From 3b47970f837be5726be8b4598a7380e92704620f Mon Sep 17 00:00:00 2001 From: vicpaton Date: Thu, 12 Sep 2024 18:53:35 +0200 Subject: [PATCH] updated vignette --- .../vignettes/3_evaluation_offt_path.ipynb | 1305 +++++++++++------ 1 file changed, 870 insertions(+), 435 deletions(-) diff --git a/docs/src/vignettes/3_evaluation_offt_path.ipynb b/docs/src/vignettes/3_evaluation_offt_path.ipynb index 1b205e4..20ef3f4 100644 --- a/docs/src/vignettes/3_evaluation_offt_path.ipynb +++ b/docs/src/vignettes/3_evaluation_offt_path.ipynb @@ -34,42 +34,6 @@ "import decoupler as dc" ] }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'os' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[26], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mcreate_heatmap\u001b[49m\u001b[43m(\u001b[49m\u001b[43mofftarget_results\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[43mvalue_col\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mperc_offtargets\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43my_axis_col\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcell_drug\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43mx_axis_col\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmethod\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43mcmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcoolwarm\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", - "Cell \u001b[0;32mIn[25], line 54\u001b[0m, in \u001b[0;36mcreate_heatmap\u001b[0;34m(results, terms, y_axis_col, x_axis_col, value_col, title, x_label, y_label, cmap, filepath, render)\u001b[0m\n\u001b[1;32m 51\u001b[0m plt\u001b[38;5;241m.\u001b[39mtight_layout()\n\u001b[1;32m 53\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m filepath:\n\u001b[0;32m---> 54\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mos\u001b[49m\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mdirname(filepath):\n\u001b[1;32m 55\u001b[0m os\u001b[38;5;241m.\u001b[39mmakedirs(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mdirname(filepath), exist_ok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 56\u001b[0m plt\u001b[38;5;241m.\u001b[39msavefig(filepath)\n", - "\u001b[0;31mNameError\u001b[0m: name 'os' is not defined" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "nc.visual.create_heatmap(offtarget_results,\n", - " value_col='perc_offtargets',\n", - " y_axis_col='cell_drug',\n", - " x_axis_col='method',\n", - " cmap='coolwarm')" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -135,43 +99,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(CORNETO) Sep 12 01:08:02 PM - INFO : 1/1 inputs mapped to the graph\n", - "(CORNETO) Sep 12 01:08:02 PM - INFO : 17/25 outputs mapped to the graph\n", - "(CORNETO) Sep 12 01:08:02 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", - "(CORNETO) Sep 12 01:08:02 PM - INFO : Finished. Final size: V x E = (1188, 5786).\n", - "(CORNETO) Sep 12 01:08:02 PM - INFO : 1/1 inputs after pruning.\n", - "(CORNETO) Sep 12 01:08:02 PM - INFO : 12/17 outputs after pruning.\n", - "(CORNETO) Sep 12 01:08:02 PM - INFO : Converting into a flow graph...\n", - "(CORNETO) Sep 12 01:08:02 PM - INFO : Creating a network flow problem...\n", - "(CORNETO) Sep 12 01:08:03 PM - INFO : Preprocess completed.\n", - "(CVXPY) Sep 12 01:08:03 PM: Your problem has 26879 variables, 65436 constraints, and 0 parameters.\n", - "(CVXPY) Sep 12 01:08:03 PM: It is compliant with the following grammars: DCP, DQCP\n", - "(CVXPY) Sep 12 01:08:03 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", - "(CVXPY) Sep 12 01:08:03 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", - "(CVXPY) Sep 12 01:08:03 PM: Your problem is compiled with the CPP canonicalization backend.\n", - "(CVXPY) Sep 12 01:08:03 PM: Compiling problem (target solver=GUROBI).\n", - "(CVXPY) Sep 12 01:08:03 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", - "(CVXPY) Sep 12 01:08:03 PM: Applying reduction CvxAttr2Constr\n", - "(CVXPY) Sep 12 01:08:03 PM: Applying reduction Qp2SymbolicQp\n", - "(CVXPY) Sep 12 01:08:03 PM: Applying reduction QpMatrixStuffing\n", - "(CVXPY) Sep 12 01:08:03 PM: Applying reduction GUROBI\n", - "(CVXPY) Sep 12 01:08:03 PM: Finished problem compilation (took 1.097e-01 seconds).\n", - "(CVXPY) Sep 12 01:08:03 PM: Invoking solver GUROBI to obtain a solution.\n", - "(CVXPY) Sep 12 01:08:43 PM: Problem status: optimal\n", - "(CVXPY) Sep 12 01:08:43 PM: Optimal value: 1.336e+01\n", - "(CVXPY) Sep 12 01:08:43 PM: Compilation took 1.097e-01 seconds\n", - "(CVXPY) Sep 12 01:08:43 PM: Solver (including time spent in interface) took 4.071e+01 seconds\n", - "(CORNETO) Sep 12 01:08:43 PM - INFO : Finished in 41.77 s.\n" - ] - } - ], + "outputs": [], "source": [ "# topological methods\n", "shortest_path_network, shortest_paths_list = nc.methods.run_shortest_paths(graph, sources, measurements)\n", @@ -374,378 +304,751 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "However, this was just a showcase using one drug and one cell line combination. However, in order to get a more unbiased metric, we can repeat the same analysis using the whole PANACEA dataset. Here, we only provided TF activity estimations for those cell line-drug combinations that had a minimum of biological signal (DGE genes > 30)." + "However, this was just an example using one drug and one cell line combination. However, in order to get a more unbiased metric, we can repeat the same analysis using the whole PANACEA dataset. Here, we provide a one_liner that performs systematic network inference using the methods implemented in NetworkCommons, using the different cell line and drug combinations available in the PANACEA dataset. Here, due to computational power constrains, we will do this on one cell line, H1793, and all available drugs. " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(CORNETO) Sep 12 02:24:47 PM - INFO : 1/1 inputs mapped to the graph\n", - "(CORNETO) Sep 12 02:24:47 PM - INFO : 20/25 outputs mapped to the graph\n", - "(CORNETO) Sep 12 02:24:47 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", - "(CORNETO) Sep 12 02:24:47 PM - INFO : Finished. Final size: V x E = (0, 0).\n", - "(CORNETO) Sep 12 02:24:47 PM - INFO : 0/1 inputs after pruning.\n", - "(CORNETO) Sep 12 02:24:47 PM - INFO : 0/20 outputs after pruning.\n", - "(CORNETO) Sep 12 02:24:47 PM - INFO : Converting into a flow graph...\n", - "(CORNETO) Sep 12 02:24:47 PM - INFO : Creating a network flow problem...\n", - "(CORNETO) Sep 12 02:24:47 PM - INFO : Preprocess completed.\n", - "(CVXPY) Sep 12 02:24:47 PM: Your problem has 210 variables, 556 constraints, and 0 parameters.\n", - "(CVXPY) Sep 12 02:24:47 PM: It is compliant with the following grammars: DCP, DQCP\n", - "(CVXPY) Sep 12 02:24:47 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", - "(CVXPY) Sep 12 02:24:47 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", - "(CVXPY) Sep 12 02:24:47 PM: Your problem is compiled with the CPP canonicalization backend.\n", - "(CVXPY) Sep 12 02:24:47 PM: Compiling problem (target solver=GUROBI).\n", - "(CVXPY) Sep 12 02:24:47 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", - "(CVXPY) Sep 12 02:24:47 PM: Applying reduction CvxAttr2Constr\n", - "(CVXPY) Sep 12 02:24:47 PM: Applying reduction Qp2SymbolicQp\n", - "(CVXPY) Sep 12 02:24:47 PM: Applying reduction QpMatrixStuffing\n", - "(CVXPY) Sep 12 02:24:47 PM: Applying reduction GUROBI\n", - "(CVXPY) Sep 12 02:24:47 PM: Finished problem compilation (took 4.238e-02 seconds).\n", - "(CVXPY) Sep 12 02:24:47 PM: Invoking solver GUROBI to obtain a solution.\n", - "(CVXPY) Sep 12 02:24:47 PM: Problem status: infeasible\n", - "(CVXPY) Sep 12 02:24:47 PM: Optimal value: inf\n", - "(CVXPY) Sep 12 02:24:47 PM: Compilation took 4.238e-02 seconds\n", - "(CVXPY) Sep 12 02:24:47 PM: Solver (including time spent in interface) took 9.094e-03 seconds\n", - "(CORNETO) Sep 12 02:24:47 PM - INFO : Finished in 0.15 s.\n", - "(CORNETO) Sep 12 02:24:48 PM - INFO : 1/1 inputs mapped to the graph\n", - "(CORNETO) Sep 12 02:24:48 PM - INFO : 19/25 outputs mapped to the graph\n", - "(CORNETO) Sep 12 02:24:48 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", - "(CORNETO) Sep 12 02:24:48 PM - INFO : Finished. Final size: V x E = (1186, 5787).\n", - "(CORNETO) Sep 12 02:24:48 PM - INFO : 1/1 inputs after pruning.\n", - "(CORNETO) Sep 12 02:24:48 PM - INFO : 16/19 outputs after pruning.\n", - "(CORNETO) Sep 12 02:24:48 PM - INFO : Converting into a flow graph...\n", - "(CORNETO) Sep 12 02:24:48 PM - INFO : Creating a network flow problem...\n", - "(CORNETO) Sep 12 02:24:49 PM - INFO : Preprocess completed.\n", - "(CVXPY) Sep 12 02:24:49 PM: Your problem has 26865 variables, 65402 constraints, and 0 parameters.\n", - "(CVXPY) Sep 12 02:24:49 PM: It is compliant with the following grammars: DCP, DQCP\n", - "(CVXPY) Sep 12 02:24:49 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", - "(CVXPY) Sep 12 02:24:49 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", - "(CVXPY) Sep 12 02:24:49 PM: Your problem is compiled with the CPP canonicalization backend.\n", - "(CVXPY) Sep 12 02:24:49 PM: Compiling problem (target solver=GUROBI).\n", - "(CVXPY) Sep 12 02:24:49 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", - "(CVXPY) Sep 12 02:24:49 PM: Applying reduction CvxAttr2Constr\n", - "(CVXPY) Sep 12 02:24:49 PM: Applying reduction Qp2SymbolicQp\n", - "(CVXPY) Sep 12 02:24:49 PM: Applying reduction QpMatrixStuffing\n", - "(CVXPY) Sep 12 02:24:49 PM: Applying reduction GUROBI\n", - "(CVXPY) Sep 12 02:24:49 PM: Finished problem compilation (took 1.078e-01 seconds).\n", - "(CVXPY) Sep 12 02:24:49 PM: Invoking solver GUROBI to obtain a solution.\n", + "(CORNETO) Sep 12 02:55:41 PM - INFO : 1/1 inputs mapped to the graph\n", + "(CORNETO) Sep 12 02:55:41 PM - INFO : 23/25 outputs mapped to the graph\n", + "(CORNETO) Sep 12 02:55:41 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", + "(CORNETO) Sep 12 02:55:41 PM - INFO : Finished. Final size: V x E = (1197, 5840).\n", + "(CORNETO) Sep 12 02:55:41 PM - INFO : 1/1 inputs after pruning.\n", + "(CORNETO) Sep 12 02:55:41 PM - INFO : 22/23 outputs after pruning.\n", + "(CORNETO) Sep 12 02:55:41 PM - INFO : Converting into a flow graph...\n", + "(CORNETO) Sep 12 02:55:42 PM - INFO : Creating a network flow problem...\n", + "(CORNETO) Sep 12 02:55:42 PM - INFO : Preprocess completed.\n", + "(CVXPY) Sep 12 02:55:42 PM: Your problem has 27092 variables, 65950 constraints, and 0 parameters.\n", + "(CVXPY) Sep 12 02:55:42 PM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 12 02:55:42 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 12 02:55:42 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 12 02:55:42 PM: Your problem is compiled with the CPP canonicalization backend.\n", + "(CVXPY) Sep 12 02:55:42 PM: Compiling problem (target solver=GUROBI).\n", + "(CVXPY) Sep 12 02:55:42 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", + "(CVXPY) Sep 12 02:55:42 PM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 12 02:55:42 PM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 12 02:55:42 PM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 12 02:55:42 PM: Applying reduction GUROBI\n", + "(CVXPY) Sep 12 02:55:42 PM: Finished problem compilation (took 1.702e-01 seconds).\n", + "(CVXPY) Sep 12 02:55:42 PM: Invoking solver GUROBI to obtain a solution.\n", + "(CVXPY) Sep 12 03:00:01 PM: Problem status: optimal\n", + "(CVXPY) Sep 12 03:00:01 PM: Optimal value: 3.460e+00\n", + "(CVXPY) Sep 12 03:00:01 PM: Compilation took 1.702e-01 seconds\n", + "(CVXPY) Sep 12 03:00:01 PM: Solver (including time spent in interface) took 2.585e+02 seconds\n", + "(CORNETO) Sep 12 03:00:01 PM - INFO : Finished in 260.29 s.\n", + "(CORNETO) Sep 12 03:00:02 PM - INFO : 1/1 inputs mapped to the graph\n", + "(CORNETO) Sep 12 03:00:02 PM - INFO : 19/25 outputs mapped to the graph\n", + "(CORNETO) Sep 12 03:00:02 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", + "(CORNETO) Sep 12 03:00:03 PM - INFO : Finished. Final size: V x E = (1188, 5799).\n", + "(CORNETO) Sep 12 03:00:03 PM - INFO : 1/1 inputs after pruning.\n", + "(CORNETO) Sep 12 03:00:03 PM - INFO : 15/19 outputs after pruning.\n", + "(CORNETO) Sep 12 03:00:03 PM - INFO : Converting into a flow graph...\n", + "(CORNETO) Sep 12 03:00:04 PM - INFO : Creating a network flow problem...\n", + "(CORNETO) Sep 12 03:00:04 PM - INFO : Preprocess completed.\n", + "(CVXPY) Sep 12 03:00:04 PM: Your problem has 26922 variables, 65542 constraints, and 0 parameters.\n", + "(CVXPY) Sep 12 03:00:04 PM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 12 03:00:04 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 12 03:00:04 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 12 03:00:04 PM: Your problem is compiled with the CPP canonicalization backend.\n", + "(CVXPY) Sep 12 03:00:04 PM: Compiling problem (target solver=GUROBI).\n", + "(CVXPY) Sep 12 03:00:04 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", + "(CVXPY) Sep 12 03:00:04 PM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 12 03:00:04 PM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 12 03:00:04 PM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 12 03:00:04 PM: Applying reduction GUROBI\n", + "(CVXPY) Sep 12 03:00:04 PM: Finished problem compilation (took 2.478e-01 seconds).\n", + "(CVXPY) Sep 12 03:00:04 PM: Invoking solver GUROBI to obtain a solution.\n", + "(CVXPY) Sep 12 03:01:29 PM: Problem status: optimal\n", + "(CVXPY) Sep 12 03:01:29 PM: Optimal value: 1.034e+01\n", + "(CVXPY) Sep 12 03:01:29 PM: Compilation took 2.478e-01 seconds\n", + "(CVXPY) Sep 12 03:01:29 PM: Solver (including time spent in interface) took 8.427e+01 seconds\n", + "(CORNETO) Sep 12 03:01:29 PM - INFO : Finished in 86.14 s.\n", + "(CORNETO) Sep 12 03:01:30 PM - INFO : 1/1 inputs mapped to the graph\n", + "(CORNETO) Sep 12 03:01:30 PM - INFO : 20/25 outputs mapped to the graph\n", + "(CORNETO) Sep 12 03:01:30 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", + "(CORNETO) Sep 12 03:01:31 PM - INFO : Finished. Final size: V x E = (1190, 5799).\n", + "(CORNETO) Sep 12 03:01:31 PM - INFO : 1/1 inputs after pruning.\n", + "(CORNETO) Sep 12 03:01:31 PM - INFO : 17/20 outputs after pruning.\n", + "(CORNETO) Sep 12 03:01:31 PM - INFO : Converting into a flow graph...\n", + "(CORNETO) Sep 12 03:01:31 PM - INFO : Creating a network flow problem...\n", + "(CORNETO) Sep 12 03:01:31 PM - INFO : Preprocess completed.\n", + "(CVXPY) Sep 12 03:01:31 PM: Your problem has 26922 variables, 65538 constraints, and 0 parameters.\n", + "(CVXPY) Sep 12 03:01:31 PM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 12 03:01:31 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 12 03:01:31 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 12 03:01:31 PM: Your problem is compiled with the CPP canonicalization backend.\n", + "(CVXPY) Sep 12 03:01:31 PM: Compiling problem (target solver=GUROBI).\n", + "(CVXPY) Sep 12 03:01:31 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", + "(CVXPY) Sep 12 03:01:31 PM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 12 03:01:31 PM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 12 03:01:31 PM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 12 03:01:32 PM: Applying reduction GUROBI\n", + "(CVXPY) Sep 12 03:01:32 PM: Finished problem compilation (took 2.527e-01 seconds).\n", + "(CVXPY) Sep 12 03:01:32 PM: Invoking solver GUROBI to obtain a solution.\n", + "(CVXPY) Sep 12 03:01:35 PM: Problem status: optimal\n", + "(CVXPY) Sep 12 03:01:35 PM: Optimal value: 8.350e+00\n", + "(CVXPY) Sep 12 03:01:35 PM: Compilation took 2.527e-01 seconds\n", + "(CVXPY) Sep 12 03:01:35 PM: Solver (including time spent in interface) took 3.290e+00 seconds\n", + "(CORNETO) Sep 12 03:01:35 PM - INFO : Finished in 5.09 s.\n", + "(CORNETO) Sep 12 03:01:36 PM - INFO : 1/1 inputs mapped to the graph\n", + "(CORNETO) Sep 12 03:01:36 PM - INFO : 20/25 outputs mapped to the graph\n", + "(CORNETO) Sep 12 03:01:36 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", + "(CORNETO) Sep 12 03:01:37 PM - INFO : Finished. Final size: V x E = (1188, 5792).\n", + "(CORNETO) Sep 12 03:01:37 PM - INFO : 1/1 inputs after pruning.\n", + "(CORNETO) Sep 12 03:01:37 PM - INFO : 16/20 outputs after pruning.\n", + "(CORNETO) Sep 12 03:01:37 PM - INFO : Converting into a flow graph...\n", + "(CORNETO) Sep 12 03:01:37 PM - INFO : Creating a network flow problem...\n", + "(CORNETO) Sep 12 03:01:38 PM - INFO : Preprocess completed.\n", + "(CVXPY) Sep 12 03:01:38 PM: Your problem has 26891 variables, 65464 constraints, and 0 parameters.\n", + "(CVXPY) Sep 12 03:01:38 PM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 12 03:01:38 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 12 03:01:38 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 12 03:01:38 PM: Your problem is compiled with the CPP canonicalization backend.\n", + "(CVXPY) Sep 12 03:01:38 PM: Compiling problem (target solver=GUROBI).\n", + "(CVXPY) Sep 12 03:01:38 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", + "(CVXPY) Sep 12 03:01:38 PM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 12 03:01:38 PM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 12 03:01:38 PM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 12 03:01:38 PM: Applying reduction GUROBI\n", + "(CVXPY) Sep 12 03:01:38 PM: Finished problem compilation (took 1.927e-01 seconds).\n", + "(CVXPY) Sep 12 03:01:38 PM: Invoking solver GUROBI to obtain a solution.\n", + "(CVXPY) Sep 12 03:01:43 PM: Problem status: optimal\n", + "(CVXPY) Sep 12 03:01:43 PM: Optimal value: 9.380e+00\n", + "(CVXPY) Sep 12 03:01:43 PM: Compilation took 1.927e-01 seconds\n", + "(CVXPY) Sep 12 03:01:43 PM: Solver (including time spent in interface) took 4.522e+00 seconds\n", + "(CORNETO) Sep 12 03:01:43 PM - INFO : Finished in 6.48 s.\n", + "(CORNETO) Sep 12 03:01:44 PM - INFO : 1/1 inputs mapped to the graph\n", + "(CORNETO) Sep 12 03:01:44 PM - INFO : 23/25 outputs mapped to the graph\n", + "(CORNETO) Sep 12 03:01:44 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", + "(CORNETO) Sep 12 03:01:44 PM - INFO : Finished. Final size: V x E = (0, 0).\n", + "(CORNETO) Sep 12 03:01:44 PM - INFO : 0/1 inputs after pruning.\n", + "(CORNETO) Sep 12 03:01:44 PM - INFO : 0/23 outputs after pruning.\n", + "(CORNETO) Sep 12 03:01:44 PM - INFO : Converting into a flow graph...\n", + "(CORNETO) Sep 12 03:01:44 PM - INFO : Creating a network flow problem...\n", + "(CORNETO) Sep 12 03:01:44 PM - INFO : Preprocess completed.\n", + "(CVXPY) Sep 12 03:01:44 PM: Your problem has 210 variables, 556 constraints, and 0 parameters.\n", + "(CVXPY) Sep 12 03:01:44 PM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 12 03:01:44 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 12 03:01:44 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 12 03:01:44 PM: Your problem is compiled with the CPP canonicalization backend.\n", + "(CVXPY) Sep 12 03:01:44 PM: Compiling problem (target solver=GUROBI).\n", + "(CVXPY) Sep 12 03:01:44 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", + "(CVXPY) Sep 12 03:01:44 PM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 12 03:01:44 PM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 12 03:01:44 PM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 12 03:01:44 PM: Applying reduction GUROBI\n", + "(CVXPY) Sep 12 03:01:44 PM: Finished problem compilation (took 7.671e-02 seconds).\n", + "(CVXPY) Sep 12 03:01:44 PM: Invoking solver GUROBI to obtain a solution.\n", + "(CVXPY) Sep 12 03:01:44 PM: Problem status: infeasible\n", + "(CVXPY) Sep 12 03:01:44 PM: Optimal value: inf\n", + "(CVXPY) Sep 12 03:01:44 PM: Compilation took 7.671e-02 seconds\n", + "(CVXPY) Sep 12 03:01:44 PM: Solver (including time spent in interface) took 9.514e-03 seconds\n", + "(CORNETO) Sep 12 03:01:44 PM - INFO : Finished in 0.22 s.\n", + "(CORNETO) Sep 12 03:01:45 PM - INFO : 1/1 inputs mapped to the graph\n", + "(CORNETO) Sep 12 03:01:45 PM - INFO : 22/25 outputs mapped to the graph\n", + "(CORNETO) Sep 12 03:01:45 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", + "(CORNETO) Sep 12 03:01:45 PM - INFO : Finished. Final size: V x E = (0, 0).\n", + "(CORNETO) Sep 12 03:01:45 PM - INFO : 0/1 inputs after pruning.\n", + "(CORNETO) Sep 12 03:01:45 PM - INFO : 0/22 outputs after pruning.\n", + "(CORNETO) Sep 12 03:01:45 PM - INFO : Converting into a flow graph...\n", + "(CORNETO) Sep 12 03:01:45 PM - INFO : Creating a network flow problem...\n", + "(CORNETO) Sep 12 03:01:45 PM - INFO : Preprocess completed.\n", + "(CVXPY) Sep 12 03:01:45 PM: Your problem has 210 variables, 556 constraints, and 0 parameters.\n", + "(CVXPY) Sep 12 03:01:45 PM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 12 03:01:45 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 12 03:01:45 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 12 03:01:45 PM: Your problem is compiled with the CPP canonicalization backend.\n", + "(CVXPY) Sep 12 03:01:45 PM: Compiling problem (target solver=GUROBI).\n", + "(CVXPY) Sep 12 03:01:45 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", + "(CVXPY) Sep 12 03:01:45 PM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 12 03:01:45 PM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 12 03:01:45 PM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 12 03:01:45 PM: Applying reduction GUROBI\n", + "(CVXPY) Sep 12 03:01:45 PM: Finished problem compilation (took 6.232e-02 seconds).\n", + "(CVXPY) Sep 12 03:01:45 PM: Invoking solver GUROBI to obtain a solution.\n", + "(CVXPY) Sep 12 03:01:45 PM: Problem status: infeasible\n", + "(CVXPY) Sep 12 03:01:45 PM: Optimal value: inf\n", + "(CVXPY) Sep 12 03:01:45 PM: Compilation took 6.232e-02 seconds\n", + "(CVXPY) Sep 12 03:01:45 PM: Solver (including time spent in interface) took 8.480e-03 seconds\n", + "(CORNETO) Sep 12 03:01:45 PM - INFO : Finished in 0.21 s.\n", + "(CORNETO) Sep 12 03:01:46 PM - INFO : 1/1 inputs mapped to the graph\n", + "(CORNETO) Sep 12 03:01:46 PM - INFO : 18/25 outputs mapped to the graph\n", + "(CORNETO) Sep 12 03:01:46 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", + "(CORNETO) Sep 12 03:01:47 PM - INFO : Finished. Final size: V x E = (1184, 5783).\n", + "(CORNETO) Sep 12 03:01:47 PM - INFO : 1/1 inputs after pruning.\n", + "(CORNETO) Sep 12 03:01:47 PM - INFO : 15/18 outputs after pruning.\n", + "(CORNETO) Sep 12 03:01:47 PM - INFO : Converting into a flow graph...\n", + "(CORNETO) Sep 12 03:01:48 PM - INFO : Creating a network flow problem...\n", + "(CORNETO) Sep 12 03:01:48 PM - INFO : Preprocess completed.\n", + "(CVXPY) Sep 12 03:01:48 PM: Your problem has 26846 variables, 65358 constraints, and 0 parameters.\n", + "(CVXPY) Sep 12 03:01:48 PM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 12 03:01:48 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 12 03:01:48 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 12 03:01:48 PM: Your problem is compiled with the CPP canonicalization backend.\n", + "(CVXPY) Sep 12 03:01:48 PM: Compiling problem (target solver=GUROBI).\n", + "(CVXPY) Sep 12 03:01:48 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", + "(CVXPY) Sep 12 03:01:48 PM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 12 03:01:48 PM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 12 03:01:48 PM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 12 03:01:48 PM: Applying reduction GUROBI\n", + "(CVXPY) Sep 12 03:01:48 PM: Finished problem compilation (took 1.910e-01 seconds).\n", + "(CVXPY) Sep 12 03:01:48 PM: Invoking solver GUROBI to obtain a solution.\n", + "(CVXPY) Sep 12 03:02:21 PM: Problem status: optimal\n", + "(CVXPY) Sep 12 03:02:21 PM: Optimal value: 1.033e+01\n", + "(CVXPY) Sep 12 03:02:21 PM: Compilation took 1.910e-01 seconds\n", + "(CVXPY) Sep 12 03:02:21 PM: Solver (including time spent in interface) took 3.230e+01 seconds\n", + "(CORNETO) Sep 12 03:02:21 PM - INFO : Finished in 34.34 s.\n", + "(CORNETO) Sep 12 03:02:22 PM - INFO : 1/1 inputs mapped to the graph\n", + "(CORNETO) Sep 12 03:02:22 PM - INFO : 21/25 outputs mapped to the graph\n", + "(CORNETO) Sep 12 03:02:22 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", + "(CORNETO) Sep 12 03:02:22 PM - INFO : Finished. Final size: V x E = (0, 0).\n", + "(CORNETO) Sep 12 03:02:22 PM - INFO : 0/1 inputs after pruning.\n", + "(CORNETO) Sep 12 03:02:22 PM - INFO : 0/21 outputs after pruning.\n", + "(CORNETO) Sep 12 03:02:22 PM - INFO : Converting into a flow graph...\n", + "(CORNETO) Sep 12 03:02:22 PM - INFO : Creating a network flow problem...\n", + "(CORNETO) Sep 12 03:02:22 PM - INFO : Preprocess completed.\n", + "(CVXPY) Sep 12 03:02:22 PM: Your problem has 210 variables, 556 constraints, and 0 parameters.\n", + "(CVXPY) Sep 12 03:02:22 PM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 12 03:02:22 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 12 03:02:22 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 12 03:02:22 PM: Your problem is compiled with the CPP canonicalization backend.\n", + "(CVXPY) Sep 12 03:02:22 PM: Compiling problem (target solver=GUROBI).\n", + "(CVXPY) Sep 12 03:02:22 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", + "(CVXPY) Sep 12 03:02:22 PM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 12 03:02:22 PM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 12 03:02:22 PM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 12 03:02:22 PM: Applying reduction GUROBI\n", + "(CVXPY) Sep 12 03:02:22 PM: Finished problem compilation (took 1.054e-01 seconds).\n", + "(CVXPY) Sep 12 03:02:22 PM: Invoking solver GUROBI to obtain a solution.\n", + "(CVXPY) Sep 12 03:02:22 PM: Problem status: infeasible\n", + "(CVXPY) Sep 12 03:02:22 PM: Optimal value: inf\n", + "(CVXPY) Sep 12 03:02:22 PM: Compilation took 1.054e-01 seconds\n", + "(CVXPY) Sep 12 03:02:22 PM: Solver (including time spent in interface) took 1.369e-02 seconds\n", + "(CORNETO) Sep 12 03:02:22 PM - INFO : Finished in 0.31 s.\n", + "(CORNETO) Sep 12 03:02:24 PM - INFO : 1/1 inputs mapped to the graph\n", + "(CORNETO) Sep 12 03:02:24 PM - INFO : 18/25 outputs mapped to the graph\n", + "(CORNETO) Sep 12 03:02:24 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", + "(CORNETO) Sep 12 03:02:25 PM - INFO : Finished. Final size: V x E = (1190, 5795).