From 7ba5626177b91b9d6e143396aac542289e082e83 Mon Sep 17 00:00:00 2001 From: Antongiacomo Polimeno Date: Tue, 13 Feb 2024 09:12:23 +0100 Subject: [PATCH] more experiments --- .vscode/settings.json | 4 +- Experiments/Experiments.ipynb | 1 - Experiments/Experiments_NamesEnrichment.ipynb | 268 ----------------- ... 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Experiments/Simulator/Untitled-1.ipynb delete mode 100644 Experiments/Simulator/exhaustive.py create mode 100644 Experiments/Simulator/experiment_id delete mode 100644 Experiments/Simulator/greedy.py delete mode 100644 Experiments/Simulator/results_s155.txt delete mode 100644 Experiments/Simulator/sliding.py delete mode 100644 Images/graphs/exhaustive_performance copy.gp diff --git a/.vscode/settings.json b/.vscode/settings.json index 1f3e264..7249ca3 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -9,5 +9,7 @@ "latex", "plaintext" ], - "editor.lineNumbers": "on" + "editor.lineNumbers": "on", + "python.analysis.autoImportCompletions": true, + "python.analysis.typeCheckingMode": "off" } \ No newline at end of file diff --git a/Experiments/Experiments.ipynb b/Experiments/Experiments.ipynb deleted file mode 100644 index 463ec73..0000000 --- a/Experiments/Experiments.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"cell_id":"aefd8a20f6984fc288c93e7f6058b8b7","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":9892,"execution_start":1697471224836,"source_hash":null},"outputs":[{"ename":"","evalue":"","output_type":"error","traceback":["\u001b[1;31mRunning cells with 'venv' requires the ipykernel package.\n","\u001b[1;31mRun the following command to install 'ipykernel' into the Python environment. \n","\u001b[1;31mCommand: '\"/Users/antongiacomopolimeno/Dottorato/03_paper_in_corso/Big Data Access Control - extension/Experiments/Simulator/venv/bin/python\" -m pip install ipykernel -U --force-reinstall'"]}],"source":["%pip install pandas\n","%pip install matplotlib\n","%pip install seaborn\n","%pip install scipy\n"]},{"cell_type":"code","execution_count":52,"metadata":{"cell_id":"5d2f0b32ca7f4eeab106e5ad7b95b82f","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":6926,"execution_start":1697472454332,"source_hash":null},"outputs":[],"source":["import