5yFSaBYYd8haK7mzmzPur0HZQrT4pVY07YnGtOyVNobqXUCnBNUZdxOT7QpP4b9DkNQbexHVV6xD9JcHowqRacfkHG/md9uDK6swAci2BdQRay0j1MxrxboXEcc7DWmtn4ws0OMpXBbdrmoxr0qMUUYD9U4YUY7mtLqzgLN6s5GZB0TYXmirzXHFDW7BntNqWwhWsDFsJMiOwaF3JOrG4MbK87u97e9muOIANydvRXgQSWXBpXvN8UbmA4rIcPR4zqevHD9/TfWiIsLujpgHbkh4Ltol8LdGZ7zdLbdupu8rKmCM6PNmVXNPMt5YQ/iYrfFS6Z3HxngirGZ7DFCe5kvGy1Jq7qPjzSKAKF1ES8SYhQcsj6PPjXvu7tONIrNByffc51uofUKw9etB9fwqKoaMxW+PN6b3elZX2Y8vB9Slgh4KSxoxYmzDG+1pxzsdads7HdO1pgVKa2Fsw2hHuXJmBpl/BItclXl3qmbeWDFuaWrhYfYPds3TrSvQO/dVp1nPNKMd73RSWVasV3DCEZCDU//OvDAlQOhODyycolHzw9T9G+cIAmpY91FO7A++W3VUOfoP/rCJHtarR8yAHS+2dc2iUvF8yrkzY6oL23k8utDb4EMrH3RAONuMe467rSvItGNyTvH8ObyuzLTVGkEVIQJj7lSGpiixN2Hix72MON5pCF/Qqw/Fqn5BT4ADiIgpj9XyBoxU4pugO73N9kDkC4YFnBdfSzttGdI81NK/h5Ffc55tRguHt6Kyhc0xtrh8/a8d0Q4jz3AKYNijh9VXlca6IQVVCRFEuLZ7qWi979RltbhCWf1eGCVm6+jx+bfaoJFenkbY8RwuSfe15cKacx7OkUZPIXrVkDDhCgZ/93gboT3hS8YbQYrKxfOmyE75oiZv1HoEg5tHanF1CvYP6ukoWzVjjJgjcJ0j67trVGePxDSNAveMoS3K0fi+dUzv7XYWlEXp+b2gZwFhOjBnR5Rl7GZmBE4A7AQIFjoL0PNYPKw/voq+2jr+LPLla68xgk2YbTzz6abqOkQwCiPIkMz8fXMk9ftpk0Vg0hF/bI1UwVlU7v0ypBE9obeR2I7MMwME+a2TCzguaOMzGnx+XBrNU0zuRdrrwSCs/Z4q3KONlJMKngZ8bMdt1ikdpAI1aL90Bw6e455ulPjkY7o9PmXDCYuN4Qg43dhfVJ7da7ap2YjjnYZARRjRKJTurfewjAWloIi0wgjxRdiney3tRse94t7O7MWsYqOVdbFYjLqvurqxLX65tWX2s1kgY1yvTAHVv/3z+uN2n8PZbhjbvhp+OF4ddePV2+OPflzQOLRQulH29UbdHOVLRjrenkTYUTFi9AxZVtb2ZI40j/hbY8CINetxYp7Qxkpt3l2Ff0c93t7AzqQvGS+UYvsqrmYNssoIAoIPFx5QGa05O7SSP8zYToveNZmHem8A3QRkRlEx8fG/Bw1tZ7mX4F7P5uWdO72oaMN1i17sTR72szpj3eFzqmIO65KRlXkInMOxwci7Hx9poNSjoYQ+cNJmitUDgPYc0h/WHLOIg+Fa7lm7pEpeoM3NDCfYmlXh51RgGEFDtj/+3uV+CbO7QAMDSQHcA93pqU/P1zDaWhBo9XTcJVdZ4Lz2ZrQlA9tuaAvtHoSgiTuJOq42aKO3NBoBqkLxWcJncWQjW19zvpTYZ2Tk7p2GoM+oW02tTBF9Pp7A2W7Mg0YJoi+91HAAkck94UWpbbTBiub2jhFubEZn0+2BxWG43jP4+6aTdstdLf3dPpaZM/fV0T7//8LOeFVuzhFlV0Ji6RUOGkG85LI+AstbblgZa+xAugNXjEAx2qgeOxZM9ETZO1ns0Peedx4nFuTmODEG/WxQj8Z8ZE+DgcgGcCCubCHvqlIwGoqrDzwZW2Kvx7tEUA5DM5fINkMsaMjkrZZAGYsyCoIjoHCOiglMIJnnp1Li9AYESd3pNcU9uGN14wOhrJTeycQqDTgli15spbL6UN9/5Jctdm2IP7dHKaEsZAL7NixtGfvIE1I86eP1hln6GMIHG5Smfvr25+0+bfjMeUuZuYEZ17QAgVX+bDwd6cX93S0r+p7x716rpApi4Nz5a4fzdQQVF4fPXlWtEJ6OMHNlj7/cUavimbw+wmlLmPXnL6REHO80pptu9C87EONRuTk73l28LDO3LptixcNFYZ5nXSNNUDRPS5Bxg0IxZsb+ujHlDFZEsPdEX06RJfUVLIrISp6/etvjsW6InLOCtz/LXY0EARtEUJGN4HPaW5ApRoTdU7V5PL9OSAFV5u1ty4W92eqgWH73nb9m5Yuo7AdKUr3VXLDNeBfy0PGGgcTOL8ZWeQJ661FahveAChhvqFemoMrEoP1imxcl9xAKQjQeGBmsQ2ZrdM/qqpyUxd+eaVOeapWWESmCc3COcMn5mIUHDS2hvhdAQBXaFdY6Ds7gLCzmeXta2msPBPBYrK2zruNi5v1s+hNNlYMEx2fIr1tTqGHDxvt+lZbtfr5tBSWcxXAQD33e3gYdXYFzj8d7oW8YgW+08SHgzOJ3RmGpPssAjhcqmwACru6O9LIWejWixxnnyrNtKlh69J2dI3zs64YUoCAPq97cSdZV023kSevsZ71j4m6q8x/3S67kE5IRxzuNQfk0ys1haHIZsyugkIoMtXWpsq+RNLBInwnuleOdDke6eAMcD1bKnbIhIsVNc/7u08o5Qzm6UUY9oqlddWNgrof9XRBVg6OJrGqle7iHhudU+poN+levGoECqKcRdg6AofLA2FJz988TzBxnw9RXVd+Lesa7YB7Pb7osMrji0FmPDMAjsVcsWXNv207wd6x94c08XxZWg/hYvpzGGhy965ZS/dXoX4eg2gh9zrcguAIlxcFBOVW5KoRCMxPaeEJSfZ7QsnBFswqF1fWLzF7Y6Tift48AHoTLULFipOK1I8oUzk3Tn2yitrc3Oo6emLJdVWOBn9dFqDUK5fT9dJVoBlVaCDoiCL9OzxYazb5TcaqaCSMAYTMgSMqjaP82MNMOnQ30NuM2jKrMex0It+XPmVX15burwRJ+9ooKaEBAtkGoMefdwMZl1GeH4Dx0AhzBGi1Glpnby3pP2XjCrtgbZ7shhulvAc57AXG80xgYmn30SOdsvW/QFZyVQ18vFndfgaGN/Qg7Fe/xLEkesZOe1WU9BY4wjAQEQ37Ts96IXs7eoc0v7Nsg5Q3TV/gG/M/e024rVlr3DyFrfi+XcvXSo8nI+Ls7psJeJB8ZEutAkid011s+0IuIaK0vICJ+njPeHpSaW/e8e6ugyucql4AV9DDjzT1tWFeQNfdE6I1HkEFh1he6se7EPvezC7Zl5sUNUDR3ZICtfb0dfTuoPmWVuaSCBwHWwbpOAEo0M1Ov9yYuM3dTXAqZPW4V8rUKCszU5w6jPcxoHRpHYA1EkA69wWhhguDavF2n6KvlR9Tv/9elSopsN58jHIRmDQkz+q4BxgKyzdCnfilLhZNRo8XY8atdKihDOF4IUHBLmrvK89yq1aRc4VSftbcgOM/tkOOXHrZrL6JlgMVJPWm584TO1YurvnVoMXyz8miq3xuZ6c+IiOWQDuirjwqAaiIr8jrDVzVzW2Bk8xxhT8rNYTwcP6cZ2/4ca2M2iOjx4jZx7XEVLUekGKUzmHPMfdlGAcXKWqU0xcpZ2zUjwR3WH+W5qPf24ob5rQgiwRb1NOvPQBUdNwFVNu5F+S+yFDwqAwrEvnDx2m31OrBrPA2MddKzzdtOet9vjlJrKAh763jDOOVeSDi/7sLl8b5WX8C55fJH7g31NOPt6wxvQTCagY3KqGkYyASywnZmgEuYnY0Rs6VHLc0BnbUtyqfS/LBTcfSPXmHAAon+omapIJryeCPVSgb74eVZu1W2GRU9D+rOrqOxUehvZ50QI9mut6Y1tMrAohwZiZMbdxIMCXSkyLimweQFs+AEGc4nqOS7Yq3V/G4jebhJGVXqDbv008XhqX6PteXKzbvqHgq70gwQtBnZrZr6HvPrrfWh0B6yXvq7nSKOdzoA8/EQgUx0w/FAn/EOfW5z5xrGCYVwltATdW2UgqHPA4a6N+OD0jM9awerPmFkvRGl/njRQUuwI7/BJaxYxNgomLbppHLAXYFRDvv0fvMWLgRr7gVYZGb65pNeibxwmwRU+r3N/vdvqFUe/Lk92qfeQjaYCufJ4XFWlAMHvvSbX76mtUfAyEeE3JeetoV7TrutPcEZb19HhyDzw+vRvF2nvXK8/SHGKAiegF7LtnoGiquVMjpoi0NADolmDu67Q5caxdVaiNJ8T8XGsHbDwRy3+BA9M22Heqx33WDlCPubBmUL0X8vtVKtTNAywbo67sHaDu9RNYKDVCsbVPD/1DP1RoHjwraj9YQY7MsD9bRAwFwD1M1x/2ZFax5PmRHA1Bu0UcHm5WCOIxAs2hpxwRJINhLYFGPur6G+R6IGLYf2BIBbVipqaoUHEj74fBHk/2xJcgAAE2ouXb+jSvMR1BFSI453OnM8UM7srAwNfY/4NU7okkG5DC2vhvIqIrPuCjtxthtOd0Ybb4Os96udK6vvJ2+IUH1qubNnoZc7ao8ZDXpG0auNUUju9DpDvRu+IWY/G3kepBUwSNDfdjrupseiXtZl5t28KDNn4OyhNBC6BZv1m6Y3cNWKt6ra/B68zT5wf7cvbSjos0QforvaE1izjhmU8Qb364bg4rAzHpU/xhg8SkwQjITLrVmgL6PDYwHh6EG52xPn4omW5dT3P6455laGEWvQ3J3R1PHzNfTQj5voh9XHVC8s7iv/S0M9Btyff3ikgRqJ+s3Aei4nXTzatKz6f8aWSEOVxo+du6rah9BzXCM4f4rfseON89Lbdi8GArS4b0CQMiM5XghQDGisBecxS9uZnY4pLTxKrXLxvKYEdLhS9t15YZaWLPz/t568Y+0gMxnZvaqq7ENlHAcAoGPA/ehpMWrzXkCOSjqhR+1glaE6fu5aqgiWNZwFYwEko8CMQY7GuxthxkIOEAXMiKCP5c1uVSlbFi1q+H6vGqb1smMm+JNsaKw+5rK31bq/OyOA948SKjBx3XGPeiBR1oYoNMSLULbvLcgO99R77HzJNiTPkfYuIMJCbwiucK+4J1j6u70QVmMQKX9AL4f8y43yf2SaYRwjeFe2sO/XSIMyBdW1hs/1712nPMqwZSSxRyFj0Uwvt96uDPOMr27OI8FwL/WU/o1CVAAt6uINen3OHqdVSOh1RSn3q3/uoePnr6kAKrLcXw2oSytfa3tPrQcIABfSg9CetPq4AuccgDNs6xDh+DQKLaiSOhCR9QW0fXGJtb966v0FlOeRZIJoHpIxjmBxMWS7zdLfeaNbVZVVxsSAGVtOats9fE7di1FmzmP5zKRqifz0kF6C/96C/ao1DHYLPnd/t3bcS4jjnU7ALG6oMYPZ26MdlhdD/IlLsYyGx1kgYuaOqBFm2oIK97CitjOwYGJ8w5KXW9OMp5pQX70U2SweblpWRYlhOLjKdrLjfa/3d1vzSLOyKhq/J+oyLfdAXIzLzJEp9vUmx+Xm/4XFqOvNGzjr6m2fMRxXlJvjGlzghRGEHnNv+7ut6aNnQaA94Ur078hZLQhXtrAx1S+oOOFADHrI3A3EYKQZwCxyQUhvVCiaR2XB0E7kLMCeEcD6ySMyeUShJ+TJkZW+f6S+Cnwv2hdDD3y/gaZtOkH/7TujKmHwhe/HLDxAbT9brRxGGPyvdapMm97qQF8NqKcmERg9TskfQeihutOC0WNGjFQDPHoU4pn2eKCeZv/9tSPaJ/G/NRmwzNw6QcU2wverU4uKMVwl1rqyefYZ1M1HdNEqOVDqDU2U6boDjs/SKEE3V7zWuQrly5lVBSMGTdpiqeAMLiD3YEeI452O6Kdf0I7EG5YciFH9FOg9Rl+40bSvVkw5foiYuSNqBAcxI2e8GRxrT4RhfAm+8A0Xcxod3fxQPodjr/XN3fv93QxGbz3eQsv6j18S7lbwB1mj5XpWhQNXvlC7dJDqxbt1N5Hm7/FO6O30ZS41977c+UG9jMzT/kZw6boxjjeUeXE8sOa46mlDiwpHwI0C0wMQiIEY1TZdjdcZuF6SHe97J8MlZB4QGEQbB+AgekYF9grWcJTaelsFU79MQZrQr666N+6JjqN35++n56bvpGd/177w/c/rI5SgJMqn/3ymKQ3vUEk9/15mSLNQ9R4wkmqxl1of9sqfbfu7rcH9E1WX0OrggImnoEILDlhGdbzB063LqwAPlLuRJLAFQmM4hqj+wpx0MxnUpKxSF0dpf9cv16qEBXIPXBLvD2DnTB7aSN2rAURqUR0qOEYc73QESn0QEUd5pb1MFxu/PQ1W1WYQIePRToh6uoJ7Os0IAmRW4Hijlxxz2rlkyxYWLqlVuoAKlGQknmlTQb0nGBxcPuWMVYdi1fUCIR5fysytDWOUOLLQnTeR/5j4Gz4ra0PcD5kelJEdPBPvleNtxKhBznrPdVHuvVM36uobOCcXmar762rbn7pJG+vnKtMPZV4YHsEFpMdbSJ/AMAW7IjO2svn0LZEp9Bq8BTbJytfaqEkjGAkGO8n6C/ogkwY3pH+GtVS9rxkBrH0chH/77310+rJvfdcQ5T154bpaGyHeZnebubJZPiseo+opPH+8esn8bs1svxdBWT7aGDhB4qi9okn5QqbbZwgAfNm/nmrJgBAheKdHdars40hPT0EVxZxnm9OY+2vStCcae6TnkBkRxzsdAaMfggRgxtaUTgdKPVmQ5b7a2kVvBpxpQ5T1yk3HpbYQPDqti3AgWCAYA2Zeconttw6y3qxa2T4DjepgcKN6TRe1+3RJuMuRKgt1FX6MeDOqlwrXQJ7sWuSf52F61ePtg+ON86CjPtLLnSCYNRd1VXNfM94ARi0i98hiHD57xe5zcI5y2Wx9B0adtzzaTBMaQusFjEdnROnZ7uL5cvqtzE4QPKVO6QIWESqjyojTG/ui41Q2MHuWQEslny/AiUNJ6y9DG9HsZ5un+IJgGUrZ0Z6SkRjWvqLKZkIhesjkrXQ01v7660l/NyqSnE1lYWcfWkKorPOUNXqywKz50emF59tWUEEMiB3b3hfZ8eaRnGaDKSJzn2+uKhY+7F3DIkrobzA14JGmZeXe6wbieKdH8YYsgRR2Kp726uOiuI8VZVuYyxdqYmk3ekvhSEORkXtn7YFMHHxCZPXQ9yIYx1OtyqtzAKVhW21KvlBavV6fD9mhWsZzvMHDTcqqEmfMovz4X22Mm6Pgz4pDZy0ZYqOAYcL9/L9u8CzyDyeUe7yDfVSbZ82FebtP0x0P1G1ZXK2QD+JqDK5tzJ0Ff2y1LzgXcf6aMg7R241xOEaC14Mzj3L3mQ62z0Rf0oTVpL9bSM9UKZGPcmQNVOvbiQvJ828zCuhfH79UGy/UrVYJ1YsqeNfr/cPDDdTxQxC4+1frlcgcbAJPAzbc342xta7OzWblC6uJKdDW8PRzz8j93dZULJaPulQvkSrrjWDF9pPase7oha6Bt6Cf+rtB9enRZiJodi8gjnc6A5ku3Kx4ZAEb89P0RZBLXMwCWUPOes9youzMQQEEAgRjQXSfx8t9t/pYit+hLxDltAh42I4EySigfAolS4gow+nc6CDrjFE1CBCFFs5t+LGAIie2v/JQrHIs3QW9VtdvJ/ic8easAUbhoG8OaqWejhMzqtxroF6BgX5ze0rMO/WS2dqlgkwZK8jqqBCOcTbjXoTVhHsBKErzeoWsd0YBdgocr8embFX/Yy1AEFnwrax50UstlSN7OyGR/tweTf1+2kRNx65QKtLu3JvwueA+Zj3Ozp319o+tkR4p70OEEwFYjEtz5eBnlIoEbgHlEbxoj0PQokXFwqIzItx7jvd3331HoaGhlDNnTmrSpAlt3bqVMguD9HJzqHSi3Bsl5ofPXlW9v2Yra4OHGpRW5aUwqB31l7KYEjKTgvFATR0OKBwuDnLgBjpHF9tCFtKsMRXpgdqlC1jmmb4zP4xu3U1tAPyz54ylHNroY4FRVu30Uv4pG7S5lO7A2W6M80DGwlcDnXvu5nhQbp6c8TbG8W5dqajqoY+7cYf+1Uv7rdmhR/gbmGRsdatZUilBx1655bQKJznjLQaPkL6po+tR7InS7qP3OggOPvnbdlUSveHoBeV0T3y0gSo/FXwXHf3t8cb013PNleYGFKSxFk7ZeILajV9NLT5ZSd2/WkcDJ25W67NtNhzZcvR34zNp7UYmumO1Ymq9hxPtyVQNFgK9v26wmsOe0cG5jXYwHO5vVh5RNgonq9h2EQR7pEvpx1mzZtGrr75KP/74o3K6v/zyS+rSpQuFh4dTsWIZs7zWVnwF5d4Y1zVlwwnaHHHBUnrqDzEtLPTomcJIpYWbtlK1FlmIsuYluhpBdOsCUY7CdCvmIjXLc4kaFNMys4IJAh51gpWoFUaK/Phgcfpn/WracRDiUUWojz5jOSODnj6MkcFs+0lrj9OwprmIzm1U5+C1gCAKPw5nvJBpmgdQWEemYPaOaHqtVR7Kf2Wb5fynvOVSXA+WnyNPUPegi3QlbyND9gGVD7+sj6C9h/fThQMHqHCeHMnbAnb2oz4to0pBd6loILJNvqvxIwA0sHEIjV96WJXVIdChMtvXo+nG6XV05/g+KpGtCjWrYMx7tgXbgiEzYdlhmr9xC/UuWYACsuVL9b7zXN5GJbLloZBCtUzZD0EwCsxSBpHRh4nOxqa6vzpcX1z9DFw9t2hzupsjmBKvRVP2G8fc37a91y7anLbE5KLhM3cpZxAtUlBURq+nvwWeMjoIbOILDh7GiU7fHKn0XlDezP3YmKGMqRwvd6xEnauXoMCbp2j/zlVUIlsiVStfTY1ocwWc5sHNytLY/w7R3xs200NFNmr9807OqyuUnw4cxf24MPXRW6QyAzjOqLxDkiw2/hadv3qbiufPYdFnEQR7BCT5MrDPJOBsN2rUiL799lv1c2JiIoWEhNDw4cPpzTffdPq38fHxFBQURHFxcZQ//71bivvntih6/a+9lp+hcIx50v5SEIc65T9zx9DY0t9QloDUpwjOGiQZkyiQAppMJKrwhF/2KzNx5OwV6vTFWupXcCl9EvItBVIiJSQF0MbCH1Orrs6vg4zC/N2n6KU/dtPDRZbRmOCvKYCSz8XEpAD6Iv5/9Npzn5qybSyNXb5cS3Vvz6VPQr6hQKttuwL7Fth0kiHXxcRf36Ins49V4+NcgT3kpyVRAAU0MWYfUHmD7AoMCxgbz5ZeTTl3PqtvUXu/1HgiBVZ6kswA4pKf//gGfVDya/vrkf6+cX0cL/85VWr2sin7IQhGEHXxOn0zaaTD+6uZYF34J74j9ci3grIEJPr8WiOjh9MfFztTpWJ56euB9ahayXvX7rrXwLoInYCrtxKUJsyv+mg18HK5tfRSvvEUoNsN24p9Qk07ve7W68bfvEPjv3+d3iv+pVv3Hb4HfHNtBL309DjKTIxbfIh+sGoJ/LxfnUwVfBA89z3TneN9+/Ztyp07N82ZM4fuv/9+y+NDhgyhy5cv0/z581M8/9atW+rL+s3DSb/XHW98LCPm7FUlpnBwvx5QT2Wa/EXi1Sii+aEU6M6NOSALUe8TRLllsTGaUTMX0+jE7imNs0x0vHEdvDplIY3P3ouy2DEAVODn/pOmHYtdh/ZR7R217W7bJUZ8TtejKWlemRQBB7/vgw40J0bORXb7PG2oOjT1MTHzvLweTYnzyqrgkyuSArJQQCa5PoR7FA/OZzPgwLkRJCQF0pisi2jEAx0od/Z0WUSZabh8/baqkFq0ZRstLT84hd3g0bqozs8yHgWb/XE/To+gtP/teWG0cM9p+uD+GvRAvczz3gXvHO90t0qeP3+eEhISqHjxlKUa+PnQoUOpnj927Fh6//33U79Q//5E2bIRTZ9O9NZbRCdPEtWsSTRsGNGzz+ry0U8R3blDNGWK9vPkyUTjxhGFhxNVrEj0zjtEQ4dqv3v0UaLcuYl++kn7+fvvte/37CEKCSH67DOiAQO03/XrR1SyJNFXX2k/f/EF0cyZROhTR6k8/u6BB7Tf9e5NVKUK0ad61u6TT4j+/ZcC1q2jcXnzUZkn3qMBn7xMxfZnIerShahxY6IPP9Se+957RBs2EC1bRpQjB9Hs2USDBhFdvUrUpg1R585Eb7+tPReVAmFhRAsXaj8vWED02GNEFy4QNWtG9NBDRK+9pv3u1VcpcN8Soim6UTCciH5H8ygRoXWlLQY96se5IxbpBKIp/bXyo2nTiEaPJjp+nKhaNfVa6jiDxx9PPs5g0iSizz8nOniQqHx5InyOOM7g4YeJgoK04wxQ/fDLL0S7dhGVKqUdUxxngH0vU0Z7LTBhAtGcOUSbNhEVLkz0669EvXppv7vvPu08wHEGH31EtHQp0Zo1RHnzEs2YQdS3LyI6RJ06EbVooR1n8O672me4ZAlR1qxEc+cSPfIIrjiiVq2IevTQjjN4/XXtPOJA0d9/Ez3zDFFsrPYZDhxI9Mor2u9eeonozBmiP//Ufv7jD6IRI4iiomh0+fyUpUISkX6KUntEp6yON87dMWOIjh7VzqM33kg+zjh3cQ3gOIMff9SOI86DsmWJPv5YO84A+4NjpVeZ0NdfE02dSrR9u3Yu4/EHH9R+h3O3QgWi8eO1n3Hu4n3iXCxYkOi334gQNEtMJOrenahePe04gw8+IFq9mmjlSqJcudBXol2rN24QtW9P1LYt0ahR2nPffpsCdu2iCXN/o0C0Eb+K9wA5cyLCxLEGRAEzE4l+60/09liiY8e04wz++ku71nFcGzYkGjyY6MUXtd/hcZz3uCaBkzWiXt8mRDuJaK1+/J9GczlmhmFRQkMbEelLArVEXTQRrdR/HppA9M5bREcve79G3DpPAWUxPgCz0/TX7U9Eu4kIwsHQEXsefw8LAGqHmO8HKXT9uX0SiL74lGjLCZ/XiH59HqQGq8bTzauxlAXLFzpgWHpDrRH6edm0I9ETT2jHETz/PFFcnHacgTdrxO51FBiYSIRT8Ad9m82JCO3cy/Wfh0DhhyjgZALR4heIfpqVKdYIqlNH+zscZ4Dvr1/XjjPI4GuEuicsWkQUGEg0bx6i9ESXLmmfC+6vOM7gf/8zZY3wyo74+iMKPJ9INFRfL6CjihHUg3Cv08/nJlBIxGwn/edHiGgjER3H6AUiekK/7kED8miNCEixRhDRYSIK0y3Cl/R9QD6jqr6mzNaf2xNiCohI6j+/RpRlYiKNyvk+BRzfmcqOoMhI7ToDOHdxPp86pX3eRq8RYkdQgTNn6LU//6SXrp+jLKWTiGZAbZOIyhAFtE8gekC3G1ytEY+1oMAJutPdGv1GRLRK/8xx+Jfq5wE6mXD49MMb0CyRKMtMounrMs0aEThzJo0lojHTfqcs77x9z/katG4dERzF338n6tOH6O5dv/oalFHWiHzut9aku4z36dOnqVSpUrRx40Zqhg9J5/XXX6c1a9bQli1bMkXGO825Hk00D162ZLzT3eeQ2Y63OgYQHLSzVJl9LJxt208Zb6+3b9Q+uJuFNznjLeuRkGHw5HxO78j1lvHsBm/vO3IuCJmU+Hs5412kSBHKkiULnT2rzedl8HOJEtqYLWty5MihvhiOI+AgCL6Qn6j6V0Q7XiJKcmIcBAQSNfiS6G5+LWIrmPs5ZMrjjWPwNdF2pFWtCSBqaPaxcLRtVxi1b95u38h9cGefzP4sZD0SMhJuns+mEEAUOoDo5CwDtu2PNVjwv93gzX1HzgUh8xKvn/Pu5LLTXcabxdUaN25M33zzjUVcrUyZMjRs2DCX4mrR0dEq4y0IgiAIgiAIgiAIZhMVFUWlS5e+tzLeAKPEIKbWsGFD5YBjnNi1a9foMfQJuCA4OFi98Xz58mXoOcdGwGX5OF5Slp95kfNAkHNAAHIeCEDOAwHIeSAAOQ9cgxz2lStXlA/qinTpePfv35/OnTtHo0aNopiYGKpbty4tXrw4leCaPQIDA11GG4SU4EKSi0mQ80CQc0AAch4IQM4DAch5IAA5D5yDHm93SJeON0BZOb4EQRAEQRAEQRAE4V4mMK13QBAEQRAEQRAEQRAyMuJ4Z2KgBj969OgUqvBC5kPOA0HOAQHIeSAAOQ8EIOeBAOQ8MJZ0qWouCIIgCIIgCIIgCBkFyXgLgiAIgiAIgiAIgomI4y0IgiAIgiAIgiAIJiKOtyAIgiAIgiAIgiCYiDjegiAIgiAIgiAIgmAi4ngLgiAIgiAIgiAIgomI4y0IgiAIgiAIgiAIJiKOtyAIgiAIgiAIgiCYiDjegiAIgiAIgiAIgmAi4ngLgiAIgiAIgiAIgomI4y0IgiAIgiAIgiAIJiKOtyAIgiAIgiAIgiCYiDjegiAIgiAIgiAIgmAi4ngLgiAIgiAIgiAIgomI4y0IgiAILnjvvfcoICCAzp8/n9a7IgiCIAjCPYg43oIgCIKQBnz//fc0ZcqUVI8fOHBAOfonTpxIk/0SBEEQBMF4xPEWBEEQhHTmeL///vvieAuCIAhCBkIcb0EQBEHIBFy7di2tdyFdcPfuXbp9+3Za74YgCIKQyRDHWxAEQRDcBD3e/fr1o/z581PhwoXppZdeops3b6Z4zq+//krt27enYsWKUY4cOah69er0ww8/pHhOaGgo7d+/n9asWaN6x/HVtm1blQHv27evek67du0sv1u9erXlb//77z9q1aoV5cmTh/Lly0c9evRQr2XN0KFDKW/evHTs2DHq3r27et7DDz9Mo0ePpmzZstG5c+dSvbenn36aChQokOr92Hvd48ePU5cuXdQ+BAcH0wcffEBJSUmpHP3XXnuNQkJC1HGoUqUKjR8/PsXz+vTpQ/Xr10/xdz179lTvecGCBZbHtmzZoh7De2cuX75ML7/8suX1K1asSOPGjaPExETLc1A1gL/Ddr/88kuqUKGCei6qCgRBEATBn2T169YEQRAE4R4GTjec5rFjx9LmzZvp66+/pkuXLtHUqVMtz4GTXaNGDerVqxdlzZqV/vnnH3r++eeVQ/jCCy+o58AJHD58uHJi3377bfVY8eLFlWP44osvqtd96623qFq1aup3/P+0adNoyJAhyumFk3n9+nW1vZYtW9KuXbvUvllndvE8/A6OZ+7cualZs2bKSZ41axYNGzbM8lxkgOfMmUMPPvgg5cyZ0+kxSEhIoK5du1LTpk3p008/pcWLFyuHHtvDawM413j/q1atoieeeILq1q1LS5YsoREjRtCpU6foiy++UM9DAGH+/PkUHx+vghn4uw0bNlBgYCCtW7dOvQbA93isRYsW6me87zZt2qjXeuaZZ6hMmTK0ceNGGjlyJJ05c0YdX9tgCAIKCC7A8S5UqJBP54EgCIIgeEySIAiCIAhOGT16NNK0Sb169Urx+PPPP68e37Nnj+Wx69evp/r7Ll26JJUvXz7FYzVq1Ehq06ZNqufOnj1bveaqVatSPH7lypWkAgUKJD311FMpHo+JiUkKCgpK8fiQIUPUa7z55pupXr9Zs2ZJTZo0SfHY3Llz7W7TFn7d4cOHWx5LTExM6tGjR1L27NmTzp07px6bN2+eet6YMWNS/P1DDz2UFBAQkHT06FH187Zt29TzFi1apH7eu3ev+rlv374p9hHHvV69epafP/zww6Q8efIkHT58OMXr4/1myZIlKTIyUv0cERGhXi9//vxJsbGxTt+bIAiCIJiJlJoLgiAIgptwxppB1hosWrTI8liuXLks38fFxanydGRnUZ6Nn71l2bJlqrx64MCB6jX5K0uWLNSkSROVXbblueeeS/XY4MGDVek2ytCZ6dOnq5Jt7Kc7WGfLUcqNn5E1X758ueV4YL+QvbcGpefIanPJeL169VTWf+3atZbMdunSpdU+7ty5U2W28fz169er7Dgze/Zs9XPBggVTHIuOHTuqjDy/HoNMftGiRd16b4IgCIJgBlJqLgiCIAhuUqlSpRQ/ozQcJdDWCuQolUbp9aZNm5TjaA0c76CgIK+2feTIEfU/+sftgVJta1DmDifWlv79+6veaDjbo0aNUvu0cOFCeuWVV5QT7Qq83/Lly6d4rHLlyup/Pg4nT55Uvd/oLbeGS+bxewDnHOXvcLgB/odDjfJ4ONAo50cJ/sWLF1M43jgWe/fudehMx8bGpvi5XLlyLt+XIAiCIJiJON6CIAiC4CW2jiqyyB06dKCqVavS559/rrLI2bNnVxlg9DVbC395Cv8t+rxLlCiR6vdwtK1BLzOcZFuQJb7vvvssjjd6u2/dukWPPPIIpQVwsj/66CPVgw3HGz3vEHmrWbOm+hmON7B2vHEsOnXqRK+//rrd1+RAgL0qBEEQBEFIC8TxFgRBEAQ3QabVOnt69OhR5QSyqBmE1ODEQpEbgl+MvTJwR9llR48juw6glo6Sal9AKXfv3r1p27ZtygFHyTcE4dwB7xdl89bO7eHDh9X/fBzKli2rys6vXLmSIut96NAhy+8ZONQoU585c6YSS2MHu3Xr1hbHG9tiB5yPxdWrV30+DoIgCILgL6THWxAEQRDc5Lvvvkvx8zfffKP+79atm6V0GliPzEIpN1S1bcEoLvRs23sc2P4OCuUoJ//444/pzp07qf7O3ogwR2B/ixQpopTRMdLM02z3t99+a/ke7xU/Y0wZsv0AI8xQKm79PICsPwILfLwA+tPxt9gXqI1zAAAOOErNsX/W2W5Wl0cpP5TSbcFxg8K6IAiCIKQnJOMtCIIgCG4SERGhRlxhnBYcv99//50GDRpEderUUb/v3LmzKi3HLGqMuUJWdtKkSSpLjTFX1jRo0ECNAhszZoyaQY3noH8bo7fgwMMRhdOOknGeC47nP/roo2r29YABA1SPc2RkJP37779q1Jato+sIOLr4ezwf24Jgm7tg3BhGiGGsGZxmCKVh+xh/xj3XeP+YQ46ycfR94/gsXbpUjQ5Dfzln7wHGnOFYwMnmGd6c8cYscHzZOt4YS4aqApTMY7Y4/h7P27dvnyqdxzYRWBAEQRCEdIOpmumCIAiCkIHGiR04cECNxMqXL19SwYIFk4YNG5Z048aNFM9dsGBBUu3atZNy5syZFBoamjRu3LikyZMnq7/HeCvrMWAYw4XXwu+sR4tNmjRJjR/DaCzbMV/4HuPJMEIM26hQoULS0KFDk7Zv355i7BfGbTlj69at6rU7d+7s9nHg1z127Jj6u9y5cycVL15cHZ+EhIRU489eeeWVpODg4KRs2bIlVapUKemzzz5T48dsGTFihNoXHCtrKlasqB7H9mzB648cOVI9B6PMihQpktS8efOk8ePHJ92+fTvFODFsVxAEQRDSkgD8k9bOvyAIgiAI/mXPnj0quz516lSVRXcHZJeRUUYmXxAEQRAE95Eeb0EQBEHIhKAEHjO0+/Tpk9a7IgiCIAgZHunxFgRBEIRMBJTXDxw4QBMnTqRhw4ZZxNwEQRAEQTAPcbwFQRAEIRMxfPhwOnv2rFIef//999N6dwRBEAQhUyA93oIgCIIgCIIgCIJgItLjLQiCIAiCIAiCIAgmIo63IAiCIAiCIAiCIJiION6CIAiCIAiCIAiCYCIZTlwtMTGRTp8+Tfny5aOAgIC03h1BEARBEARBEAQhAwK5tCtXrlBwcDAFBgZmLscbTndISEha74YgCIIgCIIgCIKQCYiKiqLSpUtnLscbmW5+8/nz50/r3REEQUhXXLx6i+4kJFHxoJxpvSuCIAiCIAj3NPHx8Srpyz5opnK8ubwcTrc43oIgCMkkJiZR1++20dkrt+iJluXo1U6VKWe2LH7bfkJiEt2+m0i5svtvm4IgCIIgCGbjTouziKsJgiBkEk5evE6n424qB3ji2uPU98dNdPryDb9t/8nftlG9D5fSj2uOKQdcEARBEAQhsyCOtyAIQiZh/+k4y/eF8mSnfafiqNe362nbiYt+ybavCj9HN+8k0if/HaLmn6ygSWuPqyCAIAiCIAhCRkccbyFTcCbuhnI6oDwoCJmVA6fj1f8DG5eh+S+0oGol89P5q7fp4Z+30LFzV03d9tkrNy3fF8mbXW33o0UH6dFfttDZ+OTfmY2sAUJagvNvx8mLdPNOQlrviiAIguBnxPEWMjTIpvX/aRM1G7uSeny9nrp8uZZWHjqb1ruV6bh47TYtDjujAiCCxrVbd1Wm+cT5a35zBvfrjneN4PwUUig3/fVcM2pavpAq+x6z8ICp2z554br6v2zh3LRpZAf6+IFalCtbFtp47AJ1+2qdckbMBse71ntL6Zlp2+n81Vumb08QbBnz70F68IdNqtVDEARByFyI450JQInnuSu3MmWm5/DZK7Ql4iJB7yBntkA6fPYqPT5lO7351166euuu35zO2dujlGNxNyExU55/T/y2jZ79facKgLz25x66dTdzZ3twLT4zbYfqsW47fjU9PW2HXzJgB85ojnf1YE14Mnf2rMoBzpYlQJWBrzoUa9q2Iy9qjneZQrkpW5ZAGtSkDC18sSVVL5lfXSMvztxt+jX5y7oItY0l+89Sr2/WU6xVFl4QzCbu+h36ZX2E+h5tFoIgCELmQhzvTMCEZeHU6KPl1PjjFfTstB30zYojtPbwOb/2Vu6KvKScYH/DGb7GoYVoy1sd6cmW5ZQT/se2KOr21VqVbTSbCUvDacScvSrL0fmLtcq5yUxBkH/2nqZdkZcpa2CAOvZ/7Yymwb9sVUZoZmXFwVhaf/Q8ZQkMUMdl2YGzNPTXraYGJOBkIgAXGEBUrUTyxIfyRfPSYy3Kqe8/XHjANNGzKN3xRqadqVA0L815rhmVLpiLTl2+QZ8tPkRmceXmHVoZnhxYgMjcc7/vzPRBoLQA1z7Oh/S4Di4/cJbG/nfQlEDYlI0nLN8HF8hl+OsLguA545eE033frKMHf9hIr/65mz5dfIimbT5puWcJgpGI451G7Iy8RAMmbqK4G+Y6H/E379CUDdrNHkb34v0xNGHZYRo8eSs9PmWb6dsH+6Lj1IIGpxOOP5wMlHn6w+jinlZk+IJyZaN37qtOM59qSqUK5KKoizfohRnmG957o5MFrY6fv0aPTdlGvb7doDIfFzJ4uSsy/J8uDlffv9yxEv32WGPKmyOrqkLo88MGOhSjfT7+BP3ECIb8vO44rT9ynm7c9q/jhYDXJ7qD+VSr8jTjqaaUL0dW2nz8Io2ev9+064KvhXJF8qQa5zWsfUXVd43z89cNWkbOrIx3WSvHm7Pun/Sprb7/bdNJ04TesO4gqFC+aB5a+VobypczK+04eYlemL7LrwrrUzZE0NhFB9XanFYtDjAql+yPSZP1BwGQDp+voVafrqIGY5ar6zA2XlPaTw88OXU7/bTmuGXdMgpc1zO3Rlp+xjSB9Bh4SAtgI7y3YL8K0AuZC1wDsIdhn6YFG4+ep29XHaWwU/HqfjB35yn6fvUxendemFqj3v9nv18qFbEuiu5D5iDDzfG+F8BF/Oqs3XTiwnV6ZdZu+nlwQwpEGsoE5myPpmu3E6hisbw05v6a6gYXdjpOGV1rDp+jB77bQJOGNFSZJ7OYuS2S2KaC448v0KJiYZo0uKEyvM1Wca4RHGR5rGn5wjT3+ebU9cu1KiP+2eJw5ZCbVWZ9NFYTrYKY1b/7ztDUTSeUmjS+Jq+PoIXDW1LBPNlN2T7vw7vzw2h31GUqmDs7vd+7hqmftzV7oi+rTCaCHk+0LK8cPmQ4H/t1Gx07d43u+3o9vdW9Gj3eUsu4mg2M+xem76TtJ5MNvPw5s9Ln/epSx+rF/bIP+NxxTiAA8Xy7CpQ/Zzb6ZlA9FZBBJQbUxkd0qeLWPEhPwOcAyhVJ/dljH17vUpVe/2svfboknIrnz0n31ytlWqm5LS0rFaH+DUNo1vYoemPOXvr3xVaGz/petE9bd3rWDlZZ/p8eaaCO+fKDZ+mRX7bQ+IfqUJnCqffNSLafuEjv/aP10mMteKlDJepZJ9ivs9SRzUGAA+C2U7NUEBXNm4MK5M5OdUOCqG/DEFP3Z/7u05b+erQYoOcZX2gFql26AP2vcxVqXK4QpQXWgYjJGyLo2bblqVi+nIa89pHYqxRjJSJ45dZdir9xl4JyZ6PMDIJAcLqxNuPeOLx9JRWkNXr9E9Ini8Ni6LnpO1X1V49aJdW9z7oqykxwzmHtAb3rBlOn6sVVFSSEPw+eiVcJgl83nKDIC9fp+0fqU46s5qyLS/fH0DO/7yDE4RAYr1+moApIlCqYS+0T1kWzgW9w/fZdtfamxbX328YTNObfA9QotBANaFyGutcsQVmzZMzccMZ8V+kcnEzfDMRFHEgrD8XSeyZG1H7frBlYQ5uHKofzqdbl6asB9WjOs80pOCinynB1+3KdKnOF6JjREXhkE//ZfVp9P7pndXq8RTklroTresPRC/T89J2mZZvwXiw9rSWTS2sBHIvPHqqjvv95fQSttipBNdrZuXEngbJnCVSCVnAyN7zRnt7pUU1l3fH7V/7crZxjs0BwYfqWSPU/ypsf+mGjae/XljWHz6v/W1UqYnGkqpbIT/OHtaAuNYrT3cQk+mDhAVq4VztHzAalnnC682TPQt1rlaAS+XNS/M279NS07fTdqqN+yUBtOKodk+YVCiuHF7StUoze6aEFfxBtRyXGpWu3Dd0uZxSK5c9h9/cPNSitvmCM4Jz8avkRQ7OQ9krNrXmrRzUqli+HWpOemrrd8Oj/yQtaWwlu7KB5xSI0cXBDJfC2NeIi9fgGAm/mZdxwbn28SDPy0F4QfemGakFpOnYFjZofRgv2nDY944Gsypwd0ep7GHj4eFGRs+JQrGoBeXf+fmr72WraG33ZtH2YtS1K/f9G16r0SZ9aFFIolwoAYMwcPof+EyE8dozSgoNnUrZDfbvyqGGvve5I8lqI6hIQfTlzl7LimkB7CdaZqiXyqfPxqxVH1FdmOw5pof+C9ebPbVHq3pdWLTerdFsE5wDWwA4T1qjgK2wUrFdm34thI6LibNR91em+2sE0rH0leq9XDZr1TDP68ZH6KiCI9fGlmbvpjkmfEZJRbHpEnL+m1uK5u07RNyuPUp/vN6rghNnH4YHvN1D/iZup93cbTJ9wYgvuN2hzu5OQpMRWX5y5i5p9spIe+3WrOjf3RF021Ub2N+J4pxG1SgfR2D611PdTN51Updg/rD5Gc3dGq7IbIwwwLFowYkGvusEpfocsx4LhLalZ+cJ0OyGRVoefU6JjLcetohGz96gSXCOckP/CzqjIPoyrIc1CaVTP6rRmRDv667nmakHDduH0o9TQaGDYXrl5Vzm9yPjbggznkGZl1feoPMBxN5ojsZohh/JWjt4VzpuDnmxVXmX7EXzBMUCpk1lEXdKMu0rF8lKd0kF06fodGvrrNrWoHdX3zyzWHTmn/m9dqWiKx5FF+unRhioQAyC4hsXVTGDYfLPyiMXJ+/7hBrTujXb0aNOy6qb32ZJw5fCZfdNhxxtZXmueaFmOxj1YS0X+kZ1t89kqeuvvfYYJgLHjjeymPVB18+mDtVWQDsfji+WHqeW4lWrmtq/HBOXNyCIAR1llVEV8/3B9yp09iwoQYcwYMqJGcVlvqymYJznD2KZyUVr8ciuqG1JArRXYplnq6sie7Iy8rBz9Ja+0pte7VlHBt8vX76h7AIwNqLvz+WEGKKPkCiiU269/ox39+EgD5QC/0rGy2h9kZR/5eYvKgBhNmF7pgzW5f6MQldlY93p7OvJRd1rxWhvq26C0Ovc+XnSI5u8+Rf7mwBntPRfUs9CoQDHq3rReXwtbVixCpQrmttyjMjN4/wh8QtxxwbCWKjgPvlx+hP7bd4YyC/1+2kQ131tCI+fu9Vv71eXrt6n9+NWqygn3vkd/MccOcwUHO1/tVFlVQcIeReUTbJQWn6w09TzAWgTaVS2m7DJbutYsST8PbqTWKzjHaM80QwCU19rxfesoZ/+1TpXV/QFBOiQnhs/cqbQnzADJn+d+36G2AxCIxSQgBIn/N3sPPT99B41bfMiU+wEDOwfbR3YfVWBYf2GvQPAV52bfnzap8yKjII53GtKnfmn6dlA9ZWjuiY5TJ/erf+5RES70lmBh9AWejYteRs6sWVMkbw6a8VQTWv5qa3qmdXm1H7gIZ++IVqWXrL7qC+zMdq9VMkU5PUppsKAh84gIV5OxK5Sj0X7Camo4ZjlVfuc/FYzwJcLIwmqViuel7Fntn+oju1ej2rozOuCnzar028jI2pGzmsNSqXi+VL9D3/lHD2jBFzg50zadsGQYEXgxKvt6SjfuqpTIp/qJ4ewi44ZFreuX61TE2ywBJXambZ1M5u0e1ah91WJ0626i6q3kcmizyt7h5MDBG9CojHoM6tof3l+TPnqgpjomyw/Gqog7or7o7dp8/IKh+4DPlcvcm1dIfUz6NypDfz/fXDlGMEhnbImknt+sN+SmZ3G889l3vAGuUUT7P32otirBPxN3k35cc0xVxfiSDebgT4Hc2eyuRUzD0EI05bHGas3aduKScvwHTdqsphBw24g34Fri9RQl1daULZxHrYMw+q7fTlBtEGYYvyzkiEoHtHo837YirX29HU0e2lAFO5DtR7YDM9XhhJsRAOJsMwKOKCcsXTA3da1ZQjnAL3WspAICDcsWVOfeY1O2Gi4uxFnftlWKqpYKBsEmHJPP+tahp1uXV4+hGsCXz9wZWONRAWFb0cE6CFgjG5QtqKqxUBHlK3gdBF54LYSYIMjsjjfKeUHFYvnUPRoij8+00T5/tEcZGXhzRlr22mNdxlqHio+ZW6PUPRkVT2Zne7efuKQEJrHOI+OLahMkXrDewhHCWmQ2qOpCyxlAAHz6k03ViMsH65dWQUCsQyhD/2zJIVM+o/CYKxbbyBG4Xn8a3EAFTLF+wSk1MkCBoPRRfa1vXbmIcvaHd6ik7g+4F6IVCZlgVIcuMiEI8e/e0+o41yyVXwViUZmJIDnGHaI6CkkAJAV7frteJeWM7sW/fTfRYqt/0LsGvdKpMm18swP9+UwzFYiDM96uSlG/tmOZjTjeaQxKW5a/2obeVWUuJZXxh95PnNzoAfSFmDjtAkE5rSNgfOGmBwd029sdaerj2oUOUJ7s62IXE6ctUGUL5bG7oKGcp16ZAirLgTm/x8+hv+aWuhhh6ONm4C0n9NLSynacXgYXM5zRztWLq4gayp6bf7KShs/cpXrPfBXewvgyzjbbA6W9AxuXUe8fZZ7VRy2mWu8toarvLqZGH61QwRhfYWcW/UJ5UFLVszotfaW1WswQZUTE+4tlhw29seHG9Obcvap0EE6kIwVfGNxfDahLVYrnU+d8vx83maY0j8oCgCgytmvNw03Kqr7ijtWKqZ8RMEBvF0QIjTT+kM3EuY1rskLR1NcEQD/Xkpdb0+9PNFHH7mz8LXp73j6ft33uqmvHm+nXMIS2vt2Rfni4vuaAJCTS6AVhXpee4z24WosY9JjNfa65qhKBI4zAHDKPqErx9hzF68B4sc5mWgOdCQQC65cpoIyQvj9sor92RBsqPnlBP48Q8GRwHravWlwFO5a/1kZ3iEmVXHb8fA0NnLhZiY+hN9zXKihUfHAFTvtq9vUMcO+Z8nhj1ZoD4ws98EaOXOPt1yqVrLlhC0rQsTbhOhk2Y5cpDshnS8OpzWerqfFHy1WJY/Sl6ylak2qUyk/D2lVU36PvmO9jnoCgBXqXUTaLzxPnIErMMVEg2fHO3KXmXNpfrWTyPRqZz8rF86rzb/xSYwXu7AHtk5qjl1D9D5cpp/PzZYfp371n/FZ6zc5f8fw5VI8z1gRs/4HvN5rqfLOz16ZKMZr9XDPLOo/1FgHfTp+vUb3HZsKJGdwLWeemQdlCNKFfHVo9oq0lCPPdqmN03zfr1bQBtESi5xqBSdaK8PXYW59/9mhXpRj98XRTKpwnu3ISEaBAyx7sRF/bcvB6uK2VDMqZSk8C58IX/eqotjh8NnC+sZ4Y2ZLEwcauNUqoQCzsYWSdMQEIWXeU4OO8BEjKtRu/WomDQggRNpuvFQAxcTfV+0f1J9sHaEuEHYBAHCpDUR2ZkRBxtXQAnBKUmRJpZbc/rTlGY/87RAt2n1YOgbewkEuJIPfEYeCUta5cVDnCS8JiVMQTixscc29BxgxgUbEHSt7/fr4FnYm7oTKzcASREftp7TElwoPelhYV7WdL3RXK4X46R8DY/OnRBvT7lkj6ZNFBddz+2XNafcXE3aARXaqSt3ApNwwJR3zYu4ZysFAGjYwssr8ANxVEGgc1LuOT2AhnVUpbOb8Ql5o8tJEybHBTQ08dsnwPNYCwUqBysBKTkihXtqzUtLznYhv/m7NXjawDQ5qHOn1uvpzZaMrjjWjQpC3qnOv85VolrAERsJIFcqrPH9Fvoxxv9FPbA1Hvn4c0UkGDtUfO0/erjqpWDcxgf6ZNBZ+3v/HYeZXJ5NI2Z8cUN1wEpn4d2khVv+DmCEPQF3EXdzLetkGpbrVKUqNyhdTNFqqvcEb7NQrxeNvswKLawB1QIbL8lTZ0KOaKKk+GDgaCWDAKvVkPLunZbpQMInNhD9zscU2g3QAZqNdm71GPw9iCo1SvTEGlzeCt4AsbiYUcrEdY997vXVNdg1+tOKyqLzYdv6C+AMpxIZCJqghv1wEEH3B9l3QSAMF6+MvQhvTAdxuVCCCCYZ88WFtphPjKMV1o0l7rj/W5D7HDHl+vU+vBK7P2qPXZNljmLXB2Mc+dgyGo7MIXji8HZ6qXDFKOELL/qFDBOokyUHeAUfz1iiM0ad1xy+vN2an11eN+jqoSXosze8abK0usNViwxn3Yu6bqN0XGDUJrRgncOcr4of0CX1hf8MWVKdOfbGK60BQfA1QBfvdwfRUIeHrqdnXt4f7/elfv7Q9nsOhrxaJ5le4K2v+w1iI4Nnt7tDoOaIHpXKMEmQVXUcHptwXVaCO7VaPyRfLQ23+HKQcVX5g4wGBNeLt7NXqsRajHnxMCe1xVVMVqvKYj6oQUUKK8z/6+U1VqqMq1k5coPCaeFr/U2muBZHbcHQUjcb+BLlOZQoeVXYwy/L2n4qh1pSLKVgwtnFvZWN6epxYdpOD8lns0ss7WQPz2ichLKpCIUvSf1h5XXwwy1YObObfzHBGt61zAxsssgoqS8U6H3KdnnLeeuKgcUl9LzSEk5glwhJpV0IyspT72lXCmwJXzXzIolyozhXGHBeD+upqi8tIDMV6XfnOGyV7vji244FHqtO2djjTjySb0SFPNuJ2367TX20cG5agbhiYWVgRetr7Vkda93k5lo3e924kahRa09MkblfG2fc8IKnz8QC0lbrRk/1nldKDXC/1V6PkfOGlzitmz7t7QuEQbWVscV1fg85/1dFNqUq6Q+vt5u0+r8vvX5+ylvj9s9DnCC6eT+7nQ1+uMYvlzqkoELnmdsTXSkPYD9EzBuEMg442uVdz6Gzh8uBEiIMVtC96ei656vB2BDC3K3gBEX7yBHW+UmrsLDBmsBXD08XkAb0edIaDF23d2c0cZOiL+L7avqAwaXkfQCoTrwJcqpAt6jzsceVf6HwgAYS14q3tVVWqHYAmcOGTjvBWjPH5eO39CC+dxaSTiekSpH84/TN8YANGdb9f7lAHDOchlpc7WQ4DsF5wQlB9DdR6zdY1qQ0H/MLJHcKxQ5t84FIFFsjjJONZwunGeoBWGz/vR+mQIZyJYuN/1/XGTEkjE66GCAiCQiZJent6AzBLI7I43l5rD8bMG2S4cO5zrqDzyx7hPVJug7QhrDYJTcDzhgPor68/HAHoT3IKGgJBZ7Vf2bBMkQh6oV1qJwLJTaGYZPtq/OOjgCAQa17/Rnj7vV4f6NSyt9pcDqLiuUKWIEn1v1kPcV9HWBKFhd0Bb0qIXWyo9ClTq4W8REP7PB/EztkucVQEhCPFmt6o0/YkmqmIL1w0cX9yTMCUDQVpvgF3FazKCjc7AZzTv+RYqA432QOsAPkYveluCfuqSffs0I2O64/3dd99RaGgo5cyZk5o0aUJbt251+vzZs2dT1apV1fNr1apFixYtoswGIj8wBrDe/aePwPHJ6fXQ8QYw9nj2rbfgombn11HG2xHN9ZJ7lKju8lJ0i0uErXsJXYGSU6gdQ2Ea28dNz1vRNSxEcLRg45axU2pvC4xMZLZRGg/Ds5cegPnXh3MAnNLLGUsVsJ81H9SkDM17oQUNbByilGXxhQwE35ChrOlJORH6MmEw4bijdcJd4PCinAsOOLIcAxqFqGoF9KH5anzxbOhqJfO7nfGFICF639ACwVkQb7EOwoztUztVn7EjYPxzNohLwrwBAodcSeHu+7emW00t64FrwZvSsngPM962cNUE1GW9Kfu1drxdASPn1c5VaPWIdrRndGf698WWluAR+t68NUQvXOMKHPeOP9aCp1tXUIYOJiHgc8N66G0gDm08wN1RghDBQzUS3jsMXQQfMPKGz2NPQSURzh1kqWDAugIVBp89pM13R/VTq3ErVesH5l/7ojsxb5cm2oZMIsr8/3y2GR36sKsa9wiBORjUHJzBPrD4Ikaw3f/dBmo/YY1d7Qe0p/T6dr0ypLH2IUs/9/kWlgAexmTx+Q+xUYC2mrRQk0bwIK1UrK17W0/qGgK2pb44/s/qVUZTN57wKQHhDARUub90YJMy6rNGZQPK3cFHiw6qc8bdGczcXnDfN+uoz/cb1Og+V3/PGW/rPmO0PCEIjTUb+hZGKzqrIJiTpAD2BaW/aLtB4M0suCLS1RhHJG6gifTpQ3VUa2b4mK504IMullL0eV4IMR6yBDzyeZRpxXOxhvauW8oiDotqRW/vC3z+1Szt3PEFsE0XvdSKRnarqpI1nERA6bc3ekgI5iN4gfUKwUZXIGAL3wCVYWhNPf5xd6WRhHUdQWGfEkMFxPE2hFmzZtGrr75Ko0ePpp07d1KdOnWoS5cuFBtrPzqzceNGGjhwID3xxBO0a9cuuv/++9VXWFgYZTbaVCmaIiLoS6m5OxeULbi4sBbtirzsdc9trN7XieixpwY3ys34GHir8utuhslRmW3nGlrwAf153sBGBVoJHIm7OaNLzRLqM4BB520vIAwC3DxdRRTRVwyHcPHLrdUXFvf/Xmqlsn4IYPzqgcAQl48hY+Fp6RCe36R8YXq5Y2VV3sqR9+9XH/VpvBZHlTGn2JMgTO96WvBj7i7fMh9wmCCeA6eD+zvdBWInwBehKY5GI0LvjUhJaJE8av42snibvQhCeFpqbgsMHZQjqmCkF47n5Rv2hdVcgf2tERykDHFkWGAkeRuEsaxHLlpf7IH1Y7Du/EMA0ht4wgXGiLkLnH1kATeObK8qNXD8Z22L9Gr77LBjnKS76yGMWwTjkJ2G74H2lQ/0OejesDL8rMpyofUHmUXr+w1KSSEwZyt6iDJKVEFBBwRZa8yjRyUQj2XD/fHdeWFKeTf2yi312nDiu+glujCS145oR0+20ox0UL5IXnVfxqhJTBDxJwiKop0HgomY25tWcG8rRAXtVaV1rFZctb0heP3uvP2mZF4jLlxTTgNsFJRcM3CooDuCdQstA05f4/w1aj52JTX4cBl1+XKtykKiLQcTDFD50PnLNaof1rHuwtVUwQfcB9FWgv2CoNeEZeFuO99olUJb05bjFxweM5ynCMYiKRBaJLfd4COXHps5WpDXRE+rsHB88PVwY21N3Hnyksf98GhjciWs5gqcJ7in47U4iOAJ+HxYwLJCEfcCoqhGQusbdKEgzgz7Fms7BxS9meKA4L43Zd5wxNlG+3tXtFfO/2lxvI3l888/p6eeeooee+wxql69Ov3444+UO3dumjx5st3nf/XVV9S1a1caMWIEVatWjT788EOqX78+ffvtt5TZ4EWYS7G8IdbLUnP+G46mQdjIGzhKjYXCm4sa/XUA5X3+ynhbwxlnb7P+yJSyoekN6GtrVLZQCjVgb6OJcCCQwfcE3Hy51wfK1u6KLCFYw9kiX0HLAbLUGPXky8g19K5xKZ0nPFBPa3mA5oEvQnusEBtSMJc6rp4AoSfrXix/9HfbA4qrYK0+FskTWFHcW8ebJyMAb6qAMLUAFPBy+6hA6VNfOxe8VZblcWqF83j3GSAjB5B5Zv0KT4jQM94QrfMUZOmfaqVll+BwepMtZcfbkdCkI9B+hPJ/9FeCZQfPelX1AJbu19byztXd71vFvYtnvm94s706D+DPvPHXXpUBbzdhtRLihEOLEkz0ylprcuDvkdGzvgfCYO1WUzuffRVR9RQERlH9AGN9+mbvgihGgJGBXFZuDxyjcQ/WVr33aDdAtYnR8LQIBNestRvw/audtXvf5A0RypbCNbfqUCz9vvkkTVgarv7Huo5MH5xYBAggoIdqRYxFnNC3jupPRtAVY5nstShAABbnDSbKhOjtB9Y6F+/3qqG+hw7LAz9sVEKLv208Qa/O2q2Uz4+c1ZxH9IJjKkzXL9eqCTHQS0GPvKOkQXIQLI9D3ZDa+r2SS/GNBu+bA7LuVgHZgusKgUQE0zwNiHJAAfaFtwTlzmYR7/UmQAHBU1Q1IADirhaTbVsoKhbBJi8CwlxFx0EWb0BlBirJkFjwxl85pduojgR4MyKmiavdvn2bduzYQSNHjrQ8FhgYSB07dqRNmzbZ/Rs8jgy5NciQz5s3jzIbvBig/wIlTN5kqTwVV7MFI5cgSAVDq0qJZGMJhiNmkbrqE7Rs3wvHH3BGAtFiRAY9dd65tNNbQ7dRaCG1ICKSiRsvSqE9IVJXVUem0FsahhZUvf67Iy8r9XN/RxN71g6myRtOqKz7Z4vD1bgfV3Bpvj3BFE/BOYbepiGTt9K0TSdVNqlJucIqA+suOHfC3OijctTXhLLQqIs36Mvlh6mqC/VTZGZRPeBIYd+T/Wa49wo3SUSVnZFTrxRBtt4ab/u7rcEs9t83RyqBNTiyjUMLUu96pZyOBzMq483l7lCg3nbyIv2xNZJyZNMMZQgAdqhWzGlAI053/At6mPG2BplQTHrwxhBFtooF3lyJPToCximcZjhNENeB0q43Pd6eZLytQSAWaznW9eUHYqlHbc1xdBfO7Lnq73Z2LcKpwXo4c2tkKgEgV+A+ygKLnI32xtCFQxUYEKDuixwUhsONwIQnQpQIJCE7isAurimzdYXgXCK7t/pwcsUh+kQRmPZ027g3Q6DTF9bpATysK46AUzOiSxU11x2Cs1ChRhAE92VkGvG3rIbtDXwt27svoMIBZbR4DhS141U5ueOM3hf966hgebPyhS22UacaxanNp6uUHQedAFthxM3HL1rKne3ZU5gucTshSZUS4x7M4zmZlQdjqX21YkoF3RqsMQj04RxF1Ygjx9tZ24l2HzupKjLmOtH2wGfkaUDb2j7DGE9f7gsQGUMAZM3hc25f19ZjPWFP+EKd0kHK4cR5glFgnsAaD1hXvamKtO6P9yZBxcF8V6ruzsB6Vy+kgBpPi8oDe/aPM05lwh5v0xzv8+fPU0JCAhUvnnJsCX4+dMj+iKSYmBi7z8fjjrh165b6YuLjjZ+/mhbgQkQUCb2JWCQ9XdgQXWVj21vHF8YsjD2o8UJZ1hqoLUOZ2RdFc1cgCofeQhj5iI7DQIDx4A4ooeObpCMVYXdU3mEkQjwDpcodPDyOXGruTn+3IzhrvCvKuz5zXxc1GAOYpYjZ8hglgT5FZ/1YKInH5w6jCEaLEeDGil7xDUcv0Bt/7VOlVRBbgQq1uxFVnEO4wTsbLefopoKsO/rcrVU8HYH3venN9qmCNBbH243eVlswagUlh8io2F6H9kDfG9Rgjc54Q00c1zI+X1b9R/bVHbVni+Ptg+OLiDhKT1FR8ebclOPVhrevSK91ruI64+2BuJstdfQ2BRhZngZD8f55FJsvjkKd0gWU4w0D3BPHG/20PNINZc7egCwgnEVkAKGZ4Knjfcgys9l7h+2RZmWV4w0nxlPHG043SrtxDmNmrbdgTUAmFu8fVTAIknsTzEBgEtcjrk1W0DcTONePNClrcXjh5+He7s22sc5h3BCcYutMMarMIP7kKviAvmd2IjG9wRnQObh68y59vfIozd11Sn0xKNf/ZUijFPYRWnKQ/UOQDVNanK15u/X7qj3HG+8B6vpP/rbN0ueMDDaCX1jf0WKAiQOofkB1HETJbEFQ8oV2FWnMvwfpq+VH1MQCa3X+P7dHpajmsbcP6DvvVK24Wm9x7mcJCFABXASTYRex0w0tASQL0JqEbHLb8atVFhiZettSfndEX9F6AcLPXqFX/3R8jsBhXP96O48TE+evJFckeqsIDhBohv6CJy0buIfgGOHccDTW010ghomqUG8CspaJMzbVDp7AnxMqWHBdIQvvbjCYhf1cCau5Y6cqxzvyMg1t4f7fJSYm0enLmp8gpeb3EGPHjqWgoCDLV0iIPurmynGiA58SJdwiChujPYb/r58mOvYL0YVtRKeXEEXNJYo7QBT+NdGd+JTPvXWR6PB3RJf2EkUvIDr1L9HFnURHfiK6eS7lc+9eJzo4gSg+nOjkn0QxK4jObSQ6PoXoWlTK5yYmEO0fS3TtJFHENKLY9URnVxOd+IMo/gjRgc8oIPEWvRXyl/qTJPzNjRiio5P0/V5MFPU30eUwovBviG7HpXz925fpethXVCnHCeoatJkKx68guriD6OhEohtnbfb7hr7fR4hOziKKWUl0bgPR8amU7WY0/dF8BbWtXJjGV1tArSsVojdKz6HgbLEUcGKq9jw8H3+Hv8fr4PX016987isqkvUSdcmxQNv+qUVEUfOILu8jCv9W7WfK/Y7T3g/eV9TflCN2GfUKOUUDCi2mV39bQkv/HK6VO+O5CTfVcVLbxXHD8cNxxPG8dpJu7/mIAimBXioxi/LAQcPf4HPA54HPBZ8PPid8Xmq/r6fcF3y+R36irsFnqF2+bXT12FztPMD5gPPC+rk4b3D+4DzC+YTz6sI2qnTlTyqW9QK1T5iY/FycjzgvrxwjOjFT3+91RBG/E109QbT/E6LEu5bXb37jByqR7TzVufM3XTu9nujMMqLI2URxh4gOfUF056rNfp8nOvIj0aXdRKcWUq7Y/6h6zuPUO898olsXbPb7CtGhL7X9jvyL6MxSovNbiI5N1q4T/bn147+jVhWC6Nmic2jjnq1EEdOJYtdqX/j+Kq61cUSJd+j85lHqb8ZUnEe578Zqr4XXxGtjG9gWtoltp7jWLhAd+UHb7+h/1L6r74/8SAG3LtC3tRepDMSIUrPpxo042r/yPe0Y4FjgmJzfTHTsV6Lr0TbX2l26tmsMlc52lp4ps4lyXtqoX2sztc/AjTXi6XJ76KnaN+nDqiuoU6Vc6lrALHD837VSNnq/6moaVPkSdSuwlVrn2UqXT21JtUZUv/A15Qy4ST2yzfB4jcgaOZ2+63SdnqpykkZU3UUPVb5OY6stpvaV8qXYlwbFb6lrJeulHanWiLj48zSs2B+aEaqvEeoaxLWIaxLXpos1Is+tCFrTP5b+7Xeb3m50kfoUWEHR0YeIwj4iSkrUnqv+/4joWqRaQ3iNqJO4hEKzn6IacZNSrBHqf2wP23VjjRjVuRR9VH0VDawcRy9XCaOnKhym2rkOU9y+7+n8uZMpX9dqjQi9+g81zbOXqmTZY1kj1PHFcbb+GydrRKm8SfRG6dmqrPHillGWNULdE3BvwD3CwRpxMe4cDS28gOoFnaZsp+dZ1gh1nllda67WiOYlrtBzRWdTWLTNtYzzHuc/rgM7a8TVHe+pp75eajYFBcalWCPU9aautR9crhEdCuymurnCqWjs9NT7nXhHWwewHtisEfHnw6nJzZ8pK92l9ncn2Vxr7q8RnbPNpeq5jlOVu6sp7ujfljVCrXsp9vuq9t6t1ogdOxZR34LL6OFagRSw/6MUa4Rad7H+Yh3GsXaxRmS5eYbaZf2HugdHU7nb672yI7Icm0jj7itBE6r/oxzE8dX+oY6V89HH1ZZSvyrX6X9V99CzVU/Q41XO0FvVttD9lRPVc/i5bSoXok+r/UsPVL5Lb1bdRk9WOUVPV41Ua0TfKjfok2qLqUPl/Oq5qBR4oegftHj7bmqaNFddM3/cF0f/q7afBlaJpw+rraIulXOleP2ulXPSB9VW06AqcfRy1QM0rOphGlzlPL1RcQ0VDLxI2Q59TJ/8h+t/DN26eZWW/jWC+oybQjP+/CyFHaHWAps14uymd6lE1lh6rswGCr6z06kdgf9fbVWA1jxwmF5reFXtB/andbEz1CXbHHr0hyX03x/DaGvERZo95Rka8O0Sitr8Kf20YD79PHOCthbiWoP9BDtKf92bu96ng6fO0dNF/6IWxS/atSMqnvuGFgxrRjObLVcihyu6rKOfHypBH9fZQTPuT6QNQ5JoaocI+rhjNod2xJCiS6hxUARVSVxHJ/bMtKwRh06eoNa3f1Kl9IPyTHVoR2CNKJHtIj1VaIbahx/r/Udvdi5HU5utpqn9CtPH9cLoi7aXaWSDc9Qp1zIKznqWQs9+Q7VL5abnisxU00ps14hcF1ZS96D1VC8oxqEdUeHyNPqg9R16sWo4vVj1ED1S5RK9V20tda+c3XKeYD3MlniVojaNdWlH2K4RWSKnU+M8YdS24MFUdkTq+7HjNaL5zR/VU3tlnUyXLp1xaEdYrxGJ+z6k3IE36K3y/1EA1nc37AhHa0SjwpfomaJz6OCpWEra96FDO8LeGoFEBe7LlQvc9trXKHRlHXUveZweKricDh7f77avERO9jwbln0V5s92lirFfWt2PPfM1cJ9uWeQMdc6/ibLHLvbI14iLWkX35VtGpbPHUvCpz13aEc7WCE/sCFtfw9Ea4Y6vYbEjcJ9wk4Akk2YFoNQc/dxz5sxRAmnMkCFD6PLlyzR//vxUf1OmTBlVav7yyy9bHoMwG0rN9+zZ43bGG853XFwc5c/vfVQ7PQARGWQXMKNwdE+t18ddUHaC/jNE9zeN7GDYPkGxE+Vxz7WtQG+4mC/5zLTtatHHnOpHvZzxN2p+mJolyfz4SAPqqissOwPR9N4GvH/0U41esJ/aVSlKvz7W2KO/rf/hMpUBWDi8pVelWEyLT1aqrC0EflBm5wkv/7FLjeaCwI8vs6j5OGDE2exntV5LezwxZZtSnn69axXLCCojmbIhQo3PQOYB6qbuRMo/W3JI9cj1bxhC43SVZDPo+PkalUmY+VRTyzg+pssXa1XmYMpjjRzOEfeV6VtOqnmnCFCgH9Ua9Bii7NCozwVZukYfLVdZtP3vd0lV2u7oHIZ6vrWola/g9nX/9xvV9e5sneTzcmyfWl61bNi+zns9q9NQXdHWHaCCjZFcyJatfK2t19tHGweqT1BKClVZd8ua/9t3hp6bvlNVDECp3FtwfuM8h9Dcvvc6uz3THFk59KQiw4br1hfaT1itsv6/PtbI7aw/esKbf7JCCbRhFJC7yu4ZCWS6MSoS4BrEtegtaHnh6huuzLNm4qMNnM5/fmfePtW2MrR5KL2n9zF7CqpIXvtzj+r/tgaOLFrhVh8+p7LRW9/qYDcby9eEr9ekO7wwfafq5ceYQkxMsLalUDnx3aD6hm8TuiwIjKBF5LfHU9ouWLuxhvu6Hn+36ih9tiTcK/sI2X6MC4UDP9Vm/zyl3fjVqtwc79PVuFDQ98eNtO3EJfqkTy0a4MP9ACBzXnP0EjWicM2Itm5NbGDe+nsfzdgSmeK88IaX/tilJj9gKgMEIt1hcdgZNZMc1R7/DG9JvgBhu9rvL9Wut7c7qJYLd9gVeYke+H6j4X5KWgDfE8lfd3xP0zLe2bNnpwYNGtCKFSssjyUmJqqfmzVrZvdv8Lj188GyZcscPh/kyJFDvUnrr4yCLwJrLD7jjbCaM7g/0R1xn+QZ3t6XkKB0ypp9py77RVjNuoxI226cR6qqWIh4H7wVV2Pq6rNgB/28RfV6+bPP33a8HPqiUJ5oD5TfbjimlXt52n/qLn0bhihlbpRV2Rvp42xsCIuUmYWqrNDbHGzLqbjU3Nv+WndgkRweG2YNX69FvNQ7sAWZc6gR45JgdVizxdXsAcfzhbZaQGmN3r9rd/s8R9zH7XP/GkrsvVHv9fX4Q30WLRPo38SYPX+WNAIEvHCe37Ca/+oOq8O1vuK2bhjFruCy4DAPPgOUpsPpRuAwMzrdoFWlorTytTZKHA6BWF9AWfUL7bTrjp1u3GvR6w7enR/mVOGYy3KhYeItWEt+HtJQOY98f0Kv9JKXWysnsKbelsYibrasdaPH3Ci4LW+lfh3gnsAijQ/VT12ibgQsWsfjyqwDFtx65I3QojUI8oINxy54PGaS7QhvNS/srgm6loszUI7N/dC2AXJvQKl9NV2cjIVlPV6XfdABSqGH5EFLokVYzQdxOWvtC0wBsNYtcIdTmVDR3PRSc2SvJ02aRL/99hsdPHiQnnvuObp27ZpSOQeDBw9OIb720ksv0eLFi2nChAmqD/y9996j7du307Bhwygzwr0b6JvwdFQCi2p521/tCO4VYkPSGaf03g1f9gGCSugjQ7YSuNtHw/PD7Y0p8QQsSlk8MHRx84EDyormMEawKPlCAyt1cPQZezLT1JdZ7rb9tbi5wdFaYZNhYOAIo68enzcMIDNA330H3YhBxNodDsdqjmGlYubsE8NZX/RiW3P2yk3lDMNhMvMGg7mrAOef4x5r4xxfVkJ1NV8cBjgfE6Mdb2tnEuJHjmBhM0/HiTnq83Y0Hsil0KOPRib6ylngb7s+m94deBwhVPV9ARUmrLPBI/pcgYAlhI+AEdUebGRDYM4d4OTM0idz2IpbZTYgiIa+ZYxs9JX/da5CC4a1UI7urnc70fa3Oyo1b9zzoCfAYyXtZQg5GFq7lO/VL3A6UIKNChBUl7HoG1pwHE0EUeekHqjjsaVmwllYjBmDUOuuqMtqpFe+HFmpeUXfPwt7cP82PgvrtZH7u9Ef744wpqttYOQoPlOM+vOmx9sXwc9Ua4IbyuKLws6osZiwUTzJTjujqR7k8HTiB6/Lno4YtaWhPv1m+4lLbo/0YmE1XxTN7Z3jUP53l1OZUFjNdMe7f//+NH78eBo1ahTVrVuXdu/erRxrFlCLjIykM2eS1RibN29OM2bMoIkTJ6qZ3yhTR5l5zZo1KTOCsSsQfsCi5ulIK44kWY81MQKeic2OrSMQKOCIpjMxLleglBHiJI82K2txvN3JPHOGz5sZ3raGLs9RXufixoI5050/X6PKuDBiBOCm5CsojeXsgicVEDhORmW8QVvdQHHk8KpeMj267834OE+juyyM4wxkn6FIDqCIbiZ5cugZb5vIP4uHoPrE3dJcb2CxL3sZb3a8fTW0rOFIuasxZ/H6trXtG6/niQoItR19Xr2jLIev4mqgftmCKhAHsSU2mjwaJWZAdqdlRe06XOrBPcGojDeo6UF2CUDgCU4GMuWNyhX0a3YLbI64oGZvY5xi91reqZkLqcEajwoQKKVDMBBBGaxBrgzww2evqLJcBOEwMcIoUIVjvb4iw8+Ot63NgLJkBNIh3trUR1Vrd/eNEynjl4bTkv2aYDAUyR2N8/IVrPWoSgLHdGfb+ntfRA6tzwGuNvDURuVgpLejxOxWJrqRmPlbF+ezp/buLXzOrz183u156zgn2fG0HSXnKXCeIWqIxI+76uZGjBKzhqtdVoXHWoREXXFaMt7mgGz1yZMnVR/2li1bqEmTJpbfrV69mqZMmZLi+X379qXw8HD1/LCwMOrevTtlVrCo9dRnSUPR0hPYKDT6hLZkvPVF09XcYiyqRhj7UKPGTRIOBGeT/VFqDnhExaIw5/ODv1h+WN3MMXN65aFYpRrrS181A/XuEV2q0v11g93KMDI4VqzsbkTLAZfZ2ru5Icr6X5gWROvhQKHVKOpajc9wFYTh6D4CML5WP7gil57xxixXa5DhMCr44U7G257jzU6pkRlndzPe7PQju2NG4IHXFwQo7c2XxjnCpea+jBPjbWF0iqNMmutAoO/nIKqA2LGxV93g3PH2/X5Qq7ReVummgffL+gj1/4MNShviZNQoFaS0BaCu76jtxZo/9Wx3r7rBLrUIBN9pZ2WAuxrhZWaAFqrxmI+Nc2S/zRrF2XhM3nB3OoavjOhcRdkEf26PVr3dvoy1cxd2rvk+CI6d0x1vg1ouOlXX3gNsHnezrSlKzfP5bqNxcuS0izUBCSmI8IHeuj1lBA1DC1nONVeBaKNmeFuDYHBLDjS5UXmA/eQKTqOqE3G9IbCO1hN3K8JOZcIZ3hlC1Tyjc1/tYIuRx305npzQRhha9nu8b7vleKMn0Ais+2j2uFFOdMFAx5sN3Y1Hz1t6VW3B737ffNJS4oYs3Bf96xp6Y7U4Om4u7JztRiTUmznwtvB4sCOxV1L1MaOPDgsuAi1NDShjdJVpxfmAEVGugjAYBQcqmZztdtbjfVb/HFDa5x/H23Gpef5cWQ3PeKOH0FmEO3nbxpeZg7xWWXQEvWy5cuuuZf98zXinzKS5X1bIo7yM6GfEdYh2DgR43Bmhg8CDUSWNgK9vlHW6cnxh5MMgh3/1mAdidM5A5pr7tLe46CdEWw4HTLldSTCXNpWKKmcCa6+9qhDWaeEspVngHoERiMBWgI0d7wY+9Jh7Csam8ZhHBAkRDHZHCMwQx1t3ttX3Bma82eGCnYV1HmMGPS01NyLjjXY+fj//OUmQ/KoHAZuWL2Sos4dzrXkF7VzjthpXROgaGSWDclE2AwLS3Fqx1o17At+7MArR11ZIBkF1biVafjDWo4BwKSk1F9ITWExQooQRNqMXhLkt8GXWCc2OLIw+WwfDGqjOGiHeYU1j/SYJhW1Xx4Ez3r6WmgP0jCEqiM9gqV5OzWA/sD+DJ29V4j3I9k57ogntGdXZ0FIm61mL7ma8jRbYw+vAecT7tM4gIOs2TVeeRymn9ZxSM8BNjiPcrsqqjpzVegk9nd9tbI+35py4q/TpLTm41FyvcrD+fGDoGZ3xRn8covyoqog4n2zY2cLZZjP6uwHONw562HO8L1/Tto9Z6EYEoFpV1gwcOL22AQe02Hy48ABNXHssxePhZ7XrpYJB5Z0c0MP8WFdrIQJifE4aYWzCUMT1h8266uf7fKnWcgNdBiOFBVnngatsHIHZyTj3IarGgUPBXKAjwT2nf+1Inrltm/Gu7cOkD3dxVAZtcbyt9FP8wVOty9PyV1vT7Geb0bJX2yjNErPbFW1LzY/o3xuxFvH6y2XGnpSbJ4urGROQfqSJpt/w7cojdiuBYA9N05MjzxpQiWhLu6pFLUKO7pRasw0F59cIWCQQAVFX4serDmmOd3t9n42+3rAuu+OrnOIEoWS8hfTGR/fXVMJMi/bFqN5hLFhwOL5cfpj6fL+Ben+7Xo3nuKEbV+jzYKVRo0vNkW2A4+Mq6w3VaWCksfVkq/LK0IfY3LAZu2jgxM3U6fM1ysi1LXHiLKNR5cX31dbKp3+y2hac+9dm71FjtuCUo3RpfN866nfujLnyVuUe/aXuKIgmq8ob5/BxuTmMJ0S4J6+PoM5frFVZLdDHJIVWR33eY/87qEZlQe39yd+2pxJ+Qz8hqOQHx9tRj3dyxttcxxuOpb2MN/dY45TMY2CpLQwuLlOzLeW0t32zHG/AUXt7IpTnrt401MCDw4CSOpTvWysmI5PU85v1qrT640WHLIYlhI1YZ8AIBVnQv1GIOv7I5GGMDIPP/q8d0WpUERtfUXrWEf2eRgQeQIdqmoG1wklmY+Ox82qEEs67Vzt5PyrHHl3dKLdH0A0ji8Cb3aqaWtYspOQRXZPl5/XHLfoKfM/kKQjc82wmcAjVyMPT8RYjH/vDzieytf6mYrF8alqLEdV4rmDnmrPcqJqE3gGOiVFrkbW6+dydpxxWBVpzNyGRLurPM2pdHtikjLJ3UV30xl97KVaJmiaoEYwYKdp/4iZV2t2wbEFTKg161QlWFVVIOi3YkzrgZAvfM2saNG0Fdh6Ci/D5f92gtTLYA0EBzsobPX0G1xvsEFQiQkjQGfE371gC5VJqLqQ7IGbzUgdtNt83K49SwzHL1YzuL5cfUU4oRttgJiaLRrBgAwxdo8pIGBgvRfQbBmeV7cEZMCMdbzguz+mRShh0m45fUDdQGLkvz9pteR4CEOH6zZ2dVV8Z3DxU3SgxQgefAYzrtp+tUjcaGJZvd69GX/ava2q/GIIIrE7uTtabS82NVLbnLAWyek0+Xk4fLDygbuQQj8GMdSNnNDsDlQVY4HGTxXxqqL3DCXlx5i6Kung9Val5ZYOi+87gzz51j7fmAJUIMrvU3H7GmxVtUeptdEDInfYHLjU3oszblcCavYw3H38WGjKipI4DTAiEIrK/4+RF6vfTJhUUsxYVA6zgDKPQV1V1BlmSF9tr94RX/9xN/X/aRL2/20C131uqgoHoIf10cbjh/d1Mx2rFLCWL9hxfVEO9My9Mff9wk7KGCfgwdUoXUOsaMvn2eu1h2P9vzl5lhCIL00DPwAr+4b5aJdV4IVyP360+ankcazQMf5y//jC24dRxVpt1cuCIsW1itu5HWsPl17hH4zrdEqGtSVVL5DdsLWKHC5831nrYpa6A042EKAIARgUgcP8b2V0bk4dgZOOPVlDVdxdTn+830nerjilnEImjd+6rbkoQDrb2U63Kq+9xDFwlR/af1io/eEqEEUCIGGD9h9ivPXAO4HOCf2C0vaZNndGCMAv3nnZLWK1A7mymV36kN8TxvkcY1r4ifTWgrsXxglAReo/HPViLBjYOSTEr9dRl4/r5vBFYgyHK/Ss82sPIUq2nW5enJ1qWo88eqk3v96qhHl8cFkPX9IUO1QDIQMMwMyrjD1GlVzpVVt9/veKIcjyR8YIB8cfTzdR++SOjwuOMRszZ47LM2oxMK88UBygxxs32w/tr0qr/tbVkofwBxEy2v9OJfh7ckF7uWIkebVpWifXAEEcGHMqie6IuqywHRPmqGhjdd0QeB+Jqls8hX9qIq5mhaO5J+wNnvMzNeGd1mPGGorbRpf4wcHJly6LOsV7fbqAHf9ikApHIODyoO+Xr9T46nkJgVBAweR8qUI/aJZVzuSXiotoXfPbcR45ALDJcllFiBk64wIxkrK243r5dmexY8fo/av5+lflBa8prnbV100gQQOJye6zH1s4/tv/Z0nB1PHBefNBbu0cI/gOfD8aAgolrj9O/e7WWgKW6mneXGppx7g8eqK+1fH2x7LAKFH22RAtINQ7N+MEYjOri9jAEqDDyk3ucjQTByFE9q1ucvqG/blVVmIMmbaYnf9uWKjjHwcCS+XMa2poGTaRZTze1ZPPh3OO+g0A97OcNb7Y3NTkwtHmoSkLAyX9m2nZ13v+xNZKe+30HtRu/WgVqEZTE8eCqC26bM6ryAO8dTj/aH21nuC/Yc5qenrrDEiwxQ+wU9yQwe0e0GjHnaBTyKW6HzWTZbpC5wgz3MHDq0DOMchYsJtaZKxi/M7dG0Yaj51U/mxkZDmt4JA6PyLEFxh4MMuxiGYPHmaFU8q3umkAJ8+OaY0rhFqJrELjg+bYoIzPSGR7YKEQZsehjwmeAAEC/hlrJp7+AOAuyi1jYUfVQr0wBGnVfdapnp1cNx8TojHeLCkXo9a5V6G5CkhKLgcJzWpVwInrdsXpx9QVOXrhG3b5ap5wQ3Pw5C4u+czOdPgZtEOCajfYBVx4UM73UXNs+RvUgq8TnZfwN4xXN7Smbw+Gxdy6Y3eMNuLLH3kgxlByCYgaK28G4erJVOVX9gnnWeNt96pWm93vXUP2j6PNDDziOiWVsi8HBHxhN3w2qT690vKoy7ji+aKkoVzgP9flhowrMTdt0QlXpGDGyxhrcf969rxo9+/tOtf5ipFTLikVUGfH3q48qIx+n35f96xmaWbMGwc75u0+p4z9izl4ViAUos0fvO4DTjZ50wf9gXX68RTmavCGCXvxjF206ft5SnWC2mrc1AxuVUS0RaId69Jet6jEEp5DMyOhgPYbNOGldhGpBYZE1MwRQIWT3ZMty9MuGCFqtz0hnYJtyewrgqrTSBtuHADPqF73USjm4cEARfPCXjYLMLWbKow1yw9EL6ssaBAmXHzirMvO4RyPbb6R9hveJNe+J37ardbH7V+vo/rqlqEn5QrT+6AVL1Qc0L7g6wGjg0GMMMu47cP4Bqo7e7VE9xWjhU5l0lBgQx/seAxeW7RqCiBluJHCEt5+8aBVJMn5Rsx6J46jH22LoFcpt6Qc3E8zWRWRx58lLmuOtC6egj8poQxeOLyuTpgWhRfLQ38+3UBn3//bF0K7Iy8rInvlU01Q3U6PF1djgfr5t+jRYIPaFoAxKXMctPkQs7fFos1C/bJ/F1a7fSo7u4+bP5c/+UjUHCMBx6bsZiuYMKh7gYGGKAAJu9oILnHE2Yoa1N6XmPA0CBpiRoP2nfpmCqpQflS8s4AejBlUWGNeC6Q4cADJKRMdeOamtQjFKHl+YsZN+Xh9hqYDoZvAM6641S6qqK6gID5+5K9W5iGqkZhXMm3AAg+2bgfVp8OQtyqBEhjuJklQ/Pe6R2P4D9fyjOSHY5+0e1ejyjduqJQvtcKBs4dyGjTBy9571eb869NIfu1WZLUqSfxnSyNAKkPQMxvjB8V6sVxvg2mhSzpxsP8q40WuNOeVo+UNgkjVh7DneRidmbO/HaTE+EBn13x5vTJPWafoG0H7hNRrVFrgfcAAItrvRQQFUAy59pTW9/89+pQs1d9cp9QWwqWHtKqp7lxnZbk4A/P1CC/rkv0P0z+7TaqoIklX4wjH4+IFa1LhcIYrU27IyW383EMc7A4AbS+vKRdXNDWIzPGLJ7Iy3I+VEHiVmZH+3MxrqjjcyTYgiwgFPK+EUf4C+ta8G1KO3e9ykV2btVlHVRfvOpHC8IQB34sI1029u6Y2Hm5RRfYQccUf5eX2r8ngzyc3ianfupuovRjYcGXp/Od4QlbF1vM3IOGMbaCeBeA/EYuw53qhE4MBIWoirWUrNDQ58wHDhmcXWwNhrVK6gui4RBGK9CaP7nJ0BhxjVMAjMgWblCxvaS8hM6FdHGVOY7IBqA2RwkPGAarBR44qcgaqbyUMb0Wt/7lF9rFyNgOw3j7YR0g5U3Xzer66q1lu45zRlzRJADzUo7fcqKVRdwBlCBQrKrv1ZpZbWoJ8b1TYcAMQ90awqFIBRfxycx3rw/j8HKOyU1s/M8LWaUW0TOJb4sqVuSEEl8sbixwjcmgGSLd8/3EDpGSzYfVp99giS318v2C96F2hrg4ONLwjcIggAzRPYCQ//vFmtzSzI64+JM+kNcbwzCOjtgOONvgruMTXL8SzsQlyNhdXKFzHf8LJ+nxCaQ78hImzIgPkzqp4WoGe1f6MyysDnES0MFjv0YEN9OdREhye9AYMOfd9wvKEq3apyUf+VmdnJeFv32Zu9H3AEYVAi+ITP3lZV3IwebzbkcEPFTd6eI8qBQGS6zALnuWtxNXNL/a15o2tVevCHjRaBNWTB/WlkIhg75v6aSmUdjgbK4s0AQYbXOlehVzpWVhUmaeHQwIhb/mob1b+akJSkSt7NdCwEz4GKtNkzq90Ba3CWzONzW0BZMUbrIRv7qK447w94hN9eG8ebpzyEFMpc2U605CAbjTYkJEd61A42dXtw7M1y7t0FjvX0J5uq7P/Iv/eqLDyPncMkoAcbGDt2915AHO8MQsdqxVWGm/u765QOMm1cB49/iNZ7NBzN8C5n4AxvZ6CEEwrXyOx9sVybGwuVcbNKadITrDKOiCYW8mz6e94Tpd3ocA6YMdosPYPPnfu+/Ym9Hu/kGd7+Uc/NmTVQ6StYjxQzM+MNENmHkNeW45q2gjUYLcPbN9PxdEdcDZlQf4Gxe2h7QLYHxufPQxr5PcuHDPfXA+sp9Vies2sWab3GFMyTnbrV0kR9BEFISatKRdWXv4H+EJYGtPsgCM1tbxk94+0MBID9NXY1PRGUOxt9O7A+rW4QSyfOX1eBeG3cX+ayT4E43hnI2YCwxXv/HFA/D2luXl8rO/QQ74E6o+1sWC41L++nUnM4mwMalVGCWgDHYUDjMpQZwOIFpwOZPpS0YvQcQL8jj90R/Ot4W2e8Y/00w5vJkS2L7ngn2h0nZgbcL2hvPeDxWuhvN7PfLrnU/G6qsVI8fcHoUnNXPNainMrGIiDKATF/A5VfQRCEtACtSJWK5aPws1dUVV6n6jlVguBMHGe8M5/jnZlBgLa9Pm4sM5PxU4KZiH6NQlRfNXrrWNLfDKBYCEMaAk7cT81gUeVoZnk/ZbzBe71q0KEPu9LWtzooQZfMAqKFXM4FFUsGCu/ArKoHITU8i/L6nQTVS2gtIlOygJ8cbx4pZlVqniyuZo7jjTUH2WSoqXNPcar+7kLmrgWOxNUg+qamQAQki0L6ExybtHK6BUEQ0ppapVMmA1CBg/YXVCkaLXgpCPcCYhFkIJBRQv/I4pdaKeVOM509qIeDDce08SAMSt0xQxszbs2eW2wLMm0Qd8pspSu1SmnONfd5Y545ery55UDwb8YbPdaccT6uV39U8JPeAWebb1qVmvM4Me6DNhpcb5z13hpx0e/93c7E1VjRHO0xmUlQSRAEIT0A8VuwUbcVOTGD8YaZzVYTBCCOdwYD2RV/9DY318fEsHgQc1yfE4mxV2nd95dZ4Iz32sPnVMUBFN4RUca4HbNnRwvJWJdSs8DhsVjteqhQLE+aZ7zNnKPNivprDmtKpQwr62M9SIuMtxkzvAVBEAT3wMQdbkWCwBYHY6XMXMisiOMteEXzikUsWdZLVurm/u7vFqDqW1TNcT91+Qb9sj5CzbAGQ5r7T71U0BSd2fHF/G58YZazPxX+LY63dcbb5B5vnqqA94/JAsf04BvgWZ1mi+iw442RVvYUzaWkURAEwf9gTnOlYnlVMmD90fMqMQBYj0YQMhvieAtegWwqZkOirHbh3tOWx3lmrT/7uzM7yLTy3MxP/juk+lrR5w9xJyGN+rxvJ1jU/THLFKrL/oBbTG76OeONyoq2emZj9vZoi7AZO+Fmj7TL76DU/GwajBITBEEQUme9f1l/nDYdv6A0N/o3Cknr3RKENEEcb8FrHmygjUSYs/OU+h+CUuuOaH08DUO1nk/BPwxqUsaSVURQZELfOiLqlAZYRordumvp7/Zn9UeObCkz3omJSXT1Fvd4m+d4g74NtfVg7s5otf2lB87Spet3qGDubFSpeF6/ZLzRWw/RRyb6kpZxh7K4IAiC4H94hjsqonj8LewUQciMyDgxwWt61w2msYsOKrXKo7FXKCGRKCb+pip3ZbElgfwmqjX/hRZ0/uotle0W0ZK0IU/25Iy3pb+7qH/KzK0z3izuhmy3LrBuasYbYEwIph0gy/z9qmO07sg59fijTcumGjloNHn1SgPOehfWS8sh9ghKFxIjTxAEIS1oUbEIPVi/NM3dFa3uR4+3lGo8IfMijrfgNVAKxpza5QfP0rRNJ6mUnlVqVqGw6Ya2kJqCfixpFhzPLbWUmnPGu2gaZLzvaBlvBGLY6c6u93+bBV7/3fuq07AZu+jrlUeUgZU9SyA90sx8rQEISqLaAMcdAmvseEddSlbQFQRBEPwP9D8m9KtDL3espILB0t8tZGakFlXwiaHNQ9X/s7ZH0T97zqQoKxKEzEaeHOx437VkvMv7MeOdk3u89Yw3j9PCnG1/0KNWSepQtZhldvYrnSr7rb+6cN7sKcbVoMf8jC5uV1ocb0EQhDQFSubidAuZHcl4Cz7RomJhJbJ24Ew87TsVR6hwblelWFrvliCk6Uix+Bt36KguLFbZ5P5m+xlv3fG+6l9Vb7Q4fDuoPm0/eZFqlQqiArn9V4HRpFxhiroYrZRzIeYDpxvij8i6F/NT4EEQBEEQBMGvGe8TJ07QE088QeXKlaNcuXJRhQoVaPTo0XT7dvLYKXu0bdtWGW7WX88++6wZuygYBD6j59tVUN/nypaFxvWpbfrMXkFI7+JqCEJB5Au9x/4sc7YdJ8YZ7yJ+dDxRbt+qUlG/Ot3WyrmYZ29dZo4WmECk3wVBEARBEDJaxvvQoUOUmJhIP/30E1WsWJHCwsLoqaeeomvXrtH48eOd/i2e98EHH1h+zp1bSgTTO/fVDqZCubNTmcK5paRTyNRwxnv7yUvq/6ol8vnV6bMdJ+bvjHda0rJiEVVxcyjmCsXG30wWVhNFc0EQBEEQMqrj3bVrV/XFlC9fnsLDw+mHH35w6XjD0S5RooQZuyWYSPOKRdJ6FwQhzQkO0vqZeYZ39eD8ft1+TptxYv7u8U5LMC+9ZnCQqjZYe+Q8Reu93ugrFARBEARByDTianFxcVSokOsRU9OnT6ciRYpQzZo1aeTIkXT9umY8OeLWrVsUHx+f4ksQBCEt6FwjZdCwWkn/Ot6248Qyk+MN2lXRys2nbzlJUZLxFgRBEAQhszneR48epW+++YaeeeYZp88bNGgQ/f7777Rq1SrldE+bNo0eeeQRp38zduxYCgoKsnyFhIQYvPeCIAjuASE1zFFnIDzoT5J7vDXH+/xVTVejiK74ndHB6DIcg12Rl2nZgbPqMRklJgiCIAjCPed4v/nmm6nEz2y/0N9tzalTp1TZed++fVX/tjOefvpp6tKlC9WqVYsefvhhmjp1Kv3999907Ngxh38DBx3ZdP6Kiory5C0JgiAYBtbA7rVKqu/R2l2lRD6/bj9nNu7xznyl5gCjyx5pqs0Nv3rrrpotXjekQFrvliAIgiAIgmc93q+99hoNHTrU6XPQz82cPn2a2rVrR82bN6eJEyd6vHNNmjSxZMyhjG6PHDlyqC9BEIT0wP11g2ni2mNUL6SgxRFOi4w3RmldvJa5HG/wXNsKtO7IOcqXMxuN7FZVerwFQRAEQbj3HO+iRYuqL3dAphtOd4MGDejXX3+lwEDPq9p3796t/i9ZUssgCYIgpHfKF81Lq//XjvLmNEW70s053gl08dptSkxCFp7U1IHMQpG8OWjpK23SejcEQRAEQRDM7/GG042Z3GXKlFEq5ufOnaOYmBj1Zf2cqlWr0tatW9XPKCf/8MMPaceOHWoO+IIFC2jw4MHUunVrql27thm7KQiCYAolgnKqGd7+xjJO7G6ipcy8cJ7slDWL33Q0BeH/7d0HfFPl+sDxJ+mki1E2ZRYQF6AoCCogDvS6cG+RP4p773Hlch1c5Yq4BcV13XoVvc4r7utAQVFRQEBASikbWlroyvl/njc9aZKmbdI2o+nv6yemSU7OeXPy5nCe87wDAAAEEJYzw48++sg0D9dbTk6Oz2uWZZn78vJyM8WYPWp5cnKyzJ07V2bMmGHm+9ZB0k466SS57bbbwlFEAIg7nunEyis9c3hrBhgAAABxGHhrP/D6+oL36tXLE4QrDbQ///zzcBQHAFoEO+NdVuGSDYW7Wlz/bgAAgFhF+0MAiBPeg6v9sanY/M3gYgAAANFH4A0AccJ7OrHF6wrN37tHeC5xAAAA1ETgDQBxmPFesq7I/L1Hl8jOJQ4AAICaIj/sLgAgLNKrRlLfUVphbmq3zmS8AQAAoo2MNwDECR1IbUDn6gx3j3ZpUZnWDAAAAL4IvAEgjhw3uKvnb+8gHAAAANFD4A0AceTYgdWBNwOrAQAAxAYCbwCIIzp92IjcbPP3/r3aRbs4AAAAYHA1AIg/D52xjywpKJID+7aPdlEAAABA4A0A8Sc7I0UO7JsS7WIAAAAgXgNvy7LMfWFhYbSLAgAAAACIU3bMacegLSrwLioqMvfdu3ePdlEAAAAAAHFOY9DWrVvXuYzDCiY8b0ZcLpfk5+dLZmamOByOaBcn5q/Q6AWKNWvWSFYWox+3VNQDUAegqAdQ1AMo6gEU9aB+Gkpr0N21a1dxOp0tK+OtHzgnJyfaxWhW9IfEjwnUA1AHoKgHUNQDKOoBFPWgbvVlum1MJwYAAAAAQBgReAMAAAAAEEYE3i1YSkqKTJ482dyj5aIegDoART2Aoh5AUQ+gqAdNK+4GVwMAAAAAIJaQ8QYAAAAAIIwIvAEAAAAACCMCbwAAAAAAwojAGwAAAACAMCLwBgAAAAAgjAi8AQAAAAAIIwJvAAAAAADCiMAbAAAAAIAwIvAGAAAAACCMCLwBAAAAAAgjAm8AAAAAAMKIwBsAAAAAgDAi8AYAIMqeeeYZcTgcsmrVKs9zo0ePNjebvqbL6LIAAKB5IfAGACBC7r77bpkzZ06Tre+9996Tv/3tb022PgAAEB4Oy7KsMK0bAAB4ycjIkJNPPrlG1rqyslLKy8slJSXFZLWVne3+7LPPzL3+c11aWipJSUmSkJBgnrvsssvkkUceMa8BAIDYRcYbAIAo00A6NTXVE3QHoq/pMnbQjboVFxdHuwgAAHgQeAMA0AjnnXee9OrVq8bz2gTcO5DWvzUYfPbZZ83fetP31tbH259/H299r2a77XXbN81+a3mOP/74GuvYtWuXtG7dWi688MI6P5OuR7PpL7zwguy2224m4B8yZIh88cUXNZb98ccf5aijjpKsrCyT0T/00EPl22+/9by+bds2c7HgwQcf9Dy3adMmcTqdkp2d7ZOtv/jii6Vz584+6583b54ceeSRptxpaWkyatQo+eqrrwLu699++03OPPNMadu2rRx00EF1fkYAACIpMaJbAwCghfrXv/4l559/vgwdOlQmTZpknsvNzW3w+jR4zs/Pl48++sis26YB6Nlnny333nuvbNmyRdq1a+d57T//+Y8UFhaa1+vz+eefyyuvvCJXXHGFaQL/6KOPmgD4u+++k7322sss8+uvv8rBBx9sgu4bbrjBNIOfOXOmaSav7x82bJi0adPGLK9Bu65L/e9//zPl1PJpsLznnnua57/88kuzPtsnn3xignoN+idPnmyC9aefflrGjBljltV96e2UU06Rfv36mb70NL8HAMQSAm8AACJAg92LLrpI+vTpE1TgW5/hw4dL//79TeDtv75zzz1X7rrrLnn11VfNNm3PP/+8yYYHkw1etGiRzJ8/3wS96vTTTzfZ79tvv13eeOMN89xtt91m+qZrIK2fy962LqeBuAbfSoPp119/3bNuDZq1DEuWLDF/a+BtB+H2RQkNnLXshxxyiLz//vue1gN6wUGX123/97//9SnzoEGD5MUXX2zwPgUAIFxoag4AQJzRgFyzzdpU3KaBrQawZ511Vp19yb0DezvoVj169DDN1z/88EMzGJzeNPAdN26cJ+hWXbp0Mc29NRjX7LodeK9fv16WLl1qHmuwPXLkSPO8/q10eQ227Yz3woULZdmyZWZdmzdvNs3T9abN9bU5u2bQXS6XT5m9LzIAABBLCLwBAIhDmnnWvtCrV682j1977TWTnT7nnHOCer822Q4U0JeUlMjGjRvNTf/W7La/3Xff3QTFa9asMY/tYFqDbA2ctV+4PqfBtx146702WdestdKgW40fP146dOjgc3vyySfNCO/bt2/32W7v3r1D3EsAAEQGTc0BAGiE2rLHmhGOJm0afvXVV5us9y233GKame+3334BA+Vw69q1qwmKNUutTd01s60ZdQ2ir7zySnNxQAPvESNGmH7cys5mT5s2TQYPHhxwvTqYm7dWrVpF4NMAABA6Am8AABpBR9DWkbv92Zlmb8E08Q5FXevTQdWOPvpoE3hr83LNfs+YMSPoddsZZ2+///67GVlcA2alf9vNx71p320NoLt37+55TjPcGnhrAK6BdGZmpslu62jlH3zwgfzwww8yZcoUz/L2wHOaBT/ssMOCLjcAALGIpuYAADSCBoja5Pnnn3/2PLdu3Tp58803ayybnp4eMEhvKF2fqm2d2qxcByy7/vrrzZRemgUP1jfffGOCYZs2G3/rrbfkiCOOMOvSm/6tz3lPg6Z9uXWAMx08TYNm78Bbl9OR0u2m5xqca5Z7+vTpphm894jm2r9c9+0///lP2bFjR43yaVN3AACaCzLeAAA0ggazN954o5xwwglmuizt9/zYY4+Z/tDegasdTM6dO9cEmnbzax0EraHswc90u2PHjq0RXGvGW+fK1v7dOi1Xx44dg163TgGm6/SeTkx5Z6XvvPNOM6q6BtmXXHKJJCYmmunEtP+1TmfmzQ6qNUOu033ZtJ+3Dvqm29h///09z2tQrn25tdw6ivmECROkW7dusnbtWvn0009NUK/TowEA0ByQ8QYAoBE0sNXstja71im0nn32WZk6daoce+yxNZbVgFuDZZ0K64wzzjABemOceOKJcvnll5um2prd1nV6S05OltNOO838HeygarZRo0aZpuk6R7hOIaZN1zVAHjhwoGcZDYi1b7YG6fqZNSjv2bOnCYz9Lyho33I78PeezswOyHVObg2+vel84Jp5177pDz/8sPmszzzzjHTu3Nn0XwcAoLlwWDrCCQAAiEsaoM6ePVsKCgrMxYFg+45feumlJtgFAACNR8YbAIA4tWvXLjOa+UknnRR00A0AAJoefbwBAIgzGzZsMH3JX3/9ddm8ebOZsgsAAEQPgTcAAHFGRzLXKcS0T/WDDz5Y6zzYAAAgMujjDQAAAABAGNHHGwAAAACAMCLwBgAAAAAgjAi8AQAAAAAIo7gbXM3lckl+fr5kZmaaeUgBAAAAAGhqOlxaUVGRdO3aVZxOZ8sKvDXo7t69e7SLAQAAAABoAdasWSM5OTnNK/D+4osvZNq0abJgwQJZt26dvPnmmzJu3Lig36+ZbvvDZ2VlhbGkAAAAAICWqrCw0CR97Ri0WQXexcXFMmjQIPm///s/OfHEE0N+v928XINuAm8AAAAAQDgF08U55gLvo446ytwAAACaUl6eyLJlIv36idTTIjD8SvJEipaJJGaI7FgpUrpZJCVbJKN3wx6r+pbtMEIkLSf4bdvLAwAaLeYCbwAAgKY2e7bIpEmWuFwOcTgtufz27TL2xJ1RKUvOlhdl77U3iENcYmmmxOu1xjyuf1mH5LU5WXK2/TuobZtHw54QyZ3YqM8LAIiDwLu0tNTcvNvZAwAAeGe6J03SmU/cYaXlcsiDU7Lk3+sXSGLWroiWpXPSJvlqwPXicGiY6x/oNu5x/ctakrP1NbFbRNa3vAnFv7tQpMtYMt8A0NID76lTp8qUKVOiXQwAABCjtHm5y+X3pOWUvqmdpWOvyF6w3zNhpSRUBd3REPJMq1alSNFyAm8AaOmB98033yzXXHNNjZHlAAAAlPbpdjrdzcxtCQkiz1+zZ+T7epd0F5lzpYj4XwmITdo83ZGYHu1iAECzV/cs381ASkqKZwRzRjIHAAD+NLiefM9OEYfLE3TPnBmlAdY0czxslogjIQobd4r0Hh/0ti3L3TxdPjxAZMXssJcOAOJZzGW8d+zYIcuXL/c8XrlypSxcuFDatWsnPXr0iGrZAABA8zTquGLp9sc30tXZXt64aVB0RzXXwcq037Q24dZs8o5VXiOL92rYY1Xfsh2GuwP/QXfWve2i5WL9fKunH7rJztPXGwDiK/CeP3++HHLIIZ7HdjPy8ePHyzPPPBPFkgEAgOZq845SM5Bav747oz+VmNIA1g5is/f3fa0xj+tbNphtr//Unen2Rl9vAIivwHv06NFiadsmAACAJrKluMzct0tPiXZRYl9mv6reiN790B0iuza45wCPo+C7vNIlZRUucVmWuCwdhE/vLam0LNPU3vzt8v1bl7OqltFB+9zvrXq/ec79d4LTIQNzWktSQuCend+v2iLrC3dVr89Vcx2edfs9TnA65bhBXaW4tELe+TlfyivtMljSulWSnDu8l6QmRaM7A4BmE3gDAAA0tc1VgXd2enK0ixL7qvqhu+ZdIM6qzLelw6x9dZo7AB98j8ge1zd49RroTn77V1mzpaQq2HQHtpVeAax3IJqU4JAJB/aSE/ZpuoD/kyXr5YG5y+SXtdvNNsLl/w7sLbcfu0eN5/+3bJOcPXteo9b99fJNUlC4S37Nrzkyf6vkRDnngJ61vnfub+tl1ebiWi8YnLRvjnRundqo8gHwReANAABaRFNzReAdpC5jdTxz91zePnN8WyILb3A/s8d1DVr1x4vXy0vf/RnSe65+5Scp3Fkh40f0ksbSoP72t36VvK0765x2zelwSILDUf230/dvp0OXcy/j+bvq+QqXZdb/3i/r5K/H7G5e8/bhrwXmPqdtK+neNk2cTvd63Td38Ovw+9vejnrrp3z5eMkG83dqklNOGdLdvLakoEjmrdwi363cUmvgvWD1Vjn/ufl17qPflv8mDx+bIY6s/nHVwgGIJgJvAADQcpqaZxB4B6VomTjqmvJs4Y0ivU5vUFD2+e8bzf0Re3SSowd2qQpWq4NO7yBX7z9evEGe+XqVyZJrtvyCkX0a88nk9/U7TFCcnOiUuVePkg6ZKZ5t2WXwD5RDtau8Ugb//b8mI63b261zpk/g/9nv7qB58rF7yuF7dAp5/TtKK2Xu4vXm75OH5Mgd4/byZMHPfHKe/LB6a63v1abpql/HDNm7W2u/AF/EseIpuSPtQXF8ohddnO5R+HVAQACNQuANAABaUFNz+ng3vJ+3N1eDBlvToNMOvM86oKeM6t+h3vcc1Le9pKckyCOfrpC73lss2RnJcuK+Dc/C2gGrrrdHdpqEg/avPqBPtny2dKN8/vsGn8B71eYSWbNlp2lCPzw3u0Hrn3hQb8/nOG9Eb8/zg7q3MQF0ZfEa2bziA8nuspfPd6TNyT9Y5M6233DkgJpBf0meuOY85OliwIj2QNMh8AYAAHFv846qwJuMd2jzjWvQpSOaB7J5vkin0SGtVrO/67bvkpREpwzr3S6o92hG9vqxA0w/8Ec/W2GaiffMTpce7dJMIK/9kt0Dnfn2FXf3E/ceoMz99/uL1pn1Hrp7Rwmn0f07mMD79QV5ZvAzuz/74nXuPtn79WwnGSkNOxU/oE87ufUvu0tGaqL07ZjheT49JVGu6PW5XJY+TRLm1cxY/5S3zez/9OQEObhf+5orLlomTv+LLYxoDzQJAm8AANCCRjUn8G7QfONr3hDr94e8+nqLVP54o5z3cW/ZWNmhxujfJhg2j6sDY/17V7k7qNNscKijbl97xG6m7/L81VvlpMe+bvTHO3RA6E28QzFqt44i//nNXGyY9uHSGq+P3q3+bH9dFyMCNrkvyZPLM/7pk7F2zZskdy/oLlusjvLl8k3m2TG7dwq8/wO1dHAkiGT2bXBZAbgReAMAgLim/W13lFaYv9vT1Dw0njm/LXH8/pDPSwkOl5Rv+12WFIc++vW4fbqG/B5tQv3AGfvITf/+WX5as00Kd1UEHORM+2l799l2D3pWPXiZvnbknp3DPmp37/bpcue4veTnvG2ectjla5uWJGfXMep4gwXIWOvjRUsXyLfFA83jbm1ayZWH9q1nRPtJ5n0ucYpz6Eyy3UATIPAGAABxn+2uKEwVa3u6FG5OlNbh6dYb3wJkQi1JkKtO+ItUpHTzGZVbg1x3kFlzVHANPLU5dNc2rRpUDA0a/zVxmDQX7uA6DAF2bTL7iSVOn4HxNHj+y4iDZUxSF+nSupUcuVfnWucWN3InyoebB8p/5v5b+rRPk+u01QOARnNY2u4njhQWFkrr1q1l+/btkpWVFe3iAACAKPv7P0tk8g2tRCyHCRBnzRKZyCDNoVsxu7rPtzY/1kwoo13H5fe0+afHpM2iSyXBoTO4O8XByOZAo2NPAm8AABC38vJEevbUgbWqeycnJIisWiWSQ+vZ0JXkuft8a59fmh/H5/dUkifWnJ6+08lpAH/8Kr5zoBGxJ03NAQBA3Fq2TKdQ8p2TubJSZPlyAu/G9flG3H5PgeZw1+z5xm9Eep7SJMUDWqI6OngAAAA0b/36iTicvo37NOPdl0GagXr68/v56nR3M3YADULgDQAA4pZmtU+8okDE4fIE3TNnku0G6p3DvUaY4BKZN0lk8/ci6z91N2cHEDSamgMAgLjW58AN0q30Nzkxdze54bQcgm6gPjqQWmKmyFen+b3gEvlQR5XXViROd4DOoGtAUMh4AwCAuJ9OLDFrlww7qJKgGwhWhxE6qlqAF+yuGy736OlkvoGgEHgDAIC4trm4zNxnp6dEuyhA82pyPuDaupfRQdd09HQA9SLwBgAAcW3zjqrAOyM52kUBmpcBV9aS9faaZkynLANQLwJvAAAQ903NVXY6gTcQctZ78D0BX7I06B46k+nlgCAReAMAgLi1q7xSdpRWmL9pag40wB7Xiwye5gkbKi2HPL7hRCkYtZiB1YAQMKo5AACIiLw8kWXL3HNrR2qQMzvbneh0SFYrTnuABtnjOpFep5v+3Kc+v0EWbEyX/cqypUu0ywU0I2S8AQBA2M2eLdKzp8iYMe57fRzJwLtderI4HHX0VQVQN21S3mm0JGR0Nw/Xbd8V7RIBzQqXfgEAQNgz3ZMmWeJyuQNfl0vk/AsseXzF15KcVSouy6q6iVhV9+axyxKr6m8Nmru2SZUzhvaQCQf2DnlEcw28ATRel9ap5r6AwBsICYE3AAAIq6+/1mDbL9tsOWTNKqek9tgZ9Hp+X79Dpr63RE7cN0dat0oK6j2bd5Sae0Y0B5pG56rAm4w3EBoCbwAAEDbapPyCC6waUxI5Eyx59qo9pEtXS5wOh2grcL1338RkuJ1ez5W7XDLpufmyYmOxfPTbejl5SE6II5ozsBrQFLpkVWW8C4O/aAaAwBsAAIS1ibk2H/cNuhMSRGbOdMjYoa1DWt9xg7rJ/XN/l//8lB904E1Tc6BpdW7dSjonbZLs4j9ESjoxnRgQJAJvAAAQFjqCufbn9vfSSyKnnBL6+o4Z1MUE3v9bvkmOfvBLT/9v+973b/f9VubwBprUnjtfk68GXCkJDktkzlUiw2YxrRgQBAJvAAAQFjptmNNZPaiane0ePrxh68vtkCH79mgjP/y5TX7NLwzpvXvlhJZdBxBASZ7kLLtKHBp0Gy6R7y4U6TKWzDdQDwJvAABagGjMoa3buW1qifz9plYilrOqiXnjtv/UefvLj2u2mR7j3n3CpY4+4m3TkqVHdlpTfjSgZSpaJg4Ntr1ZlWZ+bwJvoG4E3gAAtIABzuzpvDRTdcIVBbLfEdu8mmbbTba9pvIyCS19T/XjjJQEueqw/tI2hGbbex+6Vbpd9K3sltbFDKbW2KC/TVqyHLJbx8atBEDDZPbTS17uTLfNkSCS2TeapQKaBQJvAABawABndnNvHejsjQc7ybyy3yQxK/TpgFolJ8pNRw0IevnlG3aY7Yw4wBWxTDuAMNGs9rBZUvntJElwuMSSBHEMnUm2GwgCgTcAAC1tgDPLKUf26Cu5A0v8pu3S7JXvY0fVVF9rtpTIS9+tkXd+zpcbj9zNPB+MFRt3mPu+HTPC8OkARFzuRLnxy06St+YXOW3MGDkhd1i0SwQ0CwTeAAC0wAHO/nZ2z5Ay0DvLKuWthfmSt3Wn/JS3XQZ3bxPU+1ZsIPAG4k1iZg/5ttghw3e2i3ZRgGaDwBsAgDimwfXU6WVy49VJjRrgrFVyghy2eyd5+6d8uenfP0uv7HRP329333BLtFu492PNtK/aXOwZkRxAfOiYmWLuNxSF3l0FaKkIvAEAiHOHjiuRbkt/kLYVbeX92/dtcF/rE/bpZgLvJQVF5hasLq1TpVOW+0QdQPPXISvV3G8oKm02sywgNu0qr/RcxLUH/LQH+lTtQhjMM9YReAMAEOc27yg1A5z16b6zUSe5o3frII+fPUQ27ij1ms7LfV89nZfpJm7+Nv3DRWSfHm2C7hMOIPZ1yHBfSNvYgMDbe5YF7QYzZdouOfH08uqgS2dT8JppwXfmheqgLCXJKft0byOJCTrKevAKd5XLp0s2SGmFSzdW3XKnarvV26zlsVQ9dlkypFdbGZHbXhri6xWb5J2f15n1eM8w4d+CSP+uLqddJvdyaSmJcvNRA6Rrm1Yhbfs/P+XLrC/+kPJK9wAg3oFvjX2iu8lTHt99os/9Ze/Ocue4vRu0DyY+8718vGRDra9npyfLgr8eLvGCwBsAgDi3ubjM3HfIaFzmQIPnI/fq3ESlAtBcdcxqWODtP8uC3v/1uhR5fPnXDZpl4drD+8vlh+oUZ8Gb/Nav8uaPa6UpJCc45dtbDg05K6sB7+Uv/ug5NjdGVmqi3HVC8IFvpcuSu95dLAWFTdNN4Plv/5QLR+ZK93ZpIb1vaUFRnUG3qkp6xw0CbwAA4tymqpPj7HSaewNouj7eGnhr9jPYFi21zbKQXtpG2mRtrWo1416X01nVcqaqdY1pWFPVqkaz1as3l8jL36+RSw/pK04zJUP9inaVy3u/rDN/H9S3vaQkOn3W67udqhY8nu37tuj5ctkmWbttp7z7yzo554CeIe2//y3fZILutmlJMvGg3r7b82ox5Nm+s/qxvU/yt+2UBz9ZbrY/+dg9JTkxuMz/vJWbTdCtAfvDZ+4rCWbdup2q9TurZriwH1eVzb1ffB/fNmeRfLdyi7y1cK1cNia0CyBvLXRf/Dh0QEd56Mx9POv0/87jCYE30ALQlwpo2eysSnYjM94AoDpUBd5llS7ZVlIubYPM+NY2y8LHU4aEdH6i/YL3u3OuCXwX/LlV9u8V3Ojq//11vQna+3RIl39NHNqoLjBPfvmH3PnuYpnz49qQA++3qjLuxw7qGnLA6p251gsP2s/+i983ymF7dApy2/nm/i97d5GR/TtIY5w8JMcE3tqC4LhB3fy6CVR3C/A0WZfqZvs6S4Y6Yd9ukpbcMkLSlvEpgRbMuy+Vw2nJ6VdvkGFHFnoOjnoE9BwkpWb/Hr3aefTArkFPHWT3W3r1+zVS4XKv0xx4qw64/v2lvEdBru5DZR+w3Y+1+dI9Jw0M+mouAF+bdrgz3u2r+mUCQGOkJCZIv8xt0t71p2zdlCtt0wcE9T4Nrs+9fpM8c292o2ZZSE1KMN1eXl+QJ//8cKkc0Cfbpx+yd39p81zV+cjnv2807x83uFujx53QoPmu9xbLgtVb5bynv/Ocsyh7Vofa+o3/tq7QLHf84G4N3r5mqrUMs/+3Ui576QdplZRQez9trzLouVljt23T70Cz3is2FsvIaZ+G/P70qtkyWgoCbyCOuftSVV9ZtlwOeWl6B/miZFFIfame/Xq1PHzmPnLEnp2D6rd09SsLZX1hw0Y6DWT+6q1y4r7d5OB+jbsyC7TUlid24E3GG0CTWDFbPug1SRIcLrG+uk2kYpZI7sSg3pq852rpdtHPcnL/AXLdyd0aNcuCBt7zVm4xt2BpvH384K7SWJ2yUmVkvw4mmP9sqTugD0W/jhmyb4/gkxqBnLZ/d3num1Wyq9xlbsHao0uWDO3d+DnYs1KTZNLBfeTpr1aaCxueJul283ivZvoO04zcqym50yETDuxtLqK0FATeQBxz96Xyu6JrOWVUl97Sa69ir/5ENfv36Lv0+V/zt5t+TJe88IO8f+XB0q9TZp3b1L5TGnRrZu3SQ3Jr9MvybMNrFGT3AbjqOa8Dt94/+tlyWbS2UAq2M1comq8nnrDkwovcF7+05clpV22QoUdur24R4p2h8Rox1zt7o8sNz23foMHNNu+wB1cj4w2gkUryROa5g27lEJe45k2SaT/1kG2ixyd3tre2lnRfLd8siVkVcs4JaY26CDkiN1tuO3p3+WOTns9Un0/Y/aXtQK866HO/PjCnjfTMTm+SXTH91EEm6K60rDr6aPuea9nLDere+Nke+nfKlK9vOlS2lpR5ffba+qxX74PsjBSTMW8K143dzdxQPwJvII5pZk1P8vVk36bNuu6Z0Cfof+wqKl1y/nPzzT8s2qTqmQlDa112+85yefLLleZv7e+kVzIb6+PFG0zg3dC5QoFoZ5t123bQrfT+5fs7yJc7Q2t5ol6Y96d8c/Ohnv6VoffxJvAG0EhFyzSc9nnKKS758df58m3xwKBWoQN77d2tdaOKocHk+Qf3kWjSY+pJQ6I7eI7+exDqvwmIDgJvII5pgHHKlevl1RkdG9yXSufH/Nuxe8rhyz83wff+d831ZN88czpWZeZ0sJPySstMr3HmsB5RnbIE8B3nwD2SrrasmDVLZGJwLSKbhAb83he/DMsph3TVliclATMy3q1O7AzKe78UyMpNxWYk2FBONvXimWZDFE3NATRapg4G5vQJvl3ilMOGHigjErsEbknnlXnVY5s2c2bcFrQ0BN5AnOs1YoN02/WrnJC7m9x4Wk6Dsn292qfLBQf3kUc/W1FvANy/U4bcfNTuTXb1tVPVetY30XyTaFmq54x1P9b7CyZZ8lnxL5LettzTFNJ/4Bv/5/Q+NckpNx21u+zWue7uFv40yy4OXalvy5Op5wXf8kR1zkqVv771q+nTWFvgbZfZc1HMsqqm+3E3uWybRuANoJHSckSGzRL57kIRq1LEkSDOoTPl/NyR0S4ZENMIvBHXmEZLZF3hLtOc9eBRVqP2wXVH7GYGMtGMtmYNq/ssVfenSnQ6JKdtq0b3WfLWMSvV3NPUHA0RaM5YzT5/9G2xpPYIfjAem2ZoZp6zX0jv6dC5Ujr+5TfZ8N6ejRrFV0evveOdxbKkoEh2u+39mhcH3JMU1KpdenKT9ekD0MLpQGpdxooULRfJ7OsOxgHUicAbcSvazUtjRcH2nea+S2t3ANtQ2vS1voHVwqFTVVPzDUVkvBG6QHPG6uObT8uR7E5dfZt3ew345z1AjircVSF/nbPIjDmgI4SHMi3X4nVF0mqvP2WP3bbIw8eMlH79HA26CNYmLdn0JXzpuz/NPLShaklTtgCIAA22CbiBoBF4o0VMo2U3L/2yZJGkZ5fXaELqM9ej12vquEFd5ZT9ujeoHFPfW2z6RQca2dN3tGJ7ZGPv+Rbdz2mfTB3QrGubVg0qw7qq0cC7tG7Y+6OtY6b7goGOlK77oymz6Yh/GuBOnV4mN16dVJVttmTmTIdMPD7037Q28f5pzTYzgOARe3byOo74/n6956DX3/mnSzaY9w/bO00OOaRx9ffuE/aSy8f0Nev2Hv3f7g/uPWKuPuswrVPcz6Wn8E8+AADRwr/CiMvm3oGm0dLmpR98syPk5qXfrdxi5q9u3SoppPctLSiSmV/8IY2loxG/+eNaufSQviG/d0dphRTtqjB/d25kxjta7L7iZRUuKdxZIa3TQvsegP3GbpNuFy2Srs728sZNgxp8HDplSI4JvB//fIW5hWpw98bN1ypVwXVDL8IBAIDoidnA+5FHHpFp06ZJQUGBDBo0SB566CEZOrT2aYwQmx5+vEKuuDTBM3ft6VdvkAOOKvSZl9Yn8+uXFc5MTZLzD+4tacmJjR7MSJuXXn9KN8nu2NlnxODamprq40c+Wy5/bCyWtxeulXOG9wqpDK98v8bcH9yvvVw8OjfgaMX+23b3l67qP+0U+XDRerl/7u/yxe8bGxR423NfZ6YmSkYzzXalJiWYix46Vdn6ol0E3gjZig3FZpyD/QdWNuri37h9usn7i9bJ6s0lAbLNvnPVe4/eq79lHdTslP1okgkAQEsVk2fir7zyilxzzTXy+OOPy7Bhw2TGjBkyduxYWbp0qXTs2DHaxUMIme4rLkkQqyr41eD7pekd5IuS0OaurXRZcvXh/UPadvtOgQYzcsjEE0Kb4kqDvb+/85s8/+2fktMurbqZuM9gRl7NTT3NxC1588c8s47/O7C3jMhtLw2RlpRoAu+1a5dJyZodkpa9e0j9qezAu7H9u6OtY2aK+S42FJZK/yj0M0fztnzDDnPft0NGo9ajF69eOP+AJioVAABoSWIy8J4+fbpccMEFMmHCBPNYA/B3331XnnrqKbnpppuiXbyWpSRPpGiZSGKGyI6VIqWbRVKyRTJ6i1TscM/lWEsg+POvlWJZCb5PWk4Z1UXnri2uzvAGyv46HSZofPunfHl1/hrTp1Hnkw7WwjXbzGBGA/fYJjOOOqjBgxnpKN7/eH+JLF1fJBOe/j7k9+v0PyP7d5CG6pGdJpd0/0yubXOfJHypYb1Tdu7zmJT3nFCzn3iAvqVLCgrd5Wim/bttnbJSpWjrKknMe83dLELrYIcRkRvURX8HG7/2rf/+vwfvx01dNnv7yt6W99/+5VDh3j/eZYrUd9HA49Hyje7AO7dj4wJvAACAuAm8y8rKZMGCBXLzzTd7nnM6nXLYYYfJN998I/FC5yQuKauUWJaR96y0/+1ycYjLxDrePabtxxoIbtrjIdmRM77G+38s3CDi6FVj7tp7JgQ3d21pRaV8uWyjGRxMBzXap0fbGgOfeQeZ9mtaqv/8lG9eH7lPRqMGM2qbniw3/2WA/PsHd/bankJLPE1KAzUvdf+dmOCQ8cN7NW76npI8ua7tdHGaT6jrdUnyDxfLmBdTpKA8+Cx6l6opuZqrY9Pfk1MG/F2c63S0OPdzljhk0x4PB6x7Tf87uEwcVd+Be9uBfw/Vj5uubP7b995WXeUI5/6pWabwfxeNOR41VcYbAAAgbgLvTZs2SWVlpXTq5DvtiT5esmRJjeVLS0vNzVZY6M7wxbpb31wkcxevl1jVOWmTfDXgMnFoP2m/k1zvx3oS3PbXy+XYf6cHDATbjS2Sbf/d2wx0FurctSmJCXLykBx54suVctMbvzTocwzvky2NNeHA3uYWFUXLxCm+0wYlOlzSKzm/xv52T39UfRHA3VddJD05UY7au7M0WyV5cop1h/uChxcN+trVUfea7ndwqbmY4rvt+h43TdkCbd8RZDnCtX8Clym830VTHI+0vH06pDd52QAAAJpl4B2qqVOnypQpU6S5SUtOMANexao9Wq2XhKqT3PpoILh7xnop3lkzuNv7L4Vy70MVsj4vSfr2DX1U8/MO7G2m49KRvd2Bl90kvTr77PAflKzqOZ3/+S/NOeBU2nRWtIl9dfBtORLk+avOEkd6d59BneKWufgQuC4m1FH3mu530LD3NkXZGrP9pipDsGUK53fRFMejYwZ2NQP1AQAARIPD0lGgYqypeVpamrz++usybtw4z/Pjx4+Xbdu2yVtvvVVvxrt79+6yfft2ycrKimjZ44r2pZzT0yfgq5UjQeT4VZHrb9vSrJgt8t2FIlale18PnSmSO1FaVl3UQfGsyNe9urZdn6YoW2O231RlCLZM4fwuOB4BAIAYpLFn69atg4o9gx+tKkKSk5NlyJAh8vHHH3uec7lc5vHw4cNrLJ+SkmI+pPcNTUBPWofNcp/E1sUOBDnJDR8NsjWQOPRT931LCro9dfGJAA2MneGve7Vuuz5NVLYGb78JyxBUmcL8XXA8AgAAzVzMZbzt6cQ0wz1z5kwzd7dOJ/bqq6+aPt7+fb8bc9UBwY4ivFwkMV1kxyqvUYR7iVQUi2T25SQXkWFG0f7Ga+Tw4REe1fwb3/rv/3vwftzUZbO3r+xtef/tXw4V7v3jXaZIfRccjwAAQAwJJfaMycBbPfzwwzJt2jQpKCiQwYMHy4MPPmjm9K4PgTcAAAAAINziIvBuKP3Qbdq0kTVr1hB4AwAAAADCwh5fTMci0wC8LrE7rHYDFRUVmXvdAQAAAAAAhDsGrS/wjruMtw7Elp+fL5mZmfE9xVITXqGhdUDLRj0AdQCKegBFPYCiHkBRD+qnobQG3V27dhWn09myMt76gXNCnSy6hWM0eCjqAagDUNQDKOoBFPUAinpQt/oy3TE7nRgAAAAAAPGEwBsAAAAAgDAi8G7BUlJSZPLkyeYeLRf1ANQBKOoBFPUAinoART1oWnE3uBoAAAAAALGEjDcAAAAAAGFE4A0AAAAAQBgReAMAAAAAEEYE3gAAAAAAhBGBNwAAAAAAYUTgDQAAAABAGBF4AwAAAAAQRgTeAAAAAACEEYE3AAAAAABhROANAAAAAEAYEXgDAAAAABBGBN4AAAAAAIQRgTcAAAAAAGFE4A0AzdBnn30mDofD3NvOO+886dWrV5NuR9en60XgfXPMMcc06LtC88VvovnS3+Fll10W7WIAaKEIvAEgCu6++26ZM2dOtIsBNBv5+fnyt7/9TRYuXNjgdbz33ntmHc3xeHHAAQdIhw4dJDU1Vfr16ydXXXWVbNy40Wc5/WwaXNZ2++qrr+rczjPPPFPrewsKCnyW3bVrl0ydOlX22GMPSUtLk27duskpp5wiv/76a9Cfa8OGDXLTTTfJ3nvvLRkZGeaz9e3bVyZMmCD/+9//PMu9+uqrpgxvvvlmjXUMGjTIvPbpp5/WeK1Hjx4yYsSIoMsDAOGUGNa1AwBqPZE++eSTZdy4cQ16/8iRI2Xnzp2SnJzc5GUDYjXwnjJlisk4Dx48uMGB9yOPPNLsgu8FCxaYz3z66adLZmamLF68WJ544gl59913zYWI9PR0s9yJJ55oAld/t9xyi+zYsUP233//oLb397//XXr37u3zXJs2bXwen3XWWfL222/LBRdcIPvuu6/5fnTfDh8+XH755Rfp2bNnndv47rvv5Oijj5aioiLzuS666CJJSUmRlStXmouSehHg888/N8e6gw46yLxHg/ETTjjBs47CwkJZtGiRJCYmmosKhxxyiOe1NWvWmJuuGwBiAYE3AESIZVkmS9SqVatGr8vpdJrsUKyqqKgQl8vFhQHUqqSkxGRKUb9///vfNZ7TAFcv3v3nP//xBJcDBw40N28afObl5cn5558f9O/xqKOOkv3226/W19euXStvvPGGXHfddTJt2jTP8wcffLCMGTPGvHb11VfX+v6tW7eai44aMOuFgwEDBvi8fuedd8rLL7/sOVZ27drVXAjwzoKrb775xhxXNdPu/5r92A7aASDaaGoOAH4nlBMnTjQnepp90ZO9iy++WMrKynyactbWRHPVqlU1+gB/+OGH5iRWTyJnzpxplisuLpZnn33W04zT7jO6evVqueSSS2S33XYzy2dnZ5uTSu/1htJvWIPfGTNmyJ577mkC9U6dOsmFF15oTny96cmrnuzm5OSYYEgzR8E2GdWyaVn++c9/mm3l5uaafffbb7+Z15csWWIChHbt2pky6L7QTFmg/adZq2uuucY0qdUsnma3/JvTqvfff9+c5OsymgHUzJl3eXX9ur6ff/7ZJ3jR5zQr6G333XeX0047zfP46aefNsFDx44dzefQprSPPfZYrZ//v//9r8lG6mfTZTXoCMa8efPkyCOPlNatW5t9PmrUqHqbAnt/96+88orJZHbu3Nnsh+OOO84EWf5ee+01GTJkiKlP7du3l7PPPtvU86bYV+r555/3rF+/Yw0C/csxevRo2WuvvUzmVjOY+nm17MHSz2xna7UZsv270XoT7OfU35hmZJV3E2qb1l9tlqy/OV2Hruv111+XWGWP57Bt27Y6l3vppZfM71sz1KHQTHRlZWWtryk9nnjr0qWLua/v4uLjjz8u69atM8cL/6Bb6fdyxhln+GToNYD+8ccfTUsfm/5e9NimFwq+/fZbc7zzfk3Xc+CBB9ZYv2bUtT7q71vf/8EHH9RZXgBoCgTeAFBFm0oOHTrUZFo0uHjwwQflnHPOMc0dNTvXEEuXLjUnkIcffrg88MADJkD717/+ZU74NHDUv/WmwbD6/vvv5euvvzbBi25fm19+/PHHJnBpSBl0vddff705+dTta9DywgsvyNixY6W8vNyz3O233y5//etfTX9JzWD16dNHjjjiCHOBIFgasD700EMyadIkue+++0wQpsGw9k3VprHal1Of1yBRs12B+mtefvnl8tNPP8nkyZPNBQ/N5vkPhqT7SwNt7RN6zz33mHJrkK8n5vYFCv1bT7q/+OILz/u+/PJL01LAOzOmQb1eGNBg0KZBtjaT1cBQy9u9e3dzMcQO2rwtW7bM1BU98df+rprB0wslH330UZ376pNPPjHb1Kay+lm164EGUBrwaxPcYNx1112mqfGNN94oV1xxhdnmYYcd5hOYaGB66qmnSkJCgimfNgvWCwO6f+yArTH7Sstw7rnnmj7H06dPN/2Otb7qMv4B4ebNm81+0t+ABlzezYLrowG/Nn9WWr/s341dlmA+p/4W9Heo7Pfrzaa/j3322cdsR78P+7vUfdwQenFr06ZN9d6C/V1r8KzLa19r/X70O9fPq8eGuujvXeuw9/dWH/1usrKyzAUSvaCj9dybXlzTi3T6+9DfqGbUtd7q8UovVtbXvFvfo8G5/4Wduuh3qccsvWDlHVzrxRK9bd++3TQ7935Ng3q9kOJN67T+nrWM9957r2mFdNJJJ5n6CQBhZQEAjHPPPddyOp3W999/X+M1l8tl7idPnmwFOnQ+/fTT5vmVK1d6nuvZs6d57oMPPqixfHp6ujV+/Pgaz5eUlNR47ptvvjHree655zzPffrpp+Y5vbfp+nSbti+//NIs88ILL/isT8vj/fyGDRus5ORk6+ijj/Z8TnXLLbeY5QKV05t+Zl0uKyvLrMvboYceau29997Wrl27PM/pNkaMGGH169evxv477LDDfMpw9dVXWwkJCda2bdvM46KiIqtNmzbWBRdc4LOdgoICq3Xr1j7P77nnntapp57qebzvvvtap5xyitnO4sWLzXNvvPGGefzTTz/V+R2MHTvW6tOnj89z9vf773//2/Pc9u3brS5dulj77LNPrd+Vfj797LpO78+q2+3du7d1+OGH17G3q9fXrVs3q7Cw0PP8q6++ap5/4IEHzOOysjKrY8eO1l577WXt3LnTs9w777xjlrv99tsbta9WrVplvpu77rrLp3y//PKLlZiY6PP8qFGjzHsff/xxq6H0d6nr0LriLZTPeemllwb8/Qb63nW9us4xY8bU+N7r+03Yy+m26rvpMSUY69at83lfTk6O9corr9T5nkWLFpllb7jhhqC2oes777zzrGeffdZ68803rdtuu81KS0uz2rdvb/35558+y86bN8/Kzc31KdOQIUNMOevTtm1ba/DgwTWe1/q8ceNGz23Hjh2e13799VezjTvuuMM8Li8vN8dRLavq1KmT9cgjj3jWo3XT/zih79dj3fLlyz3PaX3W5x966KGg9hEANBQZbwCoapKtzQ+PPfbYgH0bAzUvD4ZmfzS7HCzvJpqa3dEsjA6WpAMb/fDDDyFtW5veajNmzfJ5Z9i0Ca1mi+1RgOfOnWua0mu22ftzavYyFJo10ibiti1btpjMrmYitWmqvX39TLpPNIvm3RTYzmZ6l0FbBWhzV22CrzSrqxlMbUXg/Zk08zds2DCfkY31vZoZVLp9zaTr+rUZsv283uu+1Wangb4DzaLp+rUZ+B9//GEee9MuCd6DPWmWUDPA2iTWfxRom/Zp1c9+5plnmn1hfwZtXXDooYeazLN3k9na6Ha0mb1Nm/NrU18dQEzNnz/fjBqt2T3v8QC0tYBmAr0zuQ3ZV5pR1nLq9+v9XWjTd82A+48yra08tMVFUwvlc9bF+3vXbLV+17pfQv3deWeatb7Wd9PvMRjagkSX12yxZuX1u9EB0+orgwq2mbl+l9pyRcukrVLuuOMO01VG66m2bvDWtm1b03pBW7LosVOb6muLE20loFnkumhLDz0G+dMWRnoMsW/amsO71YNmr+1WGFpH9Tdjj1qu93ZXDe37rceNQP27tVWIZgiuNgEAAE6NSURBVOxt2idef7f6+waAcGJwNQCoakarJ4PeAVhT8B8ZuD7aTFibyurJrwal7iSNm3/QVx8N7vQ92lc5EA1WlB3UarDkTU989eS6oZ91+fLlpvzaFFxvtZVBpyHynv7Hm719u0+63eRVm2QHoifQNg2atC+plmPFihUmoNcBqewgU5sj6702w9dm1TY9edfm33ry7t8MWPenXsyw6UUR/4sy/fv3N/cahGgQ6s/+DOPHjw/4Gezt1Lfv/b8vLYeWx25ub3+vOl6APw1IvZuRN2Rf6efQ79e/HLakpCSfx/o9h2OwvVA+Z13eeecdM86BXhgpLS1t9EW3QH2LG0P3nQaNSseO0Is0ug39fQeaT16/mxdffNEc0/wHXAuFBq96UUsv0NnsixLajeXaa6/1PK8XLbXpux6/tKtIbfSCUaCLBnpBwe5aYncL8P4eNLi2L0zp71Q/uz2Ku7728MMPm7/tADxQ4O1/jFH6W/Mf9wIAmhqBNwCEoLaT8NoGIQp1BHPNOutJq2abNfDRIE+3qf0Rg8mCetPl9cTUznr5885ONwX/z2qXV0c+ri3r7z/1kWauA7EvQNjr1L65gYJa7Zdrs0+69URds1k65ZH2L9eAQfvP64m/Zqa9M3kadGpAowGb9lnWvrEa8GgW+f777w/5OwjEXof2pa9tWqxA2cBwasi+0s+hdVMHugv0vfl/hqYYzT9c9KKC9mXWftCPPvqoaTmgFw70t6jBa0Mv5tV2XPDfTw35vjXQ1HLq7ztQ4K3Bp16U0At5jaW/Ax2vwnvwvfXr15t95k1bhujFL912XYG3/r40Y62terwv0NR3gUDrqWb8dboyu3+3Tf/WCwF6wVIvtmhrFB2rwl99xxgACBcCbwCoCkL1hNF7cJ5A7CykNnf2ntfWzro1NoDXUZQ1E6qDFtm02WZ9IxcHos0pNUulWbG6gh57vl3NYHqfqGrg0JgskL0uPbG2M3WNZTcR1QsK9a1TM1t606BKg0kNIpUGVzpyujbF18DIe9ApPanXbKeO9O2dGfNvNu2f1ff+Pn///XefUadr+wxa3xqzX/wHvNJyaHns4MX+XjVg8m8hoM95z7PckH2ln0O3qS0d7Cx/ONX2mwnlc9a2Dg0ktZm6NqvWJvE2DbwbSkfkDua4oK0rGjqvuB4bamsJowG5fl7t0tBYWie8L9Rp0K38LyxofdDndDrBuuiFAh2FXAdY1ObtwfKez1sDb+/uMNqFRr87HQFfB2D7y1/+EvR6ASAS6OMNAFXzYmufRg28tM9obdkQO2jyHgHanhosFJpNDBRMazbGP/OiI4UHkznzpye0+j7tp+lPT4zt7Wvwp8Gxbsd72zrydGNocKzNTnUKNZ06yF+gacLqo5lzDVh11GnvUdlrW6cGkNrPXEdctoNJzTJrU9d//OMfnmmj/LNh/k38awvAdCR879HZtbvCc889Z7YRKCOvdHtaj7RPbKDmtsHuF92OPa2TfdFG97OOHG43+9XvQJuQezed1gy1jjKvfaAbs690RGrdX1OmTKlRZ/VxU48Srb8Z5f+7CeVz1rYO/RwapHr/zrTJvvZdbqim6uOtx5dAI5/rxQK9MBZoTAr9bejFEg1UAzWtVlpXdJR6799RoLqnrT10Gjid+s5mX2jRGSC86QUrLa+ODl8XzYbrVGQ617d9oSqY7LN+Vr1AovtWM9veGW8NurWlhs4+oGVg/m4AsYaMNwBU0WBO52TW5pI6sJQO5qMnp3oCqxkWzXDrFFt6IqtzfWuzRj1hf+qpp0w26M8//wx6WxrAaDZamzNrk0jNGmo/Ss0EaTNqbWKuc0JrP2Ndzn9KnGDo59AplLSpqfZb1bJrgK2ZUv1MOn2SDsilZdfm4Lqcbl8zRdqsWAMXHcCpMfQkWE+A9957b9NPWLPgmi3Tz6VTEGlz01Bo0K3TfekgTHqSrU3w7X2vg2hpdt/u56k0gLQzf/aJuH5nesKu2U29MODd71j3kT7WQfZ032lg/MQTT5jALtDFAw1AtC7oNHAaSGhd0M9XV6ZUL/I8+eSTJkDWOYR1wDHt/6yBhGbW9TPqBaBgBtvSz6Tv123qhRJtuq/7Wel3rdOt6etaF3RAOl1Ov3fNxmvQ4y3UfaUXD7RP9M0332yCVL1wpUH6ypUrzcUI/Q1pvaqPTgWmZdR9Zs9nH4huT3+DGmDrdjSI1t+M/naC/Zz2hQOdiksv4ujn0zqkwbn+FjW41Ayxjj2gdVf3p/f85tHo462/V704ptPWaRNtrT96cVDnT9fPd+WVV9Z4jz0gWl2Dqun3phcM9fuyW2fod61Bswa4egzSgeW0TmtTc+951/X3oXVX+2RrVl+nDNTWFvrb0+bv+puor+5qHdH16BSG+h1oCwGtszoHvB6flP9FA61/upy2zNBA2/tCkF1+u7UQgTeAmNPg8dABIA6tXr3aTCvWoUMHKyUlxUwhpVMQlZaWepZZsGCBNWzYMDMtTY8ePazp06fXOp2YTtEVyJIlS6yRI0darVq18pmya+vWrdaECRPM9D0ZGRlmyild1n8Ko2CmE7PNmjXLTPOj28rMzDTTe+n0Qvn5+Z5lKisrrSlTppipsHS50aNHm6mIgpk6yZ5ObNq0aQFfX7FihdmnnTt3tpKSksw0WMccc4z1+uuve5ax95//VG6BPqf9vO4bnUIsNTXVTGuk0yDNnz/fZzl7CqLdd9/d5/k777zTPP/Xv/61Rnnffvtta+DAgWa9vXr1su655x7rqaeeqvX7/fDDD83yWl8GDBhgvfbaa0F9hh9//NE68cQTrezsbPNeXZ9O6fXxxx/Xsber1/fSSy9ZN998s5lKS78zLYvW30BTROn0ZrqNdu3aWWeddZaVl5dXY7mG7Cul06kddNBBZmonvek+0N/M0qVLfaYT0ynLAtFpnGqbds/fW2+9Ze2xxx5mujL/qcWC+ZwVFRXW5Zdfbn7fDofDZ2qx2bNnm2ne7O9R1x1o+sBgpxNrKjqt1qRJk0yZdP/qcUfLedVVV5nXAjn99NPNb23z5s21rlc/g3+dvvXWW800X/q70vfr8e3iiy820/X527Jli5nur3///maf6TFLt/vHH38E/dl06rHrr7/efKdah+1jrh4vvvjii4Dv0Tqv5dYpCf3ZU97pcU6/a3/6mtZNf5H+TgG0TA79X7SDfwAAEBztw3rIIYeYrKC2WGjutEuEZsy1iTsAAPGKpuYAACAq9Nq/XkjQZtMAAMQzAm8AABAV2p/cnk8eAIB4xqjmAAAAAACEEX28AQAAAAAIIzLeAAAAAACEEYE3AAAAAABhFHeDq7lcLsnPz5fMzEwzaAsAAAAAAE1Ne20XFRVJ165dxel0tqzAW4Pu7t27R7sYAAAAAIAWYM2aNZKTk9OyAm/NdNsfPisrK9rFAYDI+OM5kflX6LVXHTdTZL8HRfqcG+1SAQAAxK3CwkKT9LVj0BYVeNvNyzXoJvAG0CKU5In125XiSLMnqbBEFl8l0n+cSFrdV18BAADQOMF0cWZwNQBo7oqWiUNcvs9ZlSJFy6NVIgAAAHgh8AaA5i6zn7gsv8O5I0Eks2+0SgQAAAAvBN4A0Nyl5cgDRddJhR18a9A9dCbNzAEAAGJE3PXxBoCWxuWyZOba0fKK7CEn9t0pN5xyDEE3AABADCHwBoBmLn/7TtlV7pICaS+LKjsQdAMAAMQYmpoDQDP3x8Ziz9/lFX6DrAEAACDqCLwBoJkrWLdMhqf/LJ2TNkl5JYE3AABArKGpOQA0Zytmy8lrJ8mpuS6ptBzyaPENIjIi2qUCAACAFzLeANAUSvJE1n/qvo/kNudNEmfVHN4JDksuSZ8W2TIAAACgXgTeANBYK2aLzOkp8vEY970+joSiZTqmuc9TCQ6XSNHyyGwfAAAAQSHwBoAmyDpXB8Auke8ujEzWObOfWH6H8Uqdyzuzb/i3DQAAgKAReANAE2edxaqMTNY5LUfyd5shFRpsi5j7e7dczXRiAAAAMYbB1QCgCbLODu/g25EQsazzD4knyV1LsmRA+npZUtxJKlO7yc0R2TIAAACCRcYbABojLUe+zr7bk3W2JEFk6MyIZZ11Du+C8vayPetAc890YgAAALGHwBsAGumN7UfKQUuektNX3C3zBi4QyZ0YsW3/sWmHuR/QOdPcl1cQeAMAAMQaAm8AaKR1636X3sn5sqqsqxQ5O0d029s3r5Th6T/LPtnuALzcZUV0+wAAAKgffbwBoBFKl86Sf7W/SBI6WFJpOWTRhmkie1wbkW1by5+U2W0mSUJbS6y1Tpnf9jJ5bdsRYlmWOByOiJQBAAAA9SPjDQANVZInyQsulgSHO8us93vn3xiZqcR0G99d6Nm2Du52d87D0ilxk1SS9QYAAIgpBN4A0FBFy3xHMzcH1QhNJRZg24kOl/RKzpfySgJvAACAWELgDQANlZghLsu3SbdLIjSVWGY/cfkdwnVkde1nXsbI5gAAADGFwBsAGmLFbLE+PECcDkssqzrw/aLNHZGZSiwtR/5VeUv1NGaOBLkl7zKmFAMAAIhBBN4A0JD+1fMmeZp66zhmLnHICcv/KT+nnByxYjyeN9pMY/bb7nPEcfwqeWP7WPN8BU3NAQAAYkrMBd5Tp06V/fffXzIzM6Vjx44ybtw4Wbp0abSLBQDVipaZRuXenGJJunOXlFZURqQIm3aUilWSZ6Yx69FzoMmAJyW4D+lkvAEAAGJLzAXen3/+uVx66aXy7bffykcffSTl5eVyxBFHSHFxcbSLBgBumf3E8jt8an9r7V9dWh6ZoHfTT4/KVwMmyEu5t0jGB31N0/ekBHd/c/p4AwAAxJaYm8f7gw8+8Hn8zDPPmMz3ggULZOTIkVErFwB4pOXIj+1ukkGbp7qn83IkyNz0KaZ/dWlFBILekjzpv/o607/czWWmFstJeU5+29WajDcAAECMibnA29/27dvNfbt27QK+Xlpaam62wsLCiJUNQAu1YrYM3vwPE/hq327noKmybP0JIrI0Mk3Ni5aJ06+pu1iV0itlnfwmraW8gj7eAAAAsSTmmpp7c7lcctVVV8mBBx4oe+21V619wlu3bu25de/ePeLlBNCClOSJNW+SOB0uT99u+elmaSvrzeNIZLxLkntLpd80Zpp1L7Dco6nT1BwAACC2xHTgrX29Fy1aJC+//HKty9x8880mK27f1qxZE9EyAmhhipZ5RjP3sCqlvbiPPZHo4/3jmq3y5MYTpLJqKjENumXoTNkmnczDCgJvAACAmBKzTc0vu+wyeeedd+SLL76QnJza58RNSUkxNwCIiMx+ZiA1n6bejgQpTe0jIhvD39R8xWwZvmiSHNjRZZq5y+7Xiex2pel3npzwhVmknOnEAAAAYkrMZbwtyzJB95tvvimffPKJ9O7dO9pFAoBqaTnyUtkl1U29q7LNrlbdwt/UvGr+cDvoN83cl9zveZnpxAAAAGKTMxablz///PPy4osvmrm8CwoKzG3nzp3RLhoASMXvT8jpSY+Y0cwtzTgPmiqSO1FSEhPM62XhDLwDzB+uzdylaLn5k+nEAAAAYlPMBd6PPfaY6as9evRo6dKli+f2yiuvRLtoAFq6kjxJmH+RewoxTXZXDaymz6ckOcOf8Q4wf7jJuGf2NX+S8QYAAIhNibHY1BwAmtPAappxTklwz7wQ1j7eaTmyfsCD0n7xFZKoo6pXNXPX570D7wr6eAMAAMSUmAu8ASCmB1aznJ6pxLwzzim7IpDxFpEl6afITUsyZGSX7XLveSd6gm5FU3MAAIDYFHNNzQEgVlWmdpO/rb9CKvym8dLg1+7jHe7pxAq275KC8vayKW24T9CtaGoOAAAQm8h4A0CQfl+5WJYVd5SzV98vL0zYWxJa9/MEvymJdsY7vNOJrdu+y9x3ykqt8VpSVRnKw5x1BwAAQGgIvAEgGL/9UwYsvEFeyrXczc1LZol0OcTzsifjXRH+jLfq0rpm4J3syXjTxxsAACCW0NQcAOrz2zSxFl7vHsVcD5zax/u7C93zaleJyKjmmvEudAfenQME3olO+ngDAADEIgJvAKiLBtcLb9QZu2udP9u7qXmly5KKMAa+Bdt31prxtpuaM6o5AABAbKGpOQDUpWiZRtkBXnB65s/2bmpuZ70Tq5p9N+kFgKJlkr3rFxmeXiw5ybki0qGWpuZkvAEAAGIJgTcA1CWzn1jirDl/9+B7fEYVT67KNtuBd3pKE5ZhxWyReZNExCUv9hBxOESsr24TqZglkjuxxnRiLS7wrroood+V/0jvAAAAsYCm5gBQl7QcWdbzvuopxPSwOfhekT2u81kswenwBL5NOrK5BpVVQbfSoNvcS81+5vZ0Yi2qj7delJjTU+TjMe57fQwAABBjCLwBoB4LU06Sg5Y8JXeXPSoybrXIHtcHXC4sc3mbpu61rM+vn3mLm8e7JE8sr4sSEuBiBAAAQCwg8AaAeqzfvksKytvLtowRdTZltgdYa9KMszafru1Q7Ujw6WfuaWpe0UIGVytaVrMLgN/FCAAAgFhA4A0A9Siwp/DKqjmSuDe7n3eTZrw10B82Syqrmrpb3kH30Jk+FwI8GW9XC8l4Z/bz7JfaLkYAAADEAgZXA4B6rK8KvDsFmMIrUMa7Sft4q9yJcvxraZJRvkruOGmY9MuuGlHdL/te3dS8ZWS8yypc8vTGcXJ+hzmS4HCJ5UgQh9/FCAAAgFhA4A0ATZTx9vTxrmjajLPLZcniwtZS6RooGd2Gi7RuFXA5ex7v8ibefkxaMVuS5k2SCzu6RK8zPL7hRBl/3nRp1aZntEsGAABQA03NERodtGj9pwxehBalYHupue9UX+CdFJ6M97ad5VLpcmexs+uYpyy5pUwnVjWomt2/Wz+2Zr13lJZHu2QAAAABEXgjeEzbgxZImzNv2uEOvLsE29S8Kft4i3i23yYtyWe+cH+JzhYyndiSB2oMqpbocEnp1t+jViQAAIC6EHijQXMJM20PWooNRbukc9ImOTjzF2nn2BCVpuYbi9yBd/uM2rPd3k3NK+K5j7dmu5fcV+NpHWRti7N7VIoEAABQHwJvNHwuYabtQQtQsexJ+WrABPlX75vF8VavOlt6hGtwNTvj3T4juc7lWkRTczOFWM0LC09uHCebXZ2iUiQAAID6EHgjOJn9xPKvLkzbg3hXkic9l18tCQ4rqJYe1X28o5Tx9oxqHseBd2Y/cfkdiyothzy9+Tgp3EUfbwAAEJsIvBGctBz5uf3N5gRXmRNfpu1BvDPZ1eBbenRM2CTD03+WxJ1rm7QYm3aUmfsOmcEF3mWRaGoepYEWXWs/ELG8P59TXrRulYLy9lK4k8AbAADEJqYTQ3BWzJa9N00Vp8MyU/e84bpMTsmdGO1SARHJrjq9g+/aWnqsmC23uyaJM9clrgKnyIpZZv7tuMx4a3N7z5gPTpFhTfdZ61SSJ47vLxSHpwWC25qUkXq5QQp3VYS/DAAAAA1AxhtBT91jBx/ajfRE58MMrIbI0zq3+lX3LRL1Ly1HXpbbpMJyVgfdgVp6VA0+aP9GnE08+GDljj9NJr1HypY6l0uKRB/vquNBVAZaDNQCQVzSPdndwoCMNwAAiFVkvBFUoON/spvgcLmb29LUHJFisqwXaFvvqiccIsOeCHumdU7RkfJgXh+576h0OXDwAYHrfF2DDzb2N7JitkxPcWfSrVVOkU61Z5ftjHdYRzWvq/l9uI8H2gLBcopTjz82R4KUt8rVsJs+3gAAIGaR8Ub983b/eG2NMYR16h4rQ092gQgwWVbvoFtZEcm0FmzfZfoPJ3UZU3tgmdmv5uG0KQYftDPpVYGmCXjr+MzVfbxdER3cLFIDLVamdpO/rb+iRguEhAz3NGLbyXgDAIAYReCNoObt1gas9nhGetJ7c95lUujsHN0youWoZQqpcE9pZ1mWFBTuMn93aZ1a+4IakA+bJZYkeC5MNcnggyFO45ec6DBzjg9K/iF8FyTScmR26U2e4Nd85ggNtLhs1WJZVtxRzlp9v1Qe8onI8atM9j+rlbvxVuFO+ngDAIDYRFNzBH3C73C4840zNk6QV7ceIZOKSqV1q6SoFREtiMmyOsTpH3yHOdO6raRcyqqmBuuYVffAZhoAbmt9iFz82CuyqqyrfHbq2VJHqB7SNH4+Tbvr+Mxt1/1LvhpwmXv6szk3h23Qs2c3HCqzd/SXfVstltP2z5FRXcZK2K2YLf3nTZKXdPA6bW5eMkukyyHmpaxU93GIpuYAACBWkfFG8PN2V2W+r+7wtMmq2SMtA+FWtuZ9vymkRFw6tV2YM612trtderKkJLqz2XVpk91bfq3Y1zRNX7OlpPEFSMuR7ztOrX9wN1WSJ+1+vTzoOccbqrSiUvK37ZSRGT/IQz3vlVEbL3d3SdGuKeHiP3idw/ezZVVdAGRwNQAAEKtiNvB+5JFHpFevXpKamirDhg2T7777LtpFalnScmRln+meebv9B1brlZwvG3cQeCMCSvIkacFF4vSqijp22BmrZ4jV5//Cumk78O6UFVzu2uFwSPd2aebv1ZubIPAWkQ93HSMHLXlKnm31tKdpdVPMOd5QekGhY+ImmZrzUNiD/GCb3FdnvGlqDgAAYlNMBt6vvPKKXHPNNTJ58mT54YcfZNCgQTJ27FjZsGFDtIvWoswtO0bGLb/PnVn0UilO05SWjDciIkBAqbNmOSqLJX+7OzAOl/VV6+9cXzNzLz2z3YH3n02R8RaR39cXmQx6Src6BncL5wBvflZtKpHeyfleQXcE+tsHaoHj9dmq+3iXm375AAAAsSYm+3hPnz5dLrjgApkwYYJ5/Pjjj8u7774rTz31lNx0003RLl6L8VPedvllZ3/5vM1dckjhX90n1o4EeTd5snm91ZYvREqS6m3qm5cnsmyZSL9+IjkRnn0smttuUppJ1KyfBlcNaFrdnPfDFmcPaW05fAI9HbxML/5oUNqtTauwZ7w71zWwmp8eTRx4Ly0oMvf9O2fWvWDVAG+ueReKUyrNPkoY1vRN8VdtLpaVZV3NyOZ20++w97dPy5Ev294lI7bcKonazNyvyb2d8a5wWbKzvFLSkhNj8jcQ7TLEwrE4I0Nkx47oleHrr91/jxgRA8dC+7ieqDtlpUjpZpGUbJGM3g17rOpbtsMI9+8m1G0HWre9LsTleYN5/8avQ6tXzeh4CERDzAXeZWVlsmDBArn55ps9zzmdTjnssMPkm2++kXixfMOOmJ/65sfVW819ym4XiHQ9x53NyuwrmZ/qAE4TJGGHJdYcp6zsc79s6nR2jYHY1FuvJMvUW9LF5XKI02nJLXcXy/GnlQVc1lfAJ0My55VkufvmtOptTy2RcX7bbg6y1/9Lei6/2mR9Neu3uu/9srnTOS1mP3y8uFw25V0uU3Meds8f70iQl6xbTBb486UbJSOl7sNYfTUpcP1z+y2/MKSm5qpnu3Rzv2jtdvl+1ZaQyuRflp1lLtlQ1bKkX8eM+jeeO1HWJB8kNz7zhqyt6Cb3HnC0JNZShrrKEagsth/+3Gr2/X/Tp8jY4r+JoyrIX93nPtm8QS86NGx7dW1TPfLnKLkh7ym57eAkOeagkT4neWnJCZLgdEily5Ivl20yffK9BXMcCrYcwX2S2PsdRnP73tt2D9EZnTLcdVOaWFUtuBwOS279R/SOhb7Hdd8a1ZjH9S/rkM0dT5PsDa+GvO1A61rdd0ZI/x6heZw3uN9/lWc2kWDqVSh1IdrHQzQfiU6HDOreRuKFw4qxdnn5+fnSrVs3+frrr2X48OGe52+44Qb5/PPPZd68eT7Ll5aWmputsLBQunfvLtu3b5esrCyJVec/O1/mLl4vsU5PQH+afIQno2TmU57T06fprw78pH1Q9WTcW0Vhqqx9fIyId1N1h0u6XfSpJGaFt4lwNLfdlHQQO3ORwyvbW9v+juf9oO49qq2cOqDMXPyZ8XWJzJir/X4j4x8n7i2nD+0R1LJfLd8kZz3pe5xqLM3qf3XTmKCW1UP6Qfd8Kmu37ZRwmnbyQDlxgMjls16VH7a2D6o+NoV3rzhI9uzausbz+97xkWwpLovJ30C0yxBzx+JolOEx/f34lcFhSbeLPon4sTDQcT2S9Kyv/otLwdGLbgcG+e8Rmsd5g/v955kuXeGoC9E+HqJ5yU5PlgV/PVximcaerVu3Dir2jLmMd6imTp0qU6ZMkeamQ2aKpy9oLDtyz87VQXct/W216efwDlvlp7KePs9v2dZG1vqfbFlOaW9lS5tsdzY9kKY4Fdm2NfC2s13tpE32Nmku9kneXOPkTPf3sPZbZGFZjxazH7q2biVHDhsiUlUXxw0uls+Wbqx3FOv66lIw1x2zM1JkzO4dgy7rkJ5tZcyAjrJqU3FIZaqtLDpg2/8d1Dvo7evyVx3WT2Z+8Ye4XFZY9osevw7dvZMkpCfL2DGny7JPlktaLdsKZnvBbFPt06Ot7N458D9qk0b2kVfnr6mxsa11HIfatt8WcjkacnyK9u8wmtsPuO1olCFQKwXLEZVjYaDjeiQ1VdCtEkL49wjN47zB/f7w1YVoHw/RvLSJs2mLYy7jrU3N09LS5PXXX5dx48Z5nh8/frxs27ZN3nrrrbjIeDdb2udHpw7y79upoy379e/R/js9e4q4vBZNSBBZtSr8/Xmiue1o7e+43g9AA8XCbyDaZYi1Y3E0ytCjR40ZCaN3LAx0XG+uQvj3CM3jvMH9fg2eQwwPgtxGtI+HQFNr1hnv5ORkGTJkiHz88ceewNvlcpnHl112WY3lU1JSzM1mX0fQnYBwyBLZ4wGRBVeKWNrf1ikyZIZIRZbudN8ls0QeeEDkyivdB1inU2TGDPfz4f56orntaO3v+N4PQMPEwm8g2mWIpWOxLdJlePBBkcsv9836Ru9Y6HdcjyiHSK/TRVa/0gTbdojsF/y/R2ge5w3u9z8oMt/rB9OEdSHax0OgqdkxZzC57JjLeNvTiWmGe+bMmTJ06FCZMWOGvPrqq7JkyRLp1KlTne/Ny8szGW8AAAAAAMJtzZo1klNPs42Yy3ir0047TTZu3Ci33367FBQUyODBg+WDDz6oN+hWXbt2NR88MzPT9HVE7exm+bq/aJbfclEPQB2Aoh5AUQ+gqAdQ1IP6aQ67qKjIxKDNMuON2OuTgPhFPQB1AIp6AEU9gKIeQFEPmpazidcHAAAAAAC8EHgDAAAAABBGBN4tmI4GP3nyZJ9R4dHyUA9AHYCiHkBRD6CoB1DUg6ZFH28AAAAAAMKIjDcAAAAAAGFE4A0AAAAAQBgReAMAAAAAEEYE3gAAAAAAhBGBNwAAAAAAYUTgDQAAAABAGBF4AwAAAAAQRgTeAAAAAACEEYE3AAAAAABhROANAAAAAEAYEXgDAAAAABBGBN4AAAAAAIQRgTcAAAAAAGFE4A0AiBmfffaZOBwOc28777zzpFevXk26HV2frrc+Wpa//e1vEgv7obkZPXq0uaH50d9GRkZGtIsBAHGFwBsA0GTuvvtumTNnTrSL0aKVlJSYiwWNCdq//vprs45t27ZJc/Ldd9/JJZdcIkOGDJGkpCRz8SKQNWvWyJQpU2To0KHStm1bad++vblIMHfu3KC35XK55N5775XevXtLamqqDBw4UF566aUayz3xxBMyatQo6dSpk6SkpJjlJ0yYIKtWrQppW88995wcfvjhpqz62Tp27ChHHHGEzJo1S0pLS81ylZWVkpWVJccff3yNddx///1mf4wfP77Ga7fffrt57ffffw+6TACA0BB4AwBiJvAeOXKk7Ny509yj4YG3BpWNDbx1Hc0t8H7vvffkySefNEFknz59al3urbfeknvuuUf69u0rd955p/z1r3+VoqIiE9g+/fTTQW3r1ltvlRtvvNG856GHHpIePXrImWeeKS+//LLPcj/++KMJtm+44QZ57LHH5Oyzz5b3339f9t9/f8nPz693O/p7+Mtf/mICZv1ur7vuOhNs67Y14NcLDXpTCQkJcsABB5jvz99XX30liYmJ5j7QaxrI9+/fP6jPDgAIXWID3gMAgIdlWbJr1y5p1apVo9fldDpNMAE0xMUXX2wCUq2Ll112Wa0Z3EMOOUT+/PNPkz22XXTRRTJ48GCT/dWMdF3Wrl0r9913n1x66aXy8MMPm+fOP/98k9m+/vrr5ZRTTjFBsHr00UdrvH/cuHGy3377mSz2TTfdVOe2rr76avnwww9lxowZcuWVV/q8du2118qyZcvko48+8jx30EEHmceLFy+W3Xff3Se4PvXUU+XFF1+UgoIC6dy5s3m+oqJC5s2bZ7LnAIDwIeMNAC2ABgoTJ06Url27epq7apBSVlZmXtdmxYGa5T7zzDPmee9msdo/+phjjjHBgAYPGuTMnDnTLFdcXCzPPvus+Vtvdj/q1atXm6zcbrvtZpbPzs42wYl/c9tg+zZr01sNRPbcc08TqGsz3gsvvFC2bt1a46KAZjRzcnIkLS3NBFy//vpro/alZjCPOuoo06RX+8Eeeuih8u233/oss2XLFpOZ3Hvvvc0yuqy+56effqqxvry8PBOIpaenm6yjBlp20+FQ6f7s0KGD+Vsz1vb34N1P/ZNPPpGDDz7YbK9NmzamWbIGaTZdVoNHpfXEXof9XWlGeMyYMaasWpf22GMPk8mNBVoPgrkApPXGO+hW+lk0s6zfh2a/66IZ8/Lyck+mWek+0t+Uvv+bb76p8/32mAX1tSjQJvGawT/yyCNrBN22fv36+ZRDA2/lndn+448/TLCtFyP09+L92sKFC83v1n6f/3FD66bWYa1XWqe1OTsAIHRkvAEgzmlzVu3Lqif5kyZNkgEDBpgT6tdff900XU1OTg55nUuXLpUzzjjDBLsXXHCBCaj/9a9/mayfbku3o3Jzc839999/b5q/nn766SYI1iBOgzXtV/vbb7+ZoDgUul29KKCZySuuuEJWrlxpMo8aFGtQoX1glWYvNfDWgEpvP/zwg8ns2RccQqVBuwatGkhr02Hdjl500M/x+eefy7BhwzyBjja514sLGryuX7/eLKcZUf28egHEbkasgbtmX/Vz6PO6HzU4bggNjnS/agB4wgknyIknnmie1/7HSvsw6wUAbYatAbZuX5tJH3jggWbfaECo79FMsfZX1n7BdoBqB/S6fg1cjzvuONN0+T//+Y8J/PRiiGaAQ7V9+3YTxNZHA8ZwD/ilwanWxfrqo9YzvXDhnVFWWvft1/0D2c2bN5ugVb/rv//97+Y5/e7rok3S9T3aPD1Y2tRcv5f//e9/5veo9Deh5dXm7XqxTB+fdNJJnteUf3l1u2PHjjV1+p///KepO5rl19+01i8AQIgsAEBcO/fccy2n02l9//33NV5zuVzmfvLkyVagfxKefvpp8/zKlSs9z/Xs2dM898EHH9RYPj093Ro/fnyN50tKSmo8980335j1PPfcc57nPv30U/Oc3tt0fbpN25dffmmWeeGFF3zWp+Xxfn7Dhg1WcnKydfTRR3s+p7rlllvMcoHK6U+X031jGzdunFnnihUrPM/l5+dbmZmZ1siRIz3P7dq1y6qsrPRZl+7DlJQU6+9//7vnuRkzZphtvPrqq57niouLrb59+9bYD8HauHFjjXLbBg8ebHXs2NHavHmz57mffvrJ1A+tJ7Zp06bV+N7r+i7Hjh1r9enTx+e5UaNGmVt9dBndVn23YL4vb5deemnAOl2bZcuWWampqdY555xT77Jap/w/r/3d6TZvuummGq/pd29/luzsbOvBBx+sdztXX321WX7hwoU+z5eWlprv2b5t2rTJ5/X999/fys3N9Ty+8MILrUMOOcT8fcMNN5jXbSeffLKVlpZmlZeXe57Tfa3b9a6rap999rGGDBlSb7kBADWR8QaAOKZZSM28HnvssSbT5a+2UZ/ro1lczYYFy7v5r2Y3CwsLzcBW2tRZM63nnHNO0Ot67bXXpHXr1mZQq02bNnme15GsNSP66aefmkGuNEOnme3LL7/c53NeddVVZhC4UGkG8L///a9peus9cFeXLl3M9nT0av1cmg3XZsve79PWBlo2bRmgn9d7MDB9/8knn+x5TrOt2mJAM+pNad26daZZsa63Xbt2nuc1G677UssS6ndpZ6s1k69dD/Sxfjeh0CyqfxeBQOxWAuGgLT+0dYJ+tn/84x/1Lq8tBby/Y5s9PoG+Hih7rWMhaLP+559/3jTvro/WJ+Wf6dfvSls02DSbvWPHDs9jzV5rawW7L7dmte2RzrV1w/Tp081n1rqmr2lWW7Pk/rTfuzdt7aEtMgAAoSPwBoA4tnHjRnPyvtdeezXpejXwDoUGIlOnTjX9g7WZuzuZ7KbBWih0MCl9j/YxDmTDhg2efuV2H1hv2mRap5BqyL7UYEWDZ3/a5FgvcmifXG2GrX8/8MADZmAtbQbv3S9W+7fbtIx6AcL/AkigbTSWvT9qK78GzhoMahBXFw3UJk+ebPox6/7w1pDAWy+YRJN+N9oFQrsAaHAcTICvAXqgfvgaWNuv+9PxBZQ29dcgWH+TGlBrv+vaZGZmmnvvoNoOnu0B1aZNm1ZjpHI78NbntTm7dpHQqc/UiBEjzIBqOvVaz549zQUZu0m6/0UEu3uBTX83wVwkAQDUROANAKg1813bQEqhjmCuWWcNujXbPHz4cBOc6TY14NEgNRS6vAbdL7zwQsDX/YOFaNCMuk5R9X//939yxx13mAyzjtiunz/UzxtLVqxYYQI5HSdAs6bdu3c3YwRoBlYDvYZ8Nh2ILpg+91rnQg3qg6FjFLzzzjumPumgccHQVgraskIvIHn/djSIVfUF79pPep999jHbrCvw1v2sFi1aJIMGDfKp44cddpj5W7Pn/uz+2trP2+6vrr87pX329WKUvqYXiryX92aPyg4AaBoE3gAQx/QEXZs+64l7XewMsDaJ1ubf/lnSxgbwOpCbzkOszYq9s4MNmSdagxZtRq5Zv7ouAGg2z86QezcN18x1Q7J2ui81iNGB5fwtWbLEBNYaiNqfVzOcs2fP9llOP6/3aNpaRv1u/AO4QNto7Hdg74/ayq/lsrPdta1DB1LTTO/bb79t5q22aRDaUDqYmw5MVx+tPzqgXlPS0dv1gpCOkK+DBQZLpx3T0ca12biO6m7Tabns14NpBVLf6PWaHdcAWAP0s846K+jy6YUpO7jW71TL6P271qy3ZsN1BHZdvx2UAwDCh+nEACCOaTCofZI1YJo/f36N1+0m3/bo41988YXnNXtqsFDoSX6gYFpP7r2blysdTbshUxPpXMT6Ps0k+9MmtPb2NSOoo47rdry3rUFWQ+hn0BHRdSop72nQdMRynRtZs4Z6kaO2z6t907WZvTcdaV1HnddA3abNt2fNmiUNZWc4/b8HzdJqQKjfqfdrGvhr33Uti80OwP3XYWdB/bsKaPDaUHoxRptN13dr6j7v2kRbR+u+5ZZbap2qy/58emHCu0uENhXXuuU9R7fuk8cff1y6detmAlu7Pga6yKPNvH/55ZeA4y5404sb2mpCm8Db84X7869nNq2P2qdfv1u7PDZ9rF0FvvzyS9PH327SDgAIHzLeABDntNmznnzrAFg6aJf259UmsRoIakZMM2EaUOpJvs71rVlADbCeeuopk+XV6Y9C6a+r2WhthqzNbbUvuA7cpPN+66BM2lRYs2960q/Lefd3DpZ+Dp1OTPuMa2ChZdcgSDPb+pm0b7UOVmbPO6zL6fY1sNRpnjSI8Z/DOVg6NZkGgRrU6BRaOiCVThOmmUu7D63S7emUUTrdmQY5GmRp1tI78243c9aA6txzz5UFCxaY4Fj3U6DprHRuc82ia/9q73m5/WkrAN3Hr7zyivTv3980c9f+xHrTYFOzqJrh1O/ank5Mvxfvddr9rm+99VbTHUD3rw7Qp/tam5br3/odaN9jHVROM6x2M+tQNWUfb22hYQ/+ZV9o0u/Mzvjbg/i9+eabJpDXrLD+Hvyba+tgczonuL2sfo96ccGel16nxNNuA7o/dXA5naZLBzHUQFa/Z/sChe4fbQVx2mmnmb7/ekFD64KuS/e5dkeoj14o0nECtLvGyy+/bPa97m8dWFCz1npRLVC/fa2juh2dys9/mjetk3ohQW+6XgBABAQY6RwAEGdWr15tpovq0KGDmdZIp0LS6ZZ0WiLbggULrGHDhpnpsnr06GFNnz691unEdDqlQJYsWWKm1WrVqpXPFFBbt261JkyYYLVv397KyMgw00/psrou72migplOzDZr1iwztZFuS6fz2nvvvc1USTq9l02n9JoyZYrVpUsXs9zo0aOtRYsW1dhubQJNy/XDDz+Y8uvn0GmYdJqmr7/+2mcZnU7s2muv9Wz3wAMPNNOnBZpiS7+b4447zqxL98+VV17pmRrNez/85z//Mc89/vjj9ZZby6P7Rr9L/88wd+5cUx4tV1ZWlnXsscdav/32W4113HHHHVa3bt3MVGPedeDtt9+2Bg4caKbe6tWrl3XPPfdYTz31VI16Eux0Yk3Jrj+Bbt5lsafPq+3mvd/t34Dee9O6dffdd5u6pPt5zz33tJ5//nmfZfT3pd+n7i/d10lJSWb5iRMnBpyqrTYVFRVm+2PGjLHatWtnJSYmmrpy6KGHmvqwc+fOGu9ZunSp5/P8/vvvPq/p9Hpt2rQxr73yyis13qu/DZ0a0F9t0w4CAOrn0P9FIsAHAAANpxnal156SZYvXx5wKisAABC76OMNAEAzoAOYadNkgm4AAJofMt4AAAAAAIQRGW8AAAAAAMKIwBsAAAAAgDAi8AYAAAAAIIwIvAEAAAAACKNEiTMul0vy8/MlMzNTHA5HtIsDAAAAAIhDOk55UVGRdO3aVZxOZ8sKvDXo7t69e7SLAQAAAABoAdasWSM5OTktK/DWTLf94bOysqJdHAAAAF9/PCcy/wrNlejMriL7PSjS59xolwoAEKLCwkKT9LVj0BYVeNvNyzXoJvAGAAAxpSRP5LcrRdI06FaWyOKrRPqPE0mrO1sCAIhNwXRxjrnB1b744gs59thjTTt5/QBz5syJdpEAAACaRtEyHZHG9zmrUqRoebRKBACIgJgLvIuLi2XQoEHyyCOPRLsoAAAATSuzX83TL0eCSGbfaJUIABABMdfU/KijjjI3AACAuJOWI+X7PS6O7y+SRIdLXOIU59CZNDMHgDgXc4F3qEpLS83Nu4M7AABArFrb7kw5fUmy9ErOl5R2/eXZ3JOiXSQAQEtrah6qqVOnSuvWrT03phIDAACxLH/7Tikoby/fFg+UeQVp4nLZA60BAOJVsw+8b775Ztm+fbvnptOIAQAAxKqC7bs8f+8qd0ne1p1RLQ8AIPyafVPzlJQUcwMAAGgO1nkF3p2TNsmmFe9Lj1bD6OcNAHGs2We8AQAAmpP8be4M96lt/ytfDZgg+y4/WWROT5EVs6NdNABAS8l479ixQ5Yvr57LcuXKlbJw4UJp166d9OjRI6plAwAAaIqMt2a6p+Y8LAkOu3+3S+S7C0W6jCXzDQBxKOYC7/nz58shhxzieXzNNdeY+/Hjx8szzzwTxZIBAAA0Tca7d3K+JDhcvi9YlSJFywm8ASAOxVzgPXr0aLEsRvcEAADxqaBwl2wr7yqWOMWhmW4Pp0hm3yiWDAAQLvTxBgAAiJCdZZWyraTcTCe2c5/HxCUOr1ctkXUfRrF0AIBwIfAGAACIkPWF7hHNWyUlSKseR4nDP/DWft4leVErHwAgPAi8AQAAImRXRaW5T0tOEMeO5X5Nzb36eQMA4gqBNwAAQISUV7jHsUlO1P7c/QKcitHPGwDiEYE3AABAhJRVujPeSQlO9+jlw2aJ5dXc3KKfNwDEJQJvAACACCnzzngrnbfbK/B20M8bAOISgTcAAECElFW6qjPeqmgZ/bwBoAUg8AYAAIiQsgqXb8Y7UD9vR0Jk+nlrVn39p2TXASACCLwBAAAipLwq451iZ7w9/bwTzMNKyykydKb7+XBaMVtkTk+Rj8e47/UxACBsCLwBAAAinPFOSvSavzt3omw74nc5fcXdcuCSp6Sk+/jwFmLz92LNmyTiaeLuol85AIQZgTcAAECE+3gn2xnvKm3b95Hljv3N3/lL3w9fELxitlgfDqNfOQBEGIE3AABApDPefoG3mtTtE/lqwATp+8tx4Wn+rcH8vEnukdP9RapfOQC0UATeAAAA0RpczVaSJ+en3CMJDit8zb+Llnk1L69mSYT6lQNAC0bgDQAAEOHB1WoE3kXLxBnu5t8BRlCvtEQ+zbi9aj5xAEC4EHgDAABEOuPt39Q8s5878xzO5t9VI6ibkdM19205RP8bs+NvjGwOAGFG4A0AABDtjHdajmzf+2GpqAqKLQ26w9H8O3ei/N+2V+WSVTeKOBziDGfTdgCAR2L1nwAAAAin0sraB1fL2vMiGfXvDOmWkCfTJ54sXbtq0/Cmt7a0vXSobF1703b6egNAkyPjDQAAEO3B1fSkzOmQlKwe8m3xQPmjuG3YylBaUSkry7qGv2k7AMCDwBsAACDSTc0DZLxVz3Zp5n7V5uKwlWFXuUsKytvLut0eEFfVqaC5Z2RzAAgbmpoDAADEQMZb9cxON/d/bikJWxl2lVea+509zpMfWo2Up+a8KO3Sk+RORjYHgLAh8AYAAIiQcp2/q66Md3aadE7aJCmb14iUZDY+A62Dpen83TqVWNW6SquC/9SkBNk74Vt5qOe9Zv5wa87d4hg2ywzABgBoWjQ1BwAAiHDGOynBEfD1Ydab8tWACXJtwsWNn+JrxWyxdB0fj/Gsy+WyPGVIK8+XlAUXm6BbORjZHADChsAbAAAgQuxsc3JiQs0XS/Jkt9XXeQLhRk3xVZIn1rxJ7mDaa11lRX96FkndtdL9fKCRzQEATYrAGwAAINrzeKuiZV6BciMD4VrWVbHtd8/DpDb9a54KMrI5AIQFgTcAAEAsNDXXfthNFAi7Nn0vlp0491rXztTe5s8Ep0MSM3uIDJsllriz75UWI5sDQLgQeAMAAEQ4450SKOOtAe+wWe4A2DQCT2hYIFySJ46fbhaHV2xvYvBB/5CSxC7mcaq9/dyJsnHMEjl9xd0ycunTUtZzQkM/GgCgDoxqDgAAECFlVYF3Ui2jmmsgPGtprny+4BvZZ8/95Mbcw5ukmbmJwbP38/QxT0mq7mPeoVOu/FK+jxSXVcqfW4qlb8fM0LcJAKgTGW8AAIAYmcdbde7SV74tHig/bMxo2EYy+3my5jbTnDyzr2cOb0/GW4Nyh0NyO7q3tXxDccO2CQCoE4E3AABAhDPetc3jrfp2cGecV2zc0aBtbLE6ys15l0lFVfCt9yv73GearO8qr5nxVrkdMsz84bvy5jKdGACEAU3NAQAAIj24Wh0Z79yO6SYI7m3ly7ZNudKmfZ+QtvHT0l9kTVlnuWrLI5KRWCaf5beWqfsdJbqW0orKgH3Mj814X/454FZJ2GKJzHGavuba7B0A0DQIvAEAACI9nVgdGe+0Nc/KVwMmSYLDJdZ/bwstCF4xW0YtmSSH5LrEJU6ZufMGKSjPlZ1l7oA7YMa7JE8O2X6bOPznD+8ylhHOAaCJ0NQcAAAgVvp4azPvee6gWznsIDiY5t9V73VWDaym95NS7zXZ85KqwNvOeHv38W7S+cMBAAEReAMAAEQ68K4t4120zJ1xbkgQHOC9GsD3Ss6XnWUVtWe8M/uJ1UTzhwMAAiPwBgAAiJDySqvujHdmvxqnZ1awQXCAAFqbm68q61p3xjstR6z9Z3oGYzPba8j84QCAWhF4AwAARIBlWfXP463B7rBZ7uDXjEjukMKelwW3gbQc+aXbvZ4AWrPWc5Jul4Ly9p7Au7ZRzZ39zpcT8p6X01fcLUuG/sTAagDQxAi8AQAAIpjtrm8ebw16Hcevkjm7zhSHiLRe9YDInJ5m4LT6zC07Vg5a8pQ8lvCEyPGrZHHaKeb5neV1ZLyrONK7m/nD80rbN+TjAQDqQOANAAAQAXa2u75RzW3Hpb4sCd4jjc+bVO8ga+sLlknv5Hxp3XF3kwFvleyewKbEr493ql/GW3XKSnWvo3BXCJ8KABAMphMDAACI4MBq9Wa8VdEyz+jk1VwiSx8Q2Wda4PesmC13OydJQq5LrHVOkRWzJC15tHnJ08e7PPA83qpTVoq530DgDQBNjow3AABABOfwdjpEEvR/dTGDrAVYZvH0wFnvkjyxAkxD1t65wTy25/Euragj453pzngXEHgDQJMj8AYAAIiFObz9B1kbcG2AF6qy3v5qmYu7vbXG/Fk9uFpdGW+7qXlp/eUDAISEwBsAACCCfbyD6d9tDLgycNZ7yf01st4l6+aJVT12m5sjQSrTc4PPeLemjzcAhAuBNwAAQKxlvOvKeluVIkXLqx+X5Enqb7eKwz9GH/QPcaZ3dy9SXuGb8U4K3Me7c9Im6VE+r95B3AAAoSHwBgAAiGAf76Az3p6st+/ylj5OTK9nIDYRyd5PWiUn+A6uZme8E2tmvLtveUm+GjBBZnW7Qawgpy8DAASHwBsAACCCGe+kYDPedtZ72CyxHO5AWZuTm77cHx7gCYy3OntIpeWX7tblM/tKWlXgbTc1rzXjXZInaQsv8UxfZg/ORuYbAJoGgTcAAEAkm5qHkvFWuRPFccQ34hKHV3Py6sD4u1Vb5MmNJ0il5awOuofONEF7Wi0Z7xT/jHctg7P5NGkHADQY83gDAABEcnC1UDLetood4hSrZmC89AE5fNl0cXZ0mcBcdr9OZLcr3ZlyEWmVnBhcxttMX6bPuWpkzQEAjUfGGwAAIJJNzUPNeKvMfu6+3T6cYi2e7unfbQJzHfHcS1rV6OUa9FdUumrv413VpN1VtQ1zX5U1BwA0HoE3AABABJRXWg3PeKfliGPYLE9zcksSRAZcU2/zcHtwNVVSXlnnqObapP2lzp/L6Svulr/Ju+YxAKBp0NQcAAAgAsoqKxvWx9uWO1Gu+KSDbC74Vc45/DA5ekAXcS2pzngHah6ekugUp0PEZbmbm+8qr31Uc5XdMVe+Ld4uu7ZmNqyMAICAyHgDAADE4jzeAbTt0Fu+LR4oi7Zliqz70D3MuUfN5uEOh0PSqvp56wBrpRXu4D81UMZbRHLappn7NVtKGlxGAEAzCrwfeeQR6dWrl6SmpsqwYcPku+++i3aRAAAAGqzMbmre0Iy3iPTtkGHuN29YIda8SeKsmv7Lo8vYGu+xm5trxru0KuOdUtX321+PbHfgvbm4TIpLKxpcTgBAM2hq/sorr8g111wjjz/+uAm6Z8yYIWPHjpWlS5dKx44do128lkXn7yxaJpKYIbJjpUjpZpGUbJGM3maEVTMKaj0Dr+TliSxbJtKvn0hOhMdoiea2w/I9BLG/43Y/6D7Y+HV1HewwInKD/vhvW+u//+/B+3FTl83evrK35f23fzlUuPePd5ki9V004ngUC7+BaJchFo7FGfrV7YheGb6uqrIjRkTnO2jQPN5+cjtmSOekTbJnycfiyPLr361NzrV/t9/voGfqFunrWiFlRT08I6un1lKGrNQk6Z+5TbJdf8qGVWnSO3Wj+4VwH+cQU+cNAf/dacJ/86J9PASiISYD7+nTp8sFF1wgEyZMMI81AH/33XflqaeekptuuinaxWs5VswWmTfJd2qRGpxmFNTaBmCZPVtk0iQRl0vE6RSZNUtkYoTGaonmtsP3PdS9v+N2P5h9cIEZTqiaQ2TYE+Ef/CfgtuvThGVr0PabuAz1likC30Ujjkex8BuIdhli5Vhsi0YZLrigulW2zoX9xBORrwfl9nRijch4773rdfl6wCWm33YNgab/WjFbXuk8SRIcLnEtuE1ObXuZvLr1CJ9B1/yX/6CXZtJdYi2oqyQROgYj4ucNof+7E1pdiPbxEIgWh2X5dA6KurKyMklLS5PXX39dxo0b53l+/Pjxsm3bNnnrrbd8li8tLTU3W2FhoXTv3l22b98uWVlZEqsueG6+zF28XmJV58RN8r8BEyTBvwlbABWWUw5e8pQUVLT3fb4wVfIeGyNieZ0dOFySc/Gnkpi1KxzFjolth/t7qG1/x+t+cO+D8yQhwEmmju57UJD7oqm3XZ+mKFtjtt9UZQi2TOH8LhpzPIqF30C0yxBzx+JolOHRMe7gwGf7luRc/ElEj4X2GdcZQ7vL1BMHhr6Ckjyx5vQQR6CASINu7d/tHfxo1nJOT58LVvobuTvxfbn9tCMCrt9/+bqE+xiMyJ83NPTfnWDrQrSPh2hestOTZf5th0ss09izdevWQcWeMZfx3rRpk1RWVkqnTp18ntfHS5YsqbH81KlTZcqUKdLc6D++sXXJw1ev5PygTnJVosMlPZPzZV2578G2fEt6zZMtyynlW9MkITO8B9dobjvc30Nt+zte94N7HwR+LSGEfdHU265PU5StMdtvqjIEW6ZwfheNOR7Fwm8g2mWIuWNxNMrgH3Sb7TuicizUbPuQnu0a9uaiZYGDbjXiJZGep9RY3j+I1t/IrSNrOf0LsHxdwn0MRuTPGxr6706wdSHax0M0L1YMx0pxkfHOz8+Xbt26yddffy3Dhw/3PH/DDTfI559/LvPmzYuLjPf2neWevl6xyLkzT9rN7V9zftAALEeCbDl0qbha+XbSyV8rsu+eyeJyVR9gExIsWbCoTLp2C0uxY2Lb4f4eatvf8bof3PugX8CTTUucsuWw34PaF0297fo0Rdkas/2mKkOwZQrnd9GY41Es/AaiXYZYOxZHowz77JEslt/JvtNpyQ+/Rv5YqCOat26V1LA3m4x0j5pNgDXbffyqmv1sA2Wwa1u2tuXrEO5jMCJ/3tDQf3eCrQvRPh6ieXE4RNpnpEgsa9YZ7/bt20tCQoKsX+/bDFsfd+7cucbyKSkp5mazryPoTohleriJ6WqU2kGK9nhAZMGVIlYd/wA7nCJDZkhSagcRq/oCiOrdVeSBB0rlyiur+/HMmOF+voFxRNCiue2wfg917O+43Q9mHzwoMv9yvxccIvs9EPS+aNpt16eJytbg7TdhGYIqU5i/i0Ycj2LhNxDtMsTSsdgW6TI8+GCpXH6578ncAw9E6VhYLlJYvrOBb84SCfj7myFSkaUnPwGWr/lvSOBlAyxfpwgcgxHx84aG/bsTfF2I9vEQzYylh6rYPr7YMWcwueyYy3grHcl86NCh8tBDD5nHLpdLevToIZdddlm9g6vl5eWZjDcAAAAAAOG2Zs0ayalniP6Yy3grnUpMB1Pbb7/9TACu04kVFxd7RjmvS9euXc0Hz8zMFIde0kat7Gb5ur9iuVk+wot6AOoAFPUAinoART2Aoh7UT3PYRUVFJgatT0wG3qeddpps3LhRbr/9dikoKJDBgwfLBx98UGPAtUCcTme9VxvgS39I/JhAPQB1AIp6AEU9gKIeQFEP6qZ9vIMRk4G30mblegMAAAAAoDlzRrsAAAAAAADEMwLvFkxHg588ebLPqPBoeagHoA5AUQ+gqAdQ1AMo6kHTislRzQEAAAAAiBdkvAEAAAAACCMCbwAAAAAAwojAGwAAAACAMCLwBgAAAAAgjAi8I2Dq1Kmy//77S2ZmpnTs2FHGjRsnS5cu9Vlm165dcumll0p2drZkZGTISSedJOvXr/dZ5oorrpAhQ4aYkQUHDx4ccFuvvvqqeS0tLU169uwp06ZNC6qMr732mgwYMEBSU1Nl7733lvfee8/n9R07dph51XNycqRVq1ayxx57yOOPP17verds2SJnnXWWZGVlSZs2bWTixIlmXd6f+7zzzjPbTExMNPsmXlEPaq8Hq1atEofDUeP27bffSjyhDtReBxpT5uamJdeDu+66S0aMGGHKo/XA3+bNm+XII4+Url27ms/VvXt3s53CwkKJN9SD2uvBM888E/DfBL1t2LBB4klLrQf6777+O9C7d2/zntzcXDN6dllZmc/n5hyxWkutB6vi6RxRRzVHeI0dO9Z6+umnrUWLFlkLFy60/vKXv1g9evSwduzY4Vnmoosusrp37259/PHH1vz5860DDjjAGjFihM96Lr/8cuvhhx+2zjnnHGvQoEE1tvPee+9ZiYmJ1mOPPWatWLHCeuedd6wuXbpYDz30UJ3l++qrr6yEhATr3nvvtX777Tfrtttus5KSkqxffvnFs8wFF1xg5ebmWp9++qm1cuVKa+bMmeY9b731Vp3rPvLII01Zv/32W+vLL7+0+vbta51xxhme13Uf6GefNWuW2U/HH3+8Fa+oB7XXA12XHo7mzp1rrVu3znMrKyuz4gl1oPY60NAyN0ctuR7cfvvt1vTp061rrrnGat26dY3Xt2zZYj366KPW999/b61atcocE3bbbTefuhIvqAe114OSkhKffwv0pvtr1KhRVrxpqfXg/ffft8477zzrww8/NOXRZTt27Ghde+21nmU4R6QexNs5IoF3FGzYsMFUoM8//9w83rZtm6m8r732mmeZxYsXm2W++eabGu+fPHlywB+TnpicfPLJPs89+OCDVk5OjuVyuWotz6mnnmodffTRPs8NGzbMuvDCCz2P99xzT+vvf/+7zzL77ruvdeutt9a6Xv1h6mfQEyjvH5jD4bDWrl1bY/nx48fH9UHVH/Wguh7YB9Uff/zRakmoA9V1oKFljgctpR540xPMQAFXIA888IApc7yjHtS9b3RfPPfcc1a8a4n1wKZBXe/evQO+xjliy60HK+PoHJGm5lGwfft2c9+uXTtzv2DBAikvL5fDDjvMs4w25ejRo4d88803Qa+3tLTUNP/wps028vLyZPXq1bW+T7fhvW01duxYn21rk7C3335b1q5dqxdr5NNPP5Xff/9djjjiiDrXq03I9ttvP89zuh2n0ynz5s2Tlo56ULMeHHfccaaJ1UEHHWS2E++oA9V1oKFljgctpR40RH5+vrzxxhsyatQoiXfUg9o999xzplnsySefLPGuJdcD/ez2527pqAftajwfD+eIBN4R5nK55KqrrpIDDzxQ9tprL/NcQUGBJCcn1+jn1KlTJ/NasPQHoCcoH3/8sdmOVvb77rvPvLZu3bpa36fb0G3Vte2HHnrI9NXQfhtaVu2D98gjj8jIkSPrXK/+QLxpHx39MYXyueIR9cC3HmhfJS2j9h969913zUFV+zc11wNrMKgDvnWgoWVu7lpSPQjFGWecYQKtbt26mXEBnnzySYln1IO6zZ49W84880wTIMSzllwPli9fbtZz4YUXSktHPXjIpx7E0zkigXeE6aAIixYtkpdffrnJ133BBReYQQ2OOeYYU+EPOOAAOf30081rmln6888/TeW1b3fffXfQ69YfgQ5ioJVcr7rpD0A/y9y5c83rF110kc+6UTfqga/27dvLNddcI8OGDTODi/zjH/+Qs88+O24H11LUgdDKHK+oB4Hdf//98sMPP8hbb70lK1asMMeHeEY9qJ1m1BYvXmwGYIp3LbUeaIZUg7RTTjnFlLOlox6c4lMP4uocMdpt3VuSSy+91PSh+OOPP3ye10ES9KvYunWrz/M6qIIOPhJsvw1bRUWFlZeXZ5WWlppBFHTd2lekvLzcWrZsmee2efNms7wO1HD//ffXGPhk4MCBnkFOtF+JDsDgbeLEiWYwCLV+/XqfdavZs2dbbdq08XmPlkEHWnjjjTdabP8d6kHd9cCmg4N07tzZikfUgdrrQG1ljkctrR40tI+3DsanZc7Pz7fiEfWg7nrwf//3f9bgwYOteNdS64GO89GvXz8zGFhlZWWt5eYckXoQD+eI8ZtGiCHaz0GvLr355pvyySefmCHzvemw/0lJSabZh02nENCrTsOHDw95ewkJCaZ5nl7Jeumll8w6OnToYJp29u3b13Oz+0/o697bVh999JFn29qnRG/+WSfdjjZTUdqM1Hvd9nq3bdtmrnrZ9PPre/SqVUtDPQitHixcuFC6dOki8YQ6UH8dqK3M8aSl1oOGstepfRPjCfWgfjo9kU59FM/Z7pZcDzTDOXr0aPMZn3766bhu3VQf6sHokOpBsz1HjHbk3xJcfPHF5oruZ5995jMMvl4d8p4iQK9affLJJ2aKgOHDh5ubN706pCP66QiC/fv3N3/rTa9WqY0bN5rpAXSUQ33+iiuusFJTU6158+bVO0WATi3wz3/+07xXr5L5TxGgU3joaIU6RYBehdOr1LpunfalvimE9tlnH1OG//3vf+Zqlv+0ML/++qsp77HHHmuNHj3a87niDfWg9nrwzDPPWC+++KLZrt7uuusuy+l0Wk899ZQVT6gDtdeBhpa5OWrJ9WD16tWmLFOmTLEyMjI8ZS4qKjKvv/vuu+Z3r9vSkWw1e7L77rtbBx54oBVvqAe11wPbk08+adbnn+WLJy21Hmi2VaeVPPTQQ83f3p/dG+eI1INn4ugckcA7AvT6RqCbVkjbzp07rUsuucRq27atlZaWZp1wwgk1Dj5aoQOtR09O7B+TzumXnp5u1qGVWOfMDcarr75qfqDJycnmR6MnP960LDrPXteuXc2PSOdVve++++qd5kebqOjJtf7DmpWVZU2YMKHGP6w9e/YM+LniDfWg9nqgB1U9udby6utDhw71mTIjXlAHaq8DjSlzc9OS64E2Fw1UZj1RU3pCqSeSegKq69ULNDfeeGNcBl7Ug9rrgU3rwplnnmnFs5ZaD/Tz1fbZvXGOSD14Jo7OER36v2hn3QEAAAAAiFcttzMFAAAAAAARQOANAAAAAEAYEXgDAAAAABBGBN4AAAAAAIQRgTcAAAAAAGFE4A0AAAAAQBgReAMAAAAAEEYE3gAAAAAAhBGBNwAAAAAAYUTgDQAAAABAGBF4AwAAAAAQRgTeAAAAAABI+Pw/4DplKJu0IQ4AAAAASUVORK5CYII=" 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" + } + } + ], + "console": [] + }, + { + "id": "DnEU", + "code_hash": "0b8c3b9e09a5ad7cb4b5cfbdd4094ea9", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "Average number of days between charged periods: 3.69\nAverage number of days between discharged periods: 0.62\nAverage number of days in decoupled problems: 0.53\nAverage SoC: 0.43" + } + } + ], + "console": [] + }, + { + "id": "ulZA", + "code_hash": "3fcdb73d36d54f2cc22d0f68e6b6e182", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "\n\n\n\n\n\n\n\n" + } + } + ], + "console": [] + }, + { + "id": "ecfG", + "code_hash": "fabe0c96b0ee9025d45d3e83607ab73d", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [ + { + "type": "stream", + "name": "stdout", + "text": " \n" + } + ] + }, + { + "id": "Pvdt", + "code_hash": "e8d118511de3b80a023c1d583e97500b", + "outputs": [ + { + "type": "data", + "data": { + "application/json": "{\"gamma\": \"text/plain:gamma\", \"lambda\": \"text/plain:lambda\", \"alpha\": \"text/plain:alpha\", \"beta\": \"text/plain:beta\", \"sparse\": \"text/plain:sparse\", \"Q\": \"text/plain:Q\", \"eta_storage\": \"text/plain:eta_storage\", \"eta_discharge\": \"text/plain:eta_discharge\", \"eta_charge\": \"text/plain:eta_charge\"}" + } + } + ], + "console": [] + } + ] +} \ No newline at end of file diff --git a/taylor/__marimo__/session/chapter_3.py.json b/taylor/__marimo__/session/chapter_3.py.json new file mode 100644 index 00000000..6a8052dc --- /dev/null +++ b/taylor/__marimo__/session/chapter_3.py.json @@ -0,0 +1,549 @@ +{ + "version": "1", + "metadata": { + "marimo_version": "0.14.12" + }, + "cells": [ + { + "id": "Hbol", + "code_hash": "f1a1e4332af25d02f60cca8fac568065", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "

Chapter 3 - Optimal Power Flow

\nWe use the test cases from PyPOWER (see https://github.com/rwl/pypower) to provision the test model." + } + } + ], + "console": [] + }, + { + "id": "MJUe", + "code_hash": "0c77b5d952575ec2dcf02505ac2a764a", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [] + }, + { + "id": "vblA", + "code_hash": "efed94f53e51bb883c1745832cf66cb6", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "

Coding Nomenclature

\nThe text uses the notation ||(x_{ij}||) to denote the value at the index ||(i,j||) in the matrix ||(x \\in \\mathbb{R}^{N \\times N}||) and the notation ||(x_i||) to denote the value at the index ||(i||) in the vector ||(x \n\\in \\mathbb{R}^N||). While this notation is more mathematically clear and rigorous, in code we must disambiguate cases where the variable ||(x||) denotes two different things depending on the dimensions of the variable. For example the text often use ||(x_j||) to denote ||(\\sum_j x_{i,j}||).\nTherefore the code in this notebook will always use capitalized variables for 2-dimensional arrays and lowercase variable names to 1-dimensional arrays. Thus ||(x_i||) will always be coded as x[i], while ||(x_{i,j}||) will be coded as X[i,j]. Scalars will be coded as x_<tag> to indicate that they are not arrays. In cases where variables are denoted ||(x^Y_{i}||) or ||(x^Y_{i,j}||), these will be coded as xY[i] and XY[i,j], respectively. \nAdditional variable encodings include the following:\n
    \n
  • ||(\\underline x, \\bar x, \\hat x, \\v x, \\tilde x, \\dot x, \\ddot x \\cdots \\to||) xmin, xmax, xhat, xvee, xtilde, xdot,xddot, ...
    • \n
  • ||(\\alpha,\\beta,\\gamma,\\cdots \\to||) alpha, beta, gamma, ...
    • \n
" + } + } + ], + "console": [] + }, + { + "id": "bkHC", + "code_hash": "65bd64a5c8b0b8681fbcce3a65ca62ba", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "

3.1 - Basic Formulation

" + } + } + ], + "console": [] + }, + { + "id": "lEQa", + "code_hash": "909f94036dd71747d8d6508a35ffa4cf", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "The voltage ||(v_i||) at the node ||(i||) must satisfy the limits ||(\\underline{v}_i \\le |v_i| \\le \\bar{v}_i||)." + } + } + ], + "console": [] + }, + { + "id": "PKri", + "code_hash": "8e3ff82d777b60a0f38ed14d68893e7a", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "
" + } + } + ], + "console": [] + }, + { + "id": "Xref", + "code_hash": "1f1a3ca38dde14925300bee25def6511", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "The real power flow ||(p_{ij}||) and reactive power flow ||(q_{ij}||) on the power lines from the nodes ||(i||) to the nodes ||(j||) must remain below the lines' capacities such that ||(p_{ij}^2+q_{ij}^2 \n\\le \\bar s_{ij}^2||). Note that in the pypower cases, the line limit is nominally rateA, with rateB and rateC reserved for emergency line ratings only." + } + } + ], + "console": [] + }, + { + "id": "SFPL", + "code_hash": "e81e3995a9999460b87384dda4c8ec63", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "
" + } + } + ], + "console": [] + }, + { + "id": "BYtC", + "code_hash": "c036582bf59ab90aa52f05fc1af28445", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "The real and reactive powers into or out of the node ||(i||) are the sum of the flows through the transmission lines connected to the node ||(i||). The real and reactive power at each node ||(i||) is the difference of the real generation and demand ||(p_i=pg_i-pd_i||) and the reactive generation and demand ||(q_i=qg_i-qd_i||)." + } + } + ], + "console": [] + }, + { + "id": "RGSE", + "code_hash": "84fac27fddab3887f89d6bd59886c1cd", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "
" + } + } + ], + "console": [] + }, + { + "id": "Kclp", + "code_hash": "b4a6ce902848f88f61b32bbe4cd16283", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "The nodal power injections are subject to the box constraints ||(\\underline p_i \\le p_i \\le \\bar p_i||) and ||(\\underline q_i \\le q_i \\le \\bar q_i||)." + } + } + ], + "console": [] + }, + { + "id": "emfo", + "code_hash": "db687c3b4e6b38eae2503281a7bf000f", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "
" + } + } + ], + "console": [] + }, + { + "id": "Hstk", + "code_hash": "b674acb2efb5f53fbb466243daf510e0", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "The complex line impedances are ||(z_{ij} = p_{ij}+q_{ij}j = v_i(v_i^*-v_j^*)y_{ij}^*||) and the complex line admittance is its inverse ||(y_{ij} = 1/z_{ij}||)." + } + } + ], + "console": [] + }, + { + "id": "nWHF", + "code_hash": "fc988f66a15155ee47230946d9c7ce3a", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "
" + } + } + ], + "console": [] + }, + { + "id": "iLit", + "code_hash": "a7a08af4197671b4acd7b6ab89b1da15", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "The cost of real power generation is ||(\\sum_i f_i(p_i)||)." + } + } + ], + "console": [] + }, + { + "id": "ZHCJ", + "code_hash": "1eef78ed9023fb5033a4aa6fce93cadf", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "
" + } + } + ], + "console": [] + }, + { + "id": "ROlb", + "code_hash": "40532afc17b297b5e3f92a16de55a66c", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "The total real power generation is ||(\\sum_{i \\in \\mathcal{G}} p_i||)." + } + } + ], + "console": [] + }, + { + "id": "qnkX", + "code_hash": "055b459478f4d99d7c471a686064da21", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "
" + } + } + ], + "console": [] + }, + { + "id": "TqIu", + "code_hash": "840faa84c3d0339d7090005e15788401", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "The resistive power line losses are ||(\\sum_{ij}r_{ij}I_{ij}^2 = \\sum_{ij} p_{ij}+p_{ji} = \\sum_i p_i||)." + } + } + ], + "console": [] + }, + { + "id": "Vxnm", + "code_hash": "ba226fcb6ee521fdfe8b228edc5e6c5b", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "
" + } + } + ], + "console": [] + }, + { + "id": "DnEU", + "code_hash": "5312c7d5739f131564c610cb85e5442b", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "The optimal power flow is written\n||(\\begin{array}{rl}\n \\underset{v,p,q}{\\min} & f(v,p,q)\n\\\\ \n \\mathrm{subject~to} & p_{ij}+q_{ij}=v(v_i^*+v_j^*)y_{ij}^*\n\\end{array}||)\nand Feasible Set 3.1\n||(\\begin{array}{rl}\n\\\\ \\sum_j p_{ij} = p_i\n\\\\ \\sum_j q_{ij} = q_i\n\\\\ \\underline p_i \\le p_i \\le \\bar p_i\n\\\\ \\underline q_i \\le q_i \\le \\bar q_i\n\\\\ p_{ij}^2 + q_{ij}^2 \\le \\bar s_{ij}^2\n\\\\ \\underline v_i \\le v_i \\le \\bar v_i\n\\end{array}||)" + } + } + ], + "console": [] + }, + { + "id": "ulZA", + "code_hash": "e284d85b1aa0e4906a87af03108ce584", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "
" + } + } + ], + "console": [ + { + "type": "stream", + "name": "stdout", + "text": "PYPOWER Version 5.1.18, 10-Apr-2025 -- AC Optimal Power Flow\n" + } + ] + }, + { + "id": "ecfG", + "code_hash": "e0cb79345ec73823baf630423edbe33f", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "

3.2 - Linear Approximations

\nIn polar coordinates Feasible Set 3.2 is given for the non-convex voltage-polar coordinate powerflow by\n||(\\begin{array}{l}\n \\textrm{Feasible Set 3.1}\n\\\\\n p_{ij} = g_{ij} |v_i|^2 -|v_i||v_j| ( g_{ij} \\cos(\\theta_i-\\theta_j) - b_{ij} \\sin(\\theta_i-\\theta_j) )\n\\\\\n q_{ij} = b_{ij} |v_i|^2 -|v_i||v_j| ( g_{ij} \\sin(\\theta_i-\\theta_j) + b_{ij} \\cos(\\theta_i-\\theta_j) )\n\\end{array}||)" + } + } + ], + "console": [] + }, + { + "id": "Pvdt", + "code_hash": "a7a00491169486fc0e26457ce40d9c93", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "
TODO
" + } + } + ], + "console": [] + }, + { + "id": "ZBYS", + "code_hash": "a993c2b118a6a2d265f2cd7c5d98de9e", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "

3.2.1 - Linearized powerflow

\nThe linearized power flow Feasible Set 3.3 is\n||(\\begin{array}{l}\n p_{ij} = b_{ij} ( \\theta_i - \\theta_j )\n\\\\\n \\sum_j p_{ij} = p_i\n\\\\\n \\underline p_i \\le p_i \\le \\bar p_i\n\\\\\n |p_{ij}| \\le \\bar s_{ij}\n\\end{array}||)" + } + } + ], + "console": [] + }, + { + "id": "aLJB", + "code_hash": "a7a00491169486fc0e26457ce40d9c93", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "
TODO
" + } + } + ], + "console": [] + }, + { + "id": "nHfw", + "code_hash": "d961921188cf3dc99194c580b023813c", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "

3.2.2 - Decoupled power flow

\nThe decoupled power flow Feasible Set 3.4 is\n||(\\begin{array}{l}\n \\textrm{Feasible Set 3.3}\n\\\\\n q_{ij} = b_{ij} \\left( |v_i| - |v_j| \\right)\n\\\\\n \\sum_j q_{ij} = q_i\n\\\\\n \\underline q_i \\le q_i \\le \\bar q_i\n\\\\\n \\underline v_i \\le |v_i| \\le \\bar v_i\n\\end{array}||)" + } + } + ], + "console": [] + }, + { + "id": "xXTn", + "code_hash": "a7a00491169486fc0e26457ce40d9c93", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "
TODO
" + } + } + ], + "console": [] + }, + { + "id": "AjVT", + "code_hash": "8b52ed9beb1aa92eb47f750a020980b2", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "

3.2.3 - Network Flow

\nThe simplest approximation is the network flow (or transportation model) which is given by the Feasible Set 3.5\n||(\\begin{array}{l}\n \\textrm{Feasible Set 3.1}\n\\\\\n p_{ij}+p_{ji} = 0\n\\\\\n q_{ij}+q_{ji} = 0\n\\end{array}||)\nand additional polyhedral flow capacity constraints such as\n||(\\begin{array}{l}\n |p_{ij}| + |q_{ij}| \\le \\sqrt2 \\bar s_{ij}\n\\\\\n |p_{ij}| \\le \\bar s_{ij}\n\\\\\n |q_{ij}| \\le \\bar s_{ij}\n\\end{array}||)" + } + } + ], + "console": [] + }, + { + "id": "pHFh", + "code_hash": "a7a00491169486fc0e26457ce40d9c93", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "
TODO
" + } + } + ], + "console": [] + }, + { + "id": "NCOB", + "code_hash": "3a4bee4391adec67f96211c6bc56c6c6", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "

Relaxations

" + } + } + ], + "console": [] + }, + { + "id": "aqbW", + "code_hash": "a7a00491169486fc0e26457ce40d9c93", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "
TODO
" + } + } + ], + "console": [] + }, + { + "id": "TRpd", + "code_hash": "c3389f905dc7b9b6d1b9e872f414b29d", + "outputs": [ + { + "type": "data", + "data": { + "text/plain": "" + } + } + ], + "console": [ + { + "type": "stream", + "name": "stderr", + "text": "Error trying to reload module marimo._output.formatters.formatters: cannot import name 'IbisFormatter' from 'marimo._output.formatters.df_formatters' (/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/site-packages/marimo/_output/formatters/df_formatters.py) \n" + }, + { + "type": "stream", + "name": "stderr", + "text": "
Traceback (most recent call last):\n  File "/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/site-packages/marimo/_runtime/reload/autoreload.py", line 442, in superreload\n    module = reload(module)\n             ^^^^^^^^^^^^^^\n  File "/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/importlib/__init__.py", line 131, in reload\n    _bootstrap._exec(spec, module)\n  File "<frozen importlib._bootstrap>", line 866, in _exec\n  File "<frozen importlib._bootstrap_external>", line 999, in exec_module\n  File "<frozen importlib._bootstrap>", line 488, in _call_with_frames_removed\n  File "/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/site-packages/marimo/_output/formatters/formatters.py", line 19, in <module>\n    from marimo._output.formatters.df_formatters import (\nImportError: cannot import name 'IbisFormatter' from 'marimo._output.formatters.df_formatters' (/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/site-packages/marimo/_output/formatters/df_formatters.py)\n
\n" + }, + { + "type": "stream", + "name": "stderr", + "text": "Error trying to reload module marimo._plugins.ui._impl.table: cannot import name 'ValueCount' from 'marimo._data.models' (/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/site-packages/marimo/_data/models.py) \n" + }, + { + "type": "stream", + "name": "stderr", + "text": "
Traceback (most recent call last):\n  File "/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/site-packages/marimo/_runtime/reload/autoreload.py", line 442, in superreload\n    module = reload(module)\n             ^^^^^^^^^^^^^^\n  File "/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/importlib/__init__.py", line 131, in reload\n    _bootstrap._exec(spec, module)\n  File "<frozen importlib._bootstrap>", line 866, in _exec\n  File "<frozen importlib._bootstrap_external>", line 999, in exec_module\n  File "<frozen importlib._bootstrap>", line 488, in _call_with_frames_removed\n  File "/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/site-packages/marimo/_plugins/ui/_impl/table.py", line 21, in <module>\n    from marimo._data.models import BinValue, ColumnStats, ValueCount\nImportError: cannot import name 'ValueCount' from 'marimo._data.models' (/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/site-packages/marimo/_data/models.py)\n
\n" + }, + { + "type": "stream", + "name": "stderr", + "text": "Error trying to reload module marimo._server.sessions: cannot import name 'register_formatters' from 'marimo._output.formatters.formatters' (/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/site-packages/marimo/_output/formatters/formatters.py) \n" + }, + { + "type": "stream", + "name": "stderr", + "text": "
Traceback (most recent call last):\n  File "/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/site-packages/marimo/_runtime/reload/autoreload.py", line 442, in superreload\n    module = reload(module)\n             ^^^^^^^^^^^^^^\n  File "/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/importlib/__init__.py", line 131, in reload\n    _bootstrap._exec(spec, module)\n  File "<frozen importlib._bootstrap>", line 866, in _exec\n  File "<frozen importlib._bootstrap_external>", line 999, in exec_module\n  File "<frozen importlib._bootstrap>", line 488, in _call_with_frames_removed\n  File "/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/site-packages/marimo/_server/sessions.py", line 48, in <module>\n    from marimo._output.formatters.formatters import register_formatters\nImportError: cannot import name 'register_formatters' from 'marimo._output.formatters.formatters' (/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/site-packages/marimo/_output/formatters/formatters.py)\n
\n" + }, + { + "type": "stream", + "name": "stderr", + "text": "Error trying to reload module marimo._output.formatters.df_formatters: cannot import name 'get_default_table_page_size' from 'marimo._plugins.ui._impl.table' (/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/site-packages/marimo/_plugins/ui/_impl/table.py) \n" + }, + { + "type": "stream", + "name": "stderr", + "text": "
Traceback (most recent call last):\n  File "/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/site-packages/marimo/_runtime/reload/autoreload.py", line 442, in superreload\n    module = reload(module)\n             ^^^^^^^^^^^^^^\n  File "/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/importlib/__init__.py", line 131, in reload\n    _bootstrap._exec(spec, module)\n  File "<frozen importlib._bootstrap>", line 866, in _exec\n  File "<frozen importlib._bootstrap_external>", line 999, in exec_module\n  File "<frozen importlib._bootstrap>", line 488, in _call_with_frames_removed\n  File "/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/site-packages/marimo/_output/formatters/df_formatters.py", line 21, in <module>\n    from marimo._plugins.ui._impl.table import get_default_table_page_size, table\nImportError: cannot import name 'get_default_table_page_size' from 'marimo._plugins.ui._impl.table' (/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/site-packages/marimo/_plugins/ui/_impl/table.py)\n
\n" + }, + { + "type": "stream", + "name": "stderr", + "text": "Error trying to reload module marimo._cli.cli: cannot import name 'get_notebook_status' from 'marimo._ast.load' (/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/site-packages/marimo/_ast/load.py) \n" + }, + { + "type": "stream", + "name": "stderr", + "text": "
Traceback (most recent call last):\n  File "/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/site-packages/marimo/_runtime/reload/autoreload.py", line 442, in superreload\n    module = reload(module)\n             ^^^^^^^^^^^^^^\n  File "/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/importlib/__init__.py", line 131, in reload\n    _bootstrap._exec(spec, module)\n  File "<frozen importlib._bootstrap>", line 866, in _exec\n  File "<frozen importlib._bootstrap_external>", line 999, in exec_module\n  File "<frozen importlib._bootstrap>", line 488, in _call_with_frames_removed\n  File "/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/site-packages/marimo/_cli/cli.py", line 17, in <module>\n    from marimo._ast.load import get_notebook_status\nImportError: cannot import name 'get_notebook_status' from 'marimo._ast.load' (/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/site-packages/marimo/_ast/load.py)\n
\n" + }, + { + "type": "stream", + "name": "stderr", + "text": "Error trying to reload module marimo._output.formatters.pandas_formatters: cannot import name 'include_opinionated' from 'marimo._output.formatters.df_formatters' (/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/site-packages/marimo/_output/formatters/df_formatters.py) \nTraceback (most recent call last):\n File \"/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/site-packages/marimo/_runtime/reload/autoreload.py\", line 442, in superreload\n module = reload(module)\n ^^^^^^^^^^^^^^\n File \"/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/importlib/__init__.py\", line 131, in reload\n _bootstrap._exec(spec, module)\n File \"\", line 866, in _exec\n File \"\", line 999, in exec_module\n File \"\", line 488, in _call_with_frames_removed\n File \"/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/site-packages/marimo/_output/formatters/pandas_formatters.py\", line 8, in \n from marimo._output.formatters.df_formatters import include_opinionated\nImportError: cannot import name 'include_opinionated' from 'marimo._output.formatters.df_formatters' (/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/site-packages/marimo/_output/formatters/df_formatters.py)\n" + }, + { + "type": "stream", + "name": "stderr", + "text": "Error trying to reload module marimo._server.api.lifespans: cannot import name 'SessionManager' from 'marimo._server.sessions' (/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/site-packages/marimo/_server/sessions.py) \nTraceback (most recent call last):\n File \"/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/site-packages/marimo/_runtime/reload/autoreload.py\", line 442, in superreload\n module = reload(module)\n ^^^^^^^^^^^^^^\n File \"/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/importlib/__init__.py\", line 131, in reload\n _bootstrap._exec(spec, module)\n File \"\", line 866, in _exec\n File \"\", line 999, in exec_module\n File \"\", line 488, in _call_with_frames_removed\n File \"/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/site-packages/marimo/_server/api/lifespans.py\", line 21, in \n from marimo._server.sessions import SessionManager\nImportError: cannot import name 'SessionManager' from 'marimo._server.sessions' (/Users/bmeyers/miniconda3/envs/sdt/lib/python3.12/site-packages/marimo/_server/sessions.py)\n" + } + ] + }, + { + "id": "aYJH", + "code_hash": "2037403942f1412caed182f75b81f44c", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "" + } + } + ], + "console": [] + }, + { + "id": "igMP", + "code_hash": "8af4f97dde52508bea04574fe73ba554", + "outputs": [ + { + "type": "data", + "data": { + "text/html": "" + } + } + ], + "console": [] + }, + { + "id": "SCqg", + "code_hash": "1aca8c374c3133c45e8e93b30cce839b", + "outputs": [ + { + "type": "data", + "data": { + "application/json": "{\"bus\": [\"text/plain:[1 2 3 4 5 6 7 8 9]\", \"text/plain:[3 2 2 1 1 1 1 1 1]\", \"text/plain:[ 0. 0. 0. 0. 90. 0. 100. 0. 125.]\", \"text/plain:[ 0. 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