\n", + "(CORNETO) Sep 12 03:02:25 PM - INFO : 1/1 inputs after pruning.\n", + "(CORNETO) Sep 12 03:02:25 PM - INFO : 14/18 outputs after pruning.\n", + "(CORNETO) Sep 12 03:02:25 PM - INFO : Converting into a flow graph...\n", + "(CORNETO) Sep 12 03:02:25 PM - INFO : Creating a network flow problem...\n", + "(CORNETO) Sep 12 03:02:26 PM - INFO : Preprocess completed.\n", + "(CVXPY) Sep 12 03:02:26 PM: Your problem has 26915 variables, 65522 constraints, and 0 parameters.\n", + "(CVXPY) Sep 12 03:02:26 PM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 12 03:02:26 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 12 03:02:26 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 12 03:02:26 PM: Your problem is compiled with the CPP canonicalization backend.\n", + "(CVXPY) Sep 12 03:02:26 PM: Compiling problem (target solver=GUROBI).\n", + "(CVXPY) Sep 12 03:02:26 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", + "(CVXPY) Sep 12 03:02:26 PM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 12 03:02:26 PM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 12 03:02:26 PM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 12 03:02:26 PM: Applying reduction GUROBI\n", + "(CVXPY) Sep 12 03:02:26 PM: Finished problem compilation (took 2.006e-01 seconds).\n", + "(CVXPY) Sep 12 03:02:26 PM: Invoking solver GUROBI to obtain a solution.\n", + "(CVXPY) Sep 12 03:02:41 PM: Problem status: optimal\n", + "(CVXPY) Sep 12 03:02:41 PM: Optimal value: 1.137e+01\n", + "(CVXPY) Sep 12 03:02:41 PM: Compilation took 2.006e-01 seconds\n", + "(CVXPY) Sep 12 03:02:41 PM: Solver (including time spent in interface) took 1.488e+01 seconds\n", + "(CORNETO) Sep 12 03:02:41 PM - INFO : Finished in 17.25 s.\n", + "(CORNETO) Sep 12 03:02:43 PM - INFO : 1/1 inputs mapped to the graph\n", + "(CORNETO) Sep 12 03:02:43 PM - INFO : 23/25 outputs mapped to the graph\n", + "(CORNETO) Sep 12 03:02:43 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", + "(CORNETO) Sep 12 03:02:44 PM - INFO : Finished. Final size: V x E = (1190, 5803).\n", + "(CORNETO) Sep 12 03:02:44 PM - INFO : 1/1 inputs after pruning.\n", + "(CORNETO) Sep 12 03:02:44 PM - INFO : 19/23 outputs after pruning.\n", + "(CORNETO) Sep 12 03:02:44 PM - INFO : Converting into a flow graph...\n", + "(CORNETO) Sep 12 03:02:45 PM - INFO : Creating a network flow problem...\n", + "(CORNETO) Sep 12 03:02:46 PM - INFO : Preprocess completed.\n", + "(CVXPY) Sep 12 03:02:46 PM: Your problem has 26932 variables, 65562 constraints, and 0 parameters.\n", + "(CVXPY) Sep 12 03:02:46 PM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 12 03:02:46 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 12 03:02:46 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 12 03:02:46 PM: Your problem is compiled with the CPP canonicalization backend.\n", + "(CVXPY) Sep 12 03:02:46 PM: Compiling problem (target solver=GUROBI).\n", + "(CVXPY) Sep 12 03:02:46 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", + "(CVXPY) Sep 12 03:02:46 PM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 12 03:02:46 PM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 12 03:02:46 PM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 12 03:02:46 PM: Applying reduction GUROBI\n", + "(CVXPY) Sep 12 03:02:46 PM: Finished problem compilation (took 3.662e-01 seconds).\n", + "(CVXPY) Sep 12 03:02:46 PM: Invoking solver GUROBI to obtain a solution.\n", + "\n", + "Interrupt request received\n", + "(CVXPY) Sep 12 03:30:01 PM: Problem status: user_limit\n", + "(CVXPY) Sep 12 03:30:02 PM: Optimal value: 6.440e+00\n", + "(CVXPY) Sep 12 03:30:02 PM: Compilation took 3.662e-01 seconds\n", + "(CVXPY) Sep 12 03:30:02 PM: Solver (including time spent in interface) took 1.635e+03 seconds\n", + "(CORNETO) Sep 12 03:30:04 PM - INFO : Finished in 1640.66 s.\n", + "(CORNETO) Sep 12 03:30:13 PM - INFO : 1/1 inputs mapped to the graph\n", + "(CORNETO) Sep 12 03:30:13 PM - INFO : 20/25 outputs mapped to the graph\n", + "(CORNETO) Sep 12 03:30:13 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", + "(CORNETO) Sep 12 03:30:15 PM - INFO : Finished. Final size: V x E = (1195, 5816).\n", + "(CORNETO) Sep 12 03:30:15 PM - INFO : 1/1 inputs after pruning.\n", + "(CORNETO) Sep 12 03:30:15 PM - INFO : 17/20 outputs after pruning.\n", + "(CORNETO) Sep 12 03:30:15 PM - INFO : Converting into a flow graph...\n", + "(CORNETO) Sep 12 03:30:16 PM - INFO : Creating a network flow problem...\n", + "(CORNETO) Sep 12 03:30:17 PM - INFO : Preprocess completed.\n", + "(CVXPY) Sep 12 03:30:17 PM: Your problem has 27005 variables, 65738 constraints, and 0 parameters.\n", + "(CVXPY) Sep 12 03:30:17 PM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 12 03:30:17 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 12 03:30:17 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 12 03:30:17 PM: Your problem is compiled with the CPP canonicalization backend.\n", + "(CVXPY) Sep 12 03:30:17 PM: Compiling problem (target solver=GUROBI).\n", + "(CVXPY) Sep 12 03:30:17 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", + "(CVXPY) Sep 12 03:30:17 PM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 12 03:30:17 PM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 12 03:30:17 PM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 12 03:30:17 PM: Applying reduction GUROBI\n", + "(CVXPY) Sep 12 03:30:17 PM: Finished problem compilation (took 7.224e-01 seconds).\n", + "(CVXPY) Sep 12 03:30:17 PM: Invoking solver GUROBI to obtain a solution.\n", "\n", "Interrupt request received\n", - "(CVXPY) Sep 12 02:28:36 PM: Problem status: user_limit\n", - "(CVXPY) Sep 12 02:28:36 PM: Optimal value: 9.370e+00\n", - "(CVXPY) Sep 12 02:28:36 PM: Compilation took 1.078e-01 seconds\n", - "(CVXPY) Sep 12 02:28:36 PM: Solver (including time spent in interface) took 2.269e+02 seconds\n", - "(CORNETO) Sep 12 02:28:36 PM - INFO : Finished in 228.13 s.\n", - "(CORNETO) Sep 12 02:28:37 PM - INFO : 1/1 inputs mapped to the graph\n", - "(CORNETO) Sep 12 02:28:37 PM - INFO : 17/25 outputs mapped to the graph\n", - "(CORNETO) Sep 12 02:28:37 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", - "(CORNETO) Sep 12 02:28:37 PM - INFO : Finished. Final size: V x E = (0, 0).\n", - "(CORNETO) Sep 12 02:28:37 PM - INFO : 0/1 inputs after pruning.\n", - "(CORNETO) Sep 12 02:28:37 PM - INFO : 0/17 outputs after pruning.\n", - "(CORNETO) Sep 12 02:28:37 PM - INFO : Converting into a flow graph...\n", - "(CORNETO) Sep 12 02:28:37 PM - INFO : Creating a network flow problem...\n", - "(CORNETO) Sep 12 02:28:37 PM - INFO : Preprocess completed.\n", - "(CVXPY) Sep 12 02:28:37 PM: Your problem has 210 variables, 556 constraints, and 0 parameters.\n", - "(CVXPY) Sep 12 02:28:37 PM: It is compliant with the following grammars: DCP, DQCP\n", - "(CVXPY) Sep 12 02:28:37 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", - "(CVXPY) Sep 12 02:28:37 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", - "(CVXPY) Sep 12 02:28:37 PM: Your problem is compiled with the CPP canonicalization backend.\n", - "(CVXPY) Sep 12 02:28:37 PM: Compiling problem (target solver=GUROBI).\n", - "(CVXPY) Sep 12 02:28:37 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", - "(CVXPY) Sep 12 02:28:37 PM: Applying reduction CvxAttr2Constr\n", - "(CVXPY) Sep 12 02:28:37 PM: Applying reduction Qp2SymbolicQp\n", - "(CVXPY) Sep 12 02:28:37 PM: Applying reduction QpMatrixStuffing\n", - "(CVXPY) Sep 12 02:28:37 PM: Applying reduction GUROBI\n", - "(CVXPY) Sep 12 02:28:37 PM: Finished problem compilation (took 9.378e-02 seconds).\n", - "(CVXPY) Sep 12 02:28:37 PM: Invoking solver GUROBI to obtain a solution.\n", - "(CVXPY) Sep 12 02:28:37 PM: Problem status: infeasible\n", - "(CVXPY) Sep 12 02:28:37 PM: Optimal value: inf\n", - "(CVXPY) Sep 12 02:28:37 PM: Compilation took 9.378e-02 seconds\n", - "(CVXPY) Sep 12 02:28:37 PM: Solver (including time spent in interface) took 8.900e-03 seconds\n", - "(CORNETO) Sep 12 02:28:37 PM - INFO : Finished in 0.27 s.\n", - "(CORNETO) Sep 12 02:28:39 PM - INFO : 1/1 inputs mapped to the graph\n", - "(CORNETO) Sep 12 02:28:39 PM - INFO : 19/25 outputs mapped to the graph\n", - "(CORNETO) Sep 12 02:28:39 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", - "(CORNETO) Sep 12 02:28:40 PM - INFO : Finished. Final size: V x E = (1187, 5787).\n", - "(CORNETO) Sep 12 02:28:40 PM - INFO : 1/1 inputs after pruning.\n", - "(CORNETO) Sep 12 02:28:40 PM - INFO : 16/19 outputs after pruning.\n", - "(CORNETO) Sep 12 02:28:40 PM - INFO : Converting into a flow graph...\n", - "(CORNETO) Sep 12 02:28:41 PM - INFO : Creating a network flow problem...\n", - "(CORNETO) Sep 12 02:28:41 PM - INFO : Preprocess completed.\n", - "(CVXPY) Sep 12 02:28:41 PM: Your problem has 26868 variables, 65408 constraints, and 0 parameters.\n", - "(CVXPY) Sep 12 02:28:41 PM: It is compliant with the following grammars: DCP, DQCP\n", - "(CVXPY) Sep 12 02:28:41 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", - "(CVXPY) Sep 12 02:28:41 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", - "(CVXPY) Sep 12 02:28:41 PM: Your problem is compiled with the CPP canonicalization backend.\n", - "(CVXPY) Sep 12 02:28:41 PM: Compiling problem (target solver=GUROBI).\n", - "(CVXPY) Sep 12 02:28:41 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", - "(CVXPY) Sep 12 02:28:41 PM: Applying reduction CvxAttr2Constr\n", - "(CVXPY) Sep 12 02:28:41 PM: Applying reduction Qp2SymbolicQp\n", - "(CVXPY) Sep 12 02:28:41 PM: Applying reduction QpMatrixStuffing\n", - "(CVXPY) Sep 12 02:28:41 PM: Applying reduction GUROBI\n", - "(CVXPY) Sep 12 02:28:41 PM: Finished problem compilation (took 2.038e-01 seconds).\n", - "(CVXPY) Sep 12 02:28:41 PM: Invoking solver GUROBI to obtain a solution.\n", - "(CVXPY) Sep 12 02:28:43 PM: Problem status: optimal\n", - "(CVXPY) Sep 12 02:28:43 PM: Optimal value: 9.320e+00\n", - "(CVXPY) Sep 12 02:28:43 PM: Compilation took 2.038e-01 seconds\n", - "(CVXPY) Sep 12 02:28:43 PM: Solver (including time spent in interface) took 2.259e+00 seconds\n", - "(CORNETO) Sep 12 02:28:44 PM - INFO : Finished in 4.17 s.\n", - "(CORNETO) Sep 12 02:28:45 PM - INFO : 1/1 inputs mapped to the graph\n", - "(CORNETO) Sep 12 02:28:45 PM - INFO : 18/25 outputs mapped to the graph\n", - "(CORNETO) Sep 12 02:28:45 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", - "(CORNETO) Sep 12 02:28:46 PM - INFO : Finished. Final size: V x E = (1188, 5795).\n", - "(CORNETO) Sep 12 02:28:46 PM - INFO : 1/1 inputs after pruning.\n", - "(CORNETO) Sep 12 02:28:46 PM - INFO : 14/18 outputs after pruning.\n", - "(CORNETO) Sep 12 02:28:46 PM - INFO : Converting into a flow graph...\n", - "(CORNETO) Sep 12 02:28:46 PM - INFO : Creating a network flow problem...\n", - "(CORNETO) Sep 12 02:28:47 PM - INFO : Preprocess completed.\n", - "(CVXPY) Sep 12 02:28:47 PM: Your problem has 26909 variables, 65510 constraints, and 0 parameters.\n", - "(CVXPY) Sep 12 02:28:47 PM: It is compliant with the following grammars: DCP, DQCP\n", - "(CVXPY) Sep 12 02:28:47 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", - "(CVXPY) Sep 12 02:28:47 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", - "(CVXPY) Sep 12 02:28:47 PM: Your problem is compiled with the CPP canonicalization backend.\n", - "(CVXPY) Sep 12 02:28:47 PM: Compiling problem (target solver=GUROBI).\n", - "(CVXPY) Sep 12 02:28:47 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", - "(CVXPY) Sep 12 02:28:47 PM: Applying reduction CvxAttr2Constr\n", - "(CVXPY) Sep 12 02:28:47 PM: Applying reduction Qp2SymbolicQp\n", - "(CVXPY) Sep 12 02:28:47 PM: Applying reduction QpMatrixStuffing\n", - "(CVXPY) Sep 12 02:28:47 PM: Applying reduction GUROBI\n", - "(CVXPY) Sep 12 02:28:47 PM: Finished problem compilation (took 1.679e-01 seconds).\n", - "(CVXPY) Sep 12 02:28:47 PM: Invoking solver GUROBI to obtain a solution.\n", - "(CVXPY) Sep 12 02:28:48 PM: Problem status: optimal\n", - "(CVXPY) Sep 12 02:28:48 PM: Optimal value: 1.127e+01\n", - "(CVXPY) Sep 12 02:28:48 PM: Compilation took 1.679e-01 seconds\n", - "(CVXPY) Sep 12 02:28:48 PM: Solver (including time spent in interface) took 1.382e+00 seconds\n", - "(CORNETO) Sep 12 02:28:48 PM - INFO : Finished in 3.50 s.\n", - "(CORNETO) Sep 12 02:28:49 PM - INFO : 1/1 inputs mapped to the graph\n", - "(CORNETO) Sep 12 02:28:49 PM - INFO : 18/25 outputs mapped to the graph\n", - "(CORNETO) Sep 12 02:28:49 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", - "(CORNETO) Sep 12 02:28:49 PM - INFO : Finished. Final size: V x E = (0, 0).\n", - "(CORNETO) Sep 12 02:28:49 PM - INFO : 0/1 inputs after pruning.\n", - "(CORNETO) Sep 12 02:28:49 PM - INFO : 0/18 outputs after pruning.\n", - "(CORNETO) Sep 12 02:28:49 PM - INFO : Converting into a flow graph...\n", - "(CORNETO) Sep 12 02:28:49 PM - INFO : Creating a network flow problem...\n", - "(CORNETO) Sep 12 02:28:49 PM - INFO : Preprocess completed.\n", - "(CVXPY) Sep 12 02:28:49 PM: Your problem has 210 variables, 556 constraints, and 0 parameters.\n", - "(CVXPY) Sep 12 02:28:49 PM: It is compliant with the following grammars: DCP, DQCP\n", - "(CVXPY) Sep 12 02:28:49 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", - "(CVXPY) Sep 12 02:28:49 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", - "(CVXPY) Sep 12 02:28:49 PM: Your problem is compiled with the CPP canonicalization backend.\n", - "(CVXPY) Sep 12 02:28:49 PM: Compiling problem (target solver=GUROBI).\n", - "(CVXPY) Sep 12 02:28:49 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", - "(CVXPY) Sep 12 02:28:49 PM: Applying reduction CvxAttr2Constr\n", - "(CVXPY) Sep 12 02:28:49 PM: Applying reduction Qp2SymbolicQp\n", - "(CVXPY) Sep 12 02:28:49 PM: Applying reduction QpMatrixStuffing\n", - "(CVXPY) Sep 12 02:28:49 PM: Applying reduction GUROBI\n", - "(CVXPY) Sep 12 02:28:49 PM: Finished problem compilation (took 7.491e-02 seconds).\n", - "(CVXPY) Sep 12 02:28:49 PM: Invoking solver GUROBI to obtain a solution.\n", - "(CVXPY) Sep 12 02:28:50 PM: Problem status: infeasible\n", - "(CVXPY) Sep 12 02:28:50 PM: Optimal value: inf\n", - "(CVXPY) Sep 12 02:28:50 PM: Compilation took 7.491e-02 seconds\n", - "(CVXPY) Sep 12 02:28:50 PM: Solver (including time spent in interface) took 9.006e-03 seconds\n", - "(CORNETO) Sep 12 02:28:50 PM - INFO : Finished in 0.21 s.\n", - "(CORNETO) Sep 12 02:28:51 PM - INFO : 1/1 inputs mapped to the graph\n", - "(CORNETO) Sep 12 02:28:51 PM - INFO : 18/25 outputs mapped to the graph\n", - "(CORNETO) Sep 12 02:28:51 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", - "(CORNETO) Sep 12 02:28:51 PM - INFO : Finished. Final size: V x E = (1191, 5821).\n", - "(CORNETO) Sep 12 02:28:51 PM - INFO : 1/1 inputs after pruning.\n", - "(CORNETO) Sep 12 02:28:51 PM - INFO : 15/18 outputs after pruning.\n", - "(CORNETO) Sep 12 02:28:51 PM - INFO : Converting into a flow graph...\n", - "(CORNETO) Sep 12 02:28:52 PM - INFO : Creating a network flow problem...\n", - "(CORNETO) Sep 12 02:28:52 PM - INFO : Preprocess completed.\n", - "(CVXPY) Sep 12 02:28:52 PM: Your problem has 27019 variables, 65780 constraints, and 0 parameters.\n", - "(CVXPY) Sep 12 02:28:52 PM: It is compliant with the following grammars: DCP, DQCP\n", - "(CVXPY) Sep 12 02:28:52 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", - "(CVXPY) Sep 12 02:28:52 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", - "(CVXPY) Sep 12 02:28:52 PM: Your problem is compiled with the CPP canonicalization backend.\n", - "(CVXPY) Sep 12 02:28:52 PM: Compiling problem (target solver=GUROBI).\n", - "(CVXPY) Sep 12 02:28:52 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", - "(CVXPY) Sep 12 02:28:52 PM: Applying reduction CvxAttr2Constr\n", - "(CVXPY) Sep 12 02:28:52 PM: Applying reduction Qp2SymbolicQp\n", - "(CVXPY) Sep 12 02:28:52 PM: Applying reduction QpMatrixStuffing\n", - "(CVXPY) Sep 12 02:28:52 PM: Applying reduction GUROBI\n", - "(CVXPY) Sep 12 02:28:52 PM: Finished problem compilation (took 1.511e-01 seconds).\n", - "(CVXPY) Sep 12 02:28:52 PM: Invoking solver GUROBI to obtain a solution.\n", - "(CVXPY) Sep 12 02:28:56 PM: Problem status: optimal\n", - "(CVXPY) Sep 12 02:28:56 PM: Optimal value: 1.033e+01\n", - "(CVXPY) Sep 12 02:28:56 PM: Compilation took 1.511e-01 seconds\n", - "(CVXPY) Sep 12 02:28:56 PM: Solver (including time spent in interface) took 3.469e+00 seconds\n", - "(CORNETO) Sep 12 02:28:56 PM - INFO : Finished in 4.93 s.\n", - "(CORNETO) Sep 12 02:28:57 PM - INFO : 1/1 inputs mapped to the graph\n", - "(CORNETO) Sep 12 02:28:57 PM - INFO : 22/25 outputs mapped to the graph\n", - "(CORNETO) Sep 12 02:28:57 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", - "(CORNETO) Sep 12 02:28:57 PM - INFO : Finished. Final size: V x E = (1191, 5823).\n", - "(CORNETO) Sep 12 02:28:57 PM - INFO : 1/1 inputs after pruning.\n", - "(CORNETO) Sep 12 02:28:57 PM - INFO : 21/22 outputs after pruning.\n", - "(CORNETO) Sep 12 02:28:57 PM - INFO : Converting into a flow graph...\n", - "(CORNETO) Sep 12 02:28:57 PM - INFO : Creating a network flow problem...\n", - "(CORNETO) Sep 12 02:28:58 PM - INFO : Preprocess completed.\n", - "(CVXPY) Sep 12 02:28:58 PM: Your problem has 27009 variables, 65752 constraints, and 0 parameters.\n", - "(CVXPY) Sep 12 02:28:58 PM: It is compliant with the following grammars: DCP, DQCP\n", - "(CVXPY) Sep 12 02:28:58 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", - "(CVXPY) Sep 12 02:28:58 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", - "(CVXPY) Sep 12 02:28:58 PM: Your problem is compiled with the CPP canonicalization backend.\n", - "(CVXPY) Sep 12 02:28:58 PM: Compiling problem (target solver=GUROBI).\n", - "(CVXPY) Sep 12 02:28:58 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", - "(CVXPY) Sep 12 02:28:58 PM: Applying reduction CvxAttr2Constr\n", - "(CVXPY) Sep 12 02:28:58 PM: Applying reduction Qp2SymbolicQp\n", - "(CVXPY) Sep 12 02:28:58 PM: Applying reduction QpMatrixStuffing\n", - "(CVXPY) Sep 12 02:28:58 PM: Applying reduction GUROBI\n", - "(CVXPY) Sep 12 02:28:58 PM: Finished problem compilation (took 1.356e-01 seconds).\n", - "(CVXPY) Sep 12 02:28:58 PM: Invoking solver GUROBI to obtain a solution.\n", - "(CVXPY) Sep 12 02:29:03 PM: Problem status: optimal\n", - "(CVXPY) Sep 12 02:29:03 PM: Optimal value: 4.420e+00\n", - "(CVXPY) Sep 12 02:29:03 PM: Compilation took 1.356e-01 seconds\n", - "(CVXPY) Sep 12 02:29:03 PM: Solver (including time spent in interface) took 5.320e+00 seconds\n", - "(CORNETO) Sep 12 02:29:03 PM - INFO : Finished in 6.77 s.\n", - "(CORNETO) Sep 12 02:29:04 PM - INFO : 1/1 inputs mapped to the graph\n", - "(CORNETO) Sep 12 02:29:04 PM - INFO : 22/25 outputs mapped to the graph\n", - "(CORNETO) Sep 12 02:29:04 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", - "(CORNETO) Sep 12 02:29:04 PM - INFO : Finished. Final size: V x E = (0, 0).\n", - "(CORNETO) Sep 12 02:29:04 PM - INFO : 0/1 inputs after pruning.\n", - "(CORNETO) Sep 12 02:29:04 PM - INFO : 0/22 outputs after pruning.\n", - "(CORNETO) Sep 12 02:29:04 PM - INFO : Converting into a flow graph...\n", - "(CORNETO) Sep 12 02:29:04 PM - INFO : Creating a network flow problem...\n", - "(CORNETO) Sep 12 02:29:04 PM - INFO : Preprocess completed.\n", - "(CVXPY) Sep 12 02:29:04 PM: Your problem has 210 variables, 556 constraints, and 0 parameters.\n", - "(CVXPY) Sep 12 02:29:04 PM: It is compliant with the following grammars: DCP, DQCP\n", - "(CVXPY) Sep 12 02:29:04 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", - "(CVXPY) Sep 12 02:29:04 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", - "(CVXPY) Sep 12 02:29:04 PM: Your problem is compiled with the CPP canonicalization backend.\n", - "(CVXPY) Sep 12 02:29:04 PM: Compiling problem (target solver=GUROBI).\n", - "(CVXPY) Sep 12 02:29:04 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", - "(CVXPY) Sep 12 02:29:04 PM: Applying reduction CvxAttr2Constr\n", - "(CVXPY) Sep 12 02:29:04 PM: Applying reduction Qp2SymbolicQp\n", - "(CVXPY) Sep 12 02:29:04 PM: Applying reduction QpMatrixStuffing\n", - "(CVXPY) Sep 12 02:29:04 PM: Applying reduction GUROBI\n", - "(CVXPY) Sep 12 02:29:04 PM: Finished problem compilation (took 7.783e-02 seconds).\n", - "(CVXPY) Sep 12 02:29:04 PM: Invoking solver GUROBI to obtain a solution.\n", - "(CVXPY) Sep 12 02:29:04 PM: Problem status: infeasible\n", - "(CVXPY) Sep 12 02:29:04 PM: Optimal value: inf\n", - "(CVXPY) Sep 12 02:29:04 PM: Compilation took 7.783e-02 seconds\n", - "(CVXPY) Sep 12 02:29:04 PM: Solver (including time spent in interface) took 1.931e-02 seconds\n", - "(CORNETO) Sep 12 02:29:04 PM - INFO : Finished in 0.21 s.\n", - "(CORNETO) Sep 12 02:29:05 PM - INFO : 1/1 inputs mapped to the graph\n", - "(CORNETO) Sep 12 02:29:05 PM - INFO : 19/25 outputs mapped to the graph\n", - "(CORNETO) Sep 12 02:29:05 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", - "(CORNETO) Sep 12 02:29:06 PM - INFO : Finished. Final size: V x E = (1186, 5782).\n", - "(CORNETO) Sep 12 02:29:06 PM - INFO : 1/1 inputs after pruning.\n", - "(CORNETO) Sep 12 02:29:06 PM - INFO : 12/19 outputs after pruning.\n", - "(CORNETO) Sep 12 02:29:06 PM - INFO : Converting into a flow graph...\n", - "(CORNETO) Sep 12 02:29:06 PM - INFO : Creating a network flow problem...\n", - "(CORNETO) Sep 12 02:29:07 PM - INFO : Preprocess completed.\n", - "(CVXPY) Sep 12 02:29:07 PM: Your problem has 26857 variables, 65384 constraints, and 0 parameters.\n", - "(CVXPY) Sep 12 02:29:07 PM: It is compliant with the following grammars: DCP, DQCP\n", - "(CVXPY) Sep 12 02:29:07 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", - "(CVXPY) Sep 12 02:29:07 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", - "(CVXPY) Sep 12 02:29:07 PM: Your problem is compiled with the CPP canonicalization backend.\n", - "(CVXPY) Sep 12 02:29:07 PM: Compiling problem (target solver=GUROBI).\n", - "(CVXPY) Sep 12 02:29:07 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", - "(CVXPY) Sep 12 02:29:07 PM: Applying reduction CvxAttr2Constr\n", - "(CVXPY) Sep 12 02:29:07 PM: Applying reduction Qp2SymbolicQp\n", - "(CVXPY) Sep 12 02:29:07 PM: Applying reduction QpMatrixStuffing\n", - "(CVXPY) Sep 12 02:29:07 PM: Applying reduction GUROBI\n", - "(CVXPY) Sep 12 02:29:07 PM: Finished problem compilation (took 1.393e-01 seconds).\n", - "(CVXPY) Sep 12 02:29:07 PM: Invoking solver GUROBI to obtain a solution.\n", - "(CVXPY) Sep 12 02:29:13 PM: Problem status: optimal\n", - "(CVXPY) Sep 12 02:29:13 PM: Optimal value: 1.328e+01\n", - "(CVXPY) Sep 12 02:29:13 PM: Compilation took 1.393e-01 seconds\n", - "(CVXPY) Sep 12 02:29:13 PM: Solver (including time spent in interface) took 5.993e+00 seconds\n", - "(CORNETO) Sep 12 02:29:13 PM - INFO : Finished in 7.45 s.\n", - "(CORNETO) Sep 12 02:29:14 PM - INFO : 1/1 inputs mapped to the graph\n", - "(CORNETO) Sep 12 02:29:14 PM - INFO : 21/25 outputs mapped to the graph\n", - "(CORNETO) Sep 12 02:29:14 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", - "(CORNETO) Sep 12 02:29:14 PM - INFO : Finished. Final size: V x E = (1189, 5834).\n", - "(CORNETO) Sep 12 02:29:14 PM - INFO : 1/1 inputs after pruning.\n", - "(CORNETO) Sep 12 02:29:14 PM - INFO : 20/21 outputs after pruning.\n", - "(CORNETO) Sep 12 02:29:14 PM - INFO : Converting into a flow graph...\n", - "(CORNETO) Sep 12 02:29:15 PM - INFO : Creating a network flow problem...\n", - "(CORNETO) Sep 12 02:29:15 PM - INFO : Preprocess completed.\n", - "(CVXPY) Sep 12 02:29:15 PM: Your problem has 27050 variables, 65858 constraints, and 0 parameters.\n", - "(CVXPY) Sep 12 02:29:15 PM: It is compliant with the following grammars: DCP, DQCP\n", - "(CVXPY) Sep 12 02:29:15 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", - "(CVXPY) Sep 12 02:29:15 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", - "(CVXPY) Sep 12 02:29:15 PM: Your problem is compiled with the CPP canonicalization backend.\n", - "(CVXPY) Sep 12 02:29:15 PM: Compiling problem (target solver=GUROBI).\n", - "(CVXPY) Sep 12 02:29:15 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", - "(CVXPY) Sep 12 02:29:15 PM: Applying reduction CvxAttr2Constr\n", - "(CVXPY) Sep 12 02:29:15 PM: Applying reduction Qp2SymbolicQp\n", - "(CVXPY) Sep 12 02:29:15 PM: Applying reduction QpMatrixStuffing\n", - "(CVXPY) Sep 12 02:29:15 PM: Applying reduction GUROBI\n", - "(CVXPY) Sep 12 02:29:15 PM: Finished problem compilation (took 1.444e-01 seconds).\n", - "(CVXPY) Sep 12 02:29:15 PM: Invoking solver GUROBI to obtain a solution.\n", - "(CVXPY) Sep 12 02:30:42 PM: Problem status: optimal\n", - "(CVXPY) Sep 12 02:30:42 PM: Optimal value: 5.420e+00\n", - "(CVXPY) Sep 12 02:30:42 PM: Compilation took 1.444e-01 seconds\n", - "(CVXPY) Sep 12 02:30:42 PM: Solver (including time spent in interface) took 8.706e+01 seconds\n", - "(CORNETO) Sep 12 02:30:42 PM - INFO : Finished in 88.52 s.\n", - "(CORNETO) Sep 12 02:30:44 PM - INFO : 1/1 inputs mapped to the graph\n", - "(CORNETO) Sep 12 02:30:44 PM - INFO : 20/25 outputs mapped to the graph\n", - "(CORNETO) Sep 12 02:30:44 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", - "(CORNETO) Sep 12 02:30:44 PM - INFO : Finished. Final size: V x E = (1187, 5787).\n", - "(CORNETO) Sep 12 02:30:44 PM - INFO : 1/1 inputs after pruning.\n", - "(CORNETO) Sep 12 02:30:44 PM - INFO : 15/20 outputs after pruning.\n", - "(CORNETO) Sep 12 02:30:44 PM - INFO : Converting into a flow graph...\n", - "(CORNETO) Sep 12 02:30:44 PM - INFO : Creating a network flow problem...\n", - "(CORNETO) Sep 12 02:30:45 PM - INFO : Preprocess completed.\n", - "(CVXPY) Sep 12 02:30:45 PM: Your problem has 26871 variables, 65416 constraints, and 0 parameters.\n", - "(CVXPY) Sep 12 02:30:45 PM: It is compliant with the following grammars: DCP, DQCP\n", - "(CVXPY) Sep 12 02:30:45 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", - "(CVXPY) Sep 12 02:30:45 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", - "(CVXPY) Sep 12 02:30:45 PM: Your problem is compiled with the CPP canonicalization backend.\n", - "(CVXPY) Sep 12 02:30:45 PM: Compiling problem (target solver=GUROBI).\n", - "(CVXPY) Sep 12 02:30:45 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", - "(CVXPY) Sep 12 02:30:45 PM: Applying reduction CvxAttr2Constr\n", - "(CVXPY) Sep 12 02:30:45 PM: Applying reduction Qp2SymbolicQp\n", - "(CVXPY) Sep 12 02:30:45 PM: Applying reduction QpMatrixStuffing\n", - "(CVXPY) Sep 12 02:30:45 PM: Applying reduction GUROBI\n", - "(CVXPY) Sep 12 02:30:45 PM: Finished problem compilation (took 1.354e-01 seconds).\n", - "(CVXPY) Sep 12 02:30:45 PM: Invoking solver GUROBI to obtain a solution.\n", + "(CVXPY) Sep 12 03:45:27 PM: Problem status: user_limit\n", + "(CVXPY) Sep 12 03:45:27 PM: Optimal value: 8.440e+00\n", + "(CVXPY) Sep 12 03:45:27 PM: Compilation took 7.224e-01 seconds\n", + "(CVXPY) Sep 12 03:45:27 PM: Solver (including time spent in interface) took 9.091e+02 seconds\n", + "(CORNETO) Sep 12 03:45:27 PM - INFO : Finished in 914.56 s.\n", + "(CORNETO) Sep 12 03:45:32 PM - INFO : 1/1 inputs mapped to the graph\n", + "(CORNETO) Sep 12 03:45:32 PM - INFO : 23/25 outputs mapped to the graph\n", + "(CORNETO) Sep 12 03:45:32 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", + "(CORNETO) Sep 12 03:45:32 PM - INFO : Finished. Final size: V x E = (0, 0).\n", + "(CORNETO) Sep 12 03:45:32 PM - INFO : 0/1 inputs after pruning.\n", + "(CORNETO) Sep 12 03:45:32 PM - INFO : 0/23 outputs after pruning.\n", + "(CORNETO) Sep 12 03:45:32 PM - INFO : Converting into a flow graph...\n", + "(CORNETO) Sep 12 03:45:32 PM - INFO : Creating a network flow problem...\n", + "(CORNETO) Sep 12 03:45:32 PM - INFO : Preprocess completed.\n", + "(CVXPY) Sep 12 03:45:32 PM: Your problem has 210 variables, 556 constraints, and 0 parameters.\n", + "(CVXPY) Sep 12 03:45:32 PM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 12 03:45:32 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 12 03:45:32 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 12 03:45:32 PM: Your problem is compiled with the CPP canonicalization backend.\n", + "(CVXPY) Sep 12 03:45:32 PM: Compiling problem (target solver=GUROBI).\n", + "(CVXPY) Sep 12 03:45:32 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", + "(CVXPY) Sep 12 03:45:32 PM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 12 03:45:32 PM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 12 03:45:32 PM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 12 03:45:32 PM: Applying reduction GUROBI\n", + "(CVXPY) Sep 12 03:45:32 PM: Finished problem compilation (took 2.245e-01 seconds).\n", + "(CVXPY) Sep 12 03:45:32 PM: Invoking solver GUROBI to obtain a solution.\n", + "(CVXPY) Sep 12 03:45:32 PM: Problem status: infeasible\n", + "(CVXPY) Sep 12 03:45:32 PM: Optimal value: inf\n", + "(CVXPY) Sep 12 03:45:32 PM: Compilation took 2.245e-01 seconds\n", + "(CVXPY) Sep 12 03:45:32 PM: Solver (including time spent in interface) took 2.471e-02 seconds\n", + "(CORNETO) Sep 12 03:45:32 PM - INFO : Finished in 0.88 s.\n", + "(CORNETO) Sep 12 03:45:36 PM - INFO : 1/1 inputs mapped to the graph\n", + "(CORNETO) Sep 12 03:45:36 PM - INFO : 19/25 outputs mapped to the graph\n", + "(CORNETO) Sep 12 03:45:36 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", + "(CORNETO) Sep 12 03:45:38 PM - INFO : Finished. Final size: V x E = (1192, 5807).\n", + "(CORNETO) Sep 12 03:45:38 PM - INFO : 1/1 inputs after pruning.\n", + "(CORNETO) Sep 12 03:45:38 PM - INFO : 17/19 outputs after pruning.\n", + "(CORNETO) Sep 12 03:45:38 PM - INFO : Converting into a flow graph...\n", + "(CORNETO) Sep 12 03:45:39 PM - INFO : Creating a network flow problem...\n", + "(CORNETO) Sep 12 03:45:40 PM - INFO : Preprocess completed.\n", + "(CVXPY) Sep 12 03:45:40 PM: Your problem has 26960 variables, 65630 constraints, and 0 parameters.\n", + "(CVXPY) Sep 12 03:45:40 PM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 12 03:45:40 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 12 03:45:40 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 12 03:45:40 PM: Your problem is compiled with the CPP canonicalization backend.\n", + "(CVXPY) Sep 12 03:45:40 PM: Compiling problem (target solver=GUROBI).\n", + "(CVXPY) Sep 12 03:45:40 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", + "(CVXPY) Sep 12 03:45:40 PM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 12 03:45:40 PM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 12 03:45:40 PM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 12 03:45:41 PM: Applying reduction GUROBI\n", + "(CVXPY) Sep 12 03:45:41 PM: Finished problem compilation (took 3.728e-01 seconds).\n", + "(CVXPY) Sep 12 03:45:41 PM: Invoking solver GUROBI to obtain a solution.\n", + "(CVXPY) Sep 12 03:58:30 PM: Problem status: optimal\n", + "(CVXPY) Sep 12 03:58:30 PM: Optimal value: 8.390e+00\n", + "(CVXPY) Sep 12 03:58:30 PM: Compilation took 3.728e-01 seconds\n", + "(CVXPY) Sep 12 03:58:30 PM: Solver (including time spent in interface) took 7.691e+02 seconds\n", + "(CORNETO) Sep 12 03:58:30 PM - INFO : Finished in 774.14 s.\n", + "(CORNETO) Sep 12 03:58:32 PM - INFO : 1/1 inputs mapped to the graph\n", + "(CORNETO) Sep 12 03:58:32 PM - INFO : 21/25 outputs mapped to the graph\n", + "(CORNETO) Sep 12 03:58:32 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", + "(CORNETO) Sep 12 03:58:32 PM - INFO : Finished. Final size: V x E = (0, 0).\n", + "(CORNETO) Sep 12 03:58:32 PM - INFO : 0/1 inputs after pruning.\n", + "(CORNETO) Sep 12 03:58:32 PM - INFO : 0/21 outputs after pruning.\n", + "(CORNETO) Sep 12 03:58:32 PM - INFO : Converting into a flow graph...\n", + "(CORNETO) Sep 12 03:58:32 PM - INFO : Creating a network flow problem...\n", + "(CORNETO) Sep 12 03:58:32 PM - INFO : Preprocess completed.\n", + "(CVXPY) Sep 12 03:58:32 PM: Your problem has 210 variables, 556 constraints, and 0 parameters.\n", + "(CVXPY) Sep 12 03:58:32 PM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 12 03:58:32 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 12 03:58:32 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 12 03:58:32 PM: Your problem is compiled with the CPP canonicalization backend.\n", + "(CVXPY) Sep 12 03:58:32 PM: Compiling problem (target solver=GUROBI).\n", + "(CVXPY) Sep 12 03:58:32 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", + "(CVXPY) Sep 12 03:58:32 PM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 12 03:58:32 PM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 12 03:58:32 PM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 12 03:58:32 PM: Applying reduction GUROBI\n", + "(CVXPY) Sep 12 03:58:32 PM: Finished problem compilation (took 7.904e-02 seconds).\n", + "(CVXPY) Sep 12 03:58:32 PM: Invoking solver GUROBI to obtain a solution.\n", + "(CVXPY) Sep 12 03:58:32 PM: Problem status: infeasible\n", + "(CVXPY) Sep 12 03:58:32 PM: Optimal value: inf\n", + "(CVXPY) Sep 12 03:58:32 PM: Compilation took 7.904e-02 seconds\n", + "(CVXPY) Sep 12 03:58:32 PM: Solver (including time spent in interface) took 1.147e-02 seconds\n", + "(CORNETO) Sep 12 03:58:32 PM - INFO : Finished in 0.25 s.\n", + "(CORNETO) Sep 12 03:58:34 PM - INFO : 1/1 inputs mapped to the graph\n", + "(CORNETO) Sep 12 03:58:34 PM - INFO : 15/25 outputs mapped to the graph\n", + "(CORNETO) Sep 12 03:58:34 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", + "(CORNETO) Sep 12 03:58:34 PM - INFO : Finished. Final size: V x E = (1187, 5782).\n", + "(CORNETO) Sep 12 03:58:34 PM - INFO : 1/1 inputs after pruning.\n", + "(CORNETO) Sep 12 03:58:34 PM - INFO : 11/15 outputs after pruning.\n", + "(CORNETO) Sep 12 03:58:34 PM - INFO : Converting into a flow graph...\n", + "(CORNETO) Sep 12 03:58:35 PM - INFO : Creating a network flow problem...\n", + "(CORNETO) Sep 12 03:58:36 PM - INFO : Preprocess completed.\n", + "(CVXPY) Sep 12 03:58:36 PM: Your problem has 26863 variables, 65398 constraints, and 0 parameters.\n", + "(CVXPY) Sep 12 03:58:36 PM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 12 03:58:36 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 12 03:58:36 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 12 03:58:36 PM: Your problem is compiled with the CPP canonicalization backend.\n", + "(CVXPY) Sep 12 03:58:36 PM: Compiling problem (target solver=GUROBI).\n", + "(CVXPY) Sep 12 03:58:36 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", + "(CVXPY) Sep 12 03:58:36 PM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 12 03:58:36 PM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 12 03:58:36 PM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 12 03:58:36 PM: Applying reduction GUROBI\n", + "(CVXPY) Sep 12 03:58:36 PM: Finished problem compilation (took 2.290e-01 seconds).\n", + "(CVXPY) Sep 12 03:58:36 PM: Invoking solver GUROBI to obtain a solution.\n", + "(CVXPY) Sep 12 03:59:56 PM: Problem status: optimal\n", + "(CVXPY) Sep 12 03:59:56 PM: Optimal value: 1.428e+01\n", + "(CVXPY) Sep 12 03:59:56 PM: Compilation took 2.290e-01 seconds\n", + "(CVXPY) Sep 12 03:59:56 PM: Solver (including time spent in interface) took 7.904e+01 seconds\n", + "(CORNETO) Sep 12 03:59:56 PM - INFO : Finished in 81.76 s.\n", + "(CORNETO) Sep 12 03:59:57 PM - INFO : 1/1 inputs mapped to the graph\n", + "(CORNETO) Sep 12 03:59:57 PM - INFO : 22/25 outputs mapped to the graph\n", + "(CORNETO) Sep 12 03:59:57 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", + "(CORNETO) Sep 12 03:59:57 PM - INFO : Finished. Final size: V x E = (0, 0).\n", + "(CORNETO) Sep 12 03:59:57 PM - INFO : 0/1 inputs after pruning.\n", + "(CORNETO) Sep 12 03:59:57 PM - INFO : 0/22 outputs after pruning.\n", + "(CORNETO) Sep 12 03:59:57 PM - INFO : Converting into a flow graph...\n", + "(CORNETO) Sep 12 03:59:57 PM - INFO : Creating a network flow problem...\n", + "(CORNETO) Sep 12 03:59:57 PM - INFO : Preprocess completed.\n", + "(CVXPY) Sep 12 03:59:57 PM: Your problem has 210 variables, 556 constraints, and 0 parameters.\n", + "(CVXPY) Sep 12 03:59:57 PM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 12 03:59:57 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 12 03:59:57 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 12 03:59:57 PM: Your problem is compiled with the CPP canonicalization backend.\n", + "(CVXPY) Sep 12 03:59:57 PM: Compiling problem (target solver=GUROBI).\n", + "(CVXPY) Sep 12 03:59:57 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", + "(CVXPY) Sep 12 03:59:57 PM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 12 03:59:57 PM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 12 03:59:57 PM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 12 03:59:57 PM: Applying reduction GUROBI\n", + "(CVXPY) Sep 12 03:59:57 PM: Finished problem compilation (took 6.207e-02 seconds).\n", + "(CVXPY) Sep 12 03:59:57 PM: Invoking solver GUROBI to obtain a solution.\n", + "(CVXPY) Sep 12 03:59:57 PM: Problem status: infeasible\n", + "(CVXPY) Sep 12 03:59:57 PM: Optimal value: inf\n", + "(CVXPY) Sep 12 03:59:57 PM: Compilation took 6.207e-02 seconds\n", + "(CVXPY) Sep 12 03:59:57 PM: Solver (including time spent in interface) took 7.468e-03 seconds\n", + "(CORNETO) Sep 12 03:59:57 PM - INFO : Finished in 0.19 s.\n", + "(CORNETO) Sep 12 03:59:58 PM - INFO : 1/1 inputs mapped to the graph\n", + "(CORNETO) Sep 12 03:59:58 PM - INFO : 19/25 outputs mapped to the graph\n", + "(CORNETO) Sep 12 03:59:58 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", + "(CORNETO) Sep 12 03:59:59 PM - INFO : Finished. Final size: V x E = (1189, 5808).\n", + "(CORNETO) Sep 12 03:59:59 PM - INFO : 1/1 inputs after pruning.\n", + "(CORNETO) Sep 12 03:59:59 PM - INFO : 16/19 outputs after pruning.\n", + "(CORNETO) Sep 12 03:59:59 PM - INFO : Converting into a flow graph...\n", + "(CORNETO) Sep 12 04:00:00 PM - INFO : Creating a network flow problem...\n", + "(CORNETO) Sep 12 04:00:00 PM - INFO : Preprocess completed.\n", + "(CVXPY) Sep 12 04:00:00 PM: Your problem has 26958 variables, 65630 constraints, and 0 parameters.\n", + "(CVXPY) Sep 12 04:00:00 PM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 12 04:00:00 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 12 04:00:00 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 12 04:00:00 PM: Your problem is compiled with the CPP canonicalization backend.\n", + "(CVXPY) Sep 12 04:00:00 PM: Compiling problem (target solver=GUROBI).\n", + "(CVXPY) Sep 12 04:00:00 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", + "(CVXPY) Sep 12 04:00:00 PM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 12 04:00:00 PM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 12 04:00:00 PM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 12 04:00:00 PM: Applying reduction GUROBI\n", + "(CVXPY) Sep 12 04:00:00 PM: Finished problem compilation (took 1.577e-01 seconds).\n", + "(CVXPY) Sep 12 04:00:00 PM: Invoking solver GUROBI to obtain a solution.\n", + "(CVXPY) Sep 12 04:00:25 PM: Problem status: optimal\n", + "(CVXPY) Sep 12 04:00:25 PM: Optimal value: 9.360e+00\n", + "(CVXPY) Sep 12 04:00:25 PM: Compilation took 1.577e-01 seconds\n", + "(CVXPY) Sep 12 04:00:25 PM: Solver (including time spent in interface) took 2.464e+01 seconds\n", + "(CORNETO) Sep 12 04:00:25 PM - INFO : Finished in 26.54 s.\n", + "(CORNETO) Sep 12 04:00:26 PM - INFO : 1/1 inputs mapped to the graph\n", + "(CORNETO) Sep 12 04:00:26 PM - INFO : 20/25 outputs mapped to the graph\n", + "(CORNETO) Sep 12 04:00:26 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", + "(CORNETO) Sep 12 04:00:26 PM - INFO : Finished. Final size: V x E = (0, 0).\n", + "(CORNETO) Sep 12 04:00:26 PM - INFO : 0/1 inputs after pruning.\n", + "(CORNETO) Sep 12 04:00:26 PM - INFO : 0/20 outputs after pruning.\n", + "(CORNETO) Sep 12 04:00:26 PM - INFO : Converting into a flow graph...\n", + "(CORNETO) Sep 12 04:00:26 PM - INFO : Creating a network flow problem...\n", + "(CORNETO) Sep 12 04:00:26 PM - INFO : Preprocess completed.\n", + "(CVXPY) Sep 12 04:00:26 PM: Your problem has 210 variables, 556 constraints, and 0 parameters.\n", + "(CVXPY) Sep 12 04:00:26 PM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 12 04:00:26 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 12 04:00:26 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 12 04:00:26 PM: Your problem is compiled with the CPP canonicalization backend.\n", + "(CVXPY) Sep 12 04:00:26 PM: Compiling problem (target solver=GUROBI).\n", + "(CVXPY) Sep 12 04:00:26 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", + "(CVXPY) Sep 12 04:00:26 PM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 12 04:00:26 PM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 12 04:00:26 PM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 12 04:00:26 PM: Applying reduction GUROBI\n", + "(CVXPY) Sep 12 04:00:26 PM: Finished problem compilation (took 6.065e-02 seconds).\n", + "(CVXPY) Sep 12 04:00:26 PM: Invoking solver GUROBI to obtain a solution.\n", + "(CVXPY) Sep 12 04:00:26 PM: Problem status: infeasible\n", + "(CVXPY) Sep 12 04:00:26 PM: Optimal value: inf\n", + "(CVXPY) Sep 12 04:00:26 PM: Compilation took 6.065e-02 seconds\n", + "(CVXPY) Sep 12 04:00:26 PM: Solver (including time spent in interface) took 6.036e-03 seconds\n", + "(CORNETO) Sep 12 04:00:26 PM - INFO : Finished in 0.17 s.\n", + "(CORNETO) Sep 12 04:00:27 PM - INFO : 1/1 inputs mapped to the graph\n", + "(CORNETO) Sep 12 04:00:27 PM - INFO : 18/25 outputs mapped to the graph\n", + "(CORNETO) Sep 12 04:00:27 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", + "(CORNETO) Sep 12 04:00:27 PM - INFO : Finished. Final size: V x E = (1192, 5819).\n", + "(CORNETO) Sep 12 04:00:27 PM - INFO : 1/1 inputs after pruning.\n", + "(CORNETO) Sep 12 04:00:27 PM - INFO : 18/18 outputs after pruning.\n", + "(CORNETO) Sep 12 04:00:27 PM - INFO : Converting into a flow graph...\n", + "(CORNETO) Sep 12 04:00:28 PM - INFO : Creating a network flow problem...\n", + "(CORNETO) Sep 12 04:00:28 PM - INFO : Preprocess completed.\n", + "(CVXPY) Sep 12 04:00:28 PM: Your problem has 27005 variables, 65742 constraints, and 0 parameters.\n", + "(CVXPY) Sep 12 04:00:28 PM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 12 04:00:28 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 12 04:00:28 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 12 04:00:28 PM: Your problem is compiled with the CPP canonicalization backend.\n", + "(CVXPY) Sep 12 04:00:28 PM: Compiling problem (target solver=GUROBI).\n", + "(CVXPY) Sep 12 04:00:28 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", + "(CVXPY) Sep 12 04:00:28 PM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 12 04:00:28 PM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 12 04:00:28 PM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 12 04:00:28 PM: Applying reduction GUROBI\n", + "(CVXPY) Sep 12 04:00:28 PM: Finished problem compilation (took 1.303e-01 seconds).\n", + "(CVXPY) Sep 12 04:00:28 PM: Invoking solver GUROBI to obtain a solution.\n", + "(CVXPY) Sep 12 04:00:32 PM: Problem status: optimal\n", + "(CVXPY) Sep 12 04:00:32 PM: Optimal value: 7.350e+00\n", + "(CVXPY) Sep 12 04:00:32 PM: Compilation took 1.303e-01 seconds\n", + "(CVXPY) Sep 12 04:00:32 PM: Solver (including time spent in interface) took 3.805e+00 seconds\n", + "(CORNETO) Sep 12 04:00:32 PM - INFO : Finished in 5.10 s.\n", + "(CORNETO) Sep 12 04:00:33 PM - INFO : 1/1 inputs mapped to the graph\n", + "(CORNETO) Sep 12 04:00:33 PM - INFO : 21/25 outputs mapped to the graph\n", + "(CORNETO) Sep 12 04:00:33 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", + "(CORNETO) Sep 12 04:00:33 PM - INFO : Finished. Final size: V x E = (1189, 5811).\n", + "(CORNETO) Sep 12 04:00:33 PM - INFO : 1/1 inputs after pruning.\n", + "(CORNETO) Sep 12 04:00:33 PM - INFO : 19/21 outputs after pruning.\n", + "(CORNETO) Sep 12 04:00:33 PM - INFO : Converting into a flow graph...\n", + "(CORNETO) Sep 12 04:00:34 PM - INFO : Creating a network flow problem...\n", + "(CORNETO) Sep 12 04:00:34 PM - INFO : Preprocess completed.\n", + "(CVXPY) Sep 12 04:00:34 PM: Your problem has 26961 variables, 65636 constraints, and 0 parameters.\n", + "(CVXPY) Sep 12 04:00:34 PM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 12 04:00:34 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 12 04:00:34 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 12 04:00:34 PM: Your problem is compiled with the CPP canonicalization backend.\n", + "(CVXPY) Sep 12 04:00:34 PM: Compiling problem (target solver=GUROBI).\n", + "(CVXPY) Sep 12 04:00:34 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", + "(CVXPY) Sep 12 04:00:34 PM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 12 04:00:34 PM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 12 04:00:34 PM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 12 04:00:34 PM: Applying reduction GUROBI\n", + "(CVXPY) Sep 12 04:00:34 PM: Finished problem compilation (took 1.739e-01 seconds).\n", + "(CVXPY) Sep 12 04:00:34 PM: Invoking solver GUROBI to obtain a solution.\n", + "(CVXPY) Sep 12 04:03:43 PM: Problem status: optimal\n", + "(CVXPY) Sep 12 04:03:43 PM: Optimal value: 6.370e+00\n", + "(CVXPY) Sep 12 04:03:43 PM: Compilation took 1.739e-01 seconds\n", + "(CVXPY) Sep 12 04:03:43 PM: Solver (including time spent in interface) took 1.891e+02 seconds\n", + "(CORNETO) Sep 12 04:03:44 PM - INFO : Finished in 190.60 s.\n", + "(CORNETO) Sep 12 04:03:45 PM - INFO : 1/1 inputs mapped to the graph\n", + "(CORNETO) Sep 12 04:03:45 PM - INFO : 19/25 outputs mapped to the graph\n", + "(CORNETO) Sep 12 04:03:45 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", + "(CORNETO) Sep 12 04:03:45 PM - INFO : Finished. Final size: V x E = (1187, 5792).\n", + "(CORNETO) Sep 12 04:03:45 PM - INFO : 1/1 inputs after pruning.\n", + "(CORNETO) Sep 12 04:03:45 PM - INFO : 15/19 outputs after pruning.\n", + "(CORNETO) Sep 12 04:03:45 PM - INFO : Converting into a flow graph...\n", + "(CORNETO) Sep 12 04:03:46 PM - INFO : Creating a network flow problem...\n", + "(CORNETO) Sep 12 04:03:46 PM - INFO : Preprocess completed.\n", + "(CVXPY) Sep 12 04:03:46 PM: Your problem has 26891 variables, 65466 constraints, and 0 parameters.\n", + "(CVXPY) Sep 12 04:03:46 PM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 12 04:03:46 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 12 04:03:46 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 12 04:03:46 PM: Your problem is compiled with the CPP canonicalization backend.\n", + "(CVXPY) Sep 12 04:03:46 PM: Compiling problem (target solver=GUROBI).\n", + "(CVXPY) Sep 12 04:03:46 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", + "(CVXPY) Sep 12 04:03:46 PM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 12 04:03:46 PM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 12 04:03:46 PM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 12 04:03:46 PM: Applying reduction GUROBI\n", + "(CVXPY) Sep 12 04:03:46 PM: Finished problem compilation (took 1.569e-01 seconds).\n", + "(CVXPY) Sep 12 04:03:46 PM: Invoking solver GUROBI to obtain a solution.\n", + "(CVXPY) Sep 12 04:04:06 PM: Problem status: optimal\n", + "(CVXPY) Sep 12 04:04:06 PM: Optimal value: 1.031e+01\n", + "(CVXPY) Sep 12 04:04:06 PM: Compilation took 1.569e-01 seconds\n", + "(CVXPY) Sep 12 04:04:06 PM: Solver (including time spent in interface) took 1.975e+01 seconds\n", + "(CORNETO) Sep 12 04:04:06 PM - INFO : Finished in 21.41 s.\n", + "(CORNETO) Sep 12 04:04:07 PM - INFO : 1/1 inputs mapped to the graph\n", + "(CORNETO) Sep 12 04:04:07 PM - INFO : 18/25 outputs mapped to the graph\n", + "(CORNETO) Sep 12 04:04:07 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", + "(CORNETO) Sep 12 04:04:07 PM - INFO : Finished. Final size: V x E = (1189, 5782).\n", + "(CORNETO) Sep 12 04:04:07 PM - INFO : 1/1 inputs after pruning.\n", + "(CORNETO) Sep 12 04:04:07 PM - INFO : 15/18 outputs after pruning.\n", + "(CORNETO) Sep 12 04:04:07 PM - INFO : Converting into a flow graph...\n", + "(CORNETO) Sep 12 04:04:08 PM - INFO : Creating a network flow problem...\n", + "(CORNETO) Sep 12 04:04:08 PM - INFO : Preprocess completed.\n", + "(CVXPY) Sep 12 04:04:08 PM: Your problem has 26857 variables, 65378 constraints, and 0 parameters.\n", + "(CVXPY) Sep 12 04:04:08 PM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 12 04:04:08 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 12 04:04:08 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 12 04:04:08 PM: Your problem is compiled with the CPP canonicalization backend.\n", + "(CVXPY) Sep 12 04:04:08 PM: Compiling problem (target solver=GUROBI).\n", + "(CVXPY) Sep 12 04:04:08 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", + "(CVXPY) Sep 12 04:04:08 PM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 12 04:04:08 PM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 12 04:04:08 PM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 12 04:04:08 PM: Applying reduction GUROBI\n", + "(CVXPY) Sep 12 04:04:08 PM: Finished problem compilation (took 1.171e-01 seconds).\n", + "(CVXPY) Sep 12 04:04:08 PM: Invoking solver GUROBI to obtain a solution.\n", + "(CVXPY) Sep 12 04:04:30 PM: Problem status: optimal\n", + "(CVXPY) Sep 12 04:04:30 PM: Optimal value: 1.036e+01\n", + "(CVXPY) Sep 12 04:04:30 PM: Compilation took 1.171e-01 seconds\n", + "(CVXPY) Sep 12 04:04:30 PM: Solver (including time spent in interface) took 2.177e+01 seconds\n", + "(CORNETO) Sep 12 04:04:30 PM - INFO : Finished in 23.06 s.\n", + "(CORNETO) Sep 12 04:04:31 PM - INFO : 1/1 inputs mapped to the graph\n", + "(CORNETO) Sep 12 04:04:31 PM - INFO : 17/25 outputs mapped to the graph\n", + "(CORNETO) Sep 12 04:04:31 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", + "(CORNETO) Sep 12 04:04:32 PM - INFO : Finished. Final size: V x E = (1190, 5789).\n", + "(CORNETO) Sep 12 04:04:32 PM - INFO : 1/1 inputs after pruning.\n", + "(CORNETO) Sep 12 04:04:32 PM - INFO : 15/17 outputs after pruning.\n", + "(CORNETO) Sep 12 04:04:32 PM - INFO : Converting into a flow graph...\n", + "(CORNETO) Sep 12 04:04:32 PM - INFO : Creating a network flow problem...\n", + "(CORNETO) Sep 12 04:04:33 PM - INFO : Preprocess completed.\n", + "(CVXPY) Sep 12 04:04:33 PM: Your problem has 26888 variables, 65454 constraints, and 0 parameters.\n", + "(CVXPY) Sep 12 04:04:33 PM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 12 04:04:33 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 12 04:04:33 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 12 04:04:33 PM: Your problem is compiled with the CPP canonicalization backend.\n", + "(CVXPY) Sep 12 04:04:33 PM: Compiling problem (target solver=GUROBI).\n", + "(CVXPY) Sep 12 04:04:33 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", + "(CVXPY) Sep 12 04:04:33 PM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 12 04:04:33 PM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 12 04:04:33 PM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 12 04:04:33 PM: Applying reduction GUROBI\n", + "(CVXPY) Sep 12 04:04:33 PM: Finished problem compilation (took 1.318e-01 seconds).\n", + "(CVXPY) Sep 12 04:04:33 PM: Invoking solver GUROBI to obtain a solution.\n", + "(CVXPY) Sep 12 04:05:45 PM: Problem status: optimal\n", + "(CVXPY) Sep 12 04:05:45 PM: Optimal value: 1.036e+01\n", + "(CVXPY) Sep 12 04:05:45 PM: Compilation took 1.318e-01 seconds\n", + "(CVXPY) Sep 12 04:05:45 PM: Solver (including time spent in interface) took 7.204e+01 seconds\n", + "(CORNETO) Sep 12 04:05:45 PM - INFO : Finished in 73.86 s.\n", + "(CORNETO) Sep 12 04:05:46 PM - INFO : 1/1 inputs mapped to the graph\n", + "(CORNETO) Sep 12 04:05:46 PM - INFO : 17/25 outputs mapped to the graph\n", + "(CORNETO) Sep 12 04:05:46 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", + "(CORNETO) Sep 12 04:05:47 PM - INFO : Finished. Final size: V x E = (1189, 5787).\n", + "(CORNETO) Sep 12 04:05:47 PM - INFO : 1/1 inputs after pruning.\n", + "(CORNETO) Sep 12 04:05:47 PM - INFO : 12/17 outputs after pruning.\n", + "(CORNETO) Sep 12 04:05:47 PM - INFO : Converting into a flow graph...\n", + "(CORNETO) Sep 12 04:05:48 PM - INFO : Creating a network flow problem...\n", + "(CORNETO) Sep 12 04:05:48 PM - INFO : Preprocess completed.\n", + "(CVXPY) Sep 12 04:05:48 PM: Your problem has 26886 variables, 65452 constraints, and 0 parameters.\n", + "(CVXPY) Sep 12 04:05:48 PM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 12 04:05:48 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 12 04:05:48 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 12 04:05:48 PM: Your problem is compiled with the CPP canonicalization backend.\n", + "(CVXPY) Sep 12 04:05:48 PM: Compiling problem (target solver=GUROBI).\n", + "(CVXPY) Sep 12 04:05:48 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", + "(CVXPY) Sep 12 04:05:48 PM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 12 04:05:48 PM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 12 04:05:48 PM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 12 04:05:48 PM: Applying reduction GUROBI\n", + "(CVXPY) Sep 12 04:05:48 PM: Finished problem compilation (took 1.527e-01 seconds).\n", + "(CVXPY) Sep 12 04:05:48 PM: Invoking solver GUROBI to obtain a solution.\n", "\n", "Interrupt request received\n", - "(CVXPY) Sep 12 02:35:38 PM: Problem status: user_limit\n", - "(CVXPY) Sep 12 02:35:38 PM: Optimal value: 1.130e+01\n", - "(CVXPY) Sep 12 02:35:38 PM: Compilation took 1.354e-01 seconds\n", - "(CVXPY) Sep 12 02:35:38 PM: Solver (including time spent in interface) took 2.932e+02 seconds\n", - "(CORNETO) Sep 12 02:35:38 PM - INFO : Finished in 294.57 s.\n", - "(CORNETO) Sep 12 02:35:40 PM - INFO : 1/1 inputs mapped to the graph\n", - "(CORNETO) Sep 12 02:35:40 PM - INFO : 21/25 outputs mapped to the graph\n", - "(CORNETO) Sep 12 02:35:40 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", - "(CORNETO) Sep 12 02:35:40 PM - INFO : Finished. Final size: V x E = (1191, 5814).\n", - "(CORNETO) Sep 12 02:35:40 PM - INFO : 1/1 inputs after pruning.\n", - "(CORNETO) Sep 12 02:35:40 PM - INFO : 18/21 outputs after pruning.\n", - "(CORNETO) Sep 12 02:35:40 PM - INFO : Converting into a flow graph...\n", - "(CORNETO) Sep 12 02:35:41 PM - INFO : Creating a network flow problem...\n", - "(CORNETO) Sep 12 02:35:41 PM - INFO : Preprocess completed.\n", - "(CVXPY) Sep 12 02:35:41 PM: Your problem has 26982 variables, 65686 constraints, and 0 parameters.\n", - "(CVXPY) Sep 12 02:35:41 PM: It is compliant with the following grammars: DCP, DQCP\n", - "(CVXPY) Sep 12 02:35:41 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", - "(CVXPY) Sep 12 02:35:41 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", - "(CVXPY) Sep 12 02:35:41 PM: Your problem is compiled with the CPP canonicalization backend.\n", - "(CVXPY) Sep 12 02:35:41 PM: Compiling problem (target solver=GUROBI).\n", - "(CVXPY) Sep 12 02:35:41 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", - "(CVXPY) Sep 12 02:35:41 PM: Applying reduction CvxAttr2Constr\n", - "(CVXPY) Sep 12 02:35:41 PM: Applying reduction Qp2SymbolicQp\n", - "(CVXPY) Sep 12 02:35:41 PM: Applying reduction QpMatrixStuffing\n", - "(CVXPY) Sep 12 02:35:41 PM: Applying reduction GUROBI\n", - "(CVXPY) Sep 12 02:35:41 PM: Finished problem compilation (took 1.782e-01 seconds).\n", - "(CVXPY) Sep 12 02:35:41 PM: Invoking solver GUROBI to obtain a solution.\n", - "(CVXPY) Sep 12 02:35:48 PM: Problem status: optimal\n", - "(CVXPY) Sep 12 02:35:48 PM: Optimal value: 7.370e+00\n", - "(CVXPY) Sep 12 02:35:48 PM: Compilation took 1.782e-01 seconds\n", - "(CVXPY) Sep 12 02:35:48 PM: Solver (including time spent in interface) took 6.532e+00 seconds\n", - "(CORNETO) Sep 12 02:35:48 PM - INFO : Finished in 8.42 s.\n" + "(CVXPY) Sep 12 04:32:16 PM: Problem status: user_limit\n", + "(CVXPY) Sep 12 04:32:16 PM: Optimal value: 1.339e+01\n", + "(CVXPY) Sep 12 04:32:16 PM: Compilation took 1.527e-01 seconds\n", + "(CVXPY) Sep 12 04:32:16 PM: Solver (including time spent in interface) took 1.587e+03 seconds\n", + "(CORNETO) Sep 12 04:32:16 PM - INFO : Finished in 1589.62 s.\n", + "(CORNETO) Sep 12 04:32:28 PM - INFO : 1/1 inputs mapped to the graph\n", + "(CORNETO) Sep 12 04:32:28 PM - INFO : 20/25 outputs mapped to the graph\n", + "(CORNETO) Sep 12 04:32:28 PM - INFO : Pruning the graph with size: V x E = (4946, 13172)...\n", + "(CORNETO) Sep 12 04:32:29 PM - INFO : Finished. Final size: V x E = (1192, 5798).\n", + "(CORNETO) Sep 12 04:32:29 PM - INFO : 1/1 inputs after pruning.\n", + "(CORNETO) Sep 12 04:32:29 PM - INFO : 15/20 outputs after pruning.\n", + "(CORNETO) Sep 12 04:32:29 PM - INFO : Converting into a flow graph...\n", + "(CORNETO) Sep 12 04:32:30 PM - INFO : Creating a network flow problem...\n", + "(CORNETO) Sep 12 04:32:31 PM - INFO : Preprocess completed.\n", + "(CVXPY) Sep 12 04:32:31 PM: Your problem has 26930 variables, 65556 constraints, and 0 parameters.\n", + "(CVXPY) Sep 12 04:32:31 PM: It is compliant with the following grammars: DCP, DQCP\n", + "(CVXPY) Sep 12 04:32:31 PM: (If you need to solve this problem multiple times, but with different data, consider using parameters.)\n", + "(CVXPY) Sep 12 04:32:31 PM: CVXPY will first compile your problem; then, it will invoke a numerical solver to obtain a solution.\n", + "(CVXPY) Sep 12 04:32:31 PM: Your problem is compiled with the CPP canonicalization backend.\n", + "(CVXPY) Sep 12 04:32:31 PM: Compiling problem (target solver=GUROBI).\n", + "(CVXPY) Sep 12 04:32:31 PM: Reduction chain: CvxAttr2Constr -> Qp2SymbolicQp -> QpMatrixStuffing -> GUROBI\n", + "(CVXPY) Sep 12 04:32:31 PM: Applying reduction CvxAttr2Constr\n", + "(CVXPY) Sep 12 04:32:31 PM: Applying reduction Qp2SymbolicQp\n", + "(CVXPY) Sep 12 04:32:31 PM: Applying reduction QpMatrixStuffing\n", + "(CVXPY) Sep 12 04:32:31 PM: Applying reduction GUROBI\n", + "(CVXPY) Sep 12 04:32:31 PM: Finished problem compilation (took 3.051e-01 seconds).\n", + "(CVXPY) Sep 12 04:32:31 PM: Invoking solver GUROBI to obtain a solution.\n", + "(CVXPY) Sep 12 04:32:33 PM: Problem status: optimal\n", + "(CVXPY) Sep 12 04:32:33 PM: Optimal value: 1.034e+01\n", + "(CVXPY) Sep 12 04:32:33 PM: Compilation took 3.051e-01 seconds\n", + "(CVXPY) Sep 12 04:32:33 PM: Solver (including time spent in interface) took 1.731e+00 seconds\n", + "(CORNETO) Sep 12 04:32:33 PM - INFO : Finished in 4.89 s.\n" ] } ], "source": [ - "offtarget_results = nc.eval.get_offtarget_panacea_evaluation(cell='ASPC')" + "offtarget_results = nc.eval.get_offtarget_panacea_evaluation(cell='H1793')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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rbdu2uYxNTExUcXGxxowZY5vnzJkzLusrbb0///yzy9f+/v6Kjo6+Kq+MwxxciQWAS/TQQw+pZ8+e+stf/uLcd9ddd6lr16566qmnJEnLli1zeQZqSV544QV16tRJzZs314ABA3Ty5Em9+uqrCgwMLPXZoZercuXKmjFjhvr166ebbrpJvXr1UtWqVfXDDz/ogw8+0M0336xp06ZJkl555RW1bNlSN910k/7+97+rVq1a2rdvnz744APn1eWxY8dq5cqVatmypR544AF5enrq9ddfV2FhoSZNmmQ7f8+ePfXss8/Kx8dHAwYMsD1Pd+LEifrss8/UtGlTDRo0SPXr11d2dra+/vprrVq1StnZ2Rd9fxUqVNDs2bPVqVMnxcfHq3///oqIiNDBgwf12WefqXLlys4/Ffzcc8/p448/1i233KIHHnhAZ86c0auvvqr4+Hht3br1ouepWLGinn/+efXv31+tW7dW79699eOPP2rq1KmKiorSQw89VN5vSbk0btxYM2bM0NixYxUdHa1q1arp1ltv1W233abRo0erf//+atGihbZt26a33nrL5YN2ZRk/fry6du2qm2++Wf3791dOTo6mTZumBg0auBTb1q1ba/DgwZowYYLS09PVvn17VaxYUXv27NGSJUs0depU3XXXXRddb/369dWmTRs1btxYISEh2rx5s9555x0NHTr0iuaFa4x7H44AAGb54IMPLH9/f+vQoUO21yZMmGBVr17dCg8Pt55//vlyzbdq1Srr5ptvtnx9fa3KlStbt99+u7Vjxw6XMZfziK0LH+t0/lwdOnSwAgMDLR8fH6tOnTpWUlKStXnzZpdx//vf/6zu3btbQUFBlo+PjxUbG2s988wzLmO+/vprq0OHDpa/v79VqVIlq23bttYXX3xR4nn37NnjfDzZuefrXujHH3+0HnzwQatmzZpWxYoVrbCwMOuvf/2rNWvWrHJn8c0331h/+9vfrNDQUMvb29uKjIy0EhMTrdWrV7uMW7t2rdW4cWPLy8vLql27tjVz5kzno6/KY/HixdaNN95oeXt7WyEhIdbdd99tHThwwGXMlXjE1uHDh60uXbpYAQEBliTn46tOnTplPfLII1Z4eLjl6+tr3Xzzzdb69eut1q1buzziqqy8Fi1aZMXFxVne3t5WgwYNrPfff9+68847rbi4ONvYWbNmWY0bN7Z8fX2tgIAAq2HDhtbjjz/u8t9CaesdO3as9Ze//MUKCgqyfH19rbi4OGvcuHHOZ8wCl8NhWRf8PgIAAFyzGjVqpKpVq/LXtHDV455YAACuQadPn9aZM2dc9q1Zs0Zbtmy56J82Bq4WXIkFAOAatG/fPiUkJKhv376qXr26du3apZkzZyowMFD/+9//SnzeLHA14YNdAABcg4KDg9W4cWPNnj1bR44ckZ+fn7p06aKJEydSYGEErsQCAADAONwTCwAAAONQYgEAAGAcSiwAAACMQ4kFAACAcXg6AVCClrevdfcSrippy1qTyQXIxI5M7MjEjkzsyMQubVnrMsdwJRYAAADGocQCAADAOJRYAAAAGIcSCwAAAONQYgEAAGAcSiwAAACMQ4kFAACAcSixAAAAMA4lFgAAAMahxAIAAMA4lFgAAAAYhxILAAAA41BiAQAAYBxKLAAAAIxDiQUAAIBxKLEAAAAwDiUWAAAAxqHEAgAAwDiUWAAAABiHEgsAAADjUGIBAABgHEosAAAAjEOJBQAAgHEosQAAADAOJRYAAADGocQCAADAOJRYAAAAGIcSCwAAAONQYgEAAGAcSiwAAACMQ4kFAACAcSixAAAAMA4lFgAAAMahxAIAAMA4lFgAAAAYhxILAAAA41BiAQAAYBxK7CVISkpSt27dbPvXrFkjh8Oh3NxcnTp1SklJSWrYsKE8PT1LHJ+UlCSHw2Hb4uPjnWMKCgo0fPhwRUZGytfXVy1atNCXX37pMs+oUaMUFxcnPz8/BQcHKyEhQRs3brzk9zV48GB5eHhoyZIlttdGjRpV4lpXrVrlHLNw4UJ5eHjowQcfdO5r06ZNiced29q0aSNJioqK0ssvv+w8LioqSg6HQxs2bHBZx/Dhw53HnFtXo0aNSl1nYGCgbrnlFq1du/aS87ga/K1zdS2Z3VSrU2/RrBdvVL26Ae5ektuRiR2Z2JGJHZnYkYmdiZlQYq+w4uJi+fr6atiwYUpISChxzNSpU5WVleXcMjMzFRISoh49ejjHDBw4UCtXrlRKSoq2bdum9u3bKyEhQQcPHnSOiYmJ0bRp07Rt2zalpaUpKipK7du315EjR8q93hMnTmjRokV6/PHHNWfOnBLHxMfHu6w3KytLrVq1cr6enJysxx9/XAsXLtSpU6ckSe+++65z7KZNmyRJq1atcu579913S12Tj4+PnnjiiXK/h5LWuX79etWtW1e33Xab8vLyLnkud7q1ZVUNHVhHcxfu04DhXylj7zFNHt1QQYEV3b00tyETOzKxIxM7MrEjEztTM6HEXmF+fn6aMWOGBg0apLCwsBLHBAYGKiwszLlt3rxZOTk56t+/vyTp5MmTSk1N1aRJk9SqVStFR0dr1KhRio6O1owZM5zz9OnTRwkJCapdu7bi4+M1efJk5efna+vWreVe75IlS1S/fn09+eST+vzzz5WZmWkb4+np6bLesLAweXl5SZL27t2rL774Qk8++aRiYmKc5TQkJMQ5tmrVqpKk0NBQ576QkJBS1/T3v/9dGzZs0Icfflju93HhOuvXr6/Ro0fr2LFj2r179yXN4269utXQshVZ+nD1j9qXeUIvTN+jU4VndVu7kn+ergVkYkcmdmRiRyZ2ZGJnaiaU2KtAcnKyEhISFBkZKUk6c+aMiouL5ePj4zLO19dXaWlpJc5RVFSkWbNmKTAwUDfccMMlnbtv374KDAxUp06dNG/evEta+9y5c9WlSxcFBgaqb9++Sk5OvqTjS1KrVi0NGTJEI0aM0NmzZy9rjsLCQs2dO1dBQUGKjY391Wv6vXh6OhQTHaDNW3Kc+yxL2pyeo/jYym5cmfuQiR2Z2JGJHZnYkYmdyZlQYi/R8uXL5e/v77J16tTpsuc7dOiQPvroIw0cONC5LyAgQM2bN9eYMWN06NAhFRcXa8GCBVq/fr2ysrJKXI+Pj4+mTJmilStXqkqVKuU69549e7Rhwwb17NlTktS3b1/NnTtXlmW5jNu2bZvL+/3LX/4iSTp79qzmzZunvn37SpJ69eqltLQ07d2797LzOOdf//qX9u7dq7feeqvcx5y/Tl9fX7344otauHChKle+uv8jPF9g5Yry9HAoO+e0y/7s3NMKDfZy06rci0zsyMSOTOzIxI5M7EzOhBJ7idq2bav09HSXbfbs2Zc93/z58xUUFGT7AFhKSoosy1JERIS8vb31yiuvqHfv3qpQwfVbdm49X3zxhTp27KjExET99NNP5Tr3nDlz1KFDB2fp7dy5s/Ly8vTpp5+6jIuNjXV5v6mpqZKklStX6vjx4+rcubMkqUqVKmrXrl2p99ZeiqpVq+rRRx/Vs88+q6KionIdc/46v/rqK91///3q0aOHNm/eXOoxhYWFys/Pd9kKCwt/9foBAMBvixJ7ifz8/BQdHe2yRUREXNZclmVpzpw56tevn/Me03Pq1KmjtWvX6tixY8rMzNSmTZt0+vRp1a5du8T1NGvWTMnJyfL09CzXr/SLi4s1f/58ffDBB/L09JSnp6cqVaqk7OxsWwn18vJyeb81a9aU9MutCNnZ2fL19XXO8eGHH2r+/PmXfRvA+R5++GGdPHlS06dPL9f489d54403auLEiYqIiHB5+sGFJkyYoMDAQJdtwoQJv3rtlysv/7TOFFsKCXa9mT4kqKJ+zilfmf+jIRM7MrEjEzsysSMTO5MzocS60dq1a5WRkaEBAwaUOsbPz0/h4eHKycnRihUr1LVr14vOefbs2XJdSfzwww9VUFCgb775xuUq68KFC/Xuu+8qNzf3osf//PPPeu+997Ro0SKX47/55hvl5OTok08+KXMNZfH399czzzyjcePGqaCg4LLm8PDw0MmTJ0t9fcSIEcrLy3PZRowYcblL/tXOnLG0O6NAja8Pdu5zOKTGNwRr+7f5bluXO5GJHZnYkYkdmdiRiZ3JmXi6ewF/RDt27FBRUZGys7NVUFCg9PR0SXJ5rqn0y5XMpk2bqkGDBrY5VqxYIcuyFBsbq4yMDD322GOKi4tzPsHg+PHjGjdunO644w6Fh4fr6NGjeu2113Tw4EGXR3WVJjk5WV26dLF9CKx+/fp66KGH9NZbb7k89/VCKSkpCg0NVWJiohwOh8trnTt3VnJysjp27FjmOsry97//XVOmTNHbb7+tpk2bXnTsmTNndPjwYUm/PGd38eLF2rFjx0Uf1+Xt7S1vb+9fvc4radHSA3r6oTjtyijQzt0FSuwaIV+fCvpg1WF3L81tyMSOTOzIxI5M7MjEztRMKLG/gc6dO2v//v3Or2+88UZJcvnAVF5enlJTUzV16tQS5zh3RfDAgQMKCQnRnXfeqXHjxqlixV8u93t4eGjXrl2aP3++jh49qtDQUDVp0kTr1q1z+aMJJfnxxx/1wQcf6O2337a9VqFCBXXv3l3JyckXLbFz5sxR9+7dbQVWku68807169dPR48eLfeHzEpTsWJFjRkzRn369Clz7Pbt2xUeHi5JqlSpkurUqaMZM2bonnvu+VVr+L19mnZEQYEVNfDuKIUEeynj+2N6ZOQ25eSeLvvgPygysSMTOzKxIxM7MrEzNROHdeFH0QGo5e1m/qWv30rastZkcgEysSMTOzKxIxM7MrFLW9a6zDHcEwsAAADjUGL/oMaPH297nu2VeK4tAADA1YB7Yv+ghgwZosTExBJf8/X1/Z1XAwAAcGVRYv+gQkJCFBIS4u5lAAAA/Ca4nQAAAADGocQCAADAOJRYAAAAGIcSCwAAAONQYgEAAGAcSiwAAACMQ4kFAACAcSixAAAAMA4lFgAAAMahxAIAAMA4lFgAAAAYhxILAAAA41BiAQAAYBxKLAAAAIxDiQUAAIBxKLEAAAAwDiUWAAAAxqHEAgAAwDiUWAAAABiHEgsAAADjUGIBAABgHEosAAAAjEOJBQAAgHEosQAAADAOJRYAAADGocQCAADAOJRYAAAAGIcSCwAAAONQYgEAAGAcSiwAAACMQ4kFAACAcSixAAAAMA4lFgAAAMahxAIAAMA4lFgAAAAYx2FZluXuRQAAAACXwtPdCwCuRkMn57l7CVeVaQ8HkskFpj0cqJa3r3X3Mq4qactak8kFyMSOTOzIxC5tWesyx3A7AQAAAIxDiQUAAIBxKLEAAAAwDiUWAAAAxqHEAgAAwDiUWAAAABiHEgsAAADjUGIBAABgHEosAAAAjEOJBQAAgHEosQAAADAOJRYAAADGocQCAADAOJRYAAAAGIcSCwAAAONQYgEAAGAcSiwAAACMQ4kFAACAcSixAAAAMA4lFgAAAMahxAIAAMA4lFgAAAAYhxILAAAA41BiAQAAYBxKLAAAAIxDiQUAAIBxKLEAAAAwDiUWAAAAxqHEAgAAwDiUWAAAABiHEgsAAADjUGIBAABgHEosAAAAjEOJBQAAgHEosQAAADAOJRYAAADGocQCAADAONd8iU1KSlK3bt1s+9esWSOHw6Hc3FydOnVKSUlJatiwoTw9PUscn5SUJIfDYdvi4+OdYwoKCjR8+HBFRkbK19dXLVq00Jdffukyz6hRoxQXFyc/Pz8FBwcrISFBGzduLPf7Of/cnp6e+tOf/qSHH35YhYWFtrEnT55USEiIqlSpUuLrUVFRtvdTo0aNi77ucDg0ceJEjRo1qsTXzt/O5XZ+nudynDhxostali5d6jxGcv3+nP/1uc3X11fx8fGaNWtWubO7GjgcUpcW3ho1IECTh1XWyPv81bGpt7uX5VZkUrq/da6uJbObanXqLZr14o2qVzfA3UtyOzKxIxM7MrEzMZNrvsSWR3FxsXx9fTVs2DAlJCSUOGbq1KnKyspybpmZmQoJCVGPHj2cYwYOHKiVK1cqJSVF27ZtU/v27ZWQkKCDBw86x8TExGjatGnatm2b0tLSFBUVpfbt2+vIkSPlXu/cuXOVlZWlvXv3avr06UpJSdHYsWNt41JTUxUfH6+4uDgtXbq0xLlGjx7t8r6++eabi76elZWlf/zjH3r00Udd9tWoUcM2tjQ+Pj56/vnnlZOTU+73fM63336rrKws7dixQ4MHD9b999+v1atXX/I87tKuibduucFLSz49qbHzCvTeulNKaOKt1jd6uXtpbkMmJbu1ZVUNHVhHcxfu04DhXylj7zFNHt1QQYEV3b00tyETOzKxIxM7UzOhxJaDn5+fZsyYoUGDBiksLKzEMYGBgQoLC3NumzdvVk5Ojvr37y/pl6ueqampmjRpklq1aqXo6GiNGjVK0dHRmjFjhnOePn36KCEhQbVr11Z8fLwmT56s/Px8bd26tdzrDQoKUlhYmGrWrKnbbrtNXbt21ddff20bl5ycrL59+6pv375KTk4uca6AgACX91W1atWLvh4WFiY/Pz/5+/u77PPw8LCNLU1CQoLCwsI0YcKEcr/nc6pVq6awsDDVqlVLw4YNU61atUp871er2tU9tPW7M9q+94yy8y2l7zmjXfvPKDLMw91LcxsyKVmvbjW0bEWWPlz9o/ZlntAL0/foVOFZ3dau9P+2/ujIxI5M7MjEztRMKLG/keTkZCUkJCgyMlKSdObMGRUXF8vHx8dlnK+vr9LS0kqco6ioSLNmzVJgYKBuuOGGy1rH7t279emnn6pp06Yu+7/77jutX79eiYmJSkxM1Lp167R///7LOseV5uHhofHjx+vVV1/VgQMHLmsOy7L08ccf64cffrC996vZ94eKFVvTU9WCfvlPM6JKBdWu7qEde8+4eWXuQyZ2np4OxUQHaPOW///bCsuSNqfnKD62shtX5j5kYkcmdmRiZ3ImlFhJy5cvl7+/v8vWqVOny57v0KFD+uijjzRw4EDnvoCAADVv3lxjxozRoUOHVFxcrAULFmj9+vW2X62fW4+Pj4+mTJmilStXqkqVKuU+f+/evZ3Hx8bGKj4+XiNGjHAZM2fOHHXq1EnBwcEKCQlRhw4dNHfuXNtcTzzxhEsur7zyykVf9/f317p168q91tJ0795djRo10siRIy/puBo1asjf319eXl7q0qWLRo4cqVatWv3q9fxeVm4q1FffFulf/f019Z+V9UQ/f635ukibd51299LchkzsAitXlKeHQ9k5rhlk555WaPC1eZsFmdiRiR2Z2Jmciae7F3A1aNu2rcuv9CVp48aN6tu372XNN3/+fAUFBdk+AJaSkqL77rtPERER8vDw0E033aTevXvrq6++sq0nPT1dR48e1RtvvKHExERt3LhR1apVK9f5p0yZooSEBBUXFysjI0MPP/yw+vXrp0WLFkn65R7f+fPna+rUqc5j+vbtq0cffVTPPvusKlT4//+2eeyxx5SUlOT8+sIyfeHrkhQREVGudZbl+eef16233qpHH3203MesW7dOAQEBKiws1KZNmzR06FCFhITo/vvvL3F8YWGh7UNt3t7u+9DQTbEV1aSel+Z/eFJZPxcroqqH7mrjo7zjZ7Vxx7VZ2sgEAFASSqx+uec1OjraZd+v+TX2nDlz1K9fP3l5uf4Lpk6dOlq7dq2OHz+u/Px8hYeHq2fPnqpdu3aJ64mOjlazZs1Ut25dJScn266mliYsLMz5fmJjY1VQUKDevXtr7Nixio6O1ooVK3Tw4EH17NnT5bji4mKtXr1a7dq1c+6rUqWKLZvzlfX6r9GqVSt16NBBI0aMsBXl0tSqVUtBQUGSpPj4eG3cuFHjxo0rtcROmDBBzz33nMu+kSNHSpUf+jVLv2zdWvn835XHX8rZoaNnFVK5gtr9xfuaLWxkYpeXf1pnii2FBLt+6CIkqKJ+zily06rci0zsyMSOTOxMzoTbCa6wtWvXKiMjQwMGDCh1jJ+fn8LDw5WTk6MVK1aoa9euF53z7NmzJT4Cq7w8PH75AMzJkycl/XK/bq9evZSenu6y9erVq9QPeLnLxIkTtWzZMq1fv/6yjvfw8HC+75KMGDFCeXl5Llt5/7HwW/DylM5arvuss1IFR8njrwVkYnfmjKXdGQVqfH2wc5/DITW+IVjbv81348rch0zsyMSOTOxMzoQrseW0Y8cOFRUVKTs7WwUFBUpPT5ckNWrUyGVccnKymjZtqgYNGtjmWLFihSzLUmxsrDIyMvTYY48pLi7O+QSD48ePa9y4cbrjjjsUHh6uo0eP6rXXXtPBgwddHtVVltzcXB0+fFhnz57Vnj17NHr0aMXExKhevXo6cuSIli1bpvfff9+2xnvuuUfdu3dXdna2QkJCynWugoICHT582GVfpUqVVLnylbkZvGHDhrr77rtt9+KW5qefftKpU6ectxOkpKTorrvuKnW8t7d3KbcPnLrMFf86274/ow5NvZVTcFZZPxerRjUPtW3spQ3br80rjhKZlGbR0gN6+qE47coo0M7dBUrsGiFfnwr6YNXhsg/+gyITOzKxIxM7UzOhxJZT586dXT69f+ONN0r65faBc/Ly8pSamupyr+n5zl3lO3DggEJCQnTnnXdq3Lhxqljxl0v4Hh4e2rVrl+bPn6+jR48qNDRUTZo00bp161z+aEJZzpVih8OhsLAwtWrVSuPHj5enp6fefPNN+fn56a9//avtuL/+9a/y9fXVggULNGzYsHKd69lnn9Wzzz7rsm/w4MGaOXNmuddbltGjR2vx4sXlGhsbGytJ8vT0VM2aNTV48GCNGjXqiq3lt7bk05O67WYf9fyrr/wrOZR37Kz+u7VIH224/CvxpiOTkn2adkRBgRU18O4ohQR7KeP7Y3pk5Dbl5F675Z5M7MjEjkzsTM3EYZ3fwgBIkoZOznP3Eq4q0x4OJJMLTHs4UC1vX+vuZVxV0pa1JpMLkIkdmdiRiV3astZljuGeWAAAABiHEmuQ8ePH257JeiWeawsAAGAa7ok1yJAhQ5SYmFjia76+vr/zagAAANyHEmuQkJCQcj81AAAA4I+M2wkAAABgHEosAAAAjEOJBQAAgHEosQAAADAOJRYAAADGocQCAADAOJRYAAAAGIcSCwAAAONQYgEAAGAcSiwAAACMQ4kFAACAcSixAAAAMA4lFgAAAMahxAIAAMA4lFgAAAAYhxILAAAA41BiAQAAYBxKLAAAAIxDiQUAAIBxKLEAAAAwDiUWAAAAxqHEAgAAwDiUWAAAABiHEgsAAADjUGIBAABgHEosAAAAjEOJBQAAgHEosQAAADAOJRYAAADGocQCAADAOJRYAAAAGIcSCwAAAONQYgEAAGAcSiwAAACMQ4kFAACAcRyWZVnuXgQAAABwKTzdvQDgatTy9rXuXsJVJW1ZazK5AJnYkYkdmdiRiR2Z2KUta13mGG4nAAAAgHEosQAAADAOJRYAAADGocQCAADAOJRYAAAAGIcSCwAAAONQYgEAAGAcSiwAAACMQ4kFAACAcSixAAAAMA4lFgAAAMahxAIAAMA4lFgAAAAYhxILAAAA41BiAQAAYBxKLAAAAIxDiQUAAIBxKLEAAAAwDiUWAAAAxqHEAgAAwDiUWAAAABiHEgsAAADjUGIBAABgHEosAAAAjEOJBQAAgHEosQAAADAOJRYAAADGocQCAADAOJRYAAAAGIcSCwAAAONQYgEAAGAcSiwAAACMQ4kFAACAcSixAAAAMA4lFgAAAMahxAIAAMA4lFgAAAAY55ossUlJSerWrZtt/5o1a+RwOJSbm6tTp04pKSlJDRs2lKenZ4njk5KS5HA4bFt8fLxzTEFBgYYPH67IyEj5+vqqRYsW+vLLL13mGTVqlOLi4uTn56fg4GAlJCRo48aN5X4/55/b09NTf/rTn/Twww+rsLDQZVxmZqbuu+8+Va9eXV5eXoqMjNQ///lP/fzzzy7j9u7dqz59+qh69ery8fFRjRo11LVrV+3atUuStG/fPjkcDqWnp9vW0qZNGw0fPtyZ5cW2NWvWaN68eQoKCnIeP2/ePDkcDnXs2NFl3tzcXOcx57/vpUuXXnIOJvhb5+paMrupVqfeolkv3qh6dQPcvSS3IxM7MrEjEzsysSMTOxMzuSZLbHkUFxfL19dXw4YNU0JCQoljpk6dqqysLOeWmZmpkJAQ9ejRwzlm4MCBWrlypVJSUrRt2za1b99eCQkJOnjwoHNMTEyMpk2bpm3btiktLU1RUVFq3769jhw5Uu71zp07V1lZWdq7d6+mT5+ulJQUjR071vn6999/rz//+c/as2ePFi5cqIyMDM2cOVOrV69W8+bNlZ2dLUk6ffq02rVrp7y8PL377rv69ttvtXjxYjVs2FC5ubnlXk+LFi1csklMTFTHjh1d9rVo0aLEYz09PbVq1Sp99tln5T5feXMwwa0tq2rowDqau3CfBgz/Shl7j2ny6IYKCqzo7qW5DZnYkYkdmdiRiR2Z2JmaCSW2FH5+fpoxY4YGDRqksLCwEscEBgYqLCzMuW3evFk5OTnq37+/JOnkyZNKTU3VpEmT1KpVK0VHR2vUqFGKjo7WjBkznPP06dNHCQkJql27tuLj4zV58mTl5+dr69at5V5vUFCQwsLCVLNmTd12223q2rWrvv76a+frDz74oLy8vPTJJ5+odevW+tOf/qROnTpp1apVOnjwoJ5++mlJ0vbt2/Xdd99p+vTpatasmSIjI3XzzTdr7NixatasWbnX4+Xl5ZKNr6+vvL29XfZ5eXmVeKyfn5/uu+8+Pfnkk+U+X3lzMEGvbjW0bEWWPlz9o/ZlntAL0/foVOFZ3dau5J/DawGZ2JGJHZnYkYkdmdiZmgkl9gpKTk5WQkKCIiMjJUlnzpxRcXGxfHx8XMb5+voqLS2txDmKioo0a9YsBQYG6oYbbrisdezevVuffvqpmjZtKknKzs7WihUr9MADD8jX19dlbFhYmO6++24tXrxYlmWpatWqqlChgt555x0VFxdf1vmvhFGjRmnbtm165513LnuOC3MwgaenQzHRAdq8Jce5z7Kkzek5io+t7MaVuQ+Z2JGJHZnYkYkdmdiZnMk1W2KXL18uf39/l61Tp06XPd+hQ4f00UcfaeDAgc59AQEBat68ucaMGaNDhw6puLhYCxYs0Pr165WVlVXienx8fDRlyhStXLlSVapUKff5e/fu7Tw+NjZW8fHxGjFihCRpz549sixL9erVK/HYevXqKScnR0eOHFFERIReeeUVPfvsswoODtatt96qMWPG6Pvvv7+MVC5f9erV9c9//lNPP/20zpw5U+7jLpaDCQIrV5Snh0PZOadd9mfnnlZocMlXrv/oyMSOTOzIxI5M7MjEzuRMrtkS27ZtW6Wnp7tss2fPvuz55s+fr6CgINsHwFJSUmRZliIiIuTt7a1XXnlFvXv3VoUKrtGfW88XX3yhjh07KjExUT/99FO5zz9lyhSlp6dry5YtWr58uXbv3q1+/fq5jLEsq1xzPfjggzp8+LDeeustNW/eXEuWLFF8fLxWrlxZ7vVcCU888YSOHDmiOXPmlPuY8uRwvsLCQuXn57tsJn4QDACAa801W2L9/PwUHR3tskVERFzWXJZlac6cOerXr5/tPs86depo7dq1OnbsmDIzM7Vp0yadPn1atWvXLnE9zZo1U3Jysjw9PZWcnFzuNYSFhSk6OlqxsbHq0qWLnnvuOS1evFgZGRmKjo6Ww+HQzp07Szx2586dCg4OVtWqVZ37AgICdPvtt2vcuHHasmWLbrnlFucHpCpX/uXXC3l5eba5cnNzFRgYWO51X0xQUJBGjBih5557TidOnCjXMRfLoSQTJkxQYGCgyzZhwoQrsv7LkZd/WmeKLYUEu95MHxJUUT/nFLlpVe5FJnZkYkcmdmRiRyZ2JmdyzZbYK2nt2rXKyMjQgAEDSh3j5+en8PBw5eTkaMWKFeratetF5zx79uyvuiLo4eEh6ZcPl4WGhqpdu3aaPn26Tp486TLu3BXXnj17yuFwlDiXw+FQXFycjh8/LkkKCQlRlSpV9NVXX7mMy8/PV0ZGhmJiYi573Rf6xz/+oQoVKmjq1KmXdfz5OZRkxIgRysvLc9ncefvBmTOWdmcUqPH1wc59DofU+IZgbf82323rcicysSMTOzKxIxM7MrEzORNPdy/garZjxw4VFRUpOztbBQUFzueiNmrUyGVccnKymjZtqgYNGtjmWLFihSzLUmxsrDIyMvTYY48pLi7O+QSD48ePa9y4cbrjjjsUHh6uo0eP6rXXXtPBgwddHtVVltzcXB0+fFhnz57Vnj17NHr0aMXExDjvg502bZpatGihDh06aOzYsapVq5a2b9+uxx57TBERERo3bpwkKT09XSNHjlS/fv1Uv359eXl5ae3atZozZ46eeOIJ5/kefvhhjR8/Xtddd52aNWumn3/+WWPGjFHVqlX1t7/97VJivigfHx8999xzevDBB69IDhfy9vaWt7f3FVvvlbBo6QE9/VCcdmUUaOfuAiV2jZCvTwV9sOqwu5fmNmRiRyZ2ZGJHJnZkYmdqJpTYi+jcubP279/v/PrGG2+U5HpvaV5enlJTU0u9Unjuyt6BAwcUEhKiO++8U+PGjVPFir9ctvfw8NCuXbs0f/58HT16VKGhoWrSpInWrVvn8kcTynKuFDscDoWFhalVq1YaP368PD1/+RbXrVtXmzdv1siRI5WYmKjs7GyFhYWpW7duGjlypEJCQiRJNWrUUFRUlJ577jnnHzU49/VDDz3kPN/jjz8uf39/Pf/88/ruu+8UEhKim2++WZ999pntCQi/1r333quXXnpJO3bs+NU5mODTtCMKCqyogXdHKSTYSxnfH9MjI7cpJ/d02Qf/QZGJHZnYkYkdmdiRiZ2pmTis8n7aB7iGtLx9rbuXcFVJW9aaTC5AJnZkYkcmdmRiRyZ2actalzmGe2IBAABgHErsVW78+PG259leiefaAgAAmMycGwWvUUOGDFFiYmKJr13pe08BAABMQYm9yoWEhDg/dAUAAIBfcDsBAAAAjEOJBQAAgHEosQAAADAOJRYAAADGocQCAADAOJRYAAAAGIcSCwAAAONQYgEAAGAcSiwAAACMQ4kFAACAcSixAAAAMA4lFgAAAMahxAIAAMA4lFgAAAAYhxILAAAA41BiAQAAYBxKLAAAAIxDiQUAAIBxKLEAAAAwDiUWAAAAxqHEAgAAwDiUWAAAABiHEgsAAADjUGIBAABgHEosAAAAjEOJBQAAgHEosQAAADAOJRYAAADGocQCAADAOJRYAAAAGIcSCwAAAONQYgEAAGAcSiwAAACMQ4kFAACAcSixAAAAMI7DsizL3YsAAAAALoWnuxcAXI1a3r7W3Uu4qqQta00mFyATOzKxIxM7MrEjE7u0Za3LHMPtBAAAADAOJRYAAADGocQCAADAOJRYAAAAGIcSCwAAAONQYgEAAGAcSiwAAACMQ4kFAACAcSixAAAAMA4lFgAAAMahxAIAAMA4lFgAAAAYhxILAAAA41BiAQAAYBxKLAAAAIxDiQUAAIBxKLEAAAAwDiUWAAAAxqHEAgAAwDiUWAAAABiHEgsAAADjUGIBAABgHEosAAAAjEOJBQAAgHEosQAAADAOJRYAAADGocQCAADAOJRYAAAAGIcSCwAAAONQYgEAAGAcSiwAAACMQ4kFAACAcSixAAAAMA4lFgAAAMahxAIAAMA4lFgAAAAYhxILAAAA47i1xCYlJalbt262/WvWrJHD4VBubq5OnTqlpKQkNWzYUJ6eniWOT0pKksPhsG3x8fHOMQUFBRo+fLgiIyPl6+urFi1a6Msvv3SZZ9SoUYqLi5Ofn5+Cg4OVkJCgjRs3XtJ7+uyzz9S5c2eFhoaqUqVKql+/vh555BEdPHjQNjYuLk7e3t46fPiw7bU2bdq4vJfrrrtOPXr00P79+13GnTx5UiNHjlRMTIy8vb1VpUoV9ejRQ9u3b3eO2bdvX4n5nNtq1aplO//gwYPl4eGhJUuW2F4bNWqUHA6HhgwZ4rI/PT1dDodD+/btc4652CbZfwbOfS8nTpzoMvfSpUudx0iuPyPnf31u8/X1VXx8vGbNmmVbvwn+1rm6lsxuqtWpt2jWizeqXt0Ady/J7cjEjkzsyMSOTOzIxM7ETK76K7HFxcXy9fXVsGHDlJCQUOKYqVOnKisry7llZmYqJCREPXr0cI4ZOHCgVq5cqZSUFG3btk3t27dXQkKCS7mMiYnRtGnTtG3bNqWlpSkqKkrt27fXkSNHyrXW119/XQkJCQoLC1Nqaqp27NihmTNnKi8vTy+99JLL2LS0NJ08eVJ33XWX5s+fX+J8gwYNUlZWlg4dOqT33ntPmZmZ6tu3r/P1wsJCJSQkaM6cORo7dqx2796tDz/8UGfOnFHTpk21YcMGSVLNmjVd8jm3LVu2TB4eHnrwwQddznvixAktWrRIjz/+uObMmVPi2nx8fJScnKw9e/aU+Pqjjz7qcq4aNWpo9OjRLvtK4+Pjo+eff145OTmljinNt99+q6ysLO3YsUODBw/W/fffr9WrV1/yPO50a8uqGjqwjuYu3KcBw79Sxt5jmjy6oYICK7p7aW5DJnZkYkcmdmRiRyZ2pmZy1ZdYPz8/zZgxQ4MGDVJYWFiJYwIDAxUWFubcNm/erJycHPXv31/SL1crU1NTNWnSJLVq1UrR0dEaNWqUoqOjNWPGDOc8ffr0UUJCgmrXrq34+HhNnjxZ+fn52rp1a5nrPHDggIYNG6Zhw4Zpzpw5atOmjaKiotSqVSvNnj1bzz77rMv45ORk9enTR/369Su1KFaqVElhYWEKDw9Xs2bNNHToUH399dfO119++WWtX79ey5cvV2JioiIjI/WXv/xFqampqlevngYMGCDLsuTh4eGST1hYmBwOh+6//3717t1bjz76qMt5lyxZovr16+vJJ5/U559/rszMTNvaYmNj1bZtWz399NMlrt3f39/lfB4eHgoICHDZV5pz/xCYMGFCqWNKU61aNYWFhalWrVoaNmyYatWq5ZKZCXp1q6FlK7L04eoftS/zhF6YvkenCs/qtnalZ/ZHRyZ2ZGJHJnZkYkcmdqZmctWX2MuRnJyshIQERUZGSpLOnDmj4uJi+fj4uIzz9fVVWlpaiXMUFRVp1qxZCgwM1A033FDmOZcsWaKioiI9/vjjJb4eFBTk/L8LCgq0ZMkS9e3bV+3atVNeXp7WrVt30fmzs7P173//W02bNnXue/vtt9WuXTvb+ipUqKCHHnpIO3bs0JYtW2xznT59WnfeeafCwsL0xhtv2F5PTk5W3759FRgYqE6dOmnevHklrmnixIlKTU3V5s2bL7r2S+Xh4aHx48fr1Vdf1YEDBy5rDsuy9PHHH+uHH35wyexq5+npUEx0gDZv+f9XoS1L2pyeo/jYym5cmfuQiR2Z2JGJHZnYkYmdyZm4vcQuX75c/v7+LlunTp0ue75Dhw7po48+0sCBA537AgIC1Lx5c40ZM0aHDh1ScXGxFixYoPXr19t+rX1uPT4+PpoyZYpWrlypKlWqlHnePXv2qHLlygoPDy9z7KJFi1S3bl3Fx8fLw8NDvXr1UnJysm3c9OnT5e/vLz8/P4WGhurbb791uWq7e/du1atXr8RznNu/e/du22tDhw7Vd999p//85z+2Yr9nzx5t2LBBPXv2lCT17dtXc+fOlWVZtnluuukmJSYm6oknnijzPV+q7t27q1GjRho5cuQlHVejRg35+/vLy8tLXbp00ciRI9WqVasrvr7fSmDlivL0cCg757TL/uzc0woN9nLTqtyLTOzIxI5M7MjEjkzsTM7E7SW2bdu2Sk9Pd9lmz5592fPNnz9fQUFBtg+ApaSkyLIsRUREyNvbW6+88op69+6tChVcIzi3ni+++EIdO3ZUYmKifvrppzLPa1mWywePLmbOnDku97b27dtXS5YsUUFBgcu4u+++W+np6dqyZYvS0tIUHR2t9u3bu4wrqVxezMyZMzVv3jylpqaqRo0aJa6tQ4cOzuLeuXNn5eXl6dNPPy1xvrFjx2rdunX65JNPLmkd5fH8889r/vz52rlzZ7mPWbduncvP0fjx411uGblQYWGh8vPzXbbCwsIrsXwAAPAbcnuJ9fPzU3R0tMsWERFxWXNZlqU5c+aoX79+8vJy/ddDnTp1tHbtWh07dkyZmZnatGmTTp8+rdq1a5e4nmbNmik5OVmenp4lXiW9UExMjPLy8i76gSVJ2rFjhzZs2KDHH39cnp6e8vT0VLNmzZwfpjpfYGCgM5Obb77Z+UGqxYsXO89ZWsE7tz8mJsa5Ly0tTcOGDdNrr72mFi1a2I4pLi7W/Pnz9cEHHzjXVqlSJWVnZ5d6326dOnU0aNAgPfnkk5dcqMvSqlUrdejQQSNGjCj3MbVq1VJ0dLTi4+PVv39/9evXT+PGjSt1/IQJExQYGOiyXc69uFdKXv5pnSm2FBLsejN9SFBF/ZxT5KZVuReZ2JGJHZnYkYkdmdiZnInbS+yVtHbtWmVkZGjAgAGljvHz81N4eLhycnK0YsUKde3a9aJznj17tlxX5u666y55eXlp0qRJJb5+7lFQycnJatWqlbZs2eJy9fnhhx8usyx7eHhI+uWDapLUq1cvrVq1ynbf69mzZzVlyhTVr1/feb9sZmam7rzzTv397393udXifB9++KEKCgr0zTffuKxt4cKFevfdd53v4ULPPvusdu/ebSvhV8LEiRO1bNkyrV+//rKO9/DwcOZVkhEjRigvL89lu5TSfKWdOWNpd0aBGl8f7NzncEiNbwjW9m/z3bYudyITOzKxIxM7MrEjEzuTM/F09wLKY8eOHSoqKlJ2drYKCgqUnp4uSWrUqJHLuOTkZDVt2lQNGjSwzbFixQpZlqXY2FhlZGToscceU1xcnPMJBsePH9e4ceN0xx13KDw8XEePHtVrr72mgwcPujyqqzQ1a9bUlClTNHToUOXn5+uee+5RVFSUDhw4oDfffFP+/v6aOHGiUlJSNHr0aNsaBw4cqMmTJ2v79u3O59ueOHHC+QzZH3/8UWPGjJGPj4/at28vSXrooYf03nvv6fbbb9dLL72kpk2b6scff9T48eO1c+dOrVq1Sg6HQ6dOnVL37t0VERGhJ598ssTn0oaFhSk5OVldunSxfVCsfv36euihh/TWW2/ZHsclSdddd50efvhhvfDCC2XmdKkaNmyou+++W6+88kq5xv/00086deqUCgsLtWnTJqWkpOiuu+4qdby3t7e8vb2v1HKviEVLD+jph+K0K6NAO3cXKLFrhHx9KuiDVfbv27WCTOzIxI5M7MjEjkzsTM3EiBLbuXNnl4f833jjjZJc7wfNy8tTamqqpk6dWuIc566wHThwQCEhIbrzzjs1btw4Vaz4y+VzDw8P7dq1S/Pnz9fRo0cVGhqqJk2aaN26dS5/NOFiHnjgAcXExOjFF19U9+7ddfLkSUVFRem2227Tww8/rPfff18///yzunfvbju2Xr16qlevnpKTkzV58mRJ0htvvOF8ekBwcLCuv/56ffjhh4qNjZX0y/NUP/30U40fP15PPfWU9u/fr4CAALVt21YbNmxwFuWNGzfqq6++kvRL2S7J4cOH9cEHH+jtt9+2vVahQgV1795dycnJJZZY6Zfnws6YMUOnTp0qV1aXYvTo0c5bKMpyLhtPT0/VrFlTgwcP1qhRo674mn5Ln6YdUVBgRQ28O0ohwV7K+P6YHhm5TTm5p8s++A+KTOzIxI5M7MjEjkzsTM3EYV3pGxmBP4CWt6919xKuKmnLWpPJBcjEjkzsyMSOTOzIxC5tWesyx/yh7okFAADAtYESW07jx4+3Pc/2SjzXFgAAAJfOiHtirwZDhgxRYmJiia/5+vr+zqsBAAC4tlFiyykkJEQhISHuXgYAAADE7QQAAAAwECUWAAAAxqHEAgAAwDiUWAAAABiHEgsAAADjUGIBAABgHEosAAAAjEOJBQAAgHEosQAAADAOJRYAAADGocQCAADAOJRYAAAAGIcSCwAAAONQYgEAAGAcSiwAAACMQ4kFAACAcSixAAAAMA4lFgAAAMahxAIAAMA4lFgAAAAYhxILAAAA41BiAQAAYBxKLAAAAIxDiQUAAIBxKLEAAAAwDiUWAAAAxqHEAgAAwDiUWAAAABiHEgsAAADjUGIBAABgHEosAAAAjEOJBQAAgHEosQAAADAOJRYAAADGocQCAADAOA7Lsix3LwIAAAC4FJ7uXgBwNWp5+1p3L+GqkrasNZlcgEzsyMSOTOzIxI5M7NKWtS5zDLcTAAAAwDiUWAAAABiHEgsAAADjUGIBAABgHEosAAAAjEOJBQAAgHEosQAAADAOJRYAAADGocQCAADAOJRYAAAAGIcSCwAAAONQYgEAAGAcSiwAAACMQ4kFAACAcSixAAAAMA4lFgAAAMahxAIAAMA4lFgAAAAYhxILAAAA41BiAQAAYBxKLAAAAIxDiQUAAIBxKLEAAAAwDiUWAAAAxqHEAgAAwDiUWAAAABiHEgsAAADjUGIBAABgHEosAAAAjEOJBQAAgHEosQAAADAOJRYAAADGocQCAADAOJRYAAAAGIcSCwAAAONQYgEAAGAcSiwAAACMY2SJTUpKUrdu3Wz716xZI4fDodzcXJ06dUpJSUlq2LChPD09SxyflJQkh8Nh2+Lj451jCgoKNHz4cEVGRsrX11ctWrTQl19+6TLPqFGjFBcXJz8/PwUHByshIUEbN268pPf02WefqXPnzgoNDVWlSpVUv359PfLIIzp48KDLezu3Va1aVZ07d9a2bdvK9Z46duzoHBMVFSWHw6ENGza4HDt8+HC1adPGZV92drbz/Xt5eal69eq677779MMPP9jOW1LGFzpw4IC8vLzUoEGDEl93OBzy8fHR/v37XfZ369ZNSUlJpZ7vwvcdGhqqjh07auvWrWWu6Wr0t87VtWR2U61OvUWzXrxR9eoGuHtJbkcmdmRiRyZ2ZGJHJnYmZmJkiS2P4uJi+fr6atiwYUpISChxzNSpU5WVleXcMjMzFRISoh49ejjHDBw4UCtXrlRKSoq2bdum9u3bKyEhwVkuJSkmJkbTpk3Ttm3blJaWpqioKLVv315Hjhwp11pff/11JSQkKCwsTKmpqdqxY4dmzpypvLw8vfTSSy5jv/32W2VlZWnFihUqLCxUly5dVFRU5DKmY8eOLu8rKytLCxcudBnj4+OjJ5544qLrys7OVrNmzbRq1SrNnDlTGRkZWrRokTIyMtSkSRN9//335Xp/55s3b54SExOVn59fatF3OBx69tlnL3nu89/36tWr5enpqdtuu+2S53G3W1tW1dCBdTR34T4NGP6VMvYe0+TRDRUUWNHdS3MbMrEjEzsysSMTOzKxMzWTP2yJ9fPz04wZMzRo0CCFhYWVOCYwMFBhYWHObfPmzcrJyVH//v0lSSdPnlRqaqomTZqkVq1aKTo6WqNGjVJ0dLRmzJjhnKdPnz5KSEhQ7dq1FR8fr8mTJys/P79cVwEPHDigYcOGadiwYZozZ47atGmjqKgotWrVSrNnz7aVuWrVqiksLEw33XSThg8frszMTO3atctljLe3t8v7CgsLU3BwsMuYv//979qwYYM+/PDDUtf29NNP69ChQ1q1apU6deqkP/3pT2rVqpVWrFihihUr6sEHHyzz/Z3PsizNnTtX/fr1U58+fZScnFziuKFDh2rBggX63//+d0nzn/++GzVqpCeffFKZmZnl/sfE1aJXtxpatiJLH67+UfsyT+iF6Xt0qvCsbmtX8s/xtYBM7MjEjkzsyMSOTOxMzeQPW2IvR3JyshISEhQZGSlJOnPmjIqLi+Xj4+MyztfXV2lpaSXOUVRUpFmzZikwMFA33HBDmedcsmSJioqK9Pjjj5f4elBQUIn78/LytGjRIkmSl5dXmee5UK1atTRkyBCNGDFCZ8+etb1+9uxZLVq0SHfffbftHwG+vr564IEHtGLFCmVnZ5f7nJ999plOnDihhIQE9e3bV4sWLdLx48dt426++WbddtttevLJJy/5fZ1z7NgxLViwQNHR0QoNDb3seX5vnp4OxUQHaPOWHOc+y5I2p+coPrayG1fmPmRiRyZ2ZGJHJnZkYmdyJsaW2OXLl8vf399l69Sp02XPd+jQIX300UcaOHCgc19AQICaN2+uMWPG6NChQyouLtaCBQu0fv16ZWVllbgeHx8fTZkyRStXrlSVKlXKPO+ePXtUuXJlhYeHl2udNWrUkL+/v4KCgvT222/rjjvuUFxcXIlrOX8bP368ba5//etf2rt3r9566y3ba0eOHFFubq7q1atX4jrq1asny7KUkZFRrnVLv/wjoVevXvLw8FCDBg1Uu3ZtLVmypMSxEyZM0Mcff6x169aVe/7z33dAQIDef/99LV68WBUqmPNjHli5ojw9HMrOOe2yPzv3tEKDL/0fK38EZGJHJnZkYkcmdmRiZ3Im5vyv+wXatm2r9PR0l2327NmXPd/8+fMVFBRk+3BSSkqKLMtSRESEvL299corr6h37962YnRuPV988YU6duyoxMRE/fTTT2We17IsORyOcq9z3bp1+uqrrzRv3jzFxMRo5syZtjElZTNkyBDbuKpVq+rRRx/Vs88+a7uv9vz1XQm5ubl699131bdvX+e+vn37lnpLQf369XXPPfdc0tXY89/3pk2b1KFDB3Xq1Mn2IbHzFRYWKj8/32UrLCws/xsDAABu4enuBVwuPz8/RUdHu+w7cODAZc1lWZbmzJmjfv362X41X6dOHa1du1bHjx9Xfn6+wsPD1bNnT9WuXbvE9URHR6tZs2aqW7eukpOTNWLEiIueOyYmRnl5ecrKyirX1dhatWopKChIsbGx+umnn9SzZ099/vnnJa6lPB5++GFNnz5d06dPd9lftWpVBQUFaefOnSUet3PnTjkcjnKf5+2339apU6fUtGlT5z7LsnT27Fnt3r1bMTExtmOee+45xcTEaOnSpeU6x4Xve/bs2QoMDNQbb7yhsWPHlnjMhAkT9Nxzz7nsGzlypKS25TrnlZaXf1pnii2FBLveTB8SVFE/55T8D40/OjKxIxM7MrEjEzsysTM5E2OvxF5Ja9euVUZGhgYMGFDqGD8/P4WHhysnJ0crVqxQ165dLzrn2bNny3VF76677pKXl5cmTZpU4uu5ubmlHvvggw/qf//7n/7zn/+UeZ7S+Pv765lnntG4ceNUUFDg3F+hQgUlJibq7bff1uHDh12OOXnypKZPn64OHTooJCSkXOdJTk7WI4884nJ1eMuWLbrllls0Z86cEo+pWbOmhg4dqqeeekrFxcWX/N4cDocqVKigkydPljpmxIgRysvLc9nK+ofHb+nMGUu7MwrU+Pr//0E8h0NqfEOwtn+b77Z1uROZ2JGJHZnYkYkdmdiZnImxV2LLY8eOHSoqKlJ2drYKCgqUnp4uSWrUqJHLuOTkZDVt2rTEZ5euWLFClmUpNjZWGRkZeuyxxxQXF+d8gsHx48c1btw43XHHHQoPD9fRo0f12muv6eDBgy6P6ipNzZo1NWXKFA0dOlT5+fm65557FBUVpQMHDujNN9+Uv7+/7TFb51SqVEmDBg3SyJEj1a1bN+dtCYWFhbbi6enpWeo9un//+981ZcoUvf322y5XSsePH6/Vq1erXbt2mjRpkho0aKC9e/fqX//6l06fPq3XXnvNZZ68vDxnxueEhobq559/1tdff6233nrLdv9u7969NXr0aI0dO1aenvYfxxEjRuiNN97Q3r171bNnz5JD/D/nv++cnBxNmzZNx44d0+23317qMd7e3vL29r7ovL+3RUsP6OmH4rQro0A7dxcosWuEfH0q6INVh8s++A+KTOzIxI5M7MjEjkzsTM3kD11iO3fu7HI/5I033ijJ9T7PvLw8paamaurUqSXOce7K3IEDBxQSEqI777xT48aNU8WKv1x29/Dw0K5duzR//nwdPXpUoaGhatKkidatW+fyRxMu5oEHHlBMTIxefPFFde/eXSdPnlRUVJRuu+02Pfzwwxc9dujQoZo8ebKWLFmixMRESdLHH39suzUhNjbW9iiucypWrKgxY8aoT58+LvtDQ0O1YcMGjR49WoMHD9bhw4cVEhKiTp06acGCBfrTn/7kMn7NmjXOjM8ZMGCAfH19Vb9+fVuBlaTu3btr6NCh+vDDD3XHHXfYXg8JCdETTzyhp5566qI5XPi+AwICFBcXpyVLltj+gMPV7tO0IwoKrKiBd0cpJNhLGd8f0yMjtykn93TZB/9BkYkdmdiRiR2Z2JGJnamZOKwr9ckd4A+k5e1r3b2Eq0rastZkcgEysSMTOzKxIxM7MrFLW9a6zDHcEwsAAADjUGJ/Y+PHj7c9s/VKPNcWAADgWvaHvif2ajBkyBDnvaoX8vX1/Z1XAwAA8MdAif2NhYSElPsxVAAAACgfbicAAACAcSixAAAAMA4lFgAAAMahxAIAAMA4lFgAAAAYhxILAAAA41BiAQAAYBxKLAAAAIxDiQUAAIBxKLEAAAAwDiUWAAAAxqHEAgAAwDiUWAAAABiHEgsAAADjUGIBAABgHEosAAAAjEOJBQAAgHEosQAAADAOJRYAAADGocQCAADAOJRYAAAAGIcSCwAAAONQYgEAAGAcSiwAAACMQ4kFAACAcSixAAAAMA4lFgAAAMahxAIAAMA4lFgAAAAYhxILAAAA41BiAQAAYBxKLAAAAIxDiQUAAIBxKLEAAAAwDiUWAAAAxnFYlmW5exEAAADApfB09wKAq1HL29e6ewlXlbRlrcnkAmRiRyZ2ZGJHJnZkYpe2rHWZY7idAAAAAMahxAIAAMA4lFgAAAAYhxILAAAA41BiAQAAYBxKLAAAAIxDiQUAAIBxKLEAAAAwDiUWAAAAxqHEAgAAwDiUWAAAABiHEgsAAADjUGIBAABgHEosAAAAjEOJBQAAgHEosQAAADAOJRYAAADGocQCAADAOJRYAAAAGIcSCwAAAONQYgEAAGAcSiwAAACMQ4kFAACAcSixAAAAMA4lFgAAAMahxAIAAMA4lFgAAAAYhxILAAAA41BiAQAAYBxKLAAAAIxDiQUAAIBxKLEAAAAwDiUWAAAAxqHEAgAAwDiUWAAAABiHEgsAAADjUGIBAABgnD98iU1KSlK3bt1s+9esWSOHw6Hc3FydOnVKSUlJatiwoTw9PUscn5SUJIfDYdvi4+OdYwoKCjR8+HBFRkbK19dXLVq00Jdffukyz6hRoxQXFyc/Pz8FBwcrISFBGzduvKT39Nlnn6lz584KDQ1VpUqVVL9+fT3yyCM6ePCgy3s7t1WtWlWdO3fWtm3bSs3mwmMu3Nq2bety7Pz589WkSRNVqlRJAQEBat26tZYvX15mXue2qKgoSVKbNm00fPhw53Ft2rSRw+HQokWLXM738ssvO4+RpHnz5ikoKMjl6/Pn9/f3V+PGjfXuu+9eUrZXi791rq4ls5tqdeotmvXijapXN8DdS3I7MrEjEzsysSMTOzKxMzGTP3yJLY/i4mL5+vpq2LBhSkhIKHHM1KlTlZWV5dwyMzMVEhKiHj16OMcMHDhQK1euVEpKirZt26b27dsrISHBWS4lKSYmRtOmTdO2bduUlpamqKgotW/fXkeOHCnXWl9//XUlJCQoLCxMqamp2rFjh2bOnKm8vDy99NJLLmO//fZbZWVlacWKFSosLFSXLl1UVFRU4rwtWrRweX/nttdff10Oh0MPPPCAc+yjjz6qwYMHq2fPntq6das2bdqkli1bqmvXrpo2bVqJeUnS3LlznV9fWO7P5+Pjo3/96186ffp0uTI5p3Llys75v/nmG3Xo0EGJiYn69ttvL2ked7u1ZVUNHVhHcxfu04DhXylj7zFNHt1QQYEV3b00tyETOzKxIxM7MrEjEztTM6HESvLz89OMGTM0aNAghYWFlTgmMDBQYWFhzm3z5s3KyclR//79JUknT55UamqqJk2apFatWik6OlqjRo1SdHS0ZsyY4ZynT58+SkhIUO3atRUfH6/JkycrPz9fW7duLXOdBw4c0LBhwzRs2DDNmTNHbdq0UVRUlFq1aqXZs2fr2WefdRlfrVo1hYWF6aabbtLw4cOVmZmpXbt2lTi3l5eXy/sLCwtTTk6OHn30UT311FPOsr5hwwa99NJLeuGFF/Too48qOjpa9erV07hx4zR8+HA9/PDDyszMtOUlSUFBQc6vq1atWur77N27t3Jzc/XGG2+Umcn5HA6Hc/66detq7NixqlChQrmyvZr06lZDy1Zk6cPVP2pf5gm9MH2PThWe1W3tSv7ZvBaQiR2Z2JGJHZnYkYmdqZlQYi9TcnKyEhISFBkZKUk6c+aMiouL5ePj4zLO19dXaWlpJc5RVFSkWbNmKTAwUDfccEOZ51yyZImKior0+OOPl/j6+b9eP19eXp7z1/NeXl5lnkeScnNz1bVrV7Vp00Zjxoxx7l+4cKH8/f01ePBg2zGPPPKITp8+rdTU1HKdozSVK1fW008/rdGjR+v48eOXNUdxcbHmz58vSbrpppt+1Xp+T56eDsVEB2jzlhznPsuSNqfnKD62shtX5j5kYkcmdmRiRyZ2ZGJnciae7l7A72H58uXy9/d32VdcXHzZ8x06dEgfffSR3n77bee+gIAANW/eXGPGjFG9evV03XXXaeHChVq/fr2io6Nt6+nVq5dOnDih8PBwrVy5UlWqVCnzvHv27FHlypUVHh5ernXWqFFDkpxF8I477lBcXFyZx509e1Z9+vSRp6en3nrrLTkcDudru3fvVp06dUosw9WrV1flypW1e/fucq3vYh544AFNnTpVkydP1jPPPFOuY/Ly8pzf55MnT6pixYqaNWuW6tSp86vX83sJrFxRnh4OZee43kqRnXtakTUquWlV7kUmdmRiRyZ2ZGJHJnYmZ3JNlNi2bdu6/EpfkjZu3Ki+ffte1nzz589XUFCQ7QNgKSkpuu+++xQRESEPDw/ddNNN6t27t7766ivbetLT03X06FG98cYbSkxM1MaNG1WtWrWLnteyLJdCWZZ169apUqVK2rBhg8aPH6+ZM2eW67innnpK69ev16ZNmxQQYL+x27Kscq/hcnl7e2v06NH6xz/+ofvvv79cxwQEBOjrr7+WJJ04cUKrVq3SkCFDFBoaqttvv73EYwoLC1VYWGg7NwAAuLpdE7cT+Pn5KTo62mWLiIi4rLksy9KcOXPUr18/29XIOnXqaO3atTp27JgyMzO1adMmnT59WrVr1y5xPc2aNVNycrI8PT2VnJxc5rljYmKUl5fn/KBUWWrVqqXY2Fjde++9GjhwoHr27FnmMYsWLdKLL76oRYsWqW7duiWu4fvvvy/xA2KHDh1Sfn6+YmJiyrW+svTt21eRkZEaO3ZsucZXqFDB+f29/vrr9fDDD6tNmzZ6/vnnSz1mwoQJCgwMdNkmTJhwRdZ/OfLyT+tMsaWQYNeb6UOCKurnnJI/lPdHRyZ2ZGJHJnZkYkcmdiZnck2U2Ctp7dq1ysjI0IABA0od4+fnp/DwcOXk5GjFihXq2rXrRec8e/as7WpgSe666y55eXlp0qRJJb6em5tb6rEPPvig/ve//+k///lPqWPS09M1YMAATZw4UR06dChxTK9evXTs2DG9/vrrttdefPFFVaxYUXfeeefF30g5VahQQRMmTNCMGTO0b9++y5rDw8NDJ0+eLPX1ESNGKC8vz2UbMWLEZa741ztzxtLujAI1vj7Yuc/hkBrfEKzt3+a7bV3uRCZ2ZGJHJnZkYkcmdiZnck3cTlAeO3bsUFFRkbKzs1VQUKD09HRJUqNGjVzGJScnq2nTpmrQoIFtjhUrVsiyLMXGxiojI0OPPfaY4uLinE8wOH78uMaNG6c77rhD4eHhOnr0qF577TUdPHjQ5VFdpalZs6amTJmioUOHKj8/X/fcc4+ioqJ04MABvfnmm/L397c9ZuucSpUqadCgQRo5cqS6detmuy3h6NGj6tatm9q0aaO+ffvq8OHDLq97eHioatWqat68uf75z3/qscceU1FRkbp166bTp09rwYIFmjp1ql5++WXVrFmzzPdSXl26dFHTpk31+uuv67rrrrvoWMuynOs+efKkVq5cqRUrVtie2nA+b2/vq+72gUVLD+jph+K0K6NAO3cXKLFrhHx9KuiDVYfLPvgPikzsyMSOTOzIxI5M7EzNhBL7fzp37qz9+/c7v77xxhslud7/mZeXp9TUVE2dOrXEOc5dxTtw4IBCQkJ05513aty4capY8ZdL9B4eHtq1a5fmz5+vo0ePKjQ0VE2aNNG6detc/mjCxTzwwAOKiYnRiy++qO7du+vkyZOKiorSbbfdpocffviixw4dOlSTJ0/WkiVLlJiY6PLaBx98oP3792v//v0lfnAsMjLSeTX05Zdf1vXXX6/p06frX//6l/P+36VLl5Z67+mv8fzzz6tFixZljsvPz3eu3dvbW5GRkRo9erSeeOKJK76m39KnaUcUFFhRA++OUkiwlzK+P6ZHRm5TTu6lPTf3j4RM7MjEjkzsyMSOTOxMzcRh/R6f0gEM0/L2te5ewlUlbVlrMrkAmdiRiR2Z2JGJHZnYpS1rXeYY7okFAACAcSixV5Hx48fL39+/xK1Tp07uXh4AAMBVg3tiryJDhgyx3at6jq+v7++8GgAAgKsXJfYqEhISopCQEHcvAwAA4KrH7QQAAAAwDiUWAAAAxqHEAgAAwDiUWAAAABiHEgsAAADjUGIBAABgHEosAAAAjEOJBQAAgHEosQAAADAOJRYAAADGocQCAADAOJRYAAAAGIcSCwAAAONQYgEAAGAcSiwAAACMQ4kFAACAcSixAAAAMA4lFgAAAMahxAIAAMA4lFgAAAAYhxILAAAA41BiAQAAYBxKLAAAAIxDiQUAAIBxKLEAAAAwDiUWAAAAxqHEAgAAwDiUWAAAABiHEgsAAADjUGIBAABgHEosAAAAjEOJBQAAgHEosQAAADAOJRYAAADGocQCAADAOA7Lsix3LwIAAAC4FJ7uXgBwNWp5+1p3L+GqkrasNZlcgEzsyMSOTOzIxI5M7NKWtS5zDLcTAAAAwDiUWAAAABiHEgsAAADjUGIBAABgHEosAAAAjEOJBQAAgHEosQAAADAOJRYAAADGocQCAADAOJRYAAAAGIcSCwAAAONQYgEAAGAcSiwAAACMQ4kFAACAcSixAAAAMA4lFgAAAMahxAIAAMA4lFgAAAAYhxILAAAA41BiAQAAYBxKLAAAAIxDiQUAAIBxKLEAAAAwDiUWAAAAxqHEAgAAwDiUWAAAABiHEgsAAADjUGIBAABgHEosAAAAjEOJBQAAgHEosQAAADAOJRYAAADGocQCAADAOJRYAAAAGIcSCwAAAONQYgEAAGAcSiwAAACMc82W2KSkJHXr1s22f82aNXI4HMrNzdWpU6eUlJSkhg0bytPTs8TxSUlJcjgcti0+Pt45pqCgQMOHD1dkZKR8fX3VokULffnlly7zjBo1SnFxcfLz81NwcLASEhK0cePGcr+f88/t5+enunXrKikpSV999VWJ4w8cOCAvLy81aNCgxNfXrl2rW2+9VSEhIapUqZLq1q2re++9V0VFRbaxgwcPloeHh5YsWVLiekraRo0apX379snhcCg9PV2SnF9Xq1ZNBQUFLudo1KiRRo0a5fy6TZs2Gj58uMvX589/3XXXqUePHtq/f385E7y6/K1zdS2Z3VSrU2/RrBdvVL26Ae5ektuRiR2Z2JGJHZnYkYmdiZlcsyW2PIqLi+Xr66thw4YpISGhxDFTp05VVlaWc8vMzFRISIh69OjhHDNw4ECtXLlSKSkp2rZtm9q3b6+EhAQdPHjQOSYmJkbTpk3Ttm3blJaWpqioKLVv315Hjhwp93rnzp2rrKwsbd++Xa+99pqOHTumpk2b6s0337SNnTdvnhITE5Wfn28ryzt27FDHjh315z//WZ9//rm2bdumV199VV5eXiouLnYZe+LECS1atEiPP/645syZ49x/fiYvv/yyKleu7LLv0UcfLfV9FBQU6MUXXyz3+z5n0KBBysrK0qFDh/Tee+8pMzNTffv2veR53O3WllU1dGAdzV24TwOGf6WMvcc0eXRDBQVWdPfS3IZM7MjEjkzsyMSOTOxMzYQSexF+fn6aMWOGBg0apLCwsBLHBAYGKiwszLlt3rxZOTk56t+/vyTp5MmTSk1N1aRJk9SqVStFR0dr1KhRio6O1owZM5zz9OnTRwkJCapdu7bi4+M1efJk5efna+vWreVeb1BQkMLCwpwF+J133tHdd9+toUOHKicnxznOsizNnTtX/fr1U58+fZScnOwyzyeffKKwsDBNmjRJDRo0UJ06ddSxY0e98cYb8vX1dRm7ZMkS1a9fX08++aQ+//xzZWZmSpJLJoGBgXI4HC77/P39S30f//jHPzR58mT99NNP5X7vklSpUiWFhYUpPDxczZo109ChQ/X1119f0hxXg17damjZiix9uPpH7cs8oRem79GpwrO6rV3JP4PXAjKxIxM7MrEjEzsysTM1E0rsFZacnKyEhARFRkZKks6cOaPi4mL5+Pi4jPP19VVaWlqJcxQVFWnWrFkKDAzUDTfc8KvW89BDD6mgoEArV6507vvss8904sQJJSQkqG/fvlq0aJGOHz/ufD0sLExZWVn6/PPPy5w/OTlZffv2VWBgoDp16qR58+b9qvVKUu/evRUdHa3Ro0df9hzZ2dn697//raZNm/7q9fyePD0diokO0OYt5/+jQ9qcnqP42MpuXJn7kIkdmdiRiR2Z2JGJncmZXNMldvny5fL393fZOnXqdNnzHTp0SB999JEGDhzo3BcQEKDmzZtrzJgxOnTokIqLi7VgwQKtX79eWVlZJa7Hx8dHU6ZM0cqVK1WlSpXLXo8kxcXFSfrlftNzkpOT1atXL3l4eKhBgwaqXbu2y/2sPXr0UO/evdW6dWuFh4ere/fumjZtmvLz813m3rNnjzZs2KCePXtKkvr27au5c+fKsqxftWaHw6GJEydq1qxZ+u6778p93PTp0+Xv7y8/Pz+Fhobq22+/dbnFwQSBlSvK08Oh7JzTLvuzc08rNNjLTatyLzKxIxM7MrEjEzsysTM5k2u6xLZt21bp6eku2+zZsy97vvnz5ysoKMj2AbCUlBRZlqWIiAh5e3vrlVdeUe/evVWhgmv859bzxRdfqGPHjkpMTLzkX6lf6FyhdDgckqTc3Fy9++67LveK9u3b1+WWAg8PD82dO1cHDhzQpEmTFBERofHjxys+Pt6leM+ZM0cdOnRwFu3OnTsrLy9Pn3766a9asyR16NBBLVu21DPPPFPuY+6++26lp6dry5YtSktLU3R0tNq3b2/7kNj5CgsLlZ+f77IVFhb+6vUDAIDf1jVdYv38/BQdHe2yRUREXNZclmVpzpw56tevn7y8XP/lUqdOHa1du1bHjh1TZmamNm3apNOnT6t27dolrqdZs2ZKTk6Wp6en7X7VS7Vz505JUq1atSRJb7/9tk6dOqWmTZvK09NTnp6eeuKJJ5SWlqbdu3e7HBsREaF+/fpp2rRp2r59u06dOqWZM2dK+uVDb/Pnz9cHH3zgnKdSpUrKzs6+Ylc/J06cqMWLF+ubb74p1/jAwEDn9/Hmm29WcnKy9uzZo8WLF5d6zIQJExQYGOiyTZgw4Yqs/3Lk5Z/WmWJLIcGuN9OHBFXUzzn2J0NcC8jEjkzsyMSOTOzIxM7kTK7pEnslrV27VhkZGRowYECpY/z8/BQeHq6cnBytWLFCXbt2veicZ8+e/dVXBc89GeDc0xWSk5P1yCOPuFx93rJli2655ZaLls/g4GCFh4c775398MMPVVBQoG+++cZlroULF+rdd99Vbm7ur1q3JP3lL3/R3/72Nz355JOXdbyHh4ekXz5cV5oRI0YoLy/PZRsxYsRlne9KOHPG0u6MAjW+Pti5z+GQGt8QrO3f5l/kyD8uMrEjEzsysSMTOzKxMzkTT3cv4Gq3Y8cOFRUVKTs7WwUFBc5nmjZq1MhlXHJyspo2bVric1dXrFghy7IUGxurjIwMPfbYY4qLi3M+weD48eMaN26c7rjjDoWHh+vo0aN67bXXdPDgQZdHdZUlNzdXhw8fVmFhoXbv3q3XX39dS5cu1ZtvvqmgoCClp6fr66+/1ltvveW8V/ac3r17a/To0Ro7dqySk5OVnp6u7t27q06dOjp16pTefPNNbd++Xa+++qrz/Xbp0sX2wbP69evroYce0ltvvaUHH3yw3Gsvzbhx4xQfHy9Pz7J/VE+cOKHDhw9Lkn788UeNGTNGPj4+at++fanHeHt7y9vb+1ev80patPSAnn4oTrsyCrRzd4ESu0bI16eCPlh12N1LcxsysSMTOzKxIxM7MrEzNRNKbBk6d+7s8sD8G2+8UZJcPryUl5en1NRUTZ06tcQ5zl3dO3DggEJCQnTnnXdq3Lhxqljxl0v3Hh4e2rVrl+bPn6+jR48qNDRUTZo00bp161z+aEJZzpViHx8fRUREqGXLltq0aZNuuukmSb8Uz/r169sKrCR1795dQ4cO1Ycffqi//OUvSktL05AhQ3To0CH5+/srPj5eS5cuVevWrfXjjz/qgw8+0Ntvv22bp0KFCurevbuSk5OvSImNiYnRfffdp1mzZpU59o033tAbb7wh6Zcrx9dff70+/PBDxcbG/up1/J4+TTuioMCKGnh3lEKCvZTx/TE9MnKbcnJPl33wHxSZ2JGJHZnYkYkdmdiZmonD+rUfJQf+gFrevtbdS7iqpC1rTSYXIBM7MrEjEzsysSMTu7Rlrcscwz2xAAAAMA4l1gDjx4+3Pc/2SjzXFgAAwFTcE2uAIUOGKDExscTXLvwzsAAAANcCSqwBQkJCFBIS4u5lAAAAXDW4nQAAAADGocQCAADAOJRYAAAAGIcSCwAAAONQYgEAAGAcSiwAAACMQ4kFAACAcSixAAAAMA4lFgAAAMahxAIAAMA4lFgAAAAYhxILAAAA41BiAQAAYBxKLAAAAIxDiQUAAIBxKLEAAAAwDiUWAAAAxqHEAgAAwDiUWAAAABiHEgsAAADjUGIBAABgHEosAAAAjEOJBQAAgHEosQAAADAOJRYAAADGocQCAADAOJRYAAAAGIcSCwAAAONQYgEAAGAcSiwAAACMQ4kFAACAcSixAAAAMA4lFgAAAMahxAIAAMA4lFgAAAAYx2FZluXuRQAAAACXwtPdCwCuRi1vX+vuJVxV0pa1JpMLkIkdmdiRiR2Z2JGJXdqy1mWO4XYCAAAAGIcSCwAAAONQYgEAAGAcSiwAAACMQ4kFAACAcSixAAAAMA4lFgAAAMahxAIAAMA4lFgAAAAYhxILAAAA41BiAQAAYBxKLAAAAIxDiQUAAIBxKLEAAAAwDiUWAAAAxqHEAgAAwDiUWAAAABiHEgsAAADjUGIBAABgHEosAAAAjEOJBQAAgHEosQAAADAOJRYAAADGocQCAADAOJRYAAAAGIcSCwAAAONQYgEAAGAcSiwAAACMQ4kFAACAcSixAAAAMA4lFgAAAMahxAIAAMA4lFgAAAAYhxILAAAA41BiAQAAYBxKLAAAAIxDiZWUlJSkbt262favWbNGDodDubm5OnXqlJKSktSwYUN5enqWOD4pKUkOh8O2xcfHO8cUFBRo+PDhioyMlK+vr1q0aKEvv/zSZZ5Ro0YpLi5Ofn5+Cg4OVkJCgjZu3Fju93P+uf38/FS3bl0lJSXpq6++so0tLi7WlClT1LBhQ/n4+Cg4OFidOnXSf//7X+eY22+/XR07dizxXOvWrZPD4dDWrVu1b98+ORwOpaena9SoUSVmcf52LrPzszyX4cSJE13Os3TpUucxkuv35vyvz22+vr6Kj4/XrFmzyp3b1eRvnatryeymWp16i2a9eKPq1Q1w95LcjkzsyMSOTOzIxI5M7EzMhBJbTsXFxfL19dWwYcOUkJBQ4pipU6cqKyvLuWVmZiokJEQ9evRwjhk4cKBWrlyplJQUbdu2Te3bt1dCQoIOHjzoHBMTE6Np06Zp27ZtSktLU1RUlNq3b68jR46Ue71z585VVlaWtm/frtdee03Hjh1T06ZN9eabbzrHWJalXr16afTo0frnP/+pnTt3as2aNapZs6batGmjpUuXSpIGDBiglStX6sCBAyWe589//rOuv/56l/2PPvqoSxY1atTQ6NGjXfaVxsfHR88//7xycnLK/X7P+fbbb5WVlaUdO3Zo8ODBuv/++7V69epLnsedbm1ZVUMH1tHchfs0YPhXyth7TJNHN1RQYEV3L81tyMSOTOzIxI5M7MjEztRMKLHl5OfnpxkzZmjQoEEKCwsrcUxgYKDCwsKc2+bNm5WTk6P+/ftLkk6ePKnU1FRNmjRJrVq1UnR0tEaNGqXo6GjNmDHDOU+fPn2UkJCg2rVrKz4+XpMnT1Z+fr62bt1a7vUGBQUpLCzMWYDfeecd3X333Ro6dKizHP773//WO++8ozfffFMDBw5UrVq1dMMNN2jWrFm64447NHDgQB0/fly33Xabqlatqnnz5rmc49ixY1qyZIkGDBhgO7+/v79LFh4eHgoICHDZV5qEhASFhYVpwoQJ5X6/51SrVk1hYWGqVauWhg0bplq1aunrr7++5HncqVe3Glq2Iksfrv5R+zJP6IXpe3Sq8Kxua1d6Zn90ZGJHJnZkYkcmdmRiZ2omlNjfUHJyshISEhQZGSlJOnPmjIqLi+Xj4+MyztfXV2lpaSXOUVRUpFmzZikwMFA33HDDr1rPQw89pIKCAq1cuVKS9PbbbysmJka33367bewjjzyin3/+WStXrpSnp6fuuecezZs3T5ZlOccsWbJExcXF6t27969a14U8PDw0fvx4vfrqqyVe/S0Py7L08ccf64cfflDTpk2v6Pp+S56eDsVEB2jzlv9/FdqypM3pOYqPrezGlbkPmdiRiR2Z2JGJHZnYmZwJJfb/LF++XP7+/i5bp06dLnu+Q4cO6aOPPtLAgQOd+wICAtS8eXONGTNGhw4dUnFxsRYsWKD169fbfr1+bj0+Pj6aMmWKVq5cqSpVqlz2eiQpLi5OkrRv3z5J0u7du1WvXr0Sx57bv3v3bknSfffdp++++05r1651jpk7d67uvPNOBQYG/qp1laR79+5q1KiRRo4ceUnH1ahRQ/7+/vLy8lKXLl00cuRItWrV6oqv77cSWLmiPD0cys457bI/O/e0QoO93LQq9yITOzKxIxM7MrEjEzuTM6HE/p+2bdsqPT3dZZs9e/Zlzzd//nwFBQXZPgCWkpIiy7IUEREhb29vvfLKK+rdu7cqVHD9VpxbzxdffKGOHTsqMTFRP/3002WvR5LzKur5H5A6/8rqxcTFxalFixaaM2eOJCkjI0Pr1q0r8VaCK+X555/X/PnztXPnznIfs27dOpfv3/jx411u1bhQYWGh8vPzXbbCwsIrsXwAAPAbosT+Hz8/P0VHR7tsERERlzWXZVmaM2eO+vXrJy8v13/F1KlTR2vXrtWxY8eUmZmpTZs26fTp06pdu3aJ62nWrJmSk5Pl6emp5OTky35/kpxlsFatWpJ++QBZaQXx3P6YmBjnvgEDBig1NVUFBQWaO3eu6tSpo9atW/+qNV1Mq1at1KFDB40YMaLcx9SqVUvR0dGKj49X//791a9fP40bN67U8RMmTFBgYKDLdjn34l4pefmndabYUkiw6830IUEV9XNOkZtW5V5kYkcmdmRiRyZ2ZGJnciaU2N/A2rVrlZGRcdGrlH5+fgoPD1dOTo5WrFihrl27XnTOs2fP/uorhC+//LIqV67sfLpCr169tGfPHi1btsw29qWXXlJoaKjatWvn3JeYmKgKFSro7bff1ptvvqn77rvP5arub2HixIlatmyZ1q9ff1nHe3h46OTJk6W+PmLECOXl5blsl1Kar7QzZyztzihQ4+uDnfscDqnxDcHa/m2+29blTmRiRyZ2ZGJHJnZkYmdyJp7uXoBJduzYoaKiImVnZ6ugoEDp6emSpEaNGrmMS05OVtOmTdWgQQPbHCtWrJBlWYqNjVVGRoYee+wxxcXFOZ9gcPz4cY0bN0533HGHwsPDdfToUb322ms6ePCgy6O6ypKbm6vDhw+rsLBQu3fv1uuvv66lS5fqzTffVFBQkKRfSuySJUt077336oUXXtBf//pX5efn67XXXtP777+vJUuWyM/Pzzmnv7+/evbsqREjRig/P19JSUmXlN/laNiwoe6++2698sor5Rr/008/6dSpUyosLNSmTZuUkpKiu+66q9Tx3t7e8vb2vlLLvSIWLT2gpx+K066MAu3cXaDErhHy9amgD1YddvfS3IZM7MjEjkzsyMSOTOxMzYQSewk6d+6s/fv3O7++8cYbJbneV5qXl6fU1FRNnTq1xDnOXek7cOCAQkJCdOedd2rcuHGqWPGXy/geHh7atWuX5s+fr6NHjyo0NFRNmjTRunXrXP5oQlnOlWIfHx9FRESoZcuW2rRpk2666SbnGIfDoX//+996+eWXNWXKFD3wwAPy8fFR8+bNtWbNGt188822eQcMGKDk5GR17txZ1atXL/d6fo3Ro0dr8eLF5RobGxsrSfL09FTNmjU1ePBgjRo16jdc3ZX3adoRBQVW1MC7oxQS7KWM74/pkZHblJN7uuyD/6DIxI5M7MjEjkzsyMTO1EwcVnk/2QNcQ1revrbsQdeQtGWtyeQCZGJHJnZkYkcmdmRil7as7M/ccE8sAAAAjEOJNcz48eNtz7O9Es+1BQAAMAn3xBpmyJAhSkxMLPE1X1/f33k1AAAA7kGJNUxISIhCQkLcvQwAAAC34nYCAAAAGIcSCwAAAONQYgEAAGAcSiwAAACMQ4kFAACAcSixAAAAMA4lFgAAAMahxAIAAMA4lFgAAAAYhxILAAAA41BiAQAAYBxKLAAAAIxDiQUAAIBxKLEAAAAwDiUWAAAAxqHEAgAAwDiUWAAAABiHEgsAAADjUGIBAABgHEosAAAAjEOJBQAAgHEosQAAADAOJRYAAADGocQCAADAOJRYAAAAGIcSCwAAAONQYgEAAGAcSiwAAACMQ4kFAACAcSixAAAAMA4lFgAAAMahxAIAAMA4lFgAAAAYhxILAAAA41BiAQAAYBxKLAAAAIzjsCzLcvciAAAAgEvh6e4FAFejrvd/6+4lXFXemxGrlrevdfcyrippy1rzc3KB92bEauoyrouc75+3O8jkAv+83aGB4466exlXldlPVyGTC8x+ukqZY7idAAAAAMahxAIAAMA4lFgAAAAYhxILAAAA41BiAQAAYBxKLAAAAIxDiQUAAIBxKLEAAAAwDiUWAAAAxqHEAgAAwDiUWAAAABiHEgsAAADjUGIBAABgHEosAAAAjEOJBQAAgHEosQAAADAOJRYAAADGocQCAADAOJRYAAAAGIcSCwAAAONQYgEAAGAcSiwAAACMQ4kFAACAcSixAAAAMA4lFgAAAMahxAIAAMA4lFgAAAAYhxILAAAA41BiAQAAYBxKLAAAAIxDiQUAAIBxKLEAAAAwDiUWAAAAxqHEAgAAwDiUWAAAABiHEgsAAADjXNMlNikpSd26dbPtX7NmjRwOh3Jzc3Xq1CklJSWpYcOG8vT0LHF8UlKSHA6HbYuPj3eOKSgo0PDhwxUZGSlfX1+1aNFCX375pcs8o0aNUlxcnPz8/BQcHKyEhARt3Lix3O+npDW0bNnSZczy5cvVunVrBQQEqFKlSmrSpInmzZvnMmbfvn0uc4SEhKh169Zat26dbb0lnTMuLs42R0nbvHnzXLI+P/v4+HgVFxe7nC8oKMhlrVFRUXr55Zddvj43t4eHh6pXr64BAwYoJyen3Bm6W8dWQZr6dJQWTo7WwsnRev6xP+mmeD93L+uq8LfO1bVkdlOtTr1Fs168UfXqBrh7SW7Dz4l06Lsv9UHyEM0bfYumPxqn7/+3yuX1TSte1dvPd9KsETcq+Zm/6P3X++vH/VvctNrfB5mUT1BABQ28w18vPxSi6Y+HatSgIEWGe7p7WW5laibXdIktj+LiYvn6+mrYsGFKSEgocczUqVOVlZXl3DIzMxUSEqIePXo4xwwcOFArV65USkqKtm3bpvbt2yshIUEHDx50jomJidG0adO0bds2paWlKSoqSu3bt9eRI0fKvd65c+e6rOX99993vvbqq6+qa9euuvnmm7Vx40Zt3bpVvXr10pAhQ/Too4/a5lq1apWysrL0+eefq3r16rrtttv0448/uoyJj493OV9WVpbS0tJUs2ZNl32PPPKIbWzPnj1LfR/ff/+93nzzzXK/73NGjx6trKws/fDDD3rrrbf0+eefa9iwYZc8j7v8nHNaby49oocn7NcjE/dr27cn9NSQCNUM93L30tzq1pZVNXRgHc1duE8Dhn+ljL3HNHl0QwUFVnT30tyCnxPpdNFJVakep1bdny3x9aCqUbql+zPq+ej76v7gWwoIjtCyNwbo5LHs33mlvx8yKVslH4eevCdQxWelqYvz9ezrOfr3quM6cfKsu5fmNiZncvXXbDfz8/PTjBkzJEn//e9/nVcMzxcYGKjAwEDn10uXLlVOTo769+8vSTp58qRSU1P13nvvqVWrVpJ+uYq5bNkyzZgxQ2PHjpUk9enTx2XeyZMnKzk5WVu3btVf//rXcq03KChIYWFhtv2ZmZl65JFHNHz4cI0fP965/5FHHpGXl5eGDRumHj16qGnTps7XQkNDFRYWprCwMD311FNatGiRNm7cqDvuuMM5xtPTs8TzSXLZ7+/vf9GxF/rHP/6hkSNHqk+fPvL29i7XMZIUEBDgPEdERITuvfdeLVy4sNzHu9uX2467fL3g/aPq2CpIsbV8lZlV5KZVuV+vbjW0bEWWPlz9yz+iXpi+R82bhOq2dmFa8E6mm1f3++PnRIqs10qR9VqV+nrMTbe7fH3zHU9q56Z39HPWt6pRt/lvvTy3IJOydWruq+z8s5q7/Jhz39G8q7+s/ZZMzoQrsb+B5ORkJSQkKDIyUpJ05swZFRcXy8fHx2Wcr6+v0tLSSpyjqKhIs2bNUmBgoG644YZfvaZ33nlHp0+fLvGK6+DBg+Xv719q2Tt58qTzqqiX1+9zpWf48OE6c+aMXn311cue4+DBg1q2bJlLMTdJBYd0y58D5OPl0Lffn3T3ctzG09OhmOgAbd7y/28LsSxpc3qO4mMru3FlVwd+TspWfKZI2zcslpdPgEKrx7l7OVeFazWTG+p6aX/WGQ35W4AmDw/RswOCdEuj8l8o+SMyOZNr/krs8uXL5e/v77LvwnsxL8WhQ4f00Ucf6e2333buCwgIUPPmzTVmzBjVq1dP1113nRYuXKj169crOjratp5evXrpxIkTCg8P18qVK1WlSpVyn793797y8PBwfr1gwQJ169ZNu3fvVmBgoMLDw23HeHl5qXbt2tq9e7fL/hYtWqhChQo6ceKELMtS48aNbVeEt23bZsuvb9++mjlzZrnXXJJKlSpp5MiReuqppzRo0CCXK90X88QTT+hf//qXiouLderUKTVt2lSTJ0/+VWv5vUVW99Lzj0XKq6JDJwvPasLrh5R5+Nq4ulaSwMoV5enhUHbOaZf92bmnFVmjkptW5X78nJRt347P9MmCR3Tm9En5BVTV7X+fI1+/YHcvy62u9UyqBnuoTWMPfbLxpD747wnVqu6p3u39VVwsfbGt0N3LcwuTM7nmr8S2bdtW6enpLtvs2bMve7758+crKCjI9gGwlJQUWZaliIgIeXt765VXXlHv3r1VoYLrt+Dcer744gt17NhRiYmJ+umnn8p9/ilTpri8l3bt2l32e1m8eLG++eYbpaamKjo6WvPmzVPFiq73IMbGxtryGz169GWf83wDBgxQaGionn/++XIf89hjjyk9PV1bt27V6tWrJUldunQp9R8mhYWFys/Pd9kKC937H+3BH4s0fPw+PTZpvz7+PFf/vDdMNcOunXsdUT78nJQtok5T9Xz4P/rb0IWqGXeLPkkZrhMFP7t7WW51rWficEj7D5/Rf9acUOaPxfr8m0KtSz+l1jf5lH3wH5TJmVzzJdbPz0/R0dEuW0RExGXNZVmW5syZo379+tl+7V6nTh2tXbtWx44dU2ZmpjZt2qTTp0+rdu3aJa6nWbNmSk5Olqenp5KTk8u9hrCwMJf34uf3yyeWY2JilJeXp0OHDtmOKSoq0nfffaeYmBiX/TVr1lTdunXVvXt3jR8/Xv+PvTsPq7LO/z/+OnLYBAUOWpCVpggomVpfxyVzacjUTG1MSksllxlnxsrGzLTNMrVp0Ww0GxOX0VYHq9EyIxOS0UwzFDMXKjfEkkTAjfX8/vDnmY73UQHR40efj+u6ryvu+3N/zvt+c7AXN/d9n7vuussS8Pz8/Cz9u+KKKypc75nY7XZNnDhR06ZN81i3J3Xq1FFUVJQaN26sW2+9Va+++qpWr16tlStXehw/efJk1zXNJ5fJkydXS/1VVVom7T9Qoh92F2nBR7namV2kHrdePmdKTpVfUKLSMqccYe6/QDlCffVr3uV75pH3ydn5+tdUSJ36iqjfQrcmTFQNH7u+//rf3i7Lqy73nuQfLldOrvtJjZzcMjlCLt84ZHJPLv4KDZKWlqasrCwNGTLktGOCgoIUGRmpvLw8LV++XL169TrjnOXl5dVyZrBPnz7y9fXVK6+8Ytn2xhtv6MiRI+rXr99p97/77rtlt9v1+uuvn3MtldG3b1/FxcXp2WefrdL+Jy+tOHbM87WCY8eOVX5+vtsyduzYKtd7Pthskq/d5u0yvKa01KntWYW66Yb/BTSbTbqpeZi+21bgxcouLpf7+6QinM5ylZVevr/4eHK59SRrT4mudPi4rbvS4aNfDbmR6XwwuSeX/TWxFbFlyxYVFxfr4MGDKiwsVEZGhiSpRYsWbuOSkpLUunVrXX/99ZY5li9fLqfTqZiYGGVlZWn06NGKjY11PcHgyJEjmjhxonr27KnIyEjl5uZqxowZys7OdntUV1Vde+21evHFFzVq1CgFBARowIAB8vX11UcffaRx48Zp1KhRZ7wBymaz6aGHHtL48eP1pz/9STVrnrgWsbS0VPv377eMvfLKK8+55pNeeOEF3X777RUaW1hYqP3798vpdGrPnj167LHHVLduXbVr187jeH9//0o9/eB8G9Crjr757ohyD5YoMKCGOrSqresb19T4f+z1dmle9e6He/XEI7HamlWo77cXKqFXPQUG1NDHn+8/+86XIN4nUknREeXn7nZ9XXhwr3Kzv5d/zRAF1AzVNyveUIO4WxVUq66OHc3T5v++rSP5PyuqeVcvVn1+0ZOzS/n6uB4fFKLu7QK1/vsiNbjKVx1aBuhfnxw++86XKJN7QoitgO7du2vXrl2ur1u2bCnpxOUDJ+Xn5ys5OVnTpk3zOMfJM3x79+6Vw+FQnz59NHHiRNc1pj4+Ptq6davmz5+v3NxchYeHq1WrVlq1apXbhyaci5EjR6phw4Z6+eWXNW3aNJWVlSkuLk4zZ850hekzGTRokJ544glNnz5djz32mCTpu+++s9ws5u/vr+PHj1dLzZJ066236tZbb9Vnn3121rFPP/20nn76xDMS69atq1atWumzzz5TeHh4tdVzPoXU8tHIxEg5avvoyPFy7cou0vh/7NXGrUe9XZpXfZF+QKEhvhp6XwM5wvyU9eNhjXomU3mHSs6+8yWI94n0y57N+uiNQa6v//ufFyRJMf/XWx37PKu8X37StvUP6diRPAUEheqKa5qp91/ekiOisbdKPu/oydntzCnV6/8u0B86B+nOW2oq91CZ3k05rLXfXdw3MJ1PJvfE5vxtEgMgSer1523eLuGi8tHMGLW/M83bZVxU0pd05H1yio9mxmjaEv6X8lsP32mjJ6d4+E6bhk7M9XYZF5XZT9ShJ6eY/cTZn8zENbEAAAAwDiHWEJMmTVJwcLDHpVu3bt4uDwAA4ILimlhDDB8+XAkJCR63BQYGXuBqAAAAvIsQawiHwyGHw+HtMgAAAC4KXE4AAAAA4xBiAQAAYBxCLAAAAIxDiAUAAIBxCLEAAAAwDiEWAAAAxiHEAgAAwDiEWAAAABiHEAsAAADjEGIBAABgHEIsAAAAjEOIBQAAgHEIsQAAADAOIRYAAADGIcQCAADAOIRYAAAAGIcQCwAAAOMQYgEAAGAcQiwAAACMQ4gFAACAcQixAAAAMA4hFgAAAMYhxAIAAMA4hFgAAAAYhxALAAAA4xBiAQAAYBxCLAAAAIxDiAUAAIBxCLEAAAAwDiEWAAAAxiHEAgAAwDiEWAAAABiHEAsAAADjEGIBAABgHEIsAAAAjEOIBQAAgHFsTqfT6e0iAAAAgMqwe7sA4GLU/s40b5dwUUlf0pGenIKeWNETK3piRU+s6IlV+pKOZx3D5QQAAAAwDiEWAAAAxiHEAgAAwDiEWAAAABiHEAsAAADjEGIBAABgHEIsAAAAjEOIBQAAgHEIsQAAADAOIRYAAADGIcQCAADAOIRYAAAAGIcQCwAAAOMQYgEAAGAcQiwAAACMQ4gFAACAcQixAAAAMA4hFgAAAMYhxAIAAMA4hFgAAAAYhxALAAAA4xBiAQAAYBxCLAAAAIxDiAUAAIBxCLEAAAAwDiEWAAAAxiHEAgAAwDiEWAAAABiHEAsAAADjEGIBAABgHEIsAAAAjEOIBQAAgHEIsQAAADAOIRYAAADGIcQCAADAOIRYAAAAGIcQe4rExET17t3bsj41NVU2m02HDh3S8ePHlZiYqGbNmslut3scn5iYKJvNZlni4uJcYwoLCzVy5EjVr19fgYGBateundatW+c2z/jx4xUbG6ugoCCFhYUpPj5ea9eurdQxrVy5Uj169FDdunUVEBCgRo0a6Z577tGXX35pOT5Py/79+121eNr++eefu7a3aNFCktSgQYPTzmez2ZSYmChJstls+vDDD1112Gw2BQQEaNeuXW7H0Lt3b9c+J/v7276f2u/w8HB17dpVmzZtqlSvLhZ/6H6VFs1urRXJt2jWyy3VpHEtb5fkdfTEip5Y0RMremJFT6xM7AkhtgrKysoUGBiohx56SPHx8R7HTJs2TTk5Oa5lz549cjgc6tu3r2vM0KFDlZKSogULFigzM1NdunRRfHy8srOzXWOio6M1ffp0ZWZmKj09XQ0aNFCXLl104MCBCtX6+uuv6/e//73Cw8P13nvvadu2bfrggw/Url07PfLII5bx27Ztc6s7JydHV1xxhWt7XFycZXuHDh0s86xbt861PTk52TL3tGnTTluzzWbT008/XaHj+62uXbu65l+xYoXsdrt69OhR6Xm87db2dTViaCPNfWenhoz8Rlk/HdaU55opNMTX26V5DT2xoidW9MSKnljREytTe0KIrYKgoCDNnDlTw4YNU0REhMcxISEhioiIcC3r169XXl6eHnjgAUnSsWPHlJycrBdffFEdOnRQVFSUxo8fr6ioKM2cOdM1T//+/RUfH6+GDRsqLi5OU6ZMUUFBQYXOMO7evVsjR47UyJEjNX/+fN16662qX7++brjhBj388MNav369ZZ8rrrjCre6IiAjVqPG/t4ndbrds9/Pzs8xTt25d13aHw2GZOyQk5LR1jxgxQgsXLtTmzZvPeoy/5e/v75q/RYsWevzxx7Vnz54KB/6Lxb29r9aS5Tn6ZMXP2rnnqF56fYeOF5Wrx22e32uXA3piRU+s6IkVPbGiJ1am9oQQe4EkJSUpPj5e9evXlySVlpaqrKxMAQEBbuMCAwOVnp7ucY7i4mLNmjVLISEhat68+VlfMzk5WSUlJXrsscc8brfZbJU8igvj5ptvVo8ePfT4449XeY7Dhw9r4cKFioqKUnh4eDVWd37Z7TZFR9XS+o15rnVOp7Q+I09xMbW9WJn30BMremJFT6zoiRU9sTK5J4RYD5YuXarg4GC3pVu3blWeb9++fVq2bJmGDh3qWlerVi21bdtWEyZM0L59+1RWVqaFCxdqzZo1ysnJ8VhPQECApk6dqpSUFNWpU+esr7t9+3bVrl3b7WxxcnKy23FlZma67XP11Ve7bf/tNbySlJmZ6bb9d7/7XVVaclaTJ0/Wp59+qlWrVlV4n99+32rVqqX//Oc/eu+999zOJF/sQmr7yu5j08G8Erf1Bw+VKDzMesb7ckBPrOiJFT2xoidW9MTK5J7YvV3Axahz585uf9KXpLVr1+r++++v0nzz589XaGio5QawBQsWaPDgwapXr558fHx04403ql+/fvrmm28s9WRkZCg3N1dvvvmmEhIStHbtWrdrVU/n1LOtt99+uzIyMpSdna1OnTqprKzMbfuqVatUq9b/Lub29XW/HiYmJkb/+c9/XF/7+/uftYaqaNq0qQYOHKjHH39c//3vfyu0z2+/b3l5eXr99dfVrVs3ff31164z4KcqKipSUVGR27rzdUwAAKD6EGI9CAoKUlRUlNu6vXv3Vmkup9OpOXPmaMCAAZZrRxs1aqS0tDQdOXJEBQUFioyM1D333KOGDRt6rCcqKkpt2rRR48aNlZSUpLFjx57xtRs3bqz8/Hzt37/fdTY2ODhYUVFRsts9f+uvu+46hYaGnnZOPz8/S2/Ol2effVbR0dFuTy84k1O/b7Nnz1ZISIjefPNNPf/88x73mTx5sp599lm3dc8884ykzlUt+5zkF5SotMwpR5j7Lw+OUF/9mlfslZq8jZ5Y0RMremJFT6zoiZXJPTHn76yGSktLU1ZWloYMGXLaMUFBQYqMjFReXp6WL1+uXr16nXHO8vJyy9lDT+6++275+vrq73//e6Xrvhhcc801GjFihMaNG2c5Y1wRNptNNWrU0LFjx047ZuzYscrPz3dbzvbLwflUWurU9qxC3XRDmGudzSbd1DxM320r8Fpd3kRPrOiJFT2xoidW9MTK5J5wJraKtmzZouLiYh08eFCFhYXKyMiQJNdzUk9KSkpS69atdf3111vmWL58uZxOp2JiYpSVlaXRo0crNjbW9QSDI0eOaOLEierZs6ciIyOVm5urGTNmKDs72+1RXadz7bXX6pVXXtHDDz+sgwcPKjExUdddd50OHjyohQsXSpJ8fHzc9vnll190/Phxt3Xh4eGWywoulLFjx+rNN9/UTz/9pHvuueeMY4uKilzPtM3Ly9P06dN1+PBh3Xnnnafdx9/f/6K7fODdD/fqiUditTWrUN9vL1RCr3oKDKihjz/f7+3SvIaeWNETK3piRU+s6ImVqT0hxFZR9+7d3R7I37JlS0knLh84KT8/X8nJyad9JurJs3579+6Vw+FQnz59NHHiRFdg9PHx0datWzV//nzl5uYqPDxcrVq10qpVqyw3XJ3Ogw8+qCZNmmjKlCm6++67VVBQoPDwcLVt21affvqpmjVr5jY+JibGMseaNWvUpk2bCr1edXM4HBozZozGjRt31rGffvqpIiMjJZ24cS42NlaLFi1Sp06dznOV1euL9AMKDfHV0PsayBHmp6wfD2vUM5nKO1Ry9p0vUfTEip5Y0RMremJFT6xM7YnN+dvUBUCS1P7ONG+XcFFJX9KRnpyCnljREyt6YkVPrOiJVfqSjmcdwzWxAAAAMA4h1mCTJk2yPM+2Op5rCwAAcLHjmliDDR8+XAkJCR63BQYGXuBqAAAALhxCrMEcDoccDoe3ywAAALjguJwAAAAAxiHEAgAAwDiEWAAAABiHEAsAAADjEGIBAABgHEIsAAAAjEOIBQAAgHEIsQAAADAOIRYAAADGIcQCAADAOIRYAAAAGIcQCwAAAOMQYgEAAGAcQiwAAACMQ4gFAACAcQixAAAAMA4hFgAAAMYhxAIAAMA4hFgAAAAYhxALAAAA4xBiAQAAYBxCLAAAAIxDiAUAAIBxCLEAAAAwDiEWAAAAxiHEAgAAwDiEWAAAABiHEAsAAADjEGIBAABgHEIsAAAAjEOIBQAAgHEIsQAAADAOIRYAAADGIcQCAADAOIRYAAAAGIcQCwAAAOPYnE6n09tFAAAAAJVh93YBwMWo/Z1p3i7hopK+pCM9OQU9saInVvTEip5Y0ROr9CUdzzqGywkAAABgHEIsAAAAjEOIBQAAgHEIsQAAADAOIRYAAADGIcQCAADAOIRYAAAAGIcQCwAAAOMQYgEAAGAcQiwAAACMQ4gFAACAcQixAAAAMA4hFgAAAMYhxAIAAMA4hFgAAAAYhxALAAAA4xBiAQAAYBxCLAAAAIxDiAUAAIBxCLEAAAAwDiEWAAAAxiHEAgAAwDiEWAAAABiHEAsAAADjEGIBAABgHEIsAAAAjEOIBQAAgHEIsQAAADAOIRYAAADGIcQCAADAOIRYAAAAGIcQCwAAAOMQYgEAAGAcQiwAAACMQ4gFAACAcQixAAAAMA4hthISExPVu3dvy/rU1FTZbDYdOnRIx48fV2Jiopo1aya73e5xfGJiomw2m2WJi4tzjSksLNTIkSNVv359BQYGql27dlq3bp3bPOPHj1dsbKyCgoIUFham+Ph4rV27tsLHY7PZ9OGHH7qtW7lypbp3767w8HDVrFlTTZs21ahRo5Sdne0aU1ZWpqlTp6pZs2YKCAhQWFiYunXrpv/+97+uMZ06dfJ4jCeXTp06SZIaNGigV1991bVfgwYNZLPZ9NVXX7nVNXLkSNc+J4+9RYsWbl//dv6QkBDdcsstSktLq3A/LiZ/6H6VFs1urRXJt2jWyy3VpHEtb5fkdfTEip5Y0RMremJFT6xM7AkhtpqVlZUpMDBQDz30kOLj4z2OmTZtmnJyclzLnj175HA41LdvX9eYoUOHKiUlRQsWLFBmZqa6dOmi+Ph4tzAZHR2t6dOnKzMzU+np6WrQoIG6dOmiAwcOVKn2f/7zn4qPj1dERISSk5O1ZcsWvfHGG8rPz9crr7wiSXI6nbr33nv13HPP6eGHH9b333+v1NRUXXPNNerUqZMrFC9evNh1fF9//bUk6fPPP3etW7x48WnrCAgI0JgxYypdf1xcnGv+NWvWqHHjxurRo4fy8/Mr3wwvurV9XY0Y2khz39mpISO/UdZPhzXluWYKDfH1dmleQ0+s6IkVPbGiJ1b0xMrUnhBiq1lQUJBmzpypYcOGKSIiwuOYkJAQRUREuJb169crLy9PDzzwgCTp2LFjSk5O1osvvqgOHTooKipK48ePV1RUlGbOnOmap3///oqPj1fDhg0VFxenKVOmqKCgQJs2bap03Xv37tVDDz2khx56SHPmzFGnTp3UoEEDdejQQbNnz9bTTz8tSXr//ff173//W//61780dOhQXXfddWrevLlmzZqlnj17aujQoTpy5IgcDofr+OrWrStJCg8Pd61zOBynreWPf/yjvvrqK33yySeVOga73e6av2nTpnruued0+PBhbd++vdL98KZ7e1+tJctz9MmKn7Vzz1G99PoOHS8qV4/bPL+fLgf0xIqeWNETK3piRU+sTO0JIfYikJSUpPj4eNWvX1+SVFpaqrKyMgUEBLiNCwwMVHp6usc5iouLNWvWLIWEhKh58+aVrmHRokUqLi7WY4895nF7aGioJOntt99WdHS07rzzTsuYUaNG6ddff1VKSkqlX/+3rrvuOg0fPlxjx45VeXl5leYoKirS3LlzFRoaqpiYmHOq50Ky222Kjqql9RvzXOucTml9Rp7iYmp7sTLvoSdW9MSKnljREyt6YmVyTwixlbR06VIFBwe7Ld26davyfPv27dOyZcs0dOhQ17patWqpbdu2mjBhgvbt26eysjItXLhQa9asUU5Ojsd6AgICNHXqVKWkpKhOnTqVrmPHjh2qXbu2IiMjzzhu+/btatKkicdtJ9dXx5nPJ598Uj/99JPeeuutCu+TmZnp+p4EBgbq5Zdf1jvvvKPatS/uH8LfCqntK7uPTQfzStzWHzxUovAwPy9V5V30xIqeWNETK3piRU+sTO4JIbaSOnfurIyMDLdl9uzZVZ5v/vz5Cg0NtdwAtmDBAjmdTtWrV0/+/v567bXX1K9fP9Wo4f4tO1nP6tWr1bVrVyUkJOiXX36pdB1Op1M2m63CY8+3unXr6tFHH9XTTz+t4uLiCu0TExPj+p588803+vOf/6y+fftq/fr1p92nqKhIBQUFbktRUVF1HQYAADhPCLGVFBQUpKioKLelXr16VZrL6XRqzpw5GjBggPz83H/badSokdLS0nT48GHt2bNHX3/9tUpKStSwYUOP9bRp00ZJSUmy2+1KSkqqdC3R0dHKz8+3nOn1NO7777/3uO3k+ujo6Eq/vid/+9vfdOzYMb3++usVGu/n5+f6nrRs2VIvvPCC6tWr5/b0g1NNnjxZISEhbsvkyZOrpf6qyC8oUWmZU44w94vpHaG++jWvYmH+UkNPrOiJFT2xoidW9MTK5J4QYr0oLS1NWVlZGjJkyGnHBAUFKTIyUnl5eVq+fLl69ep1xjnLy8urdCbx7rvvlp+fn1588UWP2w8dOiRJuvfee7Vjxw4tWbLEMuaVV15ReHi4brvttkq/vifBwcF66qmnNHHiRBUWFlZpDh8fHx07duy028eOHav8/Hy3ZezYsVUt+ZyVljq1PatQN90Q5lpns0k3NQ/Td9sKvFaXN9ETK3piRU+s6IkVPbEyuSd2bxdwKdqyZYuKi4t18OBBFRYWKiMjQ5Lcnmsqnbihq3Xr1rr++ustcyxfvlxOp1MxMTHKysrS6NGjFRsb63qCwZEjRzRx4kT17NlTkZGRys3N1YwZM5Sdne32qK6KuuaaazR16lSNGDFCBQUFGjhwoBo0aKC9e/fqX//6l4KDg/XKK6/o3nvv1aJFizRo0CC99NJL+v3vf6+CggLNmDFD//nPf7Ro0SIFBQVV+vVP549//KOmTp2qt99+W61btz7j2NLSUu3fv1/Siefsvvfee9qyZcsZH9fl7+8vf3//aqu3Orz74V498UistmYV6vvthUroVU+BATX08ef7vV2a19ATK3piRU+s6IkVPbEytSeE2POge/fu2rVrl+vrli1bSnK/ljQ/P1/JycmaNm2axzlOnhHcu3evHA6H+vTpo4kTJ8rX98Tpfh8fH23dulXz589Xbm6uwsPD1apVK61atcrtQxMq4y9/+Yuio6P18ssv66677tKxY8fUoEED9ejRQ3/7298knfiAhPfff1+vvvqqpk6dqr/85S8KCAhQ27ZtlZqaqptvvrlKr306vr6+mjBhgvr373/Wsd99953rxrSaNWuqUaNGmjlzpgYOHFitNZ1vX6QfUGiIr4be10COMD9l/XhYo57JVN6hkrPvfImiJ1b0xIqeWNETK3piZWpPbM4LcZcOYJj2d5r5SV/nS/qSjvTkFPTEip5Y0RMremJFT6zSl3Q86xiuiQUAAIBxCLGXqEmTJlmeZ1sdz7UFAAC4GHBN7CVq+PDhSkhI8LgtMDDwAlcDAABQvQixlyiHwyGHw+HtMgAAAM4LLicAAACAcQixAAAAMA4hFgAAAMYhxAIAAMA4hFgAAAAYhxALAAAA4xBiAQAAYBxCLAAAAIxDiAUAAIBxCLEAAAAwDiEWAAAAxiHEAgAAwDiEWAAAABiHEAsAAADjEGIBAABgHEIsAAAAjEOIBQAAgHEIsQAAADAOIRYAAADGIcQCAADAOIRYAAAAGIcQCwAAAOMQYgEAAGAcQiwAAACMQ4gFAACAcQixAAAAMA4hFgAAAMYhxAIAAMA4hFgAAAAYhxALAAAA4xBiAQAAYBx7ZXdo2bKlbDabZb3NZlNAQICioqKUmJiozp07V0uBAAAAwKkqfSa2a9eu+vHHHxUUFKTOnTurc+fOCg4O1g8//KBWrVopJydH8fHx+uijj85HvQAAAEDlz8Tm5uZq1KhReuqpp9zWP//889q1a5c+++wzPfPMM5owYYJ69epVbYUCAAAAJ1X6TOz777+vfv36Wdbfe++9ev/99yVJ/fr107Zt2869OgAAAMCDSofYgIAArV692rJ+9erVCggIkCSVl5e7/hsAAACobjan0+mszA7PP/+8Jk2apGHDhqlVq1aSpHXr1mn27NkaN26cnnjiCU2dOlWffPKJUlJSzkvRAAAAuLxVOsRK0ltvvaXp06e7LhmIiYnRgw8+qP79+0uSjh075npaAWCi9nemebuEi0r6ko705BTpSzoq7buj3i7jotIxriY9OQU9saInVvTEqmNczbOOqfSNXZJ033336b777jvt9sDAwKpMCwAAAFQIH3YAAAAA41T6TGyNGjU8ftjBSWVlZedUEAAAAHA2lQ6xH3zwgdvXJSUl+vbbbzV//nw9++yz1VYYAAAAcDqVDrGePsDg7rvvVlxcnN577z0NGTKkWgoDAAAATqfarolt06aNVqxYUV3TAQAAAKdVLSH22LFjeu2111SvXr3qmA4AAAA4o0pfThAWFuZ2Y5fT6VRhYaFq1qyphQsXVmtxAAAAgCeVDrGvvvqq29c1atRQ3bp11bp1a4WFhVVXXQAAAMBpVSrElpaWateuXRo8eLCuvvrq81UTAAAAcEaVuibWbrfrpZdeUmlp6fmqBwAAADirSt/Ydeuttyotjc9QBwAAgPdU+prYbt266fHHH1dmZqZuuukmBQUFuW3v2bNntRUHAAAAeFLpEPuXv/xFkjRlyhTLNpvNxsfOAgAA4LyrdIgtLy8/H3UAAAAAFVZtn9gFAAAAXCiVOhNbXl6uefPmafHixdq5c6dsNpuuu+463X333RowYIDbhyAAAAAA50uFz8Q6nU717NlTQ4cOVXZ2tpo1a6a4uDjt2rVLiYmJuuuuu85nnQAAAIBLhc/Ezps3T19++aVWrFihzp07u2374osv1Lt3b/3rX//SwIEDq71IAAAA4LcqfCb2nXfe0bhx4ywBVjrx7NjHH39cb731VrUWBwAAAHhS4RC7adMmde3a9bTbu3Xrpo0bN1ZLUQAAAMCZVDjEHjx4UFdeeeVpt1955ZXKy8urlqIAAACAM6lwiC0rK5PdfvpLaH18fFRaWlotRQEAAABnUuEbu5xOpxITE+Xv7+9xe1FRUbUVBQAAAJxJhUPsoEGDzjqGJxMAAADgQqhwiJ07d+75rAMAAACoMD52FgAAAMYhxAIAAMA4hFgAAAAYhxALAAAA4xBiAQAAYJxzCrG1a9fWjz/+WF21AAAAABVyTiHW6XRWVx0AAABAhXE5AQAAAIxzTiH2/vvvV+3ataurFgAAAKBCKvyJXZ7MnDmzuuoAAAAAKozLCSohMTFRvXv3tqxPTU2VzWbToUOHdPz4cSUmJqpZs2ay2+0exycmJspms1mWuLg415jCwkKNHDlS9evXV2BgoNq1a6d169a5zTN+/HjFxsYqKChIYWFhio+P19q1ayt8PDabTR9++KHb1zabTV999ZXbuKKiIoWHh8tmsyk1NdUyz5/+9Cf5+Pho0aJFlrlOt4wfP147d+6UzWZTRkaGJLm+vuKKK1RYWOj2Gi1atND48eNdX3fq1EkjR450+/q381955ZXq27evdu3aVeF+XEz+0P0qLZrdWiuSb9Gsl1uqSeNa3i7J6y7nnixLTtLE0ffpwf43a1TirZrxwiPan73TbUxJcZHenjVZjwzspAf7t9PMF0ep4NCv3in4AqAnVvTEip5YXUo9IcRWs7KyMgUGBuqhhx5SfHy8xzHTpk1TTk6Oa9mzZ48cDof69u3rGjN06FClpKRowYIFyszMVJcuXRQfH6/s7GzXmOjoaE2fPl2ZmZlKT09XgwYN1KVLFx04cKDK9V9zzTWaO3eu27oPPvhAwcHBHscfPXpU7777rh577DHNmTPHtf63x/fqq6+qdu3abuseffTR09ZQWFiol19+udK1Dxs2TDk5Odq3b58++ugj7dmzR/fff3+l5/G2W9vX1YihjTT3nZ0aMvIbZf10WFOea6bQEF9vl+Y1l3tPtn+3QZ273aOxL/xLI5+ZqbLSUr367J9VdPyYa8z7c1/WxvVf6k+jX9SjE2Yr/+ABzfz7KC9WfX7REyt6YkVPrC6lnhBiq1lQUJBmzpypYcOGKSIiwuOYkJAQRUREuJb169crLy9PDzzwgCTp2LFjSk5O1osvvqgOHTooKipK48ePV1RUlNslHP3791d8fLwaNmyouLg4TZkyRQUFBdq0aVOV6x80aJDeffddHTv2vzfznDlzNGjQII/jFy1apKZNm+rxxx/Xl19+qT179kiS2/GFhITIZrO5rTtdKJakBx98UFOmTNEvv/xSqdpr1qypiIgIRUZGqk2bNhoxYoQ2bNhQqTkuBvf2vlpLlufokxU/a+eeo3rp9R06XlSuHrd5fj9dDi73njz89Ay1u7Wnrrq2ka65LkYPPPisDubu164ftkiSjh4pVPqKD5WQ+DfFNvud6jdqqkEjntUP2zbqx21V//fgYkZPrOiJFT2xupR6Qoi9CCQlJSk+Pl7169eXJJWWlqqsrEwBAQFu4wIDA5Wenu5xjuLiYs2aNUshISFq3rx5lWu56aab1KBBAyUnJ0uSdu/erS+//FIDBgw4be3333+/QkJC1K1bN82bN6/Kr31Sv379FBUVpeeee67Kcxw8eFDvv/++Wrdufc71XEh2u03RUbW0fmOea53TKa3PyFNczOV5EyU9sTp29LAkKSg4RJK0+8fvVVZaqibN27jGRF59nRx1IvTD9ovrfzrnCz2xoidW9MTK5J5UKcT+8MMPevLJJ9WvXz/X2bJly5bpu+++q9biLkZLly5VcHCw29KtW7cqz7dv3z4tW7ZMQ4cOda2rVauW2rZtqwkTJmjfvn0qKyvTwoULtWbNGuXk5HisJyAgQFOnTlVKSorq1KlT5XokafDgwa5LA+bNm6fu3burbt26lnE7duzQV199pXvuuUfSiadVzJ0795yfH2yz2fTCCy9o1qxZ+uGHHyq83+uvv67g4GAFBQUpPDxc27Ztc7vEwQQhtX1l97HpYF6J2/qDh0oUHubnpaq8i564Ky8v13tzXlaj2BaqVz9KkpSf96vsdl/VDHK/Trh2aLgK8i6+69iqGz2xoidW9MTK9J5UOsSmpaWpWbNmWrt2rRYvXqzDh08k+I0bN+qZZ56p9gIvNp07d1ZGRobbMnv27CrPN3/+fIWGhlpuAFuwYIGcTqfq1asnf39/vfbaa+rXr59q1HD/lp2sZ/Xq1eratasSEhIq/Wf4U91///1as2aNfvzxR82bN0+DBw/2OG7OnDm6/fbbXaG5e/fuys/P1xdffHFOry9Jt99+u9q3b6+nnnqqwvvcd999ysjI0MaNG5Wenq6oqCh16dLFcpPYbxUVFamgoMBtKSoqOuf6gfPlnTcna9/uLP3xby94u5SLBj2xoidW9MTK9J5UOsQ+/vjjev7555WSkiI/v/+dBbn11lstd7VfioKCghQVFeW21KtXr0pzOZ1OzZkzRwMGDHDrpSQ1atRIaWlpOnz4sPbs2aOvv/5aJSUlatiwocd62rRpo6SkJNntdiUlJVX5+CQpPDxcPXr00JAhQ3T8+HGPZ5rLyso0f/58ffzxx7Lb7bLb7apZs6YOHjxYbWc/X3jhBb333nv69ttvKzQ+JCTE9T25+eablZSUpB07dui999477T6TJ09WSEiI2zJ58uRqqb8q8gtKVFrmlCPM/YYlR6ivfs0r9lJV3kVP/uftN1/QpvWrNOq5NxVW50rX+pCwcJWWlujoEfdf2AoO/araYeEXuswLip5Y0RMremJ1KfSk0iE2MzNTd911l2X9FVdcodzc3Gop6nKRlpamrKwsDRky5LRjgoKCFBkZqby8PC1fvly9evU645zl5eXVciZx8ODBSk1N1cCBA+Xj42PZ/sknn6iwsFDffvut21npd955R4sXL9ahQ4fOuYbf/e53+sMf/qDHH3+8SvufrPu3N6mdauzYscrPz3dbxo4dW6XXqw6lpU5tzyrUTTeEudbZbNJNzcP03bYCr9XlTfTkxC+8b7/5gjLWfqG/PftP1bnS/Rfnaxs2kY/dru83/e8Re/uzd+pg7n41ir7hQpd7QdATK3piRU+sLqWeVPrDDkJDQ5WTk6PrrrvObf23335b5TOSl5otW7aouLhYBw8eVGFhoes5qC1atHAbl5SUpNatW+v666+3zLF8+XI5nU7FxMQoKytLo0ePVmxsrOsJBkeOHNHEiRPVs2dPRUZGKjc3VzNmzFB2drbbo7qqqmvXrjpw4MBpP5EtKSlJd9xxh+UmsqZNm+qRRx7RW2+9pb/+9a/nXMfEiRMVFxcnu/3sb9WjR49q//79kqSff/5ZEyZMUEBAgLp06XLaffz9/eXv73/OdVandz/cqyceidXWrEJ9v71QCb3qKTCghj7+fL+3S/Oay70nb8+arK9XLdNfxk5VQGCQ8vNOnDAIrBksP/8A1Qyqpfa/761Fc19RUHCIAmsG6Z3Zf1fDmBvUMObi+p9OdaEnVvTEip5YXUo9qXSIvffeezVmzBgtWrRINptN5eXl+u9//6tHH31UAwcOPB81Gqd79+5uD9lv2bKlJLnd8JSfn6/k5GRNmzbN4xwnzwju3btXDodDffr00cSJE+Xre+JPqj4+Ptq6davmz5+v3NxchYeHq1WrVlq1apXbhyZUlc1mO+0NYj///LM+/vhjvf3225ZtNWrU0F133aWkpKRqCbHR0dEaPHiwZs2addaxb775pt58801JUlhYmG644QZ98skniomJOec6LqQv0g8oNMRXQ+9rIEeYn7J+PKxRz2Qq71DJ2Xe+RF3uPUlbfuKDRF55apjb+sQRz6rdrT0lSQkPPCqbrYbeeOlRlZYUK65FO/X/o/f+qnC+0RMremJFT6wupZ7YnJW8lby4uFh//etfNW/ePJWVlclut6usrEz9+/fXvHnzPP7pGTBN+zvTvF3CRSV9SUd6cor0JR2V9t1Rb5dxUekYV5OenIKeWNETK3pi1TGu5lnHVPpMrJ+fn95880099dRT2rx5sw4fPqyWLVuqcePGVSoSAAAAqKxKh9iTrr32Wl177bXVWQuq0aRJkzRp0iSP22655RYtW7bsAlcEAABQfSodYsvKyjRv3jytWLFCv/zyi8rLy922V8czQnHuhg8froSEBI/bAgMDL3A1AAAA1avSIfbhhx/WvHnzdMcdd+j666+XzWY7H3XhHDkcDjkcDm+XAQAAcF5UOsS+++67ev/999W9e/fzUQ8AAABwVpX+sAM/Pz9FRUWdj1oAAACACql0iB01apSmTZumSj6ZCwAAAKg2lb6cID09XStXrtSyZcsUFxfnevj+SYsXL6624gAAAABPqvSxs3fdddf5qAUAAACokEqH2Llz556POgAAAIAKq/Q1sQAAAIC3VehM7I033qgVK1YoLCxMLVu2POOzYTds2FBtxQEAAACeVCjE9urVS/7+/pKk3r17n896AAAAgLOqUIh95plnPP43AAAA4A1cEwsAAADjVOhMbFhY2Bmvg/2tgwcPnlNBAAAAwNlUKMS++uqr57kMAAAAoOIqFGIHDRp0vusAAAAAKoxrYgEAAGAcQiwAAACMQ4gFAACAcQixAAAAMA4hFgAAAMap0NMJ/vCHP1R4wsWLF1e5GAAAAKAiKhRiQ0JCzncdAAAAQIVVKMTOnTv3fNcBAAAAVBjXxAIAAMA4FToT27JlS9lstgpNuGHDhnMqCAAAADibCoXY3r17n+cyAAAAgIqrUIh95plnzncdAAAAQIVV6ZrYQ4cOafbs2Ro7dqwOHjwo6cRlBNnZ2dVaHAAAAOBJhc7E/tamTZsUHx+vkJAQ7dy5U8OGDZPD4dDixYu1e/du/etf/zofdQIAAAAulT4T+7e//U2JiYnasWOHAgICXOu7d++uL7/8slqLAwAAADypdIhdt26d/vSnP1nW16tXT/v376+WogAAAIAzqXSI9ff3V0FBgWX99u3bVbdu3WopCgAAADiTSofYnj176rnnnlNJSYkkyWazaffu3RozZoz69OlT7QUCAAAAp6p0iH3llVd0+PBhXXHFFTp27Jg6duyoqKgo1apVSxMnTjwfNQIAAABuKv10gpCQEKWkpOi///2vNm7cqMOHD+vGG29UfHz8+agPAAAAsKh0iD3p5ptv1s0331ydtQAAAAAVUuHLCb744gs1bdrU401d+fn5iouL06pVq6q1OAAAAMATm9PpdFZkYM+ePdW5c2c98sgjHre/9tprWrlypT744INqLRAAAAA4VYVDbP369fXpp5+qSZMmHrdv3bpVXbp00e7du6u1QMAb2t+Z5u0SLirpSzrSk1PQEyt6YkVPrOiJFT2xSl/S8axjKnw5wc8//yxfX9/Tbrfb7Tpw4EBFpwMAAACqrMIhtl69etq8efNpt2/atEmRkZHVUhQAAABwJhUOsd27d9dTTz2l48ePW7YdO3ZMzzzzjHr06FGtxQEAAACeVPgRW08++aQWL16s6OhojRgxQjExMZJOXAs7Y8YMlZWV6YknnjhvhQIAAAAnVTjEXnnllVq9erX+/Oc/a+zYsTp5P5jNZtPtt9+uGTNm6MorrzxvhQIAAAAnVerDDurXr69PPvlEeXl5ysrKktPpVOPGjRUWFna+6gMAAAAsqvSJXWFhYWrVqlV11wIAAABUSIVv7AIAAAAuFoRYAAAAGIcQCwAAAOMQYgEAAGAcQiwAAACMQ4gFAACAcQixAAAAMA4hFgAAAMYhxAIAAMA4hFgAAAAYhxALAAAA4xBiAQAAYBxCLAAAAIxDiAUAAIBxCLEAAAAwDiEWAAAAxiHEAgAAwDiEWAAAABiHEAsAAADjEGIBAABgHEIsAAAAjEOIBQAAgHEIsQAAADAOIRYAAADGIcQCAADAOIRYAAAAGIcQCwAAAOMQYgEAAGAcQiwAAACMQ4g9B4mJierdu7dlfWpqqmw2mw4dOqTjx48rMTFRzZo1k91u9zg+MTFRNpvNssTFxbnGFBYWauTIkapfv74CAwPVrl07rVu3zm2e8ePHKzY2VkFBQQoLC1N8fLzWrl1b4eOx2Wz68MMPXV+XlJSoX79+qlevnqZPny6bzab9+/e77RMZGakGDRq4rdu5c6dsNptWrFihkpISjRkzRs2aNVNQUJCuuuoqDRw4UPv27fNYQ1FRkVq0aCGbzaaMjAyPY7KyslSrVi2Fhoa6rS8pKdFzzz2nRo0aKSAgQM2bN9enn35a4eO/mPyh+1VaNLu1ViTfolkvt1STxrW8XZLX0RMremJFT6zoiRU9sTKxJ4TY86ysrEyBgYF66KGHFB8f73HMtGnTlJOT41r27Nkjh8Ohvn37usYMHTpUKSkpWrBggTIzM9WlSxfFx8crOzvbNSY6OlrTp09XZmam0tPT1aBBA3Xp0kUHDhyodN1Hjx5Vz549tW7dOqWnpysxMVF2u12pqamuMd9//72OHTumvLw87dy507V+5cqV8vf3180336yjR49qw4YNeuqpp7RhwwYtXrxY27ZtU8+ePT2+7mOPPaarrrrqtHWdDNa33HKLZduTTz6pf/7zn/rHP/6hLVu2aPjw4brrrrv07bffVvr4venW9nU1YmgjzX1np4aM/EZZPx3WlOeaKTTE19uleQ09saInVvTEip5Y0RMrU3tCiD3PgoKCNHPmTA0bNkwREREex4SEhCgiIsK1rF+/Xnl5eXrggQckSceOHVNycrJefPFFdejQQVFRURo/fryioqI0c+ZM1zz9+/dXfHy8GjZsqLi4OE2ZMkUFBQXatGlTpWo+dOiQbrvtNu3bt0/p6em67rrrFBwcrFatWrmF2NTUVLVv314333yzZX2bNm0UEBCgkJAQpaSkKCEhQTExMWrTpo2mT5+ub775Rrt373Z73WXLlumzzz7Tyy+/fNrannzyScXGxiohIcGybcGCBRo3bpy6d++uhg0b6s9//rO6d++uV155pVLH72339r5aS5bn6JMVP2vnnqN66fUdOl5Urh63eX7/XA7oiRU9saInVvTEip5YmdoTQuxFKCkpSfHx8apfv74kqbS0VGVlZQoICHAbFxgYqPT0dI9zFBcXa9asWQoJCVHz5s0r/Nr79+9Xx44dJUlpaWluwbtz585auXKl6+uVK1eqU6dO6tixo9v61NRUde7c+bSvkZ+fL5vN5nY5wM8//6xhw4ZpwYIFqlmzpsf9vvjiCy1atEgzZszwuL2oqKhSPboY2e02RUfV0vqNea51Tqe0PiNPcTG1vViZ99ATK3piRU+s6IkVPbEyuSeE2HO0dOlSBQcHuy3dunWr8nz79u3TsmXLNHToUNe6WrVqqW3btpowYYL27dunsrIyLVy4UGvWrFFOTo7HegICAjR16lSlpKSoTp06FX79hx9+WMXFxUpJSbFcc9q5c2dt377d9ZppaWnq2LGjOnTooLS0NEnSjz/+qN27d582xB4/flxjxoxRv379VLv2iR8Op9OpxMREDR8+XP/3f//ncb9ff/1ViYmJmjdvnmu/U91+++2aMmWKduzYofLycqWkpGjx4sWWHl3MQmr7yu5j08G8Erf1Bw+VKDzMz0tVeRc9saInVvTEip5Y0RMrk3tCiD1HnTt3VkZGhtsye/bsKs83f/58hYaGWm4AW7BggZxOp+rVqyd/f3+99tpr6tevn2rUcP8Wnqxn9erV6tq1qxISEvTLL79U+PV79Oih7du365///KdlW7t27eTn56fU1FRt2bJFx44d04033qj/+7//04EDB/TTTz8pNTVVgYGBatOmjWX/kpISJSQkyOl0ul0G8Y9//EOFhYUaO3bsaesaNmyY+vfvrw4dOpx2zLRp09S4cWPFxsbKz89PI0aM0AMPPGDp0W8VFRWpoKDAbSkqKjrteAAAcHEgxJ6joKAgRUVFuS316tWr0lxOp1Nz5szRgAED5Ofn/ttPo0aNlJaWpsOHD2vPnj36+uuvVVJSooYNG3qsp02bNkpKSpLdbldSUlKFaxgwYIDmzJmjRx99VFOmTHHbVrNmTf3ud7/TypUrtXLlSrVv314+Pj7y9fVVu3btXOtvvvlmS/0nA+yuXbuUkpLidjb1iy++0Jo1a+Tv7y+73a6oqChJ0v/93/9p0KBBrjEvv/yy7Ha77Ha7hgwZovz8fNntds2ZM0eSVLduXX344Yc6cuSIdu3apa1btyo4ONjSo9+aPHmyQkJC3JbJkydXuF/VLb+gRKVlTjnC3C+md4T66te8Yi9V5V30xIqeWNETK3piRU+sTO4JIfYikpaWpqysLA0ZMuS0Y4KCghQZGam8vDwtX75cvXr1OuOc5eXllT6zOGjQIM2bN0+PPfaY5Sarzp07KzU1VampqerUqZNrfYcOHZSamqq0tDTLpQQnA+yOHTv0+eefKzw83G37a6+9po0bN7rOZH/yySeSpPfee08TJ06UJK1Zs8btbPdzzz2nWrVqKSMjQ3fddZfbfAEBAapXr55KS0uVnJx8xh6NHTtW+fn5bsuZzgifb6WlTm3PKtRNN4S51tls0k3Nw/TdtgKv1eVN9MSKnljREyt6YkVPrEzuid3bBVwOtmzZouLiYh08eFCFhYWu55+2aNHCbVxSUpJat26t66+/3jLH8uXL5XQ6FRMTo6ysLI0ePVqxsbGuJxgcOXJEEydOVM+ePRUZGanc3FzNmDFD2dnZbo/qqqgBAwaoRo0aGjRokJxOp0aPHi3pRIidMGGC9u/fr0cffdQ1vmPHjnrppZdUWFjoFmJLSkp09913a8OGDVq6dKnKyspcz5p1OBzy8/PTtdde6/bawcHBkk6cfb766qslSU2aNHEbs379etWoUcOtV2vXrlV2drZatGih7OxsjR8/XuXl5XrsscdOe5z+/v7y9/evdH/Op3c/3KsnHonV1qxCfb+9UAm96ikwoIY+/nz/2Xe+RNETK3piRU+s6IkVPbEytSeE2Auge/fu2rVrl+vrli1bSjpx+cBJ+fn5Sk5O1rRp0zzOcfIM4d69e+VwONSnTx9NnDhRvr4nTv/7+Pho69atmj9/vnJzcxUeHq5WrVpp1apVbh+aUBn33XefatSooQEDBqi8vFxjxoxR27Zt5e/vL6fTqZtuusk1tnXr1iopKXE9iuuk7Oxs/ec//5FkDe0nn25QXY4fP64nn3xSP/74o4KDg9W9e3ctWLDAcoPaxe6L9AMKDfHV0PsayBHmp6wfD2vUM5nKO1Ry9p0vUfTEip5Y0RMremJFT6xM7YnN+dskBUCS1P7ONG+XcFFJX9KRnpyCnljREyt6YkVPrOiJVfqSjmcdwzWxAAAAMA4h9jIxadIky/Nsq+O5tgAAAN7ANbGXieHDh3v8qFbpxKdaAQAAmIQQe5lwOBxyOBzeLgMAAKBacDkBAAAAjEOIBQAAgHEIsQAAADAOIRYAAADGIcQCAADAOIRYAAAAGIcQCwAAAOMQYgEAAGAcQiwAAACMQ4gFAACAcQixAAAAMA4hFgAAAMYhxAIAAMA4hFgAAAAYhxALAAAA4xBiAQAAYBxCLAAAAIxDiAUAAIBxCLEAAAAwDiEWAAAAxiHEAgAAwDiEWAAAABiHEAsAAADjEGIBAABgHEIsAAAAjEOIBQAAgHEIsQAAADAOIRYAAADGIcQCAADAOIRYAAAAGIcQCwAAAOMQYgEAAGAcQiwAAACMQ4gFAACAcQixAAAAMA4hFgAAAMaxOZ1Op7eLAAAAACrD7u0CgItR+zvTvF3CRSV9SUd6cgp6YkVPrOiJVfqSjnpyXrG3y7ioPJ/ox/vkFOlLOp51DJcTAAAAwDiEWAAAABiHEAsAAADjEGIBAABgHEIsAAAAjEOIBQAAgHEIsQAAADAOIRYAAADGIcQCAADAOIRYAAAAGIcQCwAAAOMQYgEAAGAcQiwAAACMQ4gFAACAcQixAAAAMA4hFgAAAMYhxAIAAMA4hFgAAAAYhxALAAAA4xBiAQAAYBxCLAAAAIxDiAUAAIBxCLEAAAAwDiEWAAAAxiHEAgAAwDiEWAAAABiHEAsAAADjEGIBAABgHEIsAAAAjEOIBQAAgHEIsQAAADAOIRYAAADGIcQCAADAOIRYAAAAGIcQCwAAAOMQYgEAAGAcQuxvJCYmqnfv3pb1qampstlsOnTokI4fP67ExEQ1a9ZMdrvd4/jExETZbDbLEhcX5xpTWFiokSNHqn79+goMDFS7du20bt06t3nGjx+v2NhYBQUFKSwsTPHx8Vq7dm2Fj+fk63711Vdu64uKihQeHi6bzabU1FS3bStXrlSPHj1Ut25dBQQEqFGjRrrnnnv05Zdfuo1zOp2aNWuWWrdureDgYIWGhur//u//9Oqrr+ro0aOu+k/WYLfbVadOHXXo0EGvvvqqioqKXHOVlJRozJgxatasmYKCgnTVVVdp4MCB2rdvn+WYPv74Y7Vu3VqBgYEKCwuz9N9T3999990K9+xi8YfuV2nR7NZakXyLZr3cUk0a1/J2SV5HT6zoiRU9sbqce9LgSpvu/71djyX46vlEPzW51mYZ8/sWPhqT4Ktn7vfVA13sCr982uPGxPcJIbaSysrKFBgYqIceekjx8fEex0ybNk05OTmuZc+ePXI4HOrbt69rzNChQ5WSkqIFCxYoMzNTXbp0UXx8vLKzs11joqOjNX36dGVmZio9PV0NGjRQly5ddODAgQrXe80112ju3Llu6z744AMFBwdbxr7++uv6/e9/r/DwcL333nvatm2bPvjgA7Vr106PPPKI29gBAwZo5MiR6tWrl1auXKmMjAw99dRT+uijj/TZZ5+5xsXFxSknJ0e7d+/WypUr1bdvX02ePFnt2rVTYWGhJOno0aPasGGDnnrqKW3YsEGLFy/Wtm3b1LNnT7fXTE5O1oABA/TAAw9o48aN+u9//6v+/ftbjmPu3Llu/ff0i8bF7Nb2dTViaCPNfWenhoz8Rlk/HdaU55opNMTX26V5DT2xoidW9MTqcu+Jr13af9CpJV+Vetx+y/U11KZpDX20plRvfFyq4lJpUBdf2X0ucKFeZur7hBBbSUFBQZo5c6aGDRumiIgIj2NCQkIUERHhWtavX6+8vDw98MADkqRjx44pOTlZL774ojp06KCoqCiNHz9eUVFRmjlzpmue/v37Kz4+Xg0bNlRcXJymTJmigoICbdq0qcL1Dho0SO+++66OHTvmWjdnzhwNGjTIbdzu3bs1cuRIjRw5UvPnz9ett96q+vXr64YbbtDDDz+s9evXu8a+//77euutt/TOO+9o3LhxatWqlRo0aKBevXrpiy++UOfOnV1j7Xa7IiIidNVVV6lZs2Z68MEHlZaWps2bN+vvf/+7q18pKSlKSEhQTEyM2rRpo+nTp+ubb77R7t27JUmlpaV6+OGH9dJLL2n48OGKjo5W06ZNlZCQYDnm0NBQt/4HBARUuF8Xg3t7X60ly3P0yYqftXPPUb30+g4dLypXj9s8v98uB/TEip5Y0ROry70nO7Kd+vzbMn2/2+lxe7umPkrdWKate5z6Oc+pf68qVa2aUpNrL694ZOr75PL6LnlJUlKS4uPjVb9+fUknAllZWZklXAUGBio9Pd3jHMXFxZo1a5ZCQkLUvHnzCr/2TTfdpAYNGig5OVnSibD65ZdfasCAAW7jkpOTVVJSoscee8zjPDbb//4E89ZbbykmJka9evXyOC4kJOSMNcXGxqpbt25avHjxacfk5+fLZrMpNDRUkrRhwwZlZ2erRo0aatmypSIjI9WtWzdt3rzZsu9f//pX1alTR7/73e80Z84cOZ2e//G6GNntNkVH1dL6jXmudU6ntD4jT3Extb1YmffQEyt6YkVPrOjJmYUFS7Vq2vRDzv/+H1FUIu094NQ1da2XHVyqTH6fEGJPsXTpUgUHB7st3bp1q/J8+/bt07JlyzR06FDXulq1aqlt27aaMGGC9u3bp7KyMi1cuFBr1qxRTk6Ox3oCAgI0depUpaSkqE6dOpWqYfDgwZozZ44kad68eerevbvq1q3rNmb79u2qXbu229nl5ORktz5kZmZKknbs2KGYmJhK1XCq2NhY7dy50+O248ePa8yYMerXr59q1z7xA/Tjjz9KOnGd7ZNPPqmlS5cqLCxMnTp10sGDB137Pvfcc3r//feVkpKiPn366C9/+Yv+8Y9/nFOtF1JIbV/ZfWw6mFfitv7goRKFh/l5qSrvoidW9MSKnljRkzMLDjwRVA8fcz/RcfiYU7UCvVGRd5j8PiHEnqJz587KyMhwW2bPnl3l+ebPn6/Q0FDLdZkLFiyQ0+lUvXr15O/vr9dee039+vVTjRru35KT9axevVpdu3ZVQkKCfvnll0rVcP/992vNmjX68ccfNW/ePA0ePNjjuN+ebZWk22+/XRkZGfr444915MgRlZWVSVK1nNl0Op2W15NO3OSVkJAgp9PpdmlFeXm5JOmJJ55Qnz59dNNNN2nu3Lmy2WxatGiRa9xTTz2lm2++WS1bttSYMWP02GOP6aWXXjptHUVFRSooKHBbfnvTGQAAuDgRYk8RFBSkqKgot6VevXpVmsvpdGrOnDkaMGCA/Pzcf5tp1KiR0tLSdPjwYe3Zs0dff/21SkpK1LBhQ4/1tGnTRklJSbLb7UpKSqpUHeHh4erRo4eGDBmi48ePezyz3LhxY+Xn52v//v2udcHBwYqKinJdBnFSdHS0tm7dWqkaTvX999/ruuuuc1t3MsDu2rVLKSkprrOwkhQZGSlJatq0qWudv7+/GjZs6Lpu1pPWrVtr7969pw2mkydPVkhIiNsyefLkczm0c5JfUKLSMqccYe4X0ztCffVrXrGXqvIuemJFT6zoiRU9ObOTZ2BPnpE9KTjQpsJjnva4NJn8PiHEnkdpaWnKysrSkCFDTjsmKChIkZGRysvL0/Llyz1eZ/pb5eXlVTpTOHjwYKWmpmrgwIHy8bHednn33XfL19fXdbPVmfTv31/bt2/XRx99ZNnmdDqVn59/xv23bt2qTz/9VH369HGtOxlgd+zYoc8//1zh4eFu+9x0003y9/fXtm3b3PbZuXOnJWT/VkZGhsLCwuTv7+9x+9ixY5Wfn++2jB079oz1n0+lpU5tzyrUTTeEudbZbNJNzcP03bYCr9XlTfTEip5Y0RMrenJmeYelwqNONYr8X4j195WurmvTngPm3Etxrkx+n9i9XYCJtmzZouLiYh08eFCFhYXKyMiQJLVo0cJtXFJSklq3bq3rr7/eMsfy5cvldDoVExOjrKwsjR49WrGxsa4nGBw5ckQTJ05Uz549FRkZqdzcXM2YMUPZ2dluj+qqqK5du+rAgQNuZzd/69prr9Urr7yihx9+WAcPHlRiYqKuu+46HTx4UAsXLpQkV/hNSEjQBx98oH79+unJJ59Uly5dVLduXWVmZmrq1Kl68MEHXZdPlJaWav/+/SovL9evv/6q1NRUPf/882rRooVGjx4t6UQYvfvuu7VhwwYtXbpUZWVlrjPCDodDfn5+ql27toYPH65nnnlG11xzjerXr++6TOBkP5YsWaKff/5Zbdq0UUBAgFJSUjRp0iQ9+uijp+2Lv7//aQOut7z74V498UistmYV6vvthUroVU+BATX08ef7z77zJYqeWNETK3pidbn3xM8uOWr/L6SGBdsU4ZCOFTmVf0RavaVMnW7w0a8FTuUVSr+/0UeFR6Xvd5d7seoLz9T3CSG2Crp3765du3a5vm7ZsqUk92tF8/PzlZycrGnTpnmc4+QZv71798rhcKhPnz6aOHGifH1PnM738fHR1q1bNX/+fOXm5io8PFytWrXSqlWr3D40oaJsNttZbwh78MEH1aRJE02ZMkV33323CgoKFB4errZt2+rTTz9Vs2bNXHO9/fbbmjVrlubMmaOJEyfKbrercePGGjhwoG6//XbXnN99950iIyPl4+OjkJAQNW3aVGPHjtWf//xnV3jMzs7Wf/7zH0nWXwRWrlypTp06SZJeeukl2e12DRgwQMeOHVPr1q31xRdfKCzsxG+Pvr6+mjFjhh555BE5nU5FRUVpypQpGjZsWKX75U1fpB9QaIivht7XQI4wP2X9eFijnslU3qGSs+98iaInVvTEip5YXe49qVfHpiFd//dn8u6/OxF7NmSVaXF6mVZtLpef3aZe7ewK8JN2/+zU/JQSlZZ5q2LvMPV9YnOa9Pwh4AJpf2eat0u4qKQv6UhPTkFPrOiJFT2xSl/SUU/Ou7ivtbzQnk/0431yivQlHc86hmtiAQAAYBxCrKEmTZpkeZ5tdTzXFgAAwARcE2uo4cOHe/zIVenEJ38BAABcygixhnI4HHI4HN4uAwAAwCu4nAAAAADGIcQCAADAOIRYAAAAGIcQCwAAAOMQYgEAAGAcQiwAAACMQ4gFAACAcQixAAAAMA4hFgAAAMYhxAIAAMA4hFgAAAAYhxALAAAA4xBiAQAAYBxCLAAAAIxDiAUAAIBxCLEAAAAwDiEWAAAAxiHEAgAAwDiEWAAAABiHEAsAAADjEGIBAABgHEIsAAAAjEOIBQAAgHEIsQAAADAOIRYAAADGIcQCAADAOIRYAAAAGIcQCwAAAOMQYgEAAGAcQiwAAACMQ4gFAACAcQixAAAAMA4hFgAAAMYhxAIAAMA4hFgAAAAYhxALAAAA49icTqfT20UAAAAAlWH3dgHAxaj9nWneLuGikr6kIz05RfqSjvrYN8bbZVxU7ijZRk9OQU+s6IkVPbG6o2TbWcdwOQEAAACMQ4gFAACAcQixAAAAMA4hFgAAAMYhxAIAAMA4hFgAAAAYhxALAAAA4xBiAQAAYBxCLAAAAIxDiAUAAIBxCLEAAAAwDiEWAAAAxiHEAgAAwDiEWAAAABiHEAsAAADjEGIBAABgHEIsAAAAjEOIBQAAgHEIsQAAADAOIRYAAADGIcQCAADAOIRYAAAAGIcQCwAAAOMQYgEAAGAcQiwAAACMQ4gFAACAcQixAAAAMA4hFgAAAMYhxAIAAMA4hFgAAAAYhxALAAAA4xBiAQAAYBxCLAAAAIxDiAUAAIBxCLEAAAAwDiEWAAAAxiHEnqPExET17t3bsj41NVU2m02HDh3S8ePHlZiYqGbNmslut3scn5iYKJvNZlni4uJcYwoLCzVy5EjVr19fgYGBateundatW+c2z/jx4xUbG6ugoCCFhYUpPj5ea9eurfDxnHzdr776ym19UVGRwsPDZbPZlJqa6jb+ww8/dH1dUlKifv36qV69etq8ebN27typIUOG6LrrrlNgYKAaNWqkZ555RsXFxW696tWrlyIjIxUUFKQWLVrorbfestS2aNEixcbGKiAgQM2aNdMnn3xiGfP999+rZ8+eCgkJUVBQkFq1aqXdu3dX+PgvFn/ofpUWzW6tFcm3aNbLLdWkcS1vl+R1l3NPGj81QneUbHNbOmYuc22v4e+nuNee1m37v9LteRt043uvye+KcC9WfP7REyt6YkVPrC6lnhBiL4CysjIFBgbqoYceUnx8vMcx06ZNU05OjmvZs2ePHA6H+vbt6xozdOhQpaSkaMGCBcrMzFSXLl0UHx+v7Oxs15jo6GhNnz5dmZmZSk9PV4MGDdSlSxcdOHCgwvVec801mjt3rtu6Dz74QMHBwWfc7+jRo+rZs6fWrVun9PR0XX/99dq6davKy8v1z3/+U999952mTp2qN954Q+PGjXPtt3r1at1www1KTk7Wpk2b9MADD2jgwIFaunSp25h+/fppyJAh+vbbb9W7d2/17t1bmzdvdo354Ycf1L59e8XGxio1NVWbNm3SU089pYCAgAof+8Xg1vZ1NWJoI819Z6eGjPxGWT8d1pTnmik0xNfbpXkNPZEKN2/X51ff7FpWd+rv2tb0lXG68o7O2nDvSK35/QAFXHWFblo03YvVXhj0xIqeWNETq0ulJ3ZvF3A5CAoK0syZMyVJ//3vf3Xo0CHLmJCQEIWEhLi+/vDDD5WXl6cHHnhAknTs2DElJyfro48+UocOHSSdOOu6ZMkSzZw5U88//7wkqX///m7zTpkyRUlJSdq0aZN+//vfV6jeQYMG6bXXXtOrr76qwMBASdKcOXM0aNAgTZgwweM+hw4d0h133KHDhw8rPT1dERERkqSuXbuqa9eurnENGzbUtm3bNHPmTL388suS5BZoJenhhx/WZ599psWLF6tHjx6SToT8rl27avTo0ZKkCRMmKCUlRdOnT9cbb7whSXriiSfUvXt3vfjii665GjVqVKFjvpjc2/tqLVmeo09W/CxJeun1HWrbKlw9bovQwn/v8XJ13kFPpPKyMhX9nGtZb68drGse6KNvBzyqX1NP/AVl49Bx6rR5mUJbN9ehtRsvdKkXDD2xoidW9MTqUukJZ2IvUklJSYqPj1f9+vUlSaWlpSorK7OcVQwMDFR6errHOYqLizVr1iyFhISoefPmFX7tm266SQ0aNFBycrIkaffu3fryyy81YMAAj+P379+vjh07SpLS0tJcAfZ08vPz5XA4KjVmzZo1lrPYt99+u9asWSNJKi8v18cff6zo6GjdfvvtuuKKK9S6dWu3Sx1MYLfbFB1VS+s35rnWOZ3S+ow8xcXU9mJl3kNPTgiKqq/f71qlzts+V4t/vayAayIlSSE3Xq8afn7KXbHaNfbIth91dFe2wtq08FK1FwY9saInVvTE6lLpCSG2GixdulTBwcFuS7du3ao83759+7Rs2TINHTrUta5WrVpq27atJkyYoH379qmsrEwLFy7UmjVrlJOT47GegIAATZ06VSkpKapTp06lahg8eLDmzJkjSZo3b566d++uunXrehz78MMPq7i4WCkpKQoNDT3jvFlZWfrHP/6hP/3pT6cd8/7772vdunWus9DSiaB85ZVXuo278sortX//fknSL7/8osOHD+uFF15Q165d9dlnn+muu+7SH/7wB6WlpVXkkC8KIbV9Zfex6WBeidv6g4dKFB7m56WqvIueSIe+3qSNQ8bq6x5DlTlivGo2qKe2K9+ST3CQ/CPqqKyoWKX5hW77FP/yq/yv9PwzeymgJ1b0xIqeWF1KPeFygmrQuXNn1+UCJ61du1b3339/leabP3++QkNDLTeALViwQIMHD1a9evXk4+OjG2+8Uf369dM333xjqScjI0O5ubl68803lZCQoLVr1+qKK66ocA3333+/Hn/8cf3444+aN2+eXnvttdOO7dGjhz788EP985//1COPPHLacdnZ2eratav69u2rYcOGeRyzcuVKPfDAA3rzzTfdbmo7m/LycklSr169XDW0aNFCq1ev1htvvOE6U3yqoqIiFRUVua3z9/ev8OsCF8KB5V+6/rswc5sOfb1Rt/6wUlf17aayY8e9WJn30BMremJFT6wupZ5wJrYaBAUFKSoqym2pV69eleZyOp2aM2eOBgwYID8/97NMjRo1Ulpamg4fPqw9e/bo66+/VklJiRo2bOixnjZt2igpKUl2u11JSUmVqiM8PFw9evTQkCFDdPz48TOeWR4wYIDmzJmjRx99VFOmTPE4Zt++fercubPatWunWbNmeRyTlpamO++8U1OnTtXAgQPdtkVEROjnn392W/fzzz+7Ll2oU6eO7Ha7mjZt6jamSZMmZ3w6weTJk13XI59cJk+efNrx51t+QYlKy5xyhLnfsOQI9dWvecWn2evSRk+sSvMLdWTHTtVsdK2K9ufKx99P9hD3pzX4XRGuop8rfkOn6eiJFT2xoidWJveEEHuRSUtLU1ZWloYMGXLaMUFBQYqMjFReXp6WL1+uXr16nXHO8vJyy9nGihg8eLBSU1M1cOBA+fj4nHHsoEGDNG/ePD322GOuG7ZOys7OVqdOnXTTTTdp7ty5qlHD+rZLTU3VHXfcob///e/64x//aNnetm1brVixwm1dSkqK2rZtK0ny8/NTq1attG3bNrcx27dvd11X7MnYsWOVn5/vtowdO/aMx3o+lZY6tT2rUDfdEOZaZ7NJNzUP03fbCrxWlzfREyufoJqq2fAaFe0/oPwNm1VeXKw6t7Z1bQ+Kvk4169dT3lcZ3ivyAqMnVvTEip5YmdwTLie4QLZs2aLi4mIdPHhQhYWFysjIkHTiT96/lZSUpNatW+v666+3zLF8+XI5nU7FxMQoKytLo0ePVmxsrOva0SNHjmjixInq2bOnIiMjlZubqxkzZig7O9vtUV0V1bVrVx04cEC1a1fs5pkBAwaoRo0aGjRokJxOp0aPHu0KsPXr19fLL7/s9qivk2dRV65cqR49eujhhx9Wnz59XNe5+vn5uW7uevjhh9WxY0e98soruuOOO/Tuu+9q/fr1bmd1R48erXvuuUcdOnRQ586d9emnn2rJkiVuz7U9lb+//0V3+cC7H+7VE4/EamtWob7fXqiEXvUUGFBDH3++39ulec3l3pMmf39MPy9dqWO79yngqivU+OkH5Swr1753l6q04LD2zE1Wk5ceV8nBfJUUHtb1rz6pvDUbLro7iasTPbGiJ1b0xOpS6gkh9gLp3r27du3a5fq6ZcuWkk5cPnBSfn6+kpOTNW3aNI9znDxLuHfvXjkcDvXp00cTJ06Ur++JP7P6+Pho69atmj9/vnJzcxUeHq5WrVpp1apVlbq+9CSbzVbpG8Luu+8+1ahRQwMGDFB5ebmuvPJKZWVlKSsrS1dffbXb2JPHPn/+fB09elSTJ092+1N+x44dXQG0Xbt2evvtt/Xkk09q3Lhxaty4sT788EO3sH/XXXfpjTfe0OTJk/XQQw8pJiZGycnJat++faWP3Zu+SD+g0BBfDb2vgRxhfsr68bBGPZOpvEMlZ9/5EnW59ySgXoRaLpwi3/BQFR84qLz/fqPV7RNUnHviiQ1bRk1Sk/Jy3fj+a6rh76fcz9K1+cFnvVz1+UVPrOiJFT2xupR6YnP+NkUBkCS1v9OcJxpcCOlLOtKTU6Qv6aiPfWO8XcZF5Y6SbfTkFPTEip5Y0ROrO0q2nXUM18QCAADAOITYy8ikSZMsz7OtjufaAgAAXGhcE3sZGT58uBISEjxuO/nxsgAAACYgxF5GHA7HWT/uFQAAwARcTgAAAADjEGIBAABgHEIsAAAAjEOIBQAAgHEIsQAAADAOIRYAAADGIcQCAADAOIRYAAAAGIcQCwAAAOMQYgEAAGAcQiwAAACMQ4gFAACAcQixAAAAMA4hFgAAAMYhxAIAAMA4hFgAAAAYhxALAAAA4xBiAQAAYBxCLAAAAIxDiAUAAIBxCLEAAAAwDiEWAAAAxiHEAgAAwDiEWAAAABiHEAsAAADjEGIBAABgHEIsAAAAjEOIBQAAgHEIsQAAADAOIRYAAADGIcQCAADAOIRYAAAAGIcQCwAAAOMQYgEAAGAcQiwAAACMQ4gFAACAcWxOp9Pp7SIAAACAyrB7uwDgYtT+zjRvl3BRSV/SkZ6cgp5Y0RMremJFT6zoiVX6ko5nHcPlBAAAADAOIRYAAADGIcQCAADAOIRYAAAAGIcQCwAAAOMQYgEAAGAcQiwAAACMQ4gFAACAcQixAAAAMA4hFgAAAMYhxAIAAMA4hFgAAAAYhxALAAAA4xBiAQAAYBxCLAAAAIxDiAUAAIBxCLEAAAAwDiEWAAAAxiHEAgAAwDiEWAAAABiHEAsAAADjEGIBAABgHEIsAAAAjEOIBQAAgHEIsQAAADAOIRYAAADGIcQCAADAOIRYAAAAGIcQCwAAAOMQYgEAAGAcQiwAAACMQ4gFAACAcQixAAAAMA4hFgAAAMYhxAIAAMA4hFgAAAAY57IOsYmJierdu7dlfWpqqmw2mw4dOqTjx48rMTFRzZo1k91u9zg+MTFRNpvNssTFxbnGFBYWauTIkapfv74CAwPVrl07rVu3zm2e8ePHKzY2VkFBQQoLC1N8fLzWrl1b4eOx2WwKCAjQrl273Nb37t1biYmJZ623a9eurjENGjRwra9Zs6aaNWum2bNne3zdd955Rz4+PvrrX//qWtepUyePr3Fy6dSpk+t1Xn31VcvrfvXVV26vMXLkSNc+J3vVokULt69/O39ISIhuueUWpaWlVbB7F5c/dL9Ki2a31orkWzTr5ZZq0riWt0vyOnpiRU+s6IkVPbGiJ1Ym9uSyDrEVUVZWpsDAQD300EOKj4/3OGbatGnKyclxLXv27JHD4VDfvn1dY4YOHaqUlBQtWLBAmZmZ6tKli+Lj45Wdne0aEx0drenTpyszM1Pp6elq0KCBunTpogMHDlS4XpvNpqeffvqs47p27epWc05Ojt555x23Mc8995xycnK0efNm3X///Ro2bJiWLVtmmSspKUmPPfaY3nnnHR0/flyStHjxYte8X3/9tSTp888/d61bvHjxaWsLCAjQmDFjKnzMJ8XFxbnmX7NmjRo3bqwePXooPz+/0nN5063t62rE0Eaa+85ODRn5jbJ+OqwpzzVTaIivt0vzGnpiRU+s6IkVPbGiJ1am9oQQexZBQUGaOXOmhg0bpoiICI9jQkJCFBER4VrWr1+vvLw8PfDAA5KkY8eOKTk5WS+++KI6dOigqKgojR8/XlFRUZo5c6Zrnv79+ys+Pl4NGzZUXFycpkyZooKCAm3atKnC9Y4YMUILFy7U5s2bzzjO39/freaIiAiFhYW5jalVq5YiIiLUsGFDjRkzRg6HQykpKW5jfvrpJ61evVqPP/64oqOjXeHU4XC45q1bt64kKTw83LXO4XCctrY//vGP+uqrr/TJJ59U+LglyW63u+Zv2rSpnnvuOR0+fFjbt2+v1Dzedm/vq7VkeY4+WfGzdu45qpde36HjReXqcZvn99/lgJ5Y0RMremJFT6zoiZWpPSHEngdJSUmKj49X/fr1JUmlpaUqKytTQECA27jAwEClp6d7nKO4uFizZs1SSEiImjdvXuHXvvnmm9WjRw89/vjjVT+AU5SXlys5OVl5eXny8/Nz2zZ37lzdcccdCgkJ0f3336+kpKRzfr3rrrtOw4cP19ixY1VeXl6lOYqKijR37lyFhoYqJibmnGu6UOx2m6Kjamn9xjzXOqdTWp+Rp7iY2l6szHvoiRU9saInVvTEip5YmdyTyz7ELl26VMHBwW5Lt27dqjzfvn37tGzZMg0dOtS1rlatWmrbtq0mTJigffv2qaysTAsXLtSaNWuUk5PjsZ6AgABNnTpVKSkpqlOnTqVqmDx5sj799FOtWrXqtGM8HfekSZPcxowZM0bBwcHy9/fX3XffrbCwMLfjKi8v17x583T//fdLku69916lp6frp59+qlS9njz55JP66aef9NZbb1V4n8zMTNexBAYG6uWXX9Y777yj2rUv7h/C3wqp7Su7j00H80rc1h88VKLwML/T7HVpoydW9MSKnljREyt6YmVyTy77ENu5c2dlZGS4Lae7gaki5s+fr9DQUMsNYAsWLJDT6VS9evXk7++v1157Tf369VONGu7fgpP1rF69Wl27dlVCQoJ++eWXStXQtGlTDRw48IxnYz0d9/Dhw93GjB49WhkZGfriiy/UunVrTZ06VVFRUa7tKSkpOnLkiLp37y5JqlOnjm677TbNmTOnUvV6UrduXT366KN6+umnVVxcXKF9YmJiXMfyzTff6M9//rP69u2r9evXn3afoqIiFRQUuC1FRUXnXD8AADi/LvsQGxQUpKioKLelXr16VZrL6XRqzpw5GjBggOXP7o0aNVJaWpoOHz6sPXv26Ouvv1ZJSYkaNmzosZ42bdooKSlJdru9Sn+if/bZZ7VhwwZ9+OGHHrd7Ou5Tr1OtU6eOoqKidMstt2jRokV66KGHtGXLFtf2pKQkHTx4UIGBgbLb7bLb7frkk080f/78Kl8G8Ft/+9vfdOzYMb3++usVGu/n5+c6lpYtW+qFF15QvXr13J5+cKrJkycrJCTEbZk8efI5115V+QUlKi1zyhHmfjG9I9RXv+ZVLMxfauiJFT2xoidW9MSKnliZ3JPLPsRWp7S0NGVlZWnIkCGnHRMUFKTIyEjl5eVp+fLl6tWr1xnnLC8vr9KZwWuuuUYjRozQuHHjVFZWVun9Pc13zz33aOzYsZKkX3/9VR999JHeffddt7O53377rfLy8vTZZ5+d82sGBwfrqaee0sSJE1VYWFilOXx8fHTs2LHTbh87dqzy8/PdlpPH6A2lpU5tzyrUTTf87yY7m026qXmYvttW4LW6vImeWNETK3piRU+s6ImVyT2xe7sAE2zZskXFxcU6ePCgCgsLlZGRIUluzymVTpyZbN26ta6//nrLHMuXL5fT6VRMTIyysrI0evRoxcbGup5gcOTIEU2cOFE9e/ZUZGSkcnNzNWPGDGVnZ7s9qqsyxo4dqzfffFM//fST7rnnHrdtRUVF2r9/v9s6u91+xutvH374YV1//fVav3690tPTFR4eroSEBNlsNrdx3bt3V1JSkttzZ6vqj3/8o6ZOnaq3335brVu3PuPY0tJS1zEVFhbqvffe05YtW874uC5/f3/5+/ufc53V6d0P9+qJR2K1NatQ328vVEKvegoMqKGPP99/9p0vUfTEip5Y0RMremJFT6xM7QkhtgK6d+/u9gECLVu2lHTi8oGT8vPzlZycrGnTpnmc4+QZvr1798rhcKhPnz6aOHGifH1PnL738fHR1q1bNX/+fOXm5io8PFytWrXSqlWr3D40oTIcDofGjBmjcePGWbZ9+umnioyMdFsXExOjrVu3nna+pk2bqkuXLnr66ae1d+9e3XXXXZYAK0l9+vTRgAEDlJubW+mb0k7l6+urCRMmqH///mcd+91337mOqWbNmmrUqJFmzpypgQMHnlMNF9oX6QcUGuKrofc1kCPMT1k/HtaoZzKVd6jk7DtfouiJFT2xoidW9MSKnliZ2hOb87dJDIAkqf2dZn7S1/mSvqQjPTkFPbGiJ1b0xIqeWNETq/QlHc86hmtiAQAAYBxCrCEmTZpkea5rdTzXFgAAwERcE2uI4cOHKyEhweO2wMDAC1wNAACAdxFiDeFwOCzPcQUAALhccTkBAAAAjEOIBQAAgHEIsQAAADAOIRYAAADGIcQCAADAOIRYAAAAGIcQCwAAAOMQYgEAAGAcQiwAAACMQ4gFAACAcQixAAAAMA4hFgAAAMYhxAIAAMA4hFgAAAAYhxALAAAA4xBiAQAAYBxCLAAAAIxDiAUAAIBxCLEAAAAwDiEWAAAAxiHEAgAAwDiEWAAAABiHEAsAAADjEGIBAABgHEIsAAAAjEOIBQAAgHEIsQAAADAOIRYAAADGIcQCAADAOIRYAAAAGIcQCwAAAOMQYgEAAGAcQiwAAACMQ4gFAACAcQixAAAAMA4hFgAAAMaxOZ1Op7eLAAAAACrD7u0CgItR+zvTvF3CRSV9SUd6cor0JR315uferuLiMiyen51T8bNjxc+O1bB40ZNTDIs/+xguJwAAAIBxCLEAAAAwDiEWAAAAxiHEAgAAwDiEWAAAABiHEAsAAADjEGIBAABgHEIsAAAAjEOIBQAAgHEIsQAAADAOIRYAAADGIcQCAADAOIRYAAAAGIcQCwAAAOMQYgEAAGAcQiwAAACMQ4gFAACAcQixAAAAMA4hFgAAAMYhxAIAAMA4hFgAAAAYhxALAAAA4xBiAQAAYBxCLAAAAIxDiAUAAIBxCLEAAAAwDiEWAAAAxiHEAgAAwDiEWAAAABiHEAsAAADjEGIBAABgHEIsAAAAjEOIBQAAgHEIsQAAADAOIRYAAADGIcQCAADAOITY30hMTFTv3r0t61NTU2Wz2XTo0CEdP35ciYmJatasmex2u8fxiYmJstlsliUuLs41prCwUCNHjlT9+vUVGBiodu3aad26dW7zjB8/XrGxsQoKClJYWJji4+O1du3aCh+PzWZTQECAdu3a5ba+d+/eSkxMPO1xn64PJx07dkzPPPOMoqOj5e/vrzp16qhv37767rvvXGMaNGjgsQcnl5Ovb7PZ9OGHH1ZLzb+dPzw8XF27dtWmTZvO2qeL0R+6X6VFs1trRfItmvVySzVpXMvbJXnd5dyTPTvWafHM4Zo5rr1e/muMdmz8/LRjU955Wi//NUbffDHvwhV4Ebmc3yenczn3hJ8dq0upJ4TYSiorK1NgYKAeeughxcfHexwzbdo05eTkuJY9e/bI4XCob9++rjFDhw5VSkqKFixYoMzMTHXp0kXx8fHKzs52jYmOjtb06dOVmZmp9PR0NWjQQF26dNGBAwcqXK/NZtPTTz9d9QM+RVFRkeLj4zVnzhw9//zz2r59uz755BOVlpaqdevW+uqrryRJ69atcx1/cnKyJGnbtm2uddOmTav2mrt27eqaf8WKFbLb7erRo0fVDtSLbm1fVyOGNtLcd3ZqyMhvlPXTYU15rplCQ3y9XZrXXO49KSk+qiuujlF8wjNnHLcjI0X7ftqo4JArLlBlF5fL/X3iyeXeE352rC6lnhBiKykoKEgzZ87UsGHDFBER4XFMSEiIIiIiXMv69euVl5enBx54QNKJM5nJycl68cUX1aFDB0VFRWn8+PGKiorSzJkzXfP0799f8fHxatiwoeLi4jRlyhQVFBRU6uziiBEjtHDhQm3evPncDvz/e/XVV7VmzRotXbpUCQkJql+/vn73u98pOTlZTZo00ZAhQ+R0OlW3bl3X8TscDknSFVdc4VoXEhJS7TX7+/u75m/RooUef/xx7dmzp1Kh/2Jwb++rtWR5jj5Z8bN27jmql17foeNF5epxm+f32+Xgcu9Jw7iOan/nI2rc4rbTjik89LNWLJqgOxJfVg2fyyOgnOpyf594crn3hJ8dq0upJ4TYCyApKUnx8fGqX7++JKm0tFRlZWUKCAhwGxcYGKj09HSPcxQXF2vWrFkKCQlR8+bNK/zaN998s3r06KHHH3+86gfwG2+//bZuu+02Sw01atTQI488oi1btmjjxo3n9BrVUfPhw4e1cOFCRUVFKTw8/JzquZDsdpuio2pp/cY81zqnU1qfkae4mNperMx76MnZOcvL9cn80WoVP0R1rmrs7XK8gveJFT05O352rEzqCSH2FEuXLlVwcLDb0q1btyrPt2/fPi1btkxDhw51ratVq5batm2rCRMmaN++fSorK9PChQu1Zs0a5eTkeKwnICBAU6dOVUpKiurUqVOpGiZPnqxPP/1Uq1atqvJxnLR9+3Y1adLE47aT67dv337Or1OVmn/7vatVq5b+85//6L333lONGua8zUNq+8ruY9PBvBK39QcPlSg8zM9LVXkXPTm7r1PeVI0adt3YaaC3S/Ea3idW9OTs+NmxMqkn5vzf/QLp3LmzMjIy3JbZs2dXeb758+crNDTUcqPUggUL5HQ6Va9ePfn7++u1115Tv379LIHrZD2rV69W165dlZCQoF9++aVSNTRt2lQDBw6strOxTqezWuY5k6rU/Nvv3ddff63bb79d3bp1s9wk9ltFRUUqKChwW4qKiqrjEIALYv/uzfpm5b/UbcBk2Ww2b5cDGIOfHSvTemL3dgEXm6CgIEVFRbmt27t3b5XmcjqdmjNnjgYMGCA/P/ffehs1aqS0tDQdOXJEBQUFioyM1D333KOGDRt6rCcqKkpt2rRR48aNlZSUpLFjx1aqlmeffVbR0dFuTwKoiujoaH3//fcet51cHx0dfU6vcVJlaz71ezd79myFhITozTff1PPPP+9xn8mTJ+vZZ591W/fMM89I6lzVss9JfkGJSsuccoS5X4PkCPXVr3nFXqnJ2+jJmWVnrdfRw7/qn0/97z3rLC9T6uK/65uV/9IfJ3zhxeouHN4nVvTkzPjZsTKtJ4TY8ygtLU1ZWVkaMmTIaccEBQUpKChIeXl5Wr58uV588cUzzlleXl6lM4XXXHONRowYoXHjxqlRo0aV3v+ke++9V0888YQ2btzodl1seXm5pk6dqqZNm1bqmt0zOdeabTabatSooWPHjp12zNixY/W3v/3NbZ2/v78+v/urSr9edSgtdWp7VqFuuiFMq776VZJks0k3NQ/T4o+zz7L3pYmenFnT3/XStbHt3NYlTx+ipr/rpevb/sFLVV14vE+s6MmZ8bNjZVpPCLFVsGXLFhUXF+vgwYMqLCxURkaGJKlFixZu45KSktS6dWtdf/31ljmWL18up9OpmJgYZWVlafTo0YqNjXU9weDIkSOaOHGievbsqcjISOXm5mrGjBnKzs52e1RXZYwdO1ZvvvmmfvrpJ91zzz1nHJufn+86rpPCw8P1yCOP6KOPPtKdd96pV155Ra1bt9bPP/+sSZMm6fvvv9fnn39erX+CqEzNRUVF2r9/vyQpLy9P06dP1+HDh3XnnXeedh9/f3/5+/tXW73V4d0P9+qJR2K1NatQ328vVEKvegoMqKGPP9/v7dK85nLvSfHxIzp0YLfr6/xf9+qXPd8rIChEtR1XKTA4zG18DR9fBdWuI8eVDU+d6pJ2ub9PPLnce8LPjtWl1BNCbBV0797d7TrLli1bSnK/VjQ/P1/JycmnfR5qfn6+xo4dq71798rhcKhPnz6aOHGifH1P/NnHx8dHW7du1fz585Wbm6vw8HC1atVKq1atcvvQhMpwOBwaM2aMxo0bd9axqampruM6aciQIZo9e7a++OILTZo0SePGjdOuXbtUq1Ytde7cWV999ZXHwH4uKlPzp59+qsjISEknbp6LjY3VokWL1KlTp2qt6Xz7Iv2AQkN8NfS+BnKE+Snrx8Ma9Uym8g6VnH3nS9Tl3pP9uzfr/Wn/u8kiNXmyJCmu9V3qNvAFb5V10bnc3yeeXO494WfH6lLqic15Ie7SAQzT/s40b5dwUUlf0pGenCJ9SUe9efoPurksDYvnZ+dU/OxY8bNjNSxe9OQUwzx/npQbnk4AAAAA4xBiDTVp0iTL82yr47m2AAAAJuCaWEMNHz5cCQkJHrcFBgZe4GoAAAAuLEKsoRwOhxwOh7fLAAAA8AouJwAAAIBxCLEAAAAwDiEWAAAAxiHEAgAAwDiEWAAAABiHEAsAAADjEGIBAABgHEIsAAAAjEOIBQAAgHEIsQAAADAOIRYAAADGIcQCAADAOIRYAAAAGIcQCwAAAOMQYgEAAGAcQiwAAACMQ4gFAACAcQixAAAAMA4hFgAAAMYhxAIAAMA4hFgAAAAYhxALAAAA4xBiAQAAYBxCLAAAAIxDiAUAAIBxCLEAAAAwDiEWAAAAxiHEAgAAwDiEWAAAABiHEAsAAADjEGIBAABgHEIsAAAAjEOIBQAAgHEIsQAAADAOIRYAAADGIcQCAADAODan0+n0dhEAAABAZdi9XQBwMWp/Z5q3S7iopC/pSE9Okb6koz72jfF2GReVO0q20ZNT0BOrO0q28e/JKfg31ip9ScezjuFyAgAAABiHEAsAAADjEGIBAABgHEIsAAAAjEOIBQAAgHEIsQAAADAOIRYAAADGIcQCAADAOIRYAAAAGIcQCwAAAOMQYgEAAGAcQiwAAACMQ4gFAACAcQixAAAAMA4hFgAAAMYhxAIAAMA4hFgAAAAYhxALAAAA4xBiAQAAYBxCLAAAAIxDiAUAAIBxCLEAAAAwDiEWAAAAxiHEAgAAwDiEWAAAABiHEAsAAADjEGIBAABgHEIsAAAAjEOIBQAAgHEIsQAAADAOIRYAAADGIcQCAADAOIRYAAAAGIcQCwAAAOMQYgEAAGAcQiwAAACMY3SITUxMVO/evS3rU1NTZbPZdOjQIR0/flyJiYlq1qyZ7Ha7x/GJiYmy2WyWJS4uzjWmsLBQI0eOVP369RUYGKh27dpp3bp1bvOMHz9esbGxCgoKUlhYmOLj47V27dpKHdPSpUvVsWNH1apVSzVr1lSrVq00b948y7gPPvhAbdq0UUhIiGrVqqW4uDiNHDnStX3evHkKDQ11+9pms6lJkyaWuRYtWiSbzaYGDRpYxp+6BAQEeOybr6+vrrvuOj322GM6fvz4aff/7bJz506NHz9eLVq0cOuhzWbT8OHD3WrMyMhw7SNJO3fulM1mU0ZGhtvXJxc/Pz9FRUXp+eefl9PprHD/LxZ/6H6VFs1urRXJt2jWyy3VpHEtb5fkdZdzTxo/NUJ3lGxzWzpmLnNtr+Hvp7jXntZt+7/S7XkbdON7r8nvinAvVnz+0RMrelJxl/O/J6djYk+MDrEVUVZWpsDAQD300EOKj4/3OGbatGnKyclxLXv27JHD4VDfvn1dY4YOHaqUlBQtWLBAmZmZ6tKli+Lj45Wdne0aEx0drenTpyszM1Pp6elq0KCBunTpogMHDlSo1n/84x/q1auXbr75Zq1du1abNm3Svffeq+HDh+vRRx91jVuxYoXuuece9enTR19//bW++eYbTZw4USUlJWecPygoSL/88ovWrFnjtj4pKUnXXnutZXzt2rXd+pKTk6Ndu3a5jenatatycnL0448/aurUqfrnP/+pZ555Rvfcc4/bfm3bttWwYcPc1l1zzTUe6wwICFBSUpJ27NhRob791ueff66cnBzt2LFDzz77rCZOnKg5c+ZUeh5vurV9XY0Y2khz39mpISO/UdZPhzXluWYKDfH1dmleQ0+kws3b9fnVN7uW1Z36u7Y1fWWcrryjszbcO1Jrfj9AAVddoZsWTfditRcGPbGiJ2fHvydWpvbkkg+xQUFBmjlzpoYNG6aIiAiPY0JCQhQREeFa1q9fr7y8PD3wwAOSpGPHjik5OVkvvviiOnTooKioKI0fP15RUVGaOXOma57+/fsrPj5eDRs2VFxcnKZMmaKCggJt2rTprHXu2bNHo0aN0siRIzVp0iQ1bdpUUVFRGjVqlF566SW98sorrrO6S5Ys0c0336zRo0crJiZG0dHR6t27t2bMmHHG17Db7erfv79bqNu7d69SU1PVv39/y3ibzebWl4iICF155ZVuY/z9/RUREaFrrrlGvXv3Vnx8vFJSUhQYGOi2n5+fn2rWrOm2zsfHx2OdMTEx6ty5s5544omz9u1U4eHhioiIUP369XXffffp5ptv1oYNGyo9jzfd2/tqLVmeo09W/Kyde47qpdd36HhRuXrc5vn9ezmgJ1J5WZmKfs51LSW/5kmS7LWDdc0DfbRl9Av6NfUrFWz4ThuHjpOj3Y0Kbd3cy1WfX/TEip6cHf+eWJnak0s+xFZFUlKS4uPjVb9+fUlSaWmpysrK3P6ULkmBgYFKT0/3OEdxcbFmzZqlkJAQNW9+9n8g/v3vf6ukpMTtjOtJf/rTnxQcHKx33nlHkhQREaHvvvtOmzdvruyhafDgwXr//fd19OhRSScuG+jataslnFbF5s2btXr1avn5+Z3zXC+88IKSk5O1fv36Ks+xfv16ffPNN2rduvU513Oh2O02RUfV0vqNea51Tqe0PiNPcTG1vViZ99CTE4Ki6uv3u1ap87bP1eJfLyvgmkhJUsiN16uGn59yV6x2jT2y7Ucd3ZWtsDYtvFTthUFPrOjJmfHviZXJPTE+xC5dulTBwcFuS7du3ao83759+7Rs2TINHTrUta5WrVpq27atJkyYoH379qmsrEwLFy7UmjVrlJOT47GegIAATZ06VSkpKapTp85ZX3f79u0KCQlRZGSkZZufn58aNmyo7du3S5IefPBBtWrVSs2aNVODBg107733as6cOSoqKjrr67Rs2VINGzbUv//9bzmdTs2bN0+DBw/2ODY/P/+svf3t8TZr1ky//PKLRo8efdY6zubGG29UQkKCxowZU6n92rVrp+DgYPn5+alVq1ZKSEjQwIEDz7meCyWktq/sPjYdzHO/NOTgoRKFh537LwcmoifSoa83aeOQsfq6x1Bljhivmg3qqe3Kt+QTHCT/iDoqKypWaX6h2z7Fv/wq/yvreqni84+eWNGTs+PfEyuTe2L3dgHnqnPnzm5/0pektWvX6v7776/SfPPnz1doaKjlBrAFCxZo8ODBqlevnnx8fHTjjTeqX79++uabbyz1ZGRkKDc3V2+++aYSEhK0du1aXXHFFVWqx5OgoCB9/PHH+uGHH7Ry5Up99dVXGjVqlKZNm6Y1a9aoZs2aZ9x/8ODBmjt3rq699lodOXJE3bt31/Tp1uuiatWqZflTfGBgoNvXJ/t/5MgRTZ06VXa7XX369Dn3g5T0/PPPq0mTJvrss88q3L/33ntPTZo0UUlJiTZv3qwHH3xQYWFheuGFFzyOLyoqsoR/f3//c64dqE4Hln/p+u/CzG069PVG3frDSl3Vt5vKjh33YmXeQ0+s6AkuN8afiQ0KClJUVJTbUq9evSrN5XQ6NWfOHA0YMMDyJ/FGjRopLS1Nhw8f1p49e/T111+rpKREDRs29FhPmzZtlJSUJLvdrqSkpLO+dnR0tPLz87Vv3z7LtuLiYv3www+Kjo621DR06FDNnj1bGzZs0JYtW/Tee++d9bXuu+8+ffXVVxo/frwGDBggu93z7zI1atQ4a29PHm/z5s01Z84crV27tkLHWxGNGjXSsGHD9Pjjj1f4CQPXXHONoqKi1KRJE/Xt21cjR47UK6+8ouPHPf8DPnnyZIWEhLgtkydPrpb6qyK/oESlZU45wtwvpneE+urXvGIvVeVd9MSqNL9QR3bsVM1G16pof658/P1kD3G/k9jvinAV/Vyxm0ovBfTEip5Y8e+Jlck9MT7EVqe0tDRlZWVpyJAhpx0TFBSkyMhI5eXlafny5erVq9cZ5ywvL6/Qn/n79OkjX19fvfLKK5Ztb7zxho4cOaJ+/fqddv8GDRqoZs2aOnLkyFlfy+FwqGfPnkpLSzvtpQRVUaNGDY0bN05PPvmkjh07Vi1zPv3009q+fbvefffdKu3v4+Oj0tJSFRd7/kEcO3as8vPz3ZaxY8eeS8nnpLTUqe1ZhbrphjDXOptNuql5mL7bVuC1uryJnlj5BNVUzYbXqGj/AeVv2Kzy4mLVubWta3tQ9HWqWb+e8r7K8F6RFxg9saInVvx7YmVyT4y/nKAitmzZouLiYh08eFCFhYWuZ4v+9vmk0okbulq3bq3rr7/eMsfy5cvldDoVExOjrKwsjR49WrGxsa4nGBw5ckQTJ05Uz549FRkZqdzcXM2YMUPZ2dluj+o6nWuvvVYvvviiRo0apYCAAA0YMEC+vr766KOPNG7cOI0aNcp1g9L48eN19OhRde/eXfXr19ehQ4f02muvqaSkRLfddluFejJv3jy9/vrrCg8//TMCnU6n9u/fb1l/xRVXqEYNz7//9O3bV6NHj9aMGTM83qRWWVdeeaX+9re/6aWXXqrQ+F9//VX79+9XaWmpMjMzNW3aNHXu3Fm1a3u+ON3f3/+iu3zg3Q/36olHYrU1q1Dfby9UQq96CgyooY8/t34vLheXe0+a/P0x/bx0pY7t3qeAq65Q46cflLOsXPveXarSgsPaMzdZTV56XCUH81VSeFjXv/qk8tZs0KG1G71d+nlDT6zoScVc7v+eeGJqTy6LENu9e3e355u2bNlSktz+RJ2fn6/k5GRNmzbN4xwnz9Dt3btXDodDffr00cSJE+Xre+L0u4+Pj7Zu3ar58+crNzdX4eHhatWqlVatWuX2oQlnMnLkSDVs2FAvv/yypk2bprKyMsXFxWnmzJmusCxJHTt21IwZMzRw4ED9/PPPCgsLU8uWLfXZZ58pJiamQq8VGBhoub71VAUFBR5vNMvJyTnt48rsdrtGjBihF198UX/+858VFBRUoXrO5NFHH9XMmTNPe0nAb518FrCPj48iIyPVvXt3TZw48ZxruJC+SD+g0BBfDb2vgRxhfsr68bBGPZOpvENnfg7wpexy70lAvQi1XDhFvuGhKj5wUHn//Uar2yeoOPfE3cRbRk1Sk/Jy3fj+a6rh76fcz9K1+cFnvVz1+UVPrOhJxVzu/554YmpPbE4TP84IOM/a35nm7RIuKulLOtKTU6Qv6aiPfSv2S+Pl4o6SbfTkFPTE6o6Sbfx7cgr+jbVKX9LxrGO4JhYAAADGIcReIJMmTbI8c7U6nmsLAABwObosrom9GAwfPlwJCQket53t2lQAAAC4I8ReIA6HQw6Hw9tlAAAAXBK4nAAAAADGIcQCAADAOIRYAAAAGIcQCwAAAOMQYgEAAGAcQiwAAACMQ4gFAACAcQixAAAAMA4hFgAAAMYhxAIAAMA4hFgAAAAYhxALAAAA4xBiAQAAYBxCLAAAAIxDiAUAAIBxCLEAAAAwDiEWAAAAxiHEAgAAwDiEWAAAABiHEAsAAADjEGIBAABgHEIsAAAAjEOIBQAAgHEIsQAAADAOIRYAAADGIcQCAADAOIRYAAAAGIcQCwAAAOMQYgEAAGAcQiwAAACMQ4gFAACAcQixAAAAMA4hFgAAAMYhxAIAAMA4hFgAAAAYx+Z0Op3eLgIAAACoDLu3CwAuRuOSirxdwkXl/7F331FRXYv3wPfQkQ4WiBpRQJrYK0axEFRMUDRRBGss0bxYEmPUFHs39iQYn1hjjfg1aswTRUWxl4DEjopSo9IRRRjm94c/Jo4XBTXhzGX2Z61Z63HnctlzHkO2d849d85QY/QYdU10DK3ya6grhs1+IDqGVln9dVW+d57D947Ur6Gu/D15zpyhxvx78pzVX1ctcx9OJyAiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2dHZEjt48GD07NlTsv3IkSNQKBTIysrC48ePMXjwYHh5ecHAwKDU/QcPHgyFQiF5eHp6qvfJzc3FuHHjUKdOHZiamsLb2xtnz57VOM60adPg5uYGMzMz2NjYwNfXF6dPny7363n2Z1tZWaFt27Y4dOiQxj6JiYn46KOP8NZbb8HIyAh16tTB2LFjkZ6errFfhw4doFAosHXrVo3tS5cuhaOjo+RnP3r0CLa2tqhatSoKCgoAAOvWrSt1XJ59JCQkYNq0aWjcuLHGOCgUCowcOVLjZ8TExKi/BwASEhKgUCgQExOj8XXJw8jICM7Ozpg1axZUKlW5x1EER3sFBrxrgElBRpgz1BjudaRvS9+m+pjUzwjTBxnho66GsLNUCEgqRtf21lj2tSO2LHbGlsXOmD/hbTT1NBMdSzhrCz0MCzDH0s9s8eOXdpg23Bp1HAxEx6owfN+Uje8d/p6Ul1z/nuhsiS0PpVIJU1NTjBkzBr6+vqXus2zZMqSmpqofiYmJsLW1xYcffqjeZ9iwYThw4AA2btyIuLg4+Pn5wdfXF8nJyep96tevj++//x5xcXGIjo6Go6MjagqmuAAAhPpJREFU/Pz8cP/+/XLnXbt2LVJTU3H8+HFUrVoV7733Hm7dugUAuHXrFpo3b44bN25gy5YtiI+Px8qVKxEZGYk2bdogIyND41gmJib45ptvUFhYWObPDQ8Ph6enJ9zc3LBr1y4AQN++fTXGpU2bNhg+fLjGttq1a5d6PBMTE4SFheHGjRvlfu0lDh48iNTUVNy4cQPTp0/H7NmzsWbNmlc+TkUyMlAgLUOF3SeLSn2+fUN9tPHQx6/HixC6uxBPilQY0sUQBvoVHFSQ9MxCbNh1H5/PvYPx8+4g7lo+vhpZE7UdjERHE6aKiQKTBlpBWQws25aDKT9lYvvBh8h/VCw6WoXh+6ZsfO/w96Q85Pz3hCX2JczMzBAaGorhw4fD3t6+1H2srKxgb2+vfpw7dw6ZmZkYMmQIgKdnKcPDw7FgwQK0b98ezs7OmDZtGpydnREaGqo+TnBwMHx9fVGvXj14enpi8eLFyMnJwcWLF8ud19raGvb29mjQoAFCQ0Px6NEjHDhwAADwn//8B0ZGRoiIiICPjw/efvttdOvWDQcPHkRycjK+/vprjWP169cPWVlZ+O9//1vmzw0LC0P//v3Rv39/hIWFAQBMTU01xsXIyAhVqlTR2KavX/pfCVdXV3Ts2FGSqTzs7Oxgb2+POnXqICQkBG3btsWFCxde+TgV6XpSMQ6cV+LyndL/YHh76uNwjBJX7hYjLVOFX6KKYFEF8CjljEJldDbuIc5feojU+4VIuVeIn3c/wOOCYrjWNRUdTZhubUyRkVOMtXvzcDulCA+yi3H5diHuZ2n/f3T+KXzflI3vHf6elIec/57ozv9LFSQsLAy+vr6oU6cOAKCoqAhKpRImJiYa+5mamiI6OrrUYzx58gSrVq2ClZUVGjVq9Fo5TE1N1cfKyMjA/v378cknn6i3l7C3t0dISAi2bdum8bG7paUlvv76a8yYMQMPHz584c+5efMmTp48iT59+qBPnz44duwY7ty581qZnzVv3jyEh4fj3Llzr32Mc+fO4fz582jVqtUb5xHFxgKwrKLAzZS//5gUFAJJ91V4u7rufeSlpwDaNbeAiZEC1249Eh1HmEYuRriTWoSRvSyweJwtpgy1RrvGxqJjaQ2+b6T43pHi78lTcv57otMldu/evTA3N9d4dOvW7bWPl5KSgt9//x3Dhg1Tb7OwsECbNm0wc+ZMpKSkQKlU4ueff8bJkyeRmppaah4TExMsWbIEBw4cQNWqVV85R35+Pr755hvo6+vDx8cHN27cgEqlgru7e6n7u7u7IzMzUzJ14ZNPPoGJiQkWL178wp+1Zs0adOvWDTY2NrC1tUWXLl2wdu3aV878vKZNm6JPnz6YOHHiK32ft7c3zM3NYWRkhBYtWqBPnz4YOHDgG+cRxcL06R/SvEea83rzHqlgbqo7f2TrvGWErUtcsGNFfYzsVwNzf0pBYtoT0bGEqWajjw7NTPBXhhJLtmTjyIVH6OdnDm8vefyH59/G983f+N55Mf6ePCXnvyc6XWI7duyImJgYjcfq1atf+3jr16+HtbW15AKwjRs3QqVSoWbNmjA2Nsby5cvRr18/6OlpDn9JnhMnTqBr167o06cP7t27V+6f369fP5ibm8PCwgLh4eEICwtDw4YN1c+/6gVOxsbGmDFjBr777js8ePBA8rxSqcT69evRv39/9bb+/ftj3bp1KC5+848hZs2ahWPHjiEiIqLc37Nt2zbExMQgNjYW27dvx6+//opJkya9cP+CggLk5ORoPEouTiPtkfzXE4ybk4AJC+7gf0ezMHaQPWrb6868vucpFMCdtCL835F8JP6lxNE/CnAs5jF8mpqU/c2kU/jeobLI+e+JTpdYMzMzODs7azxq1qz5WsdSqVRYs2YNBgwYACMjzT8QTk5OiIqKQl5eHhITE3HmzBkUFhaiXr16peZp3bo1wsLCYGBgoJ5jWh5LlixBTEwM0tLSkJaWhkGDBgEAnJ2doVAocOXKlVK/78qVK7CxsUG1atUkz/Xv3x916tTBrFmzJM/t378fycnJ6Nu3LwwMDGBgYICgoCDcuXMHkZGR5c79Ik5OThg+fDgmTZpU7gJeu3ZtODs7w93dHR9++CHGjRuHRYsW4fHjx6XuP3fuXFhZWWk85s6d+8bZ/ym5//8MwfNnBcxNFZKzB5VZkRJIu1+Im3cLsPHXB0hILsB7nWxExxImO68YqQ+UGttSHyhha6XTf9LV+L75G987L8bfk6fk/PdE+xPKRFRUFOLj4zF06NAX7mNmZgYHBwdkZmZi//796NGjx0uPWVxc/EpnBe3t7eHs7Cwpo3Z2dnj33Xfx448/4tEjzblQaWlp2LRpE/r27QuFQvrxiZ6eHubOnYvQ0FD18lYlwsLCEBQUJDmbHRQU9Erl+2WmTJmC69evS5b7Ki99fX0UFRXhyZPSPz6bPHkysrOzNR6TJ09+k8j/qMxcICdfBae3/n6rGhsCtaopcPee7vyRfZ5CARga6M7Hfc+LTyxEDVvNCyNr2OojPVv7L8SoCHzfvJiuv3eexd+Tp+T890T7FwET7PLly+qLo3Jzc9Xrkj67tinwtNC1atUKDRo0kBxj//79UKlUcHV1RXx8PCZMmAA3Nzf1CgYPHz7E7NmzERAQAAcHBzx48AA//PADkpOTNZbqehPff/89vL290aVLF8yaNQt169bFpUuXMGHCBNSsWROzZ89+4fd2794drVq1wk8//YQaNWoAAO7fv489e/Zg9+7dktc8cOBABAYGIiMjA7a2tm+Uu0aNGvj888+xcOHCcu2fnp6OtLQ0FBUVIS4uDsuWLUPHjh1haWlZ6v7GxsYwNi5t3k/FTSkwMoDGuoS25go42CqQX6BC9kPgxCUlOjbWx4McFTJzVXi3mT5y8/HCq20rmwE9quL8pYd4kFEIUxM9tG9hiQYuVTBtRZLoaMIcOPMYkwZZwd/bFOeuFMDxLUO0b2KCDfvyREerMHzflI3vHf6elIec/56wxJbB399f42r7Jk2aANCcX5qdnY3w8HAsW7as1GOUnN1LSkqCra0tevfujdmzZ8PQ0BDA07OFV69exfr16/HgwQPY2dmhRYsWOHbsmMZNE96Ei4sLzp07h6lTp6JPnz7IyMiAvb09evbsialTp5ZZNufPnw9vb2/11xs2bICZmRk6d+4s2bdz584wNTXFzz//jDFjxrxx9i+++AKhoaEvnBLwrJL1fPX19eHg4AB/f/+XFnRtULOqAsO7/z0FpXvrp2/L89eVCD9WhKMXlTAyAALbGsDECLjzlwpr9xeiSPmiI1YuVhb6GDfYAbaW+nj4uBh3kgswbUUSYq/mi44mTEJqEX7ckYNeHc3wfrsqeJClxNYDeTh9SXfmc/N9Uza+d/h7Uh5y/nuiUGn77YyIBPgqTPvfvBVpzlBj9Bh1TXQMrfJrqCuGzZZe8KjLVn9dle+d5/C9I/VrqCt/T54zZ6gx/548Z/XXZa/OxDmxRERERCQ7LLEyMGfOHMl6tv/EurZEREREcsU5sTIwcuRI9OnTp9Tnnr8DFxEREZEuYImVAVtb2ze+yp+IiIioMuF0AiIiIiKSHZZYIiIiIpIdllgiIiIikh2WWCIiIiKSHZZYIiIiIpIdllgiIiIikh2WWCIiIiKSHZZYIiIiIpIdllgiIiIikh2WWCIiIiKSHZZYIiIiIpIdllgiIiIikh2WWCIiIiKSHZZYIiIiIpIdllgiIiIikh2WWCIiIiKSHZZYIiIiIpIdllgiIiIikh2WWCIiIiKSHZZYIiIiIpIdllgiIiIikh2WWCIiIiKSHZZYIiIiIpIdllgiIiIikh2WWCIiIiKSHZZYIiIiIpIdllgiIiIikh2WWCIiIiKSHZZYIiIiIpIdllgiIiIikh2WWCIiIiKSHZZYIiIiIpIdllgiIiIikh2WWCIiIiKSHZZYIiIiIpIdhUqlUokOQURERET0KgxEByDSRu+8HyU6glaJ3uPDMXkOx0SKYyLFMZHimEhxTKSi9/iUuQ+nExARERGR7LDEEhEREZHssMQSERERkeywxBIRERGR7LDEEhEREZHssMQSERERkeywxBIRERGR7LDEEhEREZHssMQSERERkeywxBIRERGR7LDEEhEREZHssMQSERERkeywxBIRERGR7LDEEhEREZHssMQSERERkeywxBIRERGR7LDEEhEREZHssMQSERERkeywxBIRERGR7LDEEhEREZHssMQSERERkeywxBIRERGR7LDEEhEREZHssMQSERERkeywxBIRERGR7LDEEhEREZHssMQSERERkeywxBIRERGR7LDEEhEREZHssMQSERERkeywxBIRERGR7LDEEhEREZHssMQSERERkeywxBIRERGR7LDEEhEREZHssMQSERERkeywxBIRERGR7Mi6xA4ePBg9e/aUbD9y5AgUCgWysrLw+PFjDB48GF5eXjAwMCh1/8GDB0OhUEgenp6e6n1yc3Mxbtw41KlTB6ampvD29sbZs2c1jjNt2jS4ubnBzMwMNjY28PX1xenTp1/pNe3duxc+Pj6wsLBAlSpV0KJFC6xbt+6Fr+95jo6OWLp0qfprhUKBXbt2vfR1ljzWr1+vcaykpCQYGRmhQYMGpWZ99nstLS3RokUL/PrrrwCADh06vPRndejQodS8jo6OUCgUOHXqlMbPGjdunPp7gKdj3bhxY42vnz2+lZUV2rVrh6ioqFKza7te/m/hl9WtEBneDqu+awJ3FwvRkYTjmEhxTKQ4JlIcEymOiZQcx0TWJbY8lEolTE1NMWbMGPj6+pa6z7Jly5Camqp+JCYmwtbWFh9++KF6n2HDhuHAgQPYuHEj4uLi4OfnB19fXyQnJ6v3qV+/Pr7//nvExcUhOjoajo6O8PPzw/3798uVdcWKFejRowfatm2L06dP4+LFiwgKCsLIkSPxxRdfvNlAlPI6Sx6+vr5wdHRE9+7dNfZft24d+vTpg5ycnBeW8bVr1yI1NRXnzp1D27Zt8cEHHyAuLg47d+5UH//MmTMAgIMHD6q37dy584U5TUxMMHHixFd+fZ6enurjnzx5Ei4uLnjvvfeQnZ39yscSqdM71fDpMCes3ZKAoePOI/52HhbP8IK1laHoaMJwTKQ4JlIcEymOiRTHREquY1LpS6yZmRlCQ0MxfPhw2Nvbl7qPlZUV7O3t1Y9z584hMzMTQ4YMAQA8evQI4eHhWLBgAdq3bw9nZ2dMmzYNzs7OCA0NVR8nODgYvr6+qFevHjw9PbF48WLk5OTg4sWLZeZMTEzE+PHjMW7cOMyZMwceHh5wdnbG+PHjsXDhQixatOiVz+qW9Trt7e0RFhaGkydPYteuXahatap6X5VKhbVr12LAgAEIDg5GWFhYqce0traGvb096tevj5kzZ6KoqAiHDx+Gra2t+mdUq1YNAGBnZ6feZmtr+8KcI0aMwKlTp7Bv375Xen0GBgbq43t4eGDGjBnIy8vD9evXX+k4ogX1rIU9+1OxL/IvJCTmY+GPN/C4oBjvvVv6768u4JhIcUykOCZSHBMpjomUXMek0pfY1xEWFgZfX1/UqVMHAFBUVASlUgkTExON/UxNTREdHV3qMZ48eYJVq1bBysoKjRo1KvNn7tixA4WFhaWecf34449hbm6OLVu2vMarebG9e/diypQpWLt2rSTj4cOHkZ+fD19fX/Tv3x9bt27Fw4cPX3isoqIiddE1MjJ6o1x169bFyJEjMXnyZBQXF7/WMQoKCrB27VpYW1vD1dX1jfJUJAMDBeo7W+BcbKZ6m0oFnIvJhKerpcBk4nBMpDgmUhwTKY6JFMdESs5jYiA6wJvau3cvzM3NNbYplcrXPl5KSgp+//13bN68Wb3NwsICbdq0wcyZM+Hu7o4aNWpgy5YtOHnyJJydnSV5goKCkJ+fDwcHBxw4cEDjDOeLXL9+HVZWVnBwcJA8Z2RkhHr16v2jZxSvXr2KkJAQTJ48WWPaRImwsDAEBQVBX18fDRo0QL169fDLL79g8ODBGvv169cP+vr6ePToEYqLi+Ho6Ig+ffq8cb5vvvkGa9euxaZNmzBgwIByfU9cXJz6dyE/Px8WFhbYtm0bLC21+034LCtLQxjoK5CRWaixPSOrEHVqVRGUSiyOiRTHRIpjIsUxkeKYSMl5TGR/JrZjx46IiYnReKxevfq1j7d+/XpYW1tLLgDbuHEjVCoVatasCWNjYyxfvhz9+vWDnp7mEJbkOXHiBLp27Yo+ffrg3r17r53nWW96hrNEdnY2evbsCR8fH8ycOVPyfFZWFnbu3In+/furt/Xv37/UKQVLlixBTEwMfv/9d3h4eGD16tUvnSpQXtWqVcMXX3yBKVOm4MmTJ+X6HldXV/XvwPnz5zFq1Ch8+OGHOHfu3Au/p6CgADk5ORqPgoKCN85PRERE/y7Zl1gzMzM4OztrPGrWrPlax1KpVFizZg0GDBggKYxOTk6IiopCXl4eEhMTcebMGRQWFqJevXql5mndujXCwsJgYGDwwvmkz3JxcUF2djZSUlIkzz158gQ3b95E/fr1AUB9ZrG0C5aysrJgZWX1wp9TXFyM4OBg6OnpYdOmTVAoFJJ9Nm/ejMePH6NVq1YwMDCAgYEBJk6ciOjoaMnZYHt7ezg7O8PPzw9r165F3759/7HS/vnnn+PRo0f48ccfy7W/kZGR+negSZMmmDdvHmrWrKmx+sHz5s6dCysrK43H3Llz/5H8ryM7pxBFShVsbTQn09taGyI9s3xlvrLhmEhxTKQ4JlIcEymOiZScx0T2JfafFBUVhfj4eAwdOvSF+5iZmcHBwQGZmZnYv38/evTo8dJjFhcXl+vM3gcffAADAwMsWrRI8tzKlSuRn5+PgQMHAnhaePX09HD+/HmN/W7duoXs7Gx12S3NN998gxMnTuDXX3+FhUXpy2eEhYVh/PjxGme3Y2Nj0a5dO6xZs+aFx27ZsiWaNWuG2bNnl/l6y8Pc3BzffvstZs+ejdzc3Nc6RslUhxeZPHkysrOzNR6TJ09+3chvrKhIhevxuWjW0Ea9TaEAmjWywaVrOcJyicQxkeKYSHFMpDgmUhwTKTmPieznxJbH5cuX8eTJE2RkZCA3NxcxMTEAoLHOKPC0vLVq1arUdVH3798PlUoFV1dXxMfHY8KECXBzc1OvYPDw4UPMnj0bAQEBcHBwwIMHD/DDDz8gOTm51Dmnz3v77bexYMECfPHFFzAxMcGAAQNgaGiIX3/9FV999RVmzZqlzmVhYYFhw4Zh/PjxMDAwgJeXFxITEzFx4kS0bt0a3t7epf6M7du3Y968eVi7di0sLCyQlpam8by5uTni4+Nx4cIFbNq0CW5ubhrP9+vXDzNmzMCsWbNgYFD6r864ceMQGBiIL7/88rXPiD9rxIgRWLJkCTZv3oxWrVq9dN+ioiL1a8rNzcW2bdtw+fLlly7XZWxsDGNj4zfO+U/auisJX3/mhqvxubhyPRd9etSEqYkefjuYVvY3V1IcEymOiRTHRIpjIsUxkZLrmOhEifX398edO3fUXzdp0gTA0+kDJbKzsxEeHo5ly5aVeoySM3RJSUmwtbVF7969MXv2bBgaPj39rq+vj6tXr2L9+vV48OAB7Ozs0KJFCxw7dkzjpgkv89lnn6FevXpYtGgRli1bpl4NYMuWLQgKCtLYd9myZZg3bx4mTpyIO3fuwN7eHu+++y5mz55d6hQBAAgNDYVKpZJcnFVi6tSpSE9Ph4eHh6TAAkBgYCA+/fRT7Nu3DwEBAaUeo2vXrqhbty5mz55d7mkAL2NoaIiZM2ciODi4zH0vXbqkvjCuSpUqcHJyQmhoqPoMtlwcir4PaytDDAtxhK2NEeJv5WH81DhkZhWW/c2VFMdEimMixTGR4phIcUyk5DomCtWzTY60SkZGBjp37gxLS0v8/vvvqFJFu68SrEzeeV+ed/r6t0Tv8eGYPIdjIsUxkeKYSHFMpDgmUtF7fMrch3NitZitrS0OHjyIzp074+TJk6LjEBEREWkNltgKMmfOHJibm5f66Nat2wu/z87ODlOmTEHnzp0rMC0RERGRdtOJObHaYOTIkS+8CYCpqWkFpyEiIiKSN5bYCmJra/uP3ASAiIiIiDidgIiIiIhkiCWWiIiIiGSHJZaIiIiIZIclloiIiIhkhyWWiIiIiGSHJZaIiIiIZIclloiIiIhkhyWWiIiIiGSHJZaIiIiIZIclloiIiIhkhyWWiIiIiGSHJZaIiIiIZIclloiIiIhkhyWWiIiIiGSHJZaIiIiIZIclloiIiIhkhyWWiIiIiGSHJZaIiIiIZIclloiIiIhkhyWWiIiIiGSHJZaIiIiIZIclloiIiIhkhyWWiIiIiGSHJZaIiIiIZIclloiIiIhkhyWWiIiIiGSHJZaIiIiIZIclloiIiIhkhyWWiIiIiGSHJZaIiIiIZIclloiIiIhkhyWWiIiIiGSHJZaIiIiIZIclloiIiIhkhyWWiIiIiGRHoVKpVKJDEBERERG9CgPRAYi00TvvR4mOoFWi9/hwTJ4TvccHI+dnio6hVVZOtMF/D4pOoV2G+4Jj8hyOiRTHRGq4b9n7cDoBEREREckOSywRERERyQ5LLBERERHJDkssEREREckOSywRERERyQ5LLBERERHJDkssEREREckOSywRERERyQ5LLBERERHJDkssEREREckOSywRERERyQ5LLBERERHJDkssEREREckOSywRERERyQ5LLBERERHJDkssEREREckOSywRERERyQ5LLBERERHJDkssEREREckOSywRERERyQ5LLBERERHJDkssEREREckOSywRERERyQ5LLBERERHJDkssEREREckOSywRERERyQ5LLBERERHJDkssEREREckOSywRERERyQ5LLBERERHJDkssEREREckOSywRERERyQ5LLBERERHJDkssEREREckOSywRERERyQ5LLBERERHJDkssEREREcmOVpbYwYMHo2fPnpLtR44cgUKhQFZWFh4/fozBgwfDy8sLBgYGpe4/ePBgKBQKycPT01O9T25uLsaNG4c6derA1NQU3t7eOHv2rMZxpk2bBjc3N5iZmcHGxga+vr44ffp0uV/Psz/b0tISLVq0wK+//qqxz7p160rNamJiorFfWloaxo4dC2dnZ5iYmKBGjRpo27YtQkNDkZ+fr7HviRMn4O/vDxsbG5iYmMDLywuLFy+GUql85Xwl5s6dC319fSxcuFDy3Itew+rVq8v9Gkv+P5s3b57GsXft2gWFQqH++tnfhWe/LnmYmprC09MTq1atKvV1aLte/m/hl9WtEBneDqu+awJ3FwvRkYTjmPxt9khLrJxoI3kEvWsqOlqFSbxxFjtDRyL0q3fw3X9ccSP24Av3PbBlCr77jyvOH1pXcQEF4JhIcUykKtOYaGWJLQ+lUglTU1OMGTMGvr6+pe6zbNkypKamqh+JiYmwtbXFhx9+qN5n2LBhOHDgADZu3Ii4uDj4+fnB19cXycnJ6n3q16+P77//HnFxcYiOjoajoyP8/Pxw//79cuddu3YtUlNTce7cObRt2xYffPAB4uLiNPaxtLTUyJuamoo7d+6on7916xaaNGmCiIgIzJkzB3/88QdOnjyJL7/8Env37sXBg3//Iv7f//0ffHx8UKtWLRw+fBhXr17F2LFjMWvWLAQFBUGlUr1yPgBYs2YNvvzyS6xZs6bU11naawgJCSn3awQAExMTzJ8/H5mZmeUe3xLXrl1DamoqLl++jI8//hijRo1CZGTkKx9HpE7vVMOnw5ywdksCho47j/jbeVg8wwvWVoaiownDMdE0d30uvvw+S/1YujUXAHDhaqHgZBWn8Ek+qtdyhW+fqS/d70bMAaTcjoW5VfUKSiYOx0SKYyJVmcZEtiXWzMwMoaGhGD58OOzt7Uvdx8rKCvb29urHuXPnkJmZiSFDhgAAHj16hPDwcCxYsADt27eHs7Mzpk2bBmdnZ4SGhqqPExwcDF9fX9SrVw+enp5YvHgxcnJycPHixXLntba2hr29PerXr4+ZM2eiqKgIhw8f1thHoVBo5LW3t0eNGjXUz3/yyScwMDDAuXPn0KdPH7i7u6NevXro0aMHfvvtN7z//vsAgIcPH2L48OEICAjAqlWr0LhxYzg6OmLYsGFYv349duzYge3bt79yvqioKDx69AgzZsxATk4OTpw4IXmdpb0GU1PTlz7/7GsEAF9fX9jb22Pu3LnlHt8S1atXh729PerWrYsxY8agbt26uHDhwisfR6SgnrWwZ38q9kX+hYTEfCz88QYeFxTjvXdL/z3XBRwTTXmPVMh5+PfDy9kQ9zKVuJ5YJDpahann6YN33v8MLo3ffeE+uVl/IfKXmeg++Dvo6Vf+f/BwTKQ4JlKVaUxkW2JfR1hYGHx9fVGnTh0AQFFREZRKpeQje1NTU0RHR5d6jCdPnmDVqlWwsrJCo0aNXjlDUVERwsLCAABGRkbl/r709HRERETgP//5D8zMzErdp+Tj9oiICKSnp+OLL76Q7PP++++jfv362LJlyyvnCwsLQ79+/WBoaIh+/fqp9/un6evrY86cOVixYgWSkpJe6xgqlQr/+9//cPfuXbRq1eofTvjvMTBQoL6zBc7F/n0WWqUCzsVkwtPVUmAycTgmL6evB7TyMMKJi09ER9EqquJi7Fs/AS18h6LqWy6i42gFjokUx0RKTmNiIDrAi+zduxfm5uYa256fy/kqUlJS8Pvvv2Pz5s3qbRYWFmjTpg1mzpwJd3d31KhRA1u2bMHJkyfh7OwsyRMUFIT8/Hw4ODjgwIEDqFq1arl/fr9+/aCvr49Hjx6huLgYjo6O6NOnj8Y+2dnZktfcrl07/P7774iPj4dKpYKrq6vG81WrVsXjx48BAP/5z38wf/58XL9+HQDg7u5eahY3Nzf1PuXNl5OTgx07duDkyZMAgP79+6Ndu3ZYtmyZRubnX4O5uTnS0tLK9RqfFRgYiMaNG2Pq1KmvVJZr1aoFACgoKEBxcTFmzJiB9u3bl/v7RbOyNISBvgIZmZofC2dkFaJOrSqCUonFMXm5xvUNYWqiwMk/C0RH0SpnDvwXenoGaNphoOgoWoNjIsUxkZLTmGhtie3YsaPGR/oAcPr0afTv3/+1jrd+/XpYW1tLLgDbuHEjPvroI9SsWRP6+vpo2rQp+vXrh/Pnz0vyxMTE4MGDB/jvf/+LPn364PTp06hevXxzRZYsWQJfX1/cunULn332GZYvXw5bW1uNfSwsLCQffT/7UXxpzpw5g+LiYoSEhKCgQPM/Ys/Pe32TfFu2bIGTk5P67HPjxo1Rp04dbNu2DUOHDn3ha9DT0zzZ/yqvcf78+ejUqVOpZ5Rf5NixY7CwsEBBQQHOnDmDTz/9FLa2thg1alSp+xcUFEjGzdjYuNw/j0g074bGuHSrENl55X+/V3Zpd//E+cMbMHDSTo0LQnUZx0SKYyIltzHR2hJrZmYmORv6Jh8tr1mzBgMGDJB8RO7k5ISoqCg8fPgQOTk5cHBwQN++fVGvXr1S8zg7O6N169ZwcXFBWFgYJk+eXK4M9vb26u9fu3Yt/P39cfnyZY0SrKenJ3nNJZydnaFQKHDt2jWN7SU5ny2C9evXBwBcuXIF3t7ekmNduXIFHh4er5QvLCwMly5dgoHB378yxcXFWLNmjUaJfdlrKM/zz2rfvj26dOmCyZMnY/DgweX6nrp168La2hoA4OnpidOnT2P27NkvLLFz587F9OnTNbZNnToVQMdy/bx/WnZOIYqUKtjaaM5BsrU2RHqmbn5czDF5MVtLPbjXMcBP//dQdBStkhx/Dvl56fjp27/fx6piJY7snI/zhzdgxMxDAtOJwTGR4phIyW1MtLbE/pOioqIQHx+vUbaeZ2ZmBjMzM2RmZmL//v1YsGDBS49ZXFwsOYNXXi1btkSzZs0we/ZsLFu2rFzfY2dnh3fffRfff/89Ro8e/cJ5sQDg5+cHW1tbLFq0SFJid+/ejRs3bmDmzJnlzhcXF4dz587hyJEjGmdnMzIy0KFDB1y9ehVubm7leh2vat68eWjcuLFkGkV5lUyReJHJkyfj888/19hmbGyMgx+ceq2f96aKilS4Hp+LZg1tcOxUOgBAoQCaNbLBzt+Sy/juyolj8mLeXkbIzVch7qburEpQHh4te+BtN82/feHfD4VHyx5o0KaXoFRicUykOCZSchsTWZfYy5cv48mTJ8jIyEBubi5iYmIAPP2o+1lhYWFo1aoVGjRoIDnG/v371XNN4+PjMWHCBLi5ualXMHj48CFmz56NgIAAODg44MGDB/jhhx+QnJyssVTXqxo3bhwCAwPx5ZdfombNmgCenjF+dv5oierVq0NPTw8//vgj2rZti+bNm2PatGlo2LAh9PT0cPbsWVy9ehXNmjUD8LSQ//TTTwgKCsKIESPw6aefwtLSEpGRkZgwYQI++OADyXzcl+ULCwtDy5YtS51b2qJFC4SFhZW6bmxpynqNz/Py8kJISAiWL19eruPfu3cPjx8/Vk8n2LhxIz744IMX7m9sbKx10we27krC15+54Wp8Lq5cz0WfHjVhaqKH3w5Kx01XcEykFADaeBnh5J9PUKyDMwmePH6IrPt31V9npyfhXuIVmJhZwdL2LZia22jsr6dvCDPLqrCtUe/5Q1UaHBMpjolUZRoTWZdYf39/jTVGmzRpAkBzLmh2djbCw8NfeMYzOzsbkydPRlJSEmxtbdG7d2/Mnj0bhoZPP7rU19fH1atXsX79ejx48AB2dnZo0aIFjh07pnHThFfVtWtX1K1bF7Nnz8aPP/4IAOrpDM9LTU2Fvb09nJyc8Mcff2DOnDnqzMbGxvDw8MAXX3yBTz75RP09H3zwAQ4fPozZs2ejXbt2ePz4MVxcXPD1119j3LhxZc51eTbf9u3bMXHixFL36927NxYtWoQ5c+aU63WX9RpLM2PGDGzbtq1cxy85Y2tgYIDatWvj448/xrRp08r1vdriUPR9WFsZYliII2xtjBB/Kw/jp8YhM0t3z7ZxTKTcHA1gZ6WPExfzREcRIu3un9i+7O8LT46EP12Sz7NVILoNnPeib6vUOCZSHBOpyjQmCtWrXP1DpCPeeT9KdAStEr3Hh2PynOg9Phg5/9VvyFGZrZxog/+++OY/Omm4Lzgmz+GYSHFMpIaXfh8rDTq1TiwRERERVQ4ssW9ozpw5MDc3L/XRrVs30fGIiIiIKiVZz4nVBiNHjnzhRVJlrfFKRERERK+HJfYN2draSm5aQERERET/Lk4nICIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlRqFQqlegQRERERESvwkB0ACJt9M77UaIjaJXoPT4ck+dwTKSi9/jgl1PFomNolQ9b62HmliLRMbTKt/0M+N55Dv+eSEXv8SlzH04nICIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYl9xuDBg9GzZ0/J9iNHjkChUCArKwuPHz/G4MGD4eXlBQMDg1L3Hzx4MBQKheTh6emp3ic3Nxfjxo1DnTp1YGpqCm9vb5w9e1bjONOmTYObmxvMzMxgY2MDX19fnD59utyvJyoqCp06dYKtrS2qVKkCFxcXDBo0CE+ePAEArFu3DtbW1qV+r0KhwK5duwAACQkJUCgUqF69OnJzczX2a9y4MaZNm6b+ukOHDhg3bpz6e172WLduncbYPjvWnp6eUCqVGj/L2toa69atU3/t6OiIpUuXanxdcmx9fX289dZbGDp0KDIzM8s9Ztqil/9b+GV1K0SGt8Oq75rA3cVCdCThOCZSujwmt6+excYlozB/bHt8M8gdl88f1Hj+m0HupT6O7QsTlPjf93Y1oG97PYzroY9v+xnAtaZC43m3WgoEd9DD+F5Pn69hLSanNtDl986LyHFMWGJfkVKphKmpKcaMGQNfX99S91m2bBlSU1PVj8TERNja2uLDDz9U7zNs2DAcOHAAGzduRFxcHPz8/ODr64vk5GT1PvXr18f333+PuLg4REdHw9HREX5+frh//36ZOS9fvoyuXbuiefPmOHr0KOLi4rBixQoYGRlJymF55ebm4rvvvivXvrVr19YYg/Hjx8PT01NjW9++fV/4/bdu3cKGDRteOeOMGTOQmpqKu3fvYtOmTTh69CjGjBnzyscRqdM71fDpMCes3ZKAoePOI/52HhbP8IK1laHoaMJwTKR0fUwKCx7BvrYr3h/wbanPT1x2VOMROHT2038gN/er4KQVx9BAgb8ygd/PF7/geSDxvgqRMaU/ryt0/b1TGrmOCUvsKzIzM0NoaCiGDx8Oe3v7UvexsrKCvb29+nHu3DlkZmZiyJAhAIBHjx4hPDwcCxYsQPv27eHs7Ixp06bB2dkZoaGh6uMEBwfD19cX9erVg6enJxYvXoycnBxcvHixzJwRERGwt7fHggUL0KBBAzg5OaFr167473//C1NT09d67aNHj8bixYtx7969MvfV19fXGANzc3MYGBhobHtZjtGjR2Pq1KkoKCh4pYwWFhawt7dHzZo10bFjRwwaNAgXLlx4pWOIFtSzFvbsT8W+yL+QkJiPhT/ewOOCYrz3bum/b7qAYyKl62NSv1F7vPvBOHg0f7fU5y2sq2k8rv5xCHXdW8G2eu0KTlpxbqaqcCSuGNeSVKU+H5egwrFLKtz+q/TndYWuv3dKI9cxYYmtAGFhYfD19UWdOnUAAEVFRVAqlTAxMdHYz9TUFNHR0aUe48mTJ1i1ahWsrKzQqFGjMn+mvb09UlNTcfTo0Td/Af9fv3794OzsjBkzZvxjx3yRcePGoaioCCtWrHjtYyQnJ2PPnj1o1arVP5js32VgoEB9Zwuci/17CoRKBZyLyYSnq6XAZOJwTKQ4Jq8mL/sBrsVGoVn73qKjkGB870jJeUxYYp+zd+9emJubazy6dev22sdLSUnB77//jmHDhqm3WVhYoE2bNpg5cyZSUlKgVCrx888/4+TJk0hNTS01j4mJCZYsWYIDBw6gatWqZf7cDz/8EP369YOPjw8cHBwQGBiI77//Hjk5Oa/9WhQKBebNm4dVq1bh5s2br32c8qhSpQqmTp2KuXPnIjs7u9zfN3HiRJibm8PU1BS1atWCQqHA4sWL/8Wk/ywrS0MY6CuQkVmosT0jqxB2NkaCUonFMZHimLyaP6J3wdjEDB7NSj9rS7qD7x0pOY8JS+xzOnbsiJiYGI3H6tWrX/t469evh7W1teQCsI0bN0KlUqFmzZowNjbG8uXL0a9fP+jpaf5fUpLnxIkT6Nq1K/r06VPuj/PXrl2LpKQkLFiwADVr1sScOXPU81JfV5cuXfDOO+/g229Ln4f2Txo6dCjs7Owwf/78cn/PhAkTEBMTg4sXLyIyMhIA0L179xfOAy4oKEBOTo7G41WnMBCRdjt/bCcatXkPhkbGoqMQ0T+IJfY5ZmZmcHZ21njUrFnztY6lUqmwZs0aDBgwAEZGmv+acXJyQlRUFPLy8pCYmIgzZ86gsLAQ9erVKzVP69atERYWBgMDA4SFlf/q2po1a2LAgAH4/vvvcenSJTx+/BgrV64EAFhaWuLhw4coLtac5F+yUoCVlVWpx5w3bx62bduGP/74o9w5XoeBgQFmz56NZcuWISUlpVzfU7VqVTg7O8PFxQWdOnXC0qVLceLECRw+fLjU/efOnQsrKyuNx9y5c//Jl/FKsnMKUaRUwdZGczK9rbUh0jOfCEolFsdEimNSfgnXzuFB6m008/lAdBTSAnzvSMl5TFhi/0VRUVGIj4/H0KFDX7iPmZkZHBwckJmZif3796NHjx4vPWZxcfFrnym0sbGBg4MDHj58CABwdXVFUVERYmJiNPYruRCqfv36pR6nZcuW6NWrFyZNmvRaOV7Fhx9+CE9PT0yfPv21vl9fXx/A04vpSjN58mRkZ2drPCZPnvzaed9UUZEK1+Nz0ayhjXqbQgE0a2SDS9defyqInHFMpDgm5Xf+aDjecvSEw9tuoqOQFuB7R0rOY2IgOoAcXb58GU+ePEFGRgZyc3PVJbBx48Ya+4WFhaFVq1Zo0KCB5Bj79++HSqWCq6sr4uPjMWHCBLi5ualXMHj48CFmz56NgIAAODg44MGDB/jhhx+QnJyssVTXi/z000+IiYlBYGAgnJyc8PjxY2zYsAGXLl1SXyzl6ekJPz8/fPTRR1i0aBHq1auHa9euYdy4cejbt+9Lz0DPnj0bnp6eMDD493+F5s2bhy5dupRr39zcXKSlpUGlUiExMRFffvklqlWrBm9v71L3NzY2hrGxdn3EuHVXEr7+zA1X43Nx5Xou+vSoCVMTPfx2ME10NGE4JlK6PiYFjx8i46+76q8z7ych9c4VmJpbwdruLQDA40d5+PPMfnTr96WomBXK0ACwNf/7a2tzoIY18OgJkJMPmBgBVlUAC9On68faWSoAqJD3GHj4WEhkIXT9vVMauY4JS+xr8Pf3x507d9RfN2nSBMDT6QMlsrOzER4ejmXLlpV6jJIzfklJSbC1tUXv3r0xe/ZsGBo+PZ2vr6+Pq1evYv369Xjw4AHs7OzQokULHDt2TOOmCS/SsmVLREdHY+TIkUhJSYG5uTk8PT2xa9cu+Pj4qPfbtm0bpk6dio8//hgpKSmoVasWAgMDy5zzWr9+fXz00UdYtWpVmVneVKdOndCpUydERESUue+UKVMwZcoUAEC1atXQokULREREwM7O7t+O+Y85FH0f1laGGBbiCFsbI8TfysP4qXHIzCos+5srKY6JlK6PSfLtS1gzb5D669+3PJ073+Sdnug9/OmUoLhT+wCo0LB1dxERK9xbtgoM7Kyv/tqv6dP/HXurGLtPF6N+TQV6tP77+d5tn/7vqLhiHP1Td9aO1fX3TmnkOiYK1bPNi4gAAO+8HyU6glaJ3uPDMXkOx0Qqeo8PfjmlO2WoPD5srYeZW4pEx9Aq3/Yz4HvnOfx7IhW9x6fMfTgnloiIiIhkhyVWpubMmSNZz/afWNeWiIiISA44J1amRo4ciT59+pT63OveVpaIiIhILlhiZcrW1ha2traiYxAREREJwekERERERCQ7LLFEREREJDsssUREREQkOyyxRERERCQ7LLFEREREJDsssUREREQkOyyxRERERCQ7LLFEREREJDsssUREREQkOyyxRERERCQ7LLFEREREJDsssUREREQkOyyxRERERCQ7LLFEREREJDsssUREREQkOyyxRERERCQ7LLFEREREJDsssUREREQkOyyxRERERCQ7LLFEREREJDsssUREREQkOyyxRERERCQ7LLFEREREJDsssUREREQkOyyxRERERCQ7LLFEREREJDsssUREREQkOyyxRERERCQ7LLFEREREJDsssUREREQkOyyxRERERCQ7LLFEREREJDsssUREREQkOyyxRERERCQ7LLFEREREJDsKlUqlEh2CiIiIiOhVGIgOQKSN3nk/SnQErRK9x4dj8hyOiRTHRIpjIsUxkeKYSEXv8SlzH04nICIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZkXWJHTx4MHr27CnZfuTIESgUCmRlZeHx48cYPHgwvLy8YGBgUOr+gwcPhkKhkDw8PT3V++Tm5mLcuHGoU6cOTE1N4e3tjbNnz2ocZ9q0aXBzc4OZmRlsbGzg6+uL06dPl/k6/vrrLxgaGmLr1q2lPj906FA0bdpU/fWjR49ga2uLqlWroqCgQLK/o6MjFAoFTp06pbF93Lhx6NChg0bektdqYGCAqlWron379li6dKnkuB06dCh1jEaOHIl169aV+tyzj4SEBADAyZMnoa+vj+7du0tyJyQkQKFQoHr16sjNzdV4rnHjxpg2bZpGnnHjxr0wX40aNfDhhx/izp07pY6ptuvl/xZ+Wd0KkeHtsOq7JnB3sRAdSTiOiRTHRIpjIsUxkeKYSMlxTGRdYstDqVTC1NQUY8aMga+vb6n7LFu2DKmpqepHYmIibG1t8eGHH6r3GTZsGA4cOICNGzciLi4Ofn5+8PX1RXJysnqf+vXr4/vvv0dcXByio6Ph6OgIPz8/3L9//6UZa9Soge7du2PNmjWS5x4+fIjt27dj6NCh6m3h4eHw9PSEm5sbdu3aVeoxTUxMMHHixJf+XADw9PREamoq7t69i8OHD+PDDz/E3Llz4e3tLSmSw4cP1xin1NRULFiwAH379tXY1qZNG8m+tWvXBgCEhYVh9OjROHr0KFJSUkrNlJubi++++67M7M8r+ZkpKSn49ddfkZiYiP79+7/ycUTr9E41fDrMCWu3JGDouPOIv52HxTO8YG1lKDqaMBwTKY6JFMdEimMixTGRkuuYVPoSa2ZmhtDQUAwfPhz29val7mNlZQV7e3v149y5c8jMzMSQIUMAPD3zGR4ejgULFqB9+/ZwdnbGtGnT4OzsjNDQUPVxgoOD4evri3r16sHT0xOLFy9GTk4OLl68WGbOoUOHIjIyEnfv3tXY/ssvv6CoqAghISHqbWFhYejfvz/69++PsLCwUo83YsQInDp1Cvv27XvpzzUwMIC9vT3eeusteHl5YfTo0YiKisKff/6J+fPna+xbpUoVjXGyt7eHpaUlTE1NNbYZGRlJ9tXX10deXh62bduGUaNGoXv37li3bl2pmUaPHo3Fixfj3r17ZY5bafkcHBzQunVrfPrpp7hw4cIrHUMbBPWshT37U7Ev8i8kJOZj4Y838LigGO+9W/rvry7gmEhxTKQ4JlIcEymOiZRcx6TSl9jXERYWBl9fX9SpUwcAUFRUBKVSCRMTE439TE1NER0dXeoxnjx5glWrVsHKygqNGjUq82f6+/ujRo0akmK3du1a9OrVC9bW1gCAmzdv4uTJk+jTpw/69OmDY8eOlfqRed26dTFy5EhMnjwZxcXF5XjVf3Nzc0O3bt2wc+fOV/q+smzfvh1ubm5wdXVF//79sWbNGqhUKsl+/fr1g7OzM2bMmPHaPysjIwPbt29Hq1at3iRyhTMwUKC+swXOxWaqt6lUwLmYTHi6WgpMJg7HRIpjIsUxkeKYSHFMpOQ8JrIvsXv37oW5ubnGo1u3bq99vJSUFPz+++8YNmyYepuFhQXatGmDmTNnIiUlBUqlEj///DNOnjyJ1NTUUvOYmJhgyZIlOHDgAKpWrVrmz9XX18egQYOwbt06dbG7efMmjh07ho8++ki935o1a9CtWzfY2NjA1tYWXbp0wdq1a0s95jfffIPbt29j06ZNrzwObm5u6nmsJX788UfJWL/KsUvOIANA165dkZ2djaioKMl+CoUC8+bNw6pVq3Dz5s1yH78kn5mZGezs7HDt2rVSp2hoMytLQxjoK5CRWaixPSOrEHY2RoJSicUxkeKYSHFMpDgmUhwTKTmPiexLbMeOHRETE6PxWL169Wsfb/369bC2tpZcALZx40aoVCrUrFkTxsbGWL58Ofr16wc9Pc0hLMlz4sQJdO3aFX369Cn3x+IfffQRbt++jcOHDwN4ehbW0dERnTp1AvB0fu/69es15nn2798f69atK/Vsa7Vq1fDFF19gypQpePLkyasMA1QqFRQKhca2kJAQyVgHBASU63jXrl3DmTNn0K9fPwBPpzH07dv3hdMhunTpgnfeeQfffvttuTOX5IuNjUV0dDScnZ3h5+cnmdv7rIKCAuTk5Gg8SrtYjoiIiLSL7EusmZkZnJ2dNR41a9Z8rWOpVCqsWbMGAwYMgJGR5r8+nJycEBUVhby8PCQmJuLMmTMoLCxEvXr1Ss3TunVrhIWFwcDA4IVF7XkuLi5o164d1q5di+LiYmzYsAFDhgxRl8n9+/cjOTkZffv2hYGBAQwMDBAUFIQ7d+4gMjKy1GN+/vnnePToEX788cdXGosrV66gbt26GtusrKwkY21hUb6rF8PCwlBUVIS33npLnT00NBTh4eHIzs4u9XvmzZuHbdu24Y8//ijXz3g2X9u2bREWFoYbN25g27ZtL/yeuXPnwsrKSuMxd+7ccv28f0N2TiGKlCrY2mhOpre1NkR65qv9Q6Sy4JhIcUykOCZSHBMpjomUnMdE9iX2nxQVFYX4+HiNlQCeZ2ZmBgcHB2RmZmL//v3o0aPHS49ZXFz8Smf2hg4divDwcISHhyM5ORmDBw9WPxcWFoagoCDJ2dCgoKAXFmVzc3N8++23mD179kvPSD7r6tWr+N///ofevXuXO/fLFBUVYcOGDVi0aJFG7tjYWLz11lvYsmVLqd/XsmVL9OrVC5MmTXqtn6uvrw/g6YV5LzJ58mRkZ2drPCZPnvxaP++fUFSkwvX4XDRraKPeplAAzRrZ4NK1HGG5ROKYSHFMpDgmUhwTKY6JlJzHxEB0gIpw+fJlPHnyBBkZGcjNzUVMTAyAp2uPPissLAytWrVCgwYNJMfYv38/VCoVXF1dER8fjwkTJsDNzU29gsHDhw8xe/ZsBAQEwMHBAQ8ePMAPP/yA5ORkjaW6yvLhhx9izJgx+Pjjj+Hn56demur+/fvYs2cPdu/eLck3cOBABAYGIiMjA7a2tpJjjhgxAkuWLMHmzZslFzoVFRUhLS0NxcXFSE9Px5EjRzBr1iw0btwYEyZM0Ng3Pz8faWlpGtuMjY1hY2ODl9m7dy8yMzMxdOhQWFlZaTzXu3dvhIWFYeTIkaV+7+zZs+Hp6QkDg7J/VZ/N99dff2HmzJkwMTGBn5/fC7/H2NgYxsbGZR67Im3dlYSvP3PD1fhcXLmeiz49asLURA+/HUwr+5srKY6JFMdEimMixTGR4phIyXVMdKLE+vv7a1zB36RJEwDQuDI+Ozsb4eHhWLZsWanHKDlDl5SUBFtbW/Tu3RuzZ8+GoeHT0+/6+vq4evUq1q9fjwcPHsDOzg4tWrTAsWPHNG6aUJYqVaogKCgIq1at0riga8OGDTAzM0Pnzp0l39O5c2eYmpri559/xpgxYyTPGxoaYubMmQgODpY8d+nSJTg4OEBfXx9WVlbw8PDA5MmTMWrUKEm5++9//4v//ve/Gtu6dOmC//3vfy99TSWrPTxfYIGnJXbBggW4ePEiLC2lV0HWr18fH330EVatWvXSn/F8PhsbGzRs2BD79u2Dq6trmd+rTQ5F34e1lSGGhTjC1sYI8bfyMH5qHDKzCsv+5kqKYyLFMZHimEhxTKQ4JlJyHROFqrQ1joh03DvvS1dN0GXRe3w4Js/hmEhxTKQ4JlIcEymOiVT0Hp8y9+GcWCIiIiKSHZbYCjJnzhzJGqv/xLq2RERERLpIJ+bEaoORI0eiT58+pT5nampawWmIiIiI5I0ltoLY2tqWunIAEREREb06TicgIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2WGJJSIiIiLZYYklIiIiItlhiSUiIiIi2VGoVCqV6BBEJFVQUIC5c+di8uTJMDY2Fh1HK3BMpDgmUhwTKY6JFMdESm5jwhJLpKVycnJgZWWF7OxsWFpaio6jFTgmUhwTKY6JFMdEimMiJbcx4XQCIiIiIpIdllgiIiIikh2WWCIiIiKSHZZYIi1lbGyMqVOnymJyfUXhmEhxTKQ4JlIcEymOiZTcxoQXdhERERGR7PBMLBERERHJDkssEREREckOSywRERERyQ5LLBERERHJDkssERERkQ4qLCx84XMPHjyowCSvh6sTEGmRxMREKBQK1KpVCwBw5swZbN68GR4eHhgxYoTgdGJkZ2dDqVTC1tZWY3tGRgYMDAxkcWvEf0vJn2+FQiE4iXhZWVk4c+YM7t27h+LiYo3nBg4cKCiVOGfPnkVxcTFatWqlsf306dPQ19dH8+bNBSUTJykpSf239XmnTp1C69atKziReL1798aOHTskf0P++usvdO7cGX/++aegZOXDM7FEWiQ4OBiHDx8GAKSlpeHdd9/FmTNn8PXXX2PGjBmC04kRFBSErVu3SrZv374dQUFBAhKJt2HDBnh5ecHU1BSmpqZo2LAhNm7cKDqWMHv27MHbb7+Nrl274tNPP8XYsWPVj3HjxomOJ8R//vMfJCYmSrYnJyfjP//5j4BE4vn5+SEjI0Oy/fjx4+jatauAROLdvXsXw4YN09iWlpaGDh06wM3NTVCq8mOJJdIif/75J1q2bAngaUlr0KABTpw4gU2bNmHdunViwwly+vRpdOzYUbK9Q4cOOH36tIBEYi1evBijRo2Cv78/tm/fju3bt6Nr164YOXIklixZIjqeEOPHj8dHH32EvLw8ZGVlITMzU/0orbTogsuXL6Np06aS7U2aNMHly5cFJBKvdevW8PPzQ25urnrb0aNH4e/vj6lTpwpMJs6+fftw4sQJfP755wCAlJQU+Pj4wMvLC9u3bxecrmwGogMQ0d8KCwvVd0o5ePAgAgICAABubm5ITU0VGU2YgoICFBUVSbYXFhbi0aNHAhKJtWLFCoSGhmp8RB4QEABPT09MmzYNn332mcB0YiQnJ2PMmDGoUqWK6Chaw9jYGH/99Rfq1aunsT01NRUGBrr5n/7Vq1fjgw8+wPvvv4/9+/fjxIkTCAgIwKxZszB27FjR8YSoVq0aIiIi8M477wAA9u7di6ZNm2LTpk3Q09P+85zan5BIh3h6emLlypU4duwYDhw4oP6IKyUlBXZ2doLTidGyZUusWrVKsn3lypVo1qyZgERipaamwtvbW7Ld29tbZ/+h06VLF5w7d050DK3i5+eHyZMnIzs7W70tKysLX331Fd59912BycTR09PD1q1bYWhoiE6dOiEgIABz587V2QJbonbt2jhw4AA2bdqEli1bYsuWLdDX1xcdq1x4YReRFjly5AgCAwORk5ODQYMGYc2aNQCAr776ClevXsXOnTsFJ6x4x48fh6+vL1q0aIHOnTsDACIjI3H27FlERESgXbt2ghNWrAYNGiA4OBhfffWVxvZZs2Zh27ZtiIuLE5SsYu3evVv9v+/fv48ZM2ZgyJAh8PLygqGhoca+JZ9o6JLk5GS0b98e6enpaNKkCQAgJiYGNWrUwIEDB1C7dm3BCSvGxYsXJdtyc3PRr18/dO/eHaNGjVJvb9iwYUVGE8bGxqbUi0Hz8/NhbGysUWC1fToOSyyRllEqlcjJyYGNjY16W0JCAqpUqYLq1asLTCZOTEwMFi5ciJiYGPWFTJMnT4aLi4voaBUuPDwcffv2ha+vL9q2bQvgadGPjIzE9u3bERgYKDhhxSjvR50KhQJKpfJfTqOdHj58iE2bNiE2Nlb9vunXr5+k5Fdmenp6UCgUeLbqPPt1yf/Wpd+T9evXl3vfQYMG/YtJ3hxLLBGRzJw/fx5LlizBlStXAADu7u4YP368+owbET11586dcu9bp06dfzGJ9ikqKsLmzZvRpUsX1KhRQ3Sc18ISS6RF/vrrL3zxxReIjIzEvXv38PzbU1fOFDzrwoULMDQ0hJeXFwDg119/xdq1a+Hh4YFp06bByMhIcEISbcOGDejbt6/6osgST548wdatW3Vyndj169ejatWq6N69OwDgyy+/xKpVq+Dh4YEtW7boXGErLCzExx9/jG+//RZ169YVHUdrVKlSBVeuXJHt7wNLLJEW6datG+7evYtPP/0UDg4OknlLPXr0EJRMnBYtWmDSpEno3bs3bt26BQ8PD/Tq1Qtnz55F9+7dsXTpUtERK5S+vj5SU1MlU0vS09NRvXp1nfyHDsdEytXVFaGhoejUqRNOnjyJzp07Y+nSpdi7dy8MDAx0cn69lZUVYmJiWGKf0aFDB4wbNw49e/YUHeW16OY6G0RaKjo6GseOHUPjxo1FR9Ea169fV4/HL7/8Ah8fH2zevBnHjx9HUFCQzpXYF513KCgo0Nmz0iVzGp+XlJQEKysrAYnES0xMhLOzMwBg165d+OCDDzBixAi0bdsWHTp0EBtOkJ49e2LXrl06uQzdi3zyyScYP348kpKS0KxZM5iZmWk8r+0Xu7HEEmmR2rVrv7Ck6CqVSqW+jejBgwfx3nvvAXg6VnK4t/c/Zfny5QCeXoiyevVqmJubq59TKpU4evSoLO6w809q0qQJFAoFFAoFOnfurLH+qVKpxO3bt3X2Tkzm5uZIT0/H22+/jYiICPVi9iYmJjq5vjIAuLi4YMaMGTh+/HiphW3MmDGCkolTctfDZ1+7nC52Y4kl0iJLly7FpEmT8NNPP8HR0VF0HK3QvHlzzJo1C76+voiKikJoaCgA4Pbt27K9GOF1lNyNS6VSYeXKlRrL4BgZGcHR0RErV64UFU+Iko9AY2Ji0KVLF41iXzImvXv3FpROrHfffRfDhg1DkyZNcP36dfj7+wMALl26pLN/W8LCwmBtbY3z58/j/PnzGs8pFAqdLLG3b98WHeGNcE4skWDPr9n38OFDFBUVoUqVKpKlcLR9zb5/w8WLFxESEoK7d+/i888/V98ecvTo0UhPT8fmzZsFJ6xYHTt2xM6dOzWWYNN169evR9++fWFiYiI6itbIysrCN998g8TERIwaNUp9Rnrq1KkwMjLC119/LTgh0ZtjiSUSrDKt2VeRHj9+DH19fXXR37JlCwICAiQfEVZWT548we3bt+Hk5KSztxGlN/fJJ59gxowZqFq1qugoFerZdWJ13c2bN7F06VL1kn0eHh4YO3YsnJycBCcrG0ssEVUKlpaWiImJkdwrvrJ59OgRPv30U/U/fq5fv4569eph9OjRqFmzJiZNmiQ4YcVTKpVYsmQJtm/fjrt37+LJkycaz+viJxjlpSvvmxIbNmzAwoULcePGDQBA/fr1MWHCBAwYMEBwMjH279+PgIAANG7cWOPmKbGxsdizZ4/W36K4fLc8IaIKoa+vj3v37km2p6eny+Ze1qLoyr/HJ02ahNjYWBw5ckTj43NfX19s27ZNYDJxpk+fjsWLF6Nv377Izs7G559/jl69ekFPTw/Tpk0THU+r6cr7BgAWL16MUaNGwd/fH9u3b8f27dvRtWtXjBw5Uj3nXNdMmjQJn332GU6fPo3Fixdj8eLFOH36NMaNG4eJEyeKjlc2FRFpDYVCofrrr78k25OTk1UmJiYCEsmHubm56ubNm6Jj/Ovefvtt1cmTJ1UqleZrvnHjhsrCwkJkNGHq1aun2rt3r0qlejom8fHxKpVKpVq2bJmqX79+IqNpPV1536hUKpWjo6Nq/fr1ku3r1q1TOTo6CkgknrGxser69euS7deuXVMZGxsLSPRqOJGKSAtw+SQqr/v370sW9QeeXhCoq/P70tLS1Hd0Mzc3R3Z2NgDgvffew7fffisyGmmR1NRUeHt7S7Z7e3sjNTVVQCLxqlWrhpiYGLi4uGhsj4mJKfXvjLZhiSXSAlw+icqrefPm+O233zB69GgAf1+Ysnr1arRp00ZkNGFq1aqF1NRUvP3223ByckJERASaNm2Ks2fPSm5FS7rL2dkZ27dvx1dffaWxfdu2bZISpyuGDx+OESNG4NatW+qCf/z4ccyfP1+9trA2Y4kl0gIla/Vx+SQqy5w5c9CtWzdcvnwZRUVFWLZsGS5fvowTJ04gKipKdDwhAgMDERkZiVatWmH06NHo378/wsLCcPfuXd6didSmT5+Ovn374ujRoxoXMUVGRmL79u2C04nx7bffwsLCAosWLcLkyZMBAG+99RamTZsmi3VzuToBEVUKDRo0wO+//47atWuLjvKvu3nzJubNm4fY2Fjk5eWhadOmmDhxovojdV138uRJnDx5Ei4uLnj//fdFx9Fqo0aNwsyZM3Vmia3z589jyZIl6uWk3N3dMX78eDRp0kRwMvFyc3MBABYWFoKTlB9LLJGWSUpKwu7du0tdKmjx4sWCUomTmJgIhUKBWrVqAQDOnDmDzZs3w8PDAyNGjBCcjkh7ZWVl4cyZM7h375761s0lBg4cKCgVaZM1a9agY8eOqFu3rugor4UllkiLREZGIiAgAPXq1cPVq1fRoEEDJCQkQKVSoWnTpjh06JDoiBWuXbt2GDFiBAYMGIC0tDS4urrC09MTN27cwOjRozFlyhTREStccXEx4uPjSy0n7du3F5RKrGvXrmHFihUaZ9hGjx4NV1dXwcnE2LNnD0JCQpCXlwdLS0uNi/4UCoVOrp07cOBAdOzYET4+PjqzLm5ZXFxccOvWLdSsWRM+Pj7w8fFBhw4d4OzsLDpaubDEEmmRli1bolu3bpg+fTosLCwQGxuL6tWrIyQkBF27dsWoUaNER6xwNjY2OHXqFFxdXbF8+XJs27YNx48fR0REBEaOHIlbt26JjlihTp06heDgYNy5c0eyxqdCoYBSqRSUTJzw8HAEBQWhefPm6ovbTp06hbNnz2Lr1q3o3bu34IQVr379+vD398ecOXNQpUoV0XG0wrBhw3D06FHEx8erS1uHDh3g4+Ojsxd2AUBycjKOHDmCo0ePIioqCjdu3ICDgwM6dOiAn3/+WXS8l2KJJdIiFhYWiImJgZOTE2xsbBAdHQ1PT0/ExsaiR48eSEhIEB2xwpmbm+PPP/+Eo6MjAgIC0LZtW0ycOBF3796Fq6srHj16JDpihWrcuDHq16+P6dOnw8HBQbKslpWVlaBk4jg5OSEkJAQzZszQ2D516lT8/PPPuHnzpqBk4piZmSEuLo5nHEuRnJysLmxRUVG4fv06HBwckJSUJDqaUPn5+Th27Bi2bNmCTZs2QaVSoaioSHSsl+Idu4i0iJmZmXoerIODg8Z/fB88eCAqllCenp5YuXIljh07hgMHDqBr164AgJSUFNjZ2QlOV/Fu3LiBOXPmwN3dHdbW1rCystJ46KLU1NRS53j2799fZ9f/7NKlC86dOyc6hlaysbGBnZ0dbGxsYG1tDQMDA1SrVk10LCEiIiLw1VdfwdvbG3Z2dpg8eTJsbGywY8cO3L9/X3S8MnGJLSIt0rp1a0RHR8Pd3R3+/v4YP3484uLisHPnTrRu3Vp0PCHmz5+PwMBALFy4EIMGDUKjRo0AALt370bLli0Fp6t4rVq1Qnx8vGzmrFWEDh064NixY5IxiY6ORrt27QSlqni7d+9W/+/u3btjwoQJuHz5Mry8vGBoaKixb0BAQEXHE+6rr77CkSNH8Mcff8Dd3R0+Pj6YNGkS2rdvr7PLGnbt2hXVqlXD+PHjsW/fPlhbW4uO9Eo4nYBIi9y6dQt5eXlo2LAhHj58iPHjx+PEiRNwcXHB4sWLUadOHdERhVAqlcjJydH4D01CQgKqVKkii7vK/JP+7//+D9988w0mTJhQajlp2LChoGTirFy5ElOmTEGfPn3U/9g7deoUfvnlF0yfPh1vvfWWet/KXN709Mr34aquzp3W09NDtWrV8Nlnn6FXr16oX7++6EjCLV26FEePHsXRo0dhbGysnifcoUMHWYwPSywRkYyUVlQUCgVUKpVOl5Py0NXxoadiY2MRFRWFI0eO4NixYzAyMpJdafs3xcXFISoqCocOHcLevXtRvXp1rZ8nzBJLpIXOnTunXirIw8MDzZo1E5xInL/++gtffPEFIiMjce/ePckV+bpWSu7cufPS53X1bD1p2rBhA/r27Su57e6TJ0+wdetWrhOLp6V2yZIl2LRpE4qLi3Xub0kJlUqFP/74A0eOHMHhw4cRHR2N3NxceHl54Y8//hAd76VYYom0SFJSEvr164fjx4+r5yZlZWXB29sbW7duVS/4r0u6deuGu3fv4tNPPy31avwePXoISlbxCgsL4ebmhr1798Ld3V10HNnx8vLCvn37dOKubvr6+khNTZVMt0lPT0f16tV1srA9W9aOHDmC6Oho5OTkoGHDhvDx8cGSJUtER6xw77//Po4fP46cnBw0atRIveRY+/btZTE/lhd2EWmRYcOGobCwEFeuXFEv0n7t2jUMGTIEw4YNw//+9z/BCStedHQ0jh07hsaNG4uOIpyhoSEeP34sOoZsJSQkoLCwUHSMClEyveR5SUlJOruKha2tLfLy8tCoUSP4+Phg+PDhaNeunSzK2r/Fzc0NH3/8Mdq1ayfL3wuWWCItEhUVhRMnTmjcZcjV1RUrVqzQqausn1W7dm3JFAJd9p///Afz58/H6tWrYWDAP+GkqUmTJlAoFFAoFOjcubPG74hSqcTt27fVy9Tpmp9//hnt2rWDpaWl6Chaw8vLC++++65sp53wLyCRFqldu3apZ4qUSqXGFda6ZOnSpZg0aRJ++uknODo6io4j3NmzZxEZGYmIiAh4eXnBzMxM4/mdO3cKSkbaoGfPngCAmJgYdOnSBebm5urnjIyM4OjoqJN3MAOe3tmttNsyP3z4EKNHj8aaNWsEpBJryJAh6Nq1q2TaSW5uLoYMGaL1JZZzYom0yK+//oo5c+bghx9+QPPmzQE8vchr9OjRmDhxovo/UJWdjY2NxkehDx8+RFFREapUqSJZUkrX7gE/ZMiQlz6/du3aCkoiPyW3ctaFu1itX78effv2hYmJiegoWuNF84QfPHgAe3t7rb871b9BT08Pf/31l+RmD7GxsejYsaPW/31liSXSIjY2NsjPz0dRUZH6Y8CS//38GTdt/+PyJtavX1/ufQcNGvQvJqHKRJdKLP0tJycHKpUKNjY2uHHjhkZhUyqV2LNnDyZNmoSUlBSBKStWybST2NhYeHp6vnDayfbt2wWmLBunExBpkaVLl4qOoBVYTMt2//59XLt2DcDTedO6ettMKp1SqcSSJUuwfft23L17V3076xKV+R/Bz7O2tlbPEy5tLViFQoHp06cLSCZOZZl2whJLpEXKW97mzZuHrKwsnbiqlksFaSqZv7dhwwYUFxcDeDpGAwcOxIoVK1ClShXBCSteeddE/emnn1CjRg0RESvc9OnTsXr1aowfPx7ffPMNvv76ayQkJGDXrl2YMmWK6HgV6vDhw1CpVOjUqRPCw8Nha2urfs7IyAh16tTRuWsOpk6dCgBwdHSU9bQTTicgkiFLS0vExMToxMeienp6SEtLk5TYlJQUODk54dGjR4KSifHxxx/j4MGD+P7779G2bVsAT5chGzNmDN59912EhoYKTljx+A8dKScnJyxfvhzdu3eHhYUFYmJi1NtOnTqFzZs3i45Y4e7cuYO333671KXHdFlWVhZ27NiBmzdvYsKECbC1tcWFCxdQo0YN1KxZU3S8l+KZWCIZ0oV/ey5fvhzA04/6Vq9erfFxl1KpxNGjR+Hm5iYqnjDh4eHYsWMHOnTooN7m7+8PU1NT9OnTRydLLNdElUpLS4OXlxcAwNzcHNnZ2QCA9957D99++63IaMLUqVMHx44dw08//YRbt27hl19+Qc2aNbFx40bUrVsX77zzjuiIFe7ixYvw9fWFlZUVEhISMHz4cNja2mLnzp24e/cuNmzYIDriS7HEEpFWKrl7jkqlwsqVK6Gvr69+rmTO1sqVK0XFEyY/P7/Uj8SrV6+O/Px8AYnE4ZqoL1arVi2kpqbi7bffhpOTEyIiItC0aVOcPXtWMu1CV4SHh2PAgAEICQnBhQsXUFBQAADIzs7GnDlzsG/fPsEJK95nn32GwYMHY8GCBbCwsFBv9/f3R3BwsMBk5cMSS0Ra6fbt2wCAjh07YufOnbCxsRGcSDu0adMGU6dOxYYNG9Tz2B49eoTp06ejTZs2gtNVrMpyccq/ITAwEJGRkWjVqhVGjx6N/v37IywsDHfv3sVnn30mOp4Qs2bNwsqVKzFw4EBs3bpVvb1t27aYNWuWwGTinDt3DqtWrZJsr1mzJtLS0gQkejUssUSk1Q4fPiw6glZZunQpunbtilq1aqFRo0YAnq7paGxsjIiICMHpKtazF6cEBQXp7BnG0sybN0/9v/v27Yu3334bJ0+ehIuLC95//32BycS5du1aqTc7sLKyQlZWVsUH0gLGxsbIycmRbL9+/bosVjxhiSUirZeUlITdu3eXulTQ4sWLBaUSw8vLCzdu3MCmTZtw9epVAEC/fv0QEhICU1NTwenE6NSpE+7fv49atWoBAM6cOYPNmzfDw8MDI0aMEJxOO7Rp00bnztQ/z97eHvHx8ZI7/0VHR+vERbKlCQgIwIwZM9TrwSoUCty9excTJ06UxacYLLFEMtSuXTudKSyRkZEICAhAvXr1cPXqVTRo0AAJCQlQqVRo2rSp6HgVbu7cuahRowaGDx+usX3NmjW4f/8+Jk6cKCiZOMHBwRgxYgQGDBiAtLQ0+Pr6okGDBti0aRPS0tJ0bkmpEteuXcOKFStw5coVAIC7uztGjx4NV1dXwcnEGD58OMaOHYs1a9ZAoVAgJSUFJ0+exBdffKGzF7stWrQIH3zwAapXr45Hjx7Bx8cHaWlpaNOmDWbPni06Xpm4xBaRYKV9lPMilpaW/2IS7dSyZUt069YN06dPV99xqXr16ggJCUHXrl0xatQo0RErlKOjIzZv3gxvb2+N7adPn0ZQUJB6LrEusbGxwalTp+Dq6orly5dj27ZtOH78OCIiIjBy5EjcunVLdMQKFx4ejqCgIDRv3lx9BvbUqVM4e/Ystm7dKouzbP80lUqFOXPmYO7cueqLII2NjfHFF19g5syZgtOJFR0djYsXLyIvLw9NmzaFr6+v6EjlwhJLJJienl6Z6xaWLCGki+tdPrvGpY2NDaKjo+Hp6YnY2Fj06NEDCQkJoiNWKBMTE1y5cgV169bV2H7r1i14eHjg8ePHgpKJY25ujj///BOOjo4ICAhA27ZtMXHiRNy9exeurq46t5Yw8HSd2JCQEMyYMUNj+9SpU/Hzzz/j5s2bgpKJ9+TJE8THxyMvLw8eHh4aFwSSvHA6AZFgvHDp5czMzNTzYB0cHHDz5k14enoCAB48eCAymhC1a9fG8ePHJSX2+PHjOnfXoRKenp5YuXIlunfvjgMHDqjPqqWkpMDOzk5wOjFSU1PVdyp7Vv/+/bFw4UIBibSHkZERPDw8RMfQGpGRkYiMjMS9e/fUdwEssWbNGkGpyocllkgwHx8f0RG0WuvWrREdHQ13d3f4+/tj/PjxiIuLw86dO9G6dWvR8Src8OHDMW7cOBQWFqJTp04Anv5H6Msvv8T48eMFpxNj/vz5CAwMxMKFCzFo0CD1qg27d+9Gy5YtBacTo0OHDjh27BicnZ01tkdHR6Ndu3aCUon18OFDzJs374WFTRennUyfPh0zZsxA8+bN4eDgILu7mXE6AZFgFy9eLPe+DRs2/BeTaKdbt24hLy8PDRs2xMOHDzF+/HicOHECLi4uWLx4MerUqSM6YoVSqVSYNGkSli9frj5DbWJigokTJ+rsBUzA05sb5OTkaKwnnJCQgCpVqkhuR6sLVq5ciSlTpqBPnz7qf+ydOnUKv/zyC6ZPn65x1j4gIEBUzArVr18/REVFYcCAAaUWtrFjxwpKJo6DgwMWLFiAAQMGiI7yWlhiiQQrmRNb1ltRV+fEUuny8vJw5coVmJqawsXFRefXSC0qKsKRI0dw8+ZNBAcHw8LCAikpKbC0tNTJOY96enrl2k+X/q5YW1vjt99+Q9u2bUVH0Rp2dnY4c+YMnJycREd5LSyxRILduXOn3Pvq2lnHZ507d069VJCHhweaNWsmOBFpizt37qBr1664e/cuCgoKcP36ddSrVw9jx45FQUGBTt6emKTq1q2Lffv2wd3dXXQUrTFx4kSYm5vLdokxzoklEqy0Ynr58mXJwv4KhUInS2xSUhL69euH48ePw9raGgCQlZUFb29vbN26Vb3APemusWPHonnz5oiNjdW4kCswMFCyni5p8vLywr59+1C7dm3RUf51M2fOxJQpU7B+/XpUqVJFdByt8PjxY6xatQoHDx5Ew4YNYWhoqPG8tt9MhiWWSIvcunULgYGBiIuL05hiUDJ3S1c+9nvWsGHDUFhYiCtXrqgXab927RqGDBmCYcOG4X//+5/ghCTasWPHcOLECRgZGWlsd3R0RHJysqBU8pCQkIDCwkLRMSrEokWLcPPmTdSoUQOOjo6SwnbhwgVBycS5ePEiGjduDAD4888/NZ6Tw0VeLLFEWmTs2LGoW7cuIiMjUbduXZw+fRoZGRkYP348vvvuO9HxhIiKisKJEyc07jLk6uqKFStW6OxV1qSpuLi41H/gJSUlwcLCQkAi0kY9e/YUHUHrlHeJx6SkJLz11lvlnmtdUVhiibTIyZMncejQIVStWhV6enrQ19fHO++8g7lz52LMmDH4448/REescLVr1y71TJFSqdTZdVFJk5+fH5YuXYpVq1YBeHoGKS8vD1OnToW/v7/gdKQtpk6dWq79tmzZgoCAAJiZmf3LieTDw8MDMTExqFevnugoGrSrUhPpOKVSqT5zVLVqVaSkpAB4Om/22rVrIqMJs3DhQowePRrnzp1Tbzt37hzGjh2rs2enSdOiRYtw/Phx9R3LgoOD1VMJ5s+fLzoeyczHH3+Mv/76S3QMraKtawDwTCyRFmnQoAFiY2NRt25dtGrVCgsWLICRkRFWrVqldf8CriiDBw9Gfn4+WrVqBQODp3+yioqKYGBggI8++ggfffSRet+MjAxRMUmgWrVqITY2Ftu2bUNsbCzy8vIwdOhQhISEwNTUVHQ8khltLWwkxRJLpEW++eYbPHz4EAAwY8YMvPfee2jXrh3s7Oywbds2wenEWLp0qegIpOWOHj0Kb29vhISEICQkRL29qKgIR48eRfv27QWmI6J/C9eJJdJyGRkZsLGxkcWVoiLNmzcPI0eOVC/DRbpDX18fqampkjtzpaeno3r16jq5qseGDRvQt29fyU0wnjx5gq1bt2LgwIEAgM2bN6NHjx6c//kMCwsLxMbG6uynX6XR1jHhnFgiLWdra8sCWw5z5szhdAIdpVKpSn2PpKen62w5GzJkCLKzsyXbc3NzMWTIEPXXwcHBOjtGVH7a+t8gTicgokqBHyrpnl69egF4+h/YwYMHa5x1VCqVuHjxIry9vUXFE+pFxT4pKQlWVlYCEpGcaevfV5ZYIiKSpZIyplKpYGFhoXERl5GREVq3bq1zd+xq0qQJFAoFFAoFOnfurL4YEnha7G/fvo2uXbsKTCiGUqnE8ePH0bBhwzKnHNWpU0dyI4TKKjs7G0qlEra2thrbMzIyYGBgAEtLSwBP7yKpjUsassQSEZEsrV27Vn2GaMWKFTA3NxecSLySBf1jYmLQpUsXjTExMjKCo6MjevfuLSidOPr6+vDz88OVK1fKLLHP37mqMgsKCsL777+PTz75RGP79u3bsXv3buzbtw8AtPa2xLywi4gqBW298ID+XcXFxTAxMcGlS5fg4uIiOo7WWL9+PYKCgiQXdumy5s2bY/78+ejcubPoKFrD1tYWx48fh7u7u8b2q1evom3btkhPTxeUrHx4YRcREcmWnp4eXFxctP4/thWtU6dOuH//vvrrM2fOYNy4ceq7mumiWbNm4YsvvsDevXuRmpqKnJwcjYcuKigoQFFRkWR7YWEhHj16JCDRq2GJJaJKoV27dlzYXkfNmzcPEyZM0KmPgcsSHByMw4cPAwDS0tLg6+uLM2fO4Ouvv8aMGTMEpxPD398fsbGxCAgIQK1atWBjYwMbGxtYW1vDxsZGdDwhWrZsWeo/bFauXIlmzZoJSPRqOJ2AiLTOq5wVKbnwgHSXjY0N8vPzUVRUBCMjI8k/ZnRx6TUbGxucOnUKrq6uWL58ObZt24bjx48jIiICI0eOxK1bt0RHrHBRUVEvfd7Hx6eCkmiP48ePw9fXFy1atFBPs4iMjMTZs2cRERGBdu3aCU74crywi4i0jrW1dZnrEpYsIaSLC9mTJt7VTaqwsFA9H/bgwYMICAgAALi5uSE1NVVkNGF0saSWpW3btjh58iQWLlyI7du3w9TUFA0bNkRYWJgs5pjzTCwRaZ2yzpg8i/9hIpJq1aoVOnbsiO7du8PPzw+nTp1Co0aNcOrUKXzwwQdISkoSHVGIzMxMhIWF4cqVKwAADw8PDBkyRLLEFMkDSywREcmeUqnErl271OXE09MTAQEB0NfXF5xMjCNHjiAwMBA5OTkYNGgQ1qxZAwD46quvcPXqVezcuVNwwop39OhRvP/++7CyskLz5s0BAOfPn0dWVhb27NmD9u3bC05Y8S5cuABDQ0N4eXkBAH799VesXbsWHh4emDZtGoyMjAQnfDmWWCLSOhcvXiz3vg0bNvwXk5AcxMfHw9/fH8nJyXB1dQUAXLt2DbVr18Zvv/0GJycnwQnFUCqVyMnJ0bhoKSEhAVWqVEH16tUFJhPDy8sLbdq0QWhoqPofN0qlEp988glOnDiBuLg4wQkrXosWLTBp0iT07t0bt27dgoeHB3r16oWzZ8+ie/fuWj9VhyWWiLSOnp4eFApFmbc65JxYAp5eda5SqbBp0yb1x8Lp6eno378/9PT08NtvvwlOKEZRURGOHDmCmzdvIjg4GBYWFkhJSYGlpaVO3hjC1NQUMTEx6n/olLh27RoaN24siyWl/mlWVla4cOECnJycMH/+fBw6dAj79+/H8ePHERQUhMTERNERX4oXdhGR1rl9+7boCCQjUVFROHXqlMa8Rjs7O8ybNw9t27YVmEycO3fuoGvXrrh79y4KCgrw7rvvwsLCAvPnz0dBQQFWrlwpOmKFa9q0Ka5cuSIpsVeuXEGjRo0EpRJLpVKhuLgYwNMLAN977z0AT+/Q9eDBA5HRyoUlloi0Tp06dSTbLl++jLt37+LJkyfqbQqFotR9SbcYGxsjNzdXsj0vL0/r5/T9W8aOHYvmzZsjNjYWdnZ26u2BgYEYPny4wGTijBkzBmPHjkV8fDxat24NADh16hR++OEHzJs3T2Mak65MU2revDlmzZoFX19fREVFITQ0FMDTEwk1atQQnK5snE5ARFrt1q1bCAwMRFxcnMYUg5IluDidgAYOHIgLFy4gLCwMLVu2BACcPn0aw4cPR7NmzbBu3TqxAQWws7PDiRMn4OrqqnFL5oSEBHh4eCA/P190xAqnp/fy+zuV/H3RpWlKFy9eREhICO7evYvPP/8cU6dOBQCMHj0a6enp2Lx5s+CEL8czsUSk1caOHYu6desiMjISdevWxenTp5GRkYHx48fju+++Ex2PtMDy5csxaNAgtGnTBoaGhgCezgcNCAjAsmXLBKcTo7i4uNQilpSUBAsLCwGJxOM0JamGDRuWekHbwoULNVb22LJlCwICAmBmZlaR8crEM7FEpNWqVq2KQ4cOoWHDhrCyssKZM2fg6uqKQ4cOYfz48fjjjz9ERyQtcePGDVy9ehUA4O7uDmdnZ8GJxOnbty+srKywatUqWFhY4OLFi6hWrRp69OiBt99+G2vXrhUdkWTE0tISMTExqFevnugoGngmloi0mlKpVJ85qlq1KlJSUuDq6oo6derg2rVrgtORNnFxcZHFXYYqwqJFi9ClSxd4eHjg8ePHCA4Oxo0bN1C1alVs2bJFdDxhrl27hhUrVqjXE3Z3d8fo0aMlF3uRJm0938kSS0RarUGDBoiNjUXdunXRqlUrLFiwAEZGRli1apXWnRUgMZRKJdatW4fIyEjcu3dPfbV1iUOHDglKJk6tWrUQGxuLbdu2ITY2Fnl5eRg6dChCQkJgamoqOp4Q4eHhCAoKQvPmzdGmTRsATy/satCgAbZu3YrevXsLTkivitMJiEir7d+/Hw8fPkSvXr0QHx+P9957D9evX4ednR22bduGTp06iY5Ign366adYt24dunfvDgcHB/VFfyWWLFkiKJk4R48ehbe3NwwMNM9VFRUV4cSJEzp5dyonJyeEhIRgxowZGtunTp2Kn3/+GTdv3hSUTPs9e3GgNmGJJSLZycjIgI2NjaSskG6qWrUqNmzYAH9/f9FRtIa+vj5SU1Mld+ZKT09H9erVdebq+2dVqVIFFy9elMyVvnHjBho1aqSTKzaUl7aW2JevN0FEpIVsbW1ZYEnNyMhIpy/iKk3JUlHPS09P17orzCtKhw4dcOzYMcn26OhotGvXTkAielOcE0tERLI2fvx4LFu2DN9//73O/+OmV69eAJ6ueTp48GAYGxurn1Mqlbh48SK8vb1FxRMqICAAEydOxPnz5zVudvDLL79g+vTp2L17t8a+lZ1SqcTx48fRsGFDWFtbv3TfOnXqqJev0yacTkBERLJTUtZKHDp0CLa2tvD09JT8x3bnzp0VGU2oIUOGAADWr1+PPn36aFzEZWRkBEdHRwwfPhxVq1YVFVGYsm52UEKXbnZgYmKCK1euoG7duqKjvBaeiSUiItmxsrLS+DowMFBQEu2ydu1a9XJIK1asgLm5ueBE2uP5VSvo6eovt27dkm2J5ZlYIiKStUePHqG4uFg91zMhIQG7du2Cu7s7unTpIjhdxSsuLoaJiQkuXbrEdXNfg5eXF/bt24fatWuLjvKv+9///ofJkydj5syZaNasmWS+tKWlpaBk5cMSS0REsubn54devXph5MiRyMrKgpubGwwNDfHgwQMsXrwYo0aNEh2xwnl6eiIsLEw995PKT1uvxP83PDvF4tn55CUXBmr7tApOJyAiIlm7cOGCei3YHTt2oEaNGvjjjz8QHh6OKVOm6GSJnTdvHiZMmIDQ0FA0aNBAdBzSUocPHxYd4Y2wxBIRkazl5+erb00cERGBXr16QU9PD61bt8adO3cEpxNj4MCByM/PR6NGjWBkZCS5S1dGRoagZKRNfHx8REd4IyyxREQka87Ozti1axcCAwOxf/9+fPbZZwCAe/fuaf2cvn/L0qVLRUcgmcjMzERYWBiuXLkCAPDw8MCQIUNga2srOFnZOCeWiIhkbceOHQgODoZSqUTnzp0REREBAJg7dy6OHj2K33//XXBCkhNdmhN79OhRvP/++7CyskLz5s0BAOfPn0dWVhb27Nmj9bcnZoklIiLZS0tLQ2pqKho1aqS+WOXMmTOwtLSEm5ub4HRiKJVK7Nq1S32GzdPTEwEBAdDX1xecTLvpUon18vJCmzZtEBoaqv69UCqV+OSTT3DixAnExcUJTvhyLLFERESVTHx8PPz9/ZGcnAxXV1cAwLVr11C7dm389ttvcHJyEpyw4m3YsAF9+/bVuIsZADx58gRbt27FwIEDAQCbN29Gjx49dOL2vKampoiJiVH/jpS4du0aGjdujEePHglKVj7lu30FERERycaYMWPg5OSExMREXLhwARcuXMDdu3dRt25djBkzRnQ8IYYMGYLs7GzJ9tzcXPWdzgAgODhYJwosADRt2lR9pv5ZV65cQaNGjQQkejW8sIuIiKiSiYqKwqlTpzQuzrGzs8O8efPQtm1bgcnEKVn79HlJSUmSO8DpijFjxmDs2LGIj49Xryl86tQp/PDDD5g3bx4uXryo3rdhw4aiYr4QSywREVElY2xsjNzcXMn2vLw8GBkZCUgkTpMmTaBQKKBQKNC5c2cYGPxdfZRKJW7fvo2uXbsKTChOv379AABffvllqc8pFAqtvvEBSywREVEl895772HEiBEICwtDy5YtAQCnT5/GyJEjERAQIDhdxerZsycAICYmBl26dIG5ubn6OSMjIzg6OqJ3796C0ol1+/Zt0RHeCC/sIiIiqmSysrIwaNAg7NmzB4aGhgCAoqIiBAQEYN26dTr58fn69esRFBQkubCL5IslloiIqJK6ceMGrl69CgBwd3eHs7Oz4ETiJCYmQqFQoFatWgCeLsG2efNmeHh4YMSIEYLTiXPt2jWsWLFCfYGXu7s7Ro8eLVmxQBuxxBIREVGl165dO4wYMQIDBgxAWloa6tevjwYNGuDGjRsYPXo0pkyZIjpihQsPD0dQUBCaN2+ONm3aAHh6YdfZs2exdetWrZ9mwRJLRERUySiVSqxbtw6RkZG4d+8eiouLNZ4/dOiQoGTi2NjY4NSpU3B1dcXy5cuxbds2HD9+HBERERg5ciRu3bolOmKFc3JyQkhICGbMmKGxferUqfj5559x8+ZNQcnKhxd2ERERVTJjx47FunXr0L17dzRo0KDUpaV0TWFhoXo+7MGDB9UXuLm5uSE1NVVkNGFSU1PVN3l4Vv/+/bFw4UIBiV4NSywREVEls3XrVmzfvh3+/v6io2gNT09PrFy5Et27d8eBAwcwc+ZMAEBKSgrs7OwEpxOjQ4cOOHbsmGSudHR0NNq1aycoVfmxxBIREVUyRkZGOn0RV2nmz5+PwMBALFy4EIMGDVLfkWr37t3qZch0TUBAACZOnIjz589r3Ozgl19+wfTp07F7926NfbUN58QSERFVMosWLcKtW7fw/fffcyrBM5RKJXJycmBjY6PelpCQgCpVqqB69eoCk4mhp6dXrv209WYHLLFERESVQK9evTS+PnToEGxtbeHp6aleK7bEzp07KzKa1igqKsKRI0dw8+ZNBAcHw8LCAikpKbC0tNS4CQLJA6cTEBERVQLP38AgMDBQUBLtdOfOHXTt2hV3795FQUEB3n33XVhYWGD+/PkoKCjAypUrRUfUWl5eXti3bx9q164tOooGllgiIqJKYO3ater//ejRIxQXF8PMzAzA04/Md+3aBXd3d3Tp0kVURKHGjh2L5s2bIzY2VuNCrsDAQAwfPlxgMu2XkJCAwsJC0TEkWGKJiIgqmR49eqBXr14YOXIksrKy0Lp1axgaGuLBgwdYvHgxRo0aJTpihTt27BhOnDgBIyMjje2Ojo5ITk4WlIreRPlm9BIREZFsXLhwQb1E0o4dO1CjRg3cuXMHGzZswPLlywWnE6O4uLjUi5OSkpJgYWEhIBG9KZZYIiKiSiY/P19dzCIiItCrVy/o6emhdevWuHPnjuB0Yvj5+WHp0qXqrxUKBfLy8jB16lSupytTLLFERESVjLOzM3bt2oXExETs378ffn5+AIB79+7B0tJScDoxFi1ahOPHj8PDwwOPHz9GcHCweirB/PnzRcej18AltoiIiCqZHTt2IDg4GEqlEp07d0ZERAQAYO7cuTh69Ch+//13wQnFKCoqwrZt2xAbG4u8vDw0bdoUISEhMDU1FR1Nq1lYWCA2Nhb16tUTHUUDSywREVEllJaWhtTUVDRq1Ei9qP2ZM2dgaWkJNzc3wekq3tGjR+Ht7Q0DA81r2ouKinDixAm0b99eUDJxNmzYgL59+8LY2Fhj+5MnT7B161YMHDgQALB582b06NFDvdqFtmCJJSIiokpPX18fqampkjtzpaeno3r16lp5R6p/m9zHhHNiiYiIqNJTqVSl3oI3PT1d684wVpQXjUlSUpLk5hnaiOvEEhERUaVVcjtehUKBwYMHa3x0rlQqcfHiRXh7e4uKJ0STJk2gUCigUCjQuXNnjSkWSqUSt2/fRteuXQUmLB+WWCIiIqq0Ss4oqlQqWFhYaFzEZWRkhNatW+vcHbt69uwJAIiJiUGXLl1gbm6ufs7IyAiOjo7o3bu3oHTlxzmxREREVKmpVCp89NFHWLFihUZh03Xr169HUFCQ5MIuuWCJJSIiokqtuLgYJiYmuHTpElxcXETH0RqJiYlQKBSoVasWgKerV2zevBkeHh4YMWKE4HRl44VdREREVKnp6enBxcUF6enpoqNoleDgYBw+fBjA0yXZfH19cebMGXz99deYMWOG4HRlY4klIiKiSm/evHmYMGEC/vzzT9FRtMaff/6Jli1bAgC2b98OLy8vnDhxAps2bcK6devEhisHXthFREREld7AgQORn5+PRo0awcjISHKXroyMDEHJxCksLFTPhz148CACAgIAAG5ubkhNTRUZrVxYYomIiKjSW7p0qegIWsfT0xMrV65E9+7dceDAAcycORMAkJKSAjs7O8HpysYLu4iIiIh00JEjRxAYGIicnBwMGjQIa9asAQB89dVXuHr1Knbu3Ck44cuxxBIREZFOUCqV2LVrF65cuQLg6ZnIgIAA6OvrC04mjlKpRE5ODmxsbNTbEhISUKVKFcntaLUNSywRERFVevHx8fD390dycjJcXV0BANeuXUPt2rXx22+/wcnJSXBCMYqKinDkyBHcvHkTwcHBsLCwQEpKCiwtLbV+TV2WWCIiIqr0/P39oVKpsGnTJtja2gIA0tPT0b9/f+jp6eG3334TnLDi3blzB127dsXdu3dRUFCA69evo169ehg7diwKCgqwcuVK0RFfiiWWiIiIKj0zMzOcOnUKXl5eGttjY2PRtm1b5OXlCUomTs+ePWFhYYGwsDDY2dkhNjYW9erVw5EjRzB8+HDcuHFDdMSX4uoEREREVOkZGxsjNzdXsj0vLw9GRkYCEol37NgxnDhxQvL6HR0dkZycLChV+fFmB0RERFTpvffeexgxYgROnz4NlUoFlUqFU6dOYeTIker1UXVNcXExlEqlZHtSUhIsLCwEJHo1LLFERERU6S1fvhxOTk5o06YNTExMYGJigrZt28LZ2RnLli0THU8IPz8/jfVzFQoF8vLyMHXqVPj7+4sLVk6cE0tEREQ648aNG7h69SoAwN3dHc7OzoITiZOUlIQuXbpApVLhxo0baN68OW7cuIGqVavi6NGjXGKLiIiIiLRTUVERtm3bhtjYWOTl5aFp06YICQmR3JZXG7HEEhERUaWnVCqxbt06REZG4t69eyguLtZ4/tChQ4KSiXP06FF4e3vDwEDzOv+ioiKcOHEC7du3F5SsfFhiiYiIqNL79NNPsW7dOnTv3h0ODg5QKBQazy9ZskRQMnH09fWRmpoqmTaQnp6O6tWrl3rRlzbhEltERERU6W3duhXbt2+XxQVLFUWlUknKPPC0xJqZmQlI9GpYYomIiKjSMzIy0umLuJ7Vq1cvAE9XIxg8eDCMjY3VzymVSly8eBHe3t6i4pUbl9giIiKiSm/8+PFYtmwZOIsSsLKygpWVFVQqFSwsLNRfW1lZwd7eHiNGjMDPP/8sOmaZOCeWiIiIKqWSM44lDh06BFtbW3h6esLQ0FDjuZ07d1ZkNOFUKhU++ugjrFixAubm5qLjvBaWWCIiIqqUhgwZUu59165d+y8m0T7FxcUwMTHBpUuX4OLiIjrOa+GcWCIiIqqUni2mjx49QnFxsfqCpYSEBOzatQvu7u7o0qWLqIjC6OnpwcXFBenp6bItsZwTS0RERJVejx49sHHjRgBAVlYWWrdujUWLFqFnz54IDQ0VnE6MefPmYcKECfjzzz9FR3ktnE5ARERElV7VqlURFRUFT09PrF69GitWrMAff/yB8PBwTJk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targetcollectionsource
260ICOSLGbiocarta_pathwaysBIOCARTA_CTLA4_PATHWAY
387FOSL2biocarta_pathwaysBIOCARTA_RANKL_PATHWAY
938PLAUbiocarta_pathwaysBIOCARTA_FIBRINOLYSIS_PATHWAY
1091PLAUbiocarta_pathwaysBIOCARTA_PLATELETAPP_PATHWAY
1684BTG1biocarta_pathwaysBIOCARTA_BTG2_PATHWAY
............
2392873IFNA13biocarta_pathwaysBIOCARTA_INFLAM_PATHWAY
2397261GSTA2biocarta_pathwaysBIOCARTA_ARENRF2_PATHWAY
2401665SAGbiocarta_pathwaysBIOCARTA_RHODOPSIN_PATHWAY
2402175GRK1biocarta_pathwaysBIOCARTA_RHODOPSIN_PATHWAY
2403146SLC25A22biocarta_pathwaysBIOCARTA_RHODOPSIN_PATHWAY
\n", + "

4803 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " target collection source\n", + "260 ICOSLG biocarta_pathways BIOCARTA_CTLA4_PATHWAY\n", + "387 FOSL2 biocarta_pathways BIOCARTA_RANKL_PATHWAY\n", + "938 PLAU biocarta_pathways BIOCARTA_FIBRINOLYSIS_PATHWAY\n", + "1091 PLAU biocarta_pathways BIOCARTA_PLATELETAPP_PATHWAY\n", + "1684 BTG1 biocarta_pathways BIOCARTA_BTG2_PATHWAY\n", + "... ... ... ...\n", + "2392873 IFNA13 biocarta_pathways BIOCARTA_INFLAM_PATHWAY\n", + "2397261 GSTA2 biocarta_pathways BIOCARTA_ARENRF2_PATHWAY\n", + "2401665 SAG biocarta_pathways BIOCARTA_RHODOPSIN_PATHWAY\n", + "2402175 GRK1 biocarta_pathways BIOCARTA_RHODOPSIN_PATHWAY\n", + "2403146 SLC25A22 biocarta_pathways BIOCARTA_RHODOPSIN_PATHWAY\n", + "\n", + "[4803 rows x 3 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "biocarta_elements" + ] + }, + { + "cell_type": "code", + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ - "biocarta_egf_pathway = df[df['source'] == 'BIOCARTA_EGF_PATHWAY']\n", - "df = df[df['source'] != 'BIOCARTA_EGF_PATHWAY']\n" + "# we manually input the expected perturbed pathways\n", + "elements = ['BIOCARTA_EGF_PATHWAY', 'BIOCARTA_EGFR_SMRTE_PATHWAY']\n", + "\n", + "# and we take 10 random pathways to complete the list (some of them might be missing in the vis since an ora score might have not been computed for them)\n", + "elements = elements + biocarta_elements[~biocarta_elements['source'].isin(elements)].source.sample(n=10).tolist()" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
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" ] }, "metadata": {}, @@ -821,7 +1256,7 @@ "
" ] }, - "execution_count": 8, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, @@ -836,7 +1271,7 @@ } ], "source": [ - "nc.visual.create_rank_heatmap(ora_results, ['BIOCARTA_EGF_PATHWAY', 'BIOCARTA_EGFR_SMRTE_PATHWAY'], render=True)" + "nc.visual.create_heatmap(ora_results, elements, render=True)" ] }, {