pandas as pd\n","import random\n","import seaborn as sns\n","df = pd.read_csv('inmates_enriched.csv')\n","df1 = df.sample(frac = 0.1)\n","df2= df.sample(frac = 0.1)\n","\n","\n","\n","\n"]},{"cell_type":"code","execution_count":67,"metadata":{},"outputs":[{"data":{"text/plain":["0.004444979052533179"]},"execution_count":67,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAekAAAHpCAYAAACmzsSXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAiF0lEQVR4nO3de3BU9f3/8ddCyIJIEkMgl5pABCUoN6sQoy2CpFxsvaAzjlgULcVLI16YVsxUxdBOY7UFR011dATqVESdQWi9DgTFCwElGmkUGGDAeElCA02WS1gC+Xz/6I/9uRIw2WRz3muej5mdsuecHN6fruvTzW5yfM45JwAAYE43rwcAAAAtI9IAABhFpAEAMIpIAwBgFJEGAMAoIg0AgFFEGgAAo4g0AABGEWkAAIz6wUfaOadAICB+sRoAINZ4Guni4mKNHj1affr0Uf/+/XXllVdq69atYceMGzdOPp8v7Hbrrbe2+u/Yt2+fEhMTtW/fvo4eHwCAqPI00mvXrlVBQYHWr1+vVatWqampSRMnTtSBAwfCjps1a5aqq6tDt4cfftijiQEA6DxxXv7lb775Ztj9JUuWqH///iovL9fYsWND20855RSlpaV19ngAAHjK1HvSDQ0NkqTk5OSw7c8//7xSUlI0bNgwFRYW6uDBgyc8RzAYVCAQCLsBABCLPH0l/W3Nzc266667dNFFF2nYsGGh7dddd50GDBigjIwMbdq0SXPnztXWrVu1fPnyFs9TXFysoqKizhobAICo8Vm5nvRtt92mN954Q++//75OP/30Ex63Zs0aTZgwQdu3b9egQYOO2x8MBhUMBkP3A4GAMjMz1dDQoISEhKjMDgBANJh4JX377bfr1Vdf1bvvvnvSQEtSbm6uJJ0w0n6/X36/PypzAgDQmTyNtHNOs2fP1iuvvKJ33nlH2dnZ3/s1FRUVkqT09PQoTwcAgLc8jXRBQYGWLl2qlStXqk+fPqqpqZEkJSYmqlevXtqxY4eWLl2qSy+9VH379tWmTZt09913a+zYsRoxYoSXowMAEHWevift8/la3L548WLdeOON+vLLLzV9+nRVVlbqwIEDyszM1NSpU3Xfffe1+v3lQCCgxMRE3pMGAMQcMx8cixYiDQCIVaZ+ThoAAPx/RBoAAKOINAAARhFpAACMItIAABhFpAEAMIpIAwBgFJEGAMAoIg0AgFEmroIVS6qqqlRXV+f1GBFLSUlRVlaW12MAAFqBSLdBVVWVcnKGqrHxoNejRKxXr1O0ZctmQg0AMYBIt0FdXZ0aGw8q91fzlJA+0Otx2ixQvUsbFhWprq6OSANADCDSEUhIH6jkrCFejwEA+IHjg2MAABhFpAEAMIpIAwBgFJEGAMAoIg0AgFFEGgAAo4g0AABGEWkAAIwi0gAAGEWkAQAwikgDAGAUkQYAwCgiDQCAUUQaAACjiDQAAEYRaQAAjCLSAAAYRaQBADCKSAMAYBSRBgDAKCINAIBRRBoAAKOINAAARhFpAACMItIAABhFpAEAMIpIAwBgFJEGAMAoIg0AgFFEGgAAo4g0AABGEWkAAIwi0gAAGEWkAQAwikgDAGAUkQYAwCgiDQCAUUQaAACjiDQAAEYRaQAAjCLSAAAYRaQBADCKSAMAYBSRBgDAKCINAIBRRBoAAKOINAAARhFpAACMItIAABhFpAEAMIpIAwBgFJEGAMAoIg0AgFFEGgAAo4g0AABGEWkAAIwi0gAAGEWkAQAwikgDAGAUkQYAwCgiDQCAUUQaAACjiDQAAEYRaQAAjPI00sXFxRo9erT69Omj/v3768orr9TWrVvDjjl06JAKCgrUt29fnXrqqbr66qtVW1vr0cQAAHQeTyO9du1aFRQUaP369Vq1apWampo0ceJEHThwIHTM3XffrX/96196+eWXtXbtWn3zzTe66qqrPJwaAIDOEeflX/7mm2+G3V+yZIn69++v8vJyjR07Vg0NDXr22We1dOlSXXLJJZKkxYsXa+jQoVq/fr0uuOACL8YGAKBTmHpPuqGhQZKUnJwsSSovL1dTU5Py8/NDx+Tk5CgrK0tlZWUtniMYDCoQCITdAACIRWYi3dzcrLvuuksXXXSRhg0bJkmqqalRfHy8kpKSwo5NTU1VTU1Ni+cpLi5WYmJi6JaZmRnt0QEAiAozkS4oKFBlZaWWLVvWrvMUFhaqoaEhdPvyyy87aEIAADqXp+9JH3P77bfr1Vdf1bvvvqvTTz89tD0tLU2HDx9WfX192Kvp2tpapaWltXguv98vv98f7ZEBAIg6T19JO+d0++2365VXXtGaNWuUnZ0dtv+8885Tjx49VFpaGtq2detWVVVVKS8vr7PHBQCgU3n6SrqgoEBLly7VypUr1adPn9D7zImJierVq5cSExM1c+ZMzZkzR8nJyUpISNDs2bOVl5fHJ7sBAD94nkb6ySeflCSNGzcubPvixYt14403SpIWLlyobt266eqrr1YwGNSkSZP0t7/9rZMnBQCg83kaaefc9x7Ts2dPlZSUqKSkpBMmAgDADjOf7gYAAOGINAAARhFpAACMItIAABhFpAEAMIpIAwBgFJEGAMAoIg0AgFFEGgAAo4g0AABGEWkAAIwi0gAAGEWkAQAwikgDAGAUkQYAwCgiDQCAUUQaAACjiDQAAEYRaQAAjCLSAAAYRaQBADCKSAMAYBSRBgDAKCINAIBRRBoAAKOINAAARhFpAACMItIAABhFpAEAMIpIAwBgFJEGAMAoIg0AgFFEGgAAo4g0AABGEWkAAIwi0gAAGEWkAQAwikgDAGAUkQYAwCgiDQCAUUQaAACjiDQAAEYRaQAAjCLSAAAYRaQBADCKSAMAYBSRBgDAKCINAIBRRBoAAKOINAAARhFpAACMItIAABhFpAEAMIpIAwBgFJEGAMAoIg0AgFFEGgAAo4g0AABGEWkAAIwi0gAAGEWkAQAwikgDAGAUkQYAwCgiDQCAUUQaAACjiDQAAEYRaQAAjCLSAAAYRaQBADCKSAMAYBSRBgDAKCINAIBRRBoAAKOINAAARhFpAACMItIAABhFpAEAMMrTSL/77ru67LLLlJGRIZ/PpxUrVoTtv/HGG+Xz+cJukydP9mZYAAA6maeRPnDggEaOHKmSkpITHjN58mRVV1eHbi+88EInTggAgHfivPzLp0yZoilTppz0GL/fr7S0tE6aCAAAO8y/J/3OO++of//+GjJkiG677Tbt2bPnpMcHg0EFAoGwGwAAsch0pCdPnqznnntOpaWl+vOf/6y1a9dqypQpOnr06Am/pri4WImJiaFbZmZmJ04MAEDH8fTb3d/n2muvDf15+PDhGjFihAYNGqR33nlHEyZMaPFrCgsLNWfOnND9QCBAqAEAMcn0K+nvOuOMM5SSkqLt27ef8Bi/36+EhISwGwAAsSimIv3VV19pz549Sk9P93oUAACiztNvd+/fvz/sVfHOnTtVUVGh5ORkJScnq6ioSFdffbXS0tK0Y8cO3XPPPRo8eLAmTZrk4dQAAHQOTyO9ceNGjR8/PnT/2HvJM2bM0JNPPqlNmzbp73//u+rr65WRkaGJEyfqD3/4g/x+v1cjAwDQaTyN9Lhx4+ScO+H+t956qxOnAQDAlph6TxoAgK6ESAMAYBSRBgDAKCINAIBRRBoAAKOINAAARhFpAACMItIAABhFpAEAMIpIAwBgFJEGAMAoIg0AgFFEGgAAo4g0AABGEWkAAIwi0gAAGEWkAQAwikgDAGBURJE+44wztGfPnuO219fX64wzzmj3UAAAIMJI79q1S0ePHj1uezAY1Ndff93uoQAAgBTXloP/+c9/hv781ltvKTExMXT/6NGjKi0t1cCBAztsOAAAurI2RfrKK6+UJPl8Ps2YMSNsX48ePTRw4ED99a9/7bDhAADoytoU6ebmZklSdna2PvroI6WkpERlKAAA0MZIH7Nz586OngMAAHxHRJGWpNLSUpWWlmr37t2hV9jHLFq0qN2DAQDQ1UUU6aKiIs2fP1/nn3++0tPT5fP5OnouAAC6vIgi/dRTT2nJkiW6/vrrO3oeAADw/0T0c9KHDx/WhRde2NGzAACAb4ko0r/+9a+1dOnSjp4FAAB8S0Tf7j506JCefvpprV69WiNGjFCPHj3C9i9YsKBDhgMAoCuLKNKbNm3SqFGjJEmVlZVh+/gQGQAAHSOiSL/99tsdPQcAAPgOLlUJAIBREb2SHj9+/Em/rb1mzZqIBwIAAP8TUaSPvR99TFNTkyoqKlRZWXnchTcAAEBkIor0woULW9z+4IMPav/+/e0aCAAA/E+Hvic9ffp0fm83AAAdpEMjXVZWpp49e3bkKQEA6LIi+nb3VVddFXbfOafq6mpt3LhR999/f4cMBgBAVxdRpBMTE8Pud+vWTUOGDNH8+fM1ceLEDhkMAICuLqJIL168uKPnAAAA3xFRpI8pLy/X5s2bJUnnnHOOzj333A4ZCgAARBjp3bt369prr9U777yjpKQkSVJ9fb3Gjx+vZcuWqV+/fh05IwAAXVJEn+6ePXu29u3bp88++0x79+7V3r17VVlZqUAgoDvuuKOjZwQAoEuK6JX0m2++qdWrV2vo0KGhbWeffbZKSkr44BgAAB0kolfSzc3Nx11DWpJ69Oih5ubmdg8FAAAijPQll1yiO++8U998801o29dff627775bEyZM6LDhAADoyiKK9BNPPKFAIKCBAwdq0KBBGjRokLKzsxUIBPT444939IwAAHRJEb0nnZmZqY8//lirV6/Wli1bJElDhw5Vfn5+hw4HAEBX1qZX0mvWrNHZZ5+tQCAgn8+nn/3sZ5o9e7Zmz56t0aNH65xzztF7770XrVkBAOhS2hTpRx99VLNmzVJCQsJx+xITE3XLLbdowYIFHTYcAABdWZsi/emnn2ry5Mkn3D9x4kSVl5e3eygAANDGSNfW1rb4o1fHxMXF6T//+U+7hwIAAG2M9I9+9CNVVlaecP+mTZuUnp7e7qEAAEAbI33ppZfq/vvv16FDh47b19jYqHnz5ukXv/hFhw0HAEBX1qYfwbrvvvu0fPlynXXWWbr99ts1ZMgQSdKWLVtUUlKio0eP6ve//31UBgUAoKtpU6RTU1O1bt063XbbbSosLJRzTpLk8/k0adIklZSUKDU1NSqDAgDQ1bT5l5kMGDBAr7/+uv773/9q+/btcs7pzDPP1GmnnRaN+QAA6LIi+o1jknTaaadp9OjRHTkLAAD4loh+dzcAAIg+Ig0AgFFEGgAAo4g0AABGEWkAAIwi0gAAGEWkAQAwikgDAGAUkQYAwCgiDQCAUUQaAACjiDQAAEYRaQAAjCLSAAAYRaQBADCKSAMAYBSRBgDAKCINAIBRnkb63Xff1WWXXaaMjAz5fD6tWLEibL9zTg888IDS09PVq1cv5efna9u2bd4MCwBAJ/M00gcOHNDIkSNVUlLS4v6HH35Yjz32mJ566ilt2LBBvXv31qRJk3To0KFOnhQAgM4X5+VfPmXKFE2ZMqXFfc45Pfroo7rvvvt0xRVXSJKee+45paamasWKFbr22ms7c1QAADqdp5E+mZ07d6qmpkb5+fmhbYmJicrNzVVZWdkJIx0MBhUMBkP3A4FA1GcFALROVVWV6urqvB4jYikpKcrKyuq0v89spGtqaiRJqampYdtTU1ND+1pSXFysoqKiqM4GAGi7qqoq5eQMVWPjQa9HiVivXqdoy5bNnRZqs5GOVGFhoebMmRO6HwgElJmZ6eFEAABJqqurU2PjQeX+ap4S0gd6PU6bBap3acOiItXV1RHptLQ0SVJtba3S09ND22trazVq1KgTfp3f75ff74/2eACACCWkD1Ry1hCvx4gJZn9OOjs7W2lpaSotLQ1tCwQC2rBhg/Ly8jycDACAzuHpK+n9+/dr+/btofs7d+5URUWFkpOTlZWVpbvuukt//OMfdeaZZyo7O1v333+/MjIydOWVV3o3NAAAncTTSG/cuFHjx48P3T/2XvKMGTO0ZMkS3XPPPTpw4IBuvvlm1dfX6yc/+YnefPNN9ezZ06uRAQDoNJ5Gety4cXLOnXC/z+fT/PnzNX/+/E6cCgAAG8y+Jw0AQFdHpAEAMIpIAwBgFJEGAMAoIg0AgFFEGgAAo4g0AABGEWkAAIwi0gAAGEWkAQAwyuylKoGWVFVVqa6uzusxIpaSktJp16EFEPuINGJGVVWVcnKGqrHxoNejRKxXr1O0ZctmQg2gVYg0YkZdXZ0aGw8q91fzlJA+0Otx2ixQvUsbFhWprq6OSANoFSKNmJOQPlDJWUO8HgMAoo4PjgEAYBSRBgDAKCINAIBRRBoAAKOINAAARhFpAACMItIAABhFpAEAMIpIAwBgFJEGAMAoIg0AgFFEGgAAo4g0AABGEWkAAIwi0gAAGEWkAQAwikgDAGAUkQYAwCgiDQCAUUQaAACjiDQAAEYRaQAAjCLSAAAYRaQBADCKSAMAYBSRBgDAKCINAIBRRBoAAKOINAAARhFpAACMItIAABgV5/UAANCZqqqqVFdX5/UYEUtJSVFWVpbXY6CTEGkAXUZVVZVycoaqsfGg16NErFevU7Rly2ZC3UUQaQBdRl1dnRobDyr3V/OUkD7Q63HaLFC9SxsWFamuro5IdxFEGkCXk5A+UMlZQ7weA/hefHAMAACjiDQAAEYRaQAAjCLSAAAYRaQBADCKSAMAYBSRBgDAKCINAIBRRBoAAKOINAAARhFpAACMItIAABhFpAEAMIqrYHVBmzdv9nqEiMTq3EBHi9XnQqzO7SUi3YU0NuyR5NP06dO9HqVdmoKHvR4B8ATP4a6HSHchTQf3SXIadd1c9cvO8XqcNqv+d5kq//m0jhw54vUogCd4Dnc9RLoLOrV/Vkxe8D5QvcvrEQATeA53HXxwDAAAo4g0AABGEWkAAIwi0gAAGEWkAQAwikgDAGAUkQYAwCgiDQCAUUQaAACjiDQAAEYRaQAAjCLSAAAYZTrSDz74oHw+X9gtJyf2rvwCAEAkzF8F65xzztHq1atD9+PizI8MAECHMF+8uLg4paWltfr4YDCoYDAYuh8IBKIxFhCxzZs3ez1CxFJSUpSVleX1GECXYT7S27ZtU0ZGhnr27Km8vDwVFxef9F8SxcXFKioq6sQJgdZpbNgjyafp06d7PUrEevU6RVu2bCbUQCcxHenc3FwtWbJEQ4YMUXV1tYqKivTTn/5UlZWV6tOnT4tfU1hYqDlz5oTuBwIBZWZmdtbIwAk1HdwnyWnUdXPVLzv2PlsRqN6lDYuKVFdXR6SBTmI60lOmTAn9ecSIEcrNzdWAAQP00ksvaebMmS1+jd/vl9/v76wRgTY7tX+WkrOGeD0GgBhg+tPd35WUlKSzzjpL27dv93oUAACiLqYivX//fu3YsUPp6elejwIAQNSZjvRvf/tbrV27Vrt27dK6des0depUde/eXdOmTfN6NAAAos70e9JfffWVpk2bpj179qhfv376yU9+ovXr16tfv35ejwYAQNSZjvSyZcu8HgEAAM+Y/nY3AABdGZEGAMAoIg0AgFFEGgAAo4g0AABGEWkAAIwi0gAAGEWkAQAwikgDAGAUkQYAwCgiDQCAUUQaAACjiDQAAEYRaQAAjCLSAAAYRaQBADCKSAMAYBSRBgDAKCINAIBRRBoAAKOINAAARhFpAACMItIAABhFpAEAMIpIAwBgFJEGAMAoIg0AgFFEGgAAo4g0AABGEWkAAIwi0gAAGEWkAQAwikgDAGAUkQYAwCgiDQCAUUQaAACjiDQAAEYRaQAAjCLSAAAYRaQBADCKSAMAYBSRBgDAKCINAIBRRBoAAKPivB4AQGzZvHmz1yNELJZnR9dEpAG0SmPDHkk+TZ8+3etR2q0peNjrEYBWIdIAWqXp4D5JTqOum6t+2TlejxOR6n+XqfKfT+vIkSNejwK0CpEG0Can9s9SctYQr8eISKB6l9cjAG3CB8cAADCKSAMAYBSRBgDAKCINAIBRRBoAAKOINAAARhFpAACMItIAABhFpAEAMIpIAwBgFJEGAMAoIg0AgFFEGgAAo4g0AABGEWkAAIwi0gAAGEWkAQAwikgDAGAUkQYAwCgiDQCAUUQaAACjiDQAAEYRaQAAjCLSAAAYRaQBADCKSAMAYBSRBgDAKCINAIBRRBoAAKNiItIlJSUaOHCgevbsqdzcXH344YdejwQAQNSZj/SLL76oOXPmaN68efr44481cuRITZo0Sbt37/Z6NAAAoirO6wG+z4IFCzRr1izddNNNkqSnnnpKr732mhYtWqR77733uOODwaCCwWDofkNDgyQpEAi0e5b9+/dLkvZ+sVVHgo3tPl9nC1R/IUlq+HqbesT5PJ6m7ZjfW7E+vxT7a2B+bwVqqiT9rwUd0RRJ6tOnj3y+k/x/4QwLBoOue/fu7pVXXgnbfsMNN7jLL7+8xa+ZN2+ek8SNGzdu3LiZvzU0NJy0g6ZfSdfV1eno0aNKTU0N256amqotW7a0+DWFhYWaM2dO6H5zc7P27t2rvn37nvy/VlohEAgoMzNTX375pRISEtp1Lgt+aOuRfnhrYj22sR77rK+pT58+J91vOtKR8Pv98vv9YduSkpI69O9ISEgw+WBH6oe2HumHtybWYxvrsS9W12T6g2MpKSnq3r27amtrw7bX1tYqLS3No6kAAOgcpiMdHx+v8847T6WlpaFtzc3NKi0tVV5enoeTAQAQfea/3T1nzhzNmDFD559/vsaMGaNHH31UBw4cCH3auzP5/X7NmzfvuG+nx6of2nqkH96aWI9trMe+WF+TzznnvB7i+zzxxBN65JFHVFNTo1GjRumxxx5Tbm6u12MBABBVMRFpAAC6ItPvSQMA0JURaQAAjCLSAAAYRaQBADCqS0W6rZe8fPnll5WTk6OePXtq+PDhev3118P2O+f0wAMPKD09Xb169VJ+fr62bdsWdszevXv1y1/+UgkJCUpKStLMmTNDF+qIxfUMHDhQPp8v7PbQQw91yHqisably5dr4sSJoV8LW1FRcdw5Dh06pIKCAvXt21ennnqqrr766uN+gU4srWfcuHHHPUa33nqrufU0NTVp7ty5Gj58uHr37q2MjAzdcMMN+uabb8LOESvPodauJ9aeQw8++KBycnLUu3dvnXbaacrPz9eGDRvCjomVx6i164n2Y9Qm7b8MRmxYtmyZi4+Pd4sWLXKfffaZmzVrlktKSnK1tbUtHv/BBx+47t27u4cffth9/vnn7r777nM9evRw//73v0PHPPTQQy4xMdGtWLHCffrpp+7yyy932dnZrrGxMXTM5MmT3ciRI9369evde++95wYPHuymTZsWs+sZMGCAmz9/vquurg7d9u/f3+71RGtNzz33nCsqKnLPPPOMk+Q++eST485z6623uszMTFdaWuo2btzoLrjgAnfhhRfG7HouvvhiN2vWrLDH6Pt+ib8X66mvr3f5+fnuxRdfdFu2bHFlZWVuzJgx7rzzzgs7T6w8h1q7nlh7Dj3//PNu1apVbseOHa6ystLNnDnTJSQkuN27d4eOiZXHqLXrieZj1FZdJtJjxoxxBQUFoftHjx51GRkZrri4uMXjr7nmGvfzn/88bFtubq675ZZbnHPONTc3u7S0NPfII4+E9tfX1zu/3+9eeOEF55xzn3/+uZPkPvroo9Axb7zxhvP5fO7rr7+OufU4979/eBcuXNiu2U+ko9f0bTt37mwxavX19a5Hjx7u5ZdfDm3bvHmzk+TKysrasRpv1uPc/yJ95513tmv2lkRzPcd8+OGHTpL74osvnHOx9RxqyXfX41zsPoeOaWhocJLc6tWrnXOx/xh9dz3ORfcxaqsu8e3uw4cPq7y8XPn5+aFt3bp1U35+vsrKylr8mrKysrDjJWnSpEmh43fu3KmampqwYxITE5Wbmxs6pqysTElJSTr//PNDx+Tn56tbt27HfXslFtZzzEMPPaS+ffvq3HPP1SOPPKIjR45EvJZorqk1ysvL1dTUFHaenJwcZWVltek83+XVeo55/vnnlZKSomHDhqmwsFAHDx5s8zm+rbPW09DQIJ/PF7ooTiw9h1qznmNi9Tl0+PBhPf3000pMTNTIkSND54jVx6il9RwTjccoEuZ/LWhHiOSSlzU1NS0eX1NTE9p/bNvJjunfv3/Y/ri4OCUnJ4eOiaX1SNIdd9yhH//4x0pOTta6detUWFio6upqLViwIOL1RGtNrVFTU6P4+Pjj/iXa1vN8l1frkaTrrrtOAwYMUEZGhjZt2qS5c+dq69atWr58edsW8S2dsZ5Dhw5p7ty5mjZtWuhqRbH0HPqultYjxeZz6NVXX9W1116rgwcPKj09XatWrVJKSkroHLH2GJ1sPVL0HqNIdIlIo+N8+1rdI0aMUHx8vG655RYVFxfH7O/G/aG5+eabQ38ePny40tPTNWHCBO3YsUODBg3ycLITa2pq0jXXXCPnnJ588kmvx2m3k60nFp9D48ePV0VFherq6vTMM8/ommuu0YYNG46Lc6z4vvVYeoy6xLe7I7nkZVpa2kmPP/a/33fM7t27w/YfOXJEe/fubdelNr1aT0tyc3N15MgR7dq1q63LCBONNbVGWlqaDh8+rPr6+nad57u8Wk9Ljv2e++3bt0d8jmiu51jQvvjiC61atSrsVWcsPYdas56WxMJzqHfv3ho8eLAuuOACPfvss4qLi9Ozzz4bOkesPUYnW09LOuoxikSXiHQkl7zMy8sLO16SVq1aFTo+OztbaWlpYccEAgFt2LAhdExeXp7q6+tVXl4eOmbNmjVqbm5u1wVCvFpPSyoqKtStW7d2/xd1NNbUGuedd5569OgRdp6tW7eqqqqqXZdD9Wo9LTn2Y1rp6ekRnyNa6zkWtG3btmn16tXq27fvceeIledQa9bTklh8DjU3NysYDIbOEUuPUUu+vZ6WdNRjFBGvP7nWWZYtW+b8fr9bsmSJ+/zzz93NN9/skpKSXE1NjXPOueuvv97de++9oeM/+OADFxcX5/7yl7+4zZs3u3nz5rX4I0tJSUlu5cqVbtOmTe6KK65o8Uewzj33XLdhwwb3/vvvuzPPPLPDfjShs9ezbt06t3DhQldRUeF27Njh/vGPf7h+/fq5G264od3ridaa9uzZ4z755BP32muvOUlu2bJl7pNPPnHV1dWhY2699VaXlZXl1qxZ4zZu3Ojy8vJcXl5eTK5n+/btbv78+W7jxo1u586dbuXKle6MM85wY8eONbeew4cPu8svv9ydfvrprqKiIuzHXYLBYOg8sfIcas16Yu05tH//fldYWOjKysrcrl273MaNG91NN93k/H6/q6ysDJ0nVh6j1qwn2o9RW3WZSDvn3OOPP+6ysrJcfHy8GzNmjFu/fn1o38UXX+xmzJgRdvxLL73kzjrrLBcfH+/OOecc99prr4Xtb25udvfff79LTU11fr/fTZgwwW3dujXsmD179rhp06a5U0891SUkJLibbrrJ7du3LybXU15e7nJzc11iYqLr2bOnGzp0qPvTn/7kDh061CHricaaFi9e7CQdd5s3b17omMbGRveb3/zGnXbaae6UU05xU6dODYt4LK2nqqrKjR071iUnJzu/3+8GDx7sfve733XIz0l39HqO/RhZS7e33347dFysPIdas55Yew41Nja6qVOnuoyMDBcfH+/S09Pd5Zdf7j788MOwc8TKY9Sa9XTGY9QWXKoSAACjusR70gAAxCIiDQCAUUQaAACjiDQAAEYRaQAAjCLSAAAYRaQBADCKSAMAYBSRBgDAKCINAIBRRBoAAKP+DxT8LI1mEMLdAAAAAElFTkSuQmCC","text/plain":["
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","text/plain":["
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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["from scipy.spatial import distance\n","import seaborn as sns\n","\n","dis = {}\n","for col in df1.columns:\n"," dis[col.lower().replace(\" \", \"_\")] = df[col].value_counts(normalize=True).to_dict()\n","dis['age'].values()\n","\n","#sort dis['age'] by key\n","p = list(dict(sorted(dis['age'].items())).values())\n","sns.displot(p)\n","\n","dis = {}\n","for col in df2.columns:\n"," dis[col.lower().replace(\" \", \"_\")] = df2[col].value_counts(normalize=True).to_dict()\n","dis['age'].values()\n","\n","q = list(dict(sorted(dis['age'].items())).values())\n","sns.displot(q)\n","\n","sns.displot((p,q))\n","\n","distance.jensenshannon(p,q)\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"cell_id":"a5ce54c6c2a24c439f17aa0c55d619a7","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":685029,"execution_start":1697471251456,"source_hash":null},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAesAAAHpCAYAAACiOxSqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy89olMNAAAACXBIWXMAAA9hAAAPYQGoP6dpAABKTElEQVR4nO3de5gV1Znv8d9uGlpRuhEQGpIW0UzUeMHEC2ISI94Qo+d4JBcQEBTxBibKGceHZ8Z4mcw00YmaSVCi4aJj1CTnqGOciTmKIFEaJCCP0QRGDAajNDoQugVDX6rW+aO7qmvXrtr33Xvt5vt5nv1A167Lqsveb71rrVo7YYwxAgAA1qoqdwEAAEB6BGsAACxHsAYAwHIEawAALEewBgDAcgRrAAAsR7AGAMByBGtJxhi1traKR84BADYiWEv6+OOPVVdXp48//rjcRQEAIAXBGgAAyxGsAQCwHMEaAADLEawBALAcwRoAAMsRrAEAsBzBGgAAyxGsAQCwHMEaAADLEawBALAcwRoAAMsRrAEAsBzBGgAAyxGsAQCwXFmDdWNjo0477TQNGjRIw4cP16WXXqotW7YkzbN//37NnTtXQ4cO1aGHHqrJkydr586dSfNs375dX/3qVzVw4EANHz5ct9xyizo7O3tzVwAAKJmyBuuXX35Zc+fO1dq1a/XCCy+oo6NDF1xwgfbt2+fPc/PNN+uXv/ylfvGLX+jll1/WBx98oMsuu8x/33EcffWrX1V7e7vWrFmjRx55RMuXL9d3vvOdcuwSAABFlzDGmHIXwvPRRx9p+PDhevnll3XWWWeppaVFhx9+uB5//HF97WtfkyRt3rxZxx13nJqamnTGGWfoV7/6lS6++GJ98MEHGjFihCRp8eLFuvXWW/XRRx9pwIABKdtpa2tTW1ub/3dra6saGhrU0tKi2tra3tlZAACyZFWbdUtLiyRpyJAhkqQNGzaoo6ND5513nj/PscceqyOOOEJNTU2SpKamJp144ol+oJakiRMnqrW1VW+99VbkdhobG1VXV+e/GhoaSrVLAAAUzJpg7bqubrrpJn3xi1/UCSecIElqbm7WgAEDNHjw4KR5R4wYoebmZn+eYKD23vfei7JgwQK1tLT4r/fee6/IewMAQPFUl7sAnrlz5+rNN9/UK6+8UvJt1dTUqKampuTbKYTrupKkqipr7qcAAGViRSSYN2+ennvuOa1cuVKf/vSn/en19fVqb2/Xnj17kubfuXOn6uvr/XnCvcO9v715AACoZGUN1sYYzZs3T08//bReeukljRkzJun9U045Rf3799eKFSv8aVu2bNH27ds1fvx4SdL48eP1u9/9Th9++KE/zwsvvKDa2lp97nOf650dAQCghMraG/yGG27Q448/rn//93/XMccc40+vq6vTwQcfLEm6/vrr9Z//+Z9avny5amtrdeONN0qS1qxZI6nr0a2TTz5Zo0aN0t13363m5mbNmDFDV199tf75n/85q3K0traqrq7Oqt7gVIMDADxlDdaJRCJy+rJlyzRr1ixJXYOi/O///b/1xBNPqK2tTRMnTtQDDzyQVMX9pz/9Sddff71WrVqlQw45RDNnztTChQtVXZ1dkzzBGgBgM6uesy4XgjUAwGZEAgAALEewBgDAcgRrAAAsR7AGAMByBGsAACxHsAYAwHIEawAALEewBgDAcgRrAAAsR7AGAMByBGsAACxHsAYAwHIEawAALEewBgDAcgRrAAAsR7AGAMByBGsAACxHsAYAwHIEawAALEewBgDAcgRrAAAsR7AGAMByBGsAACxHsAYAwHIEawAALEewBgDAcgRrAAAsR7AGAMByBGsAACxHsAYAwHIEawAALEewBgDAcgRrAAAsR7AGAMByBGsAACxHsAYAwHIEawAALEewBgDAcgRrAAAsR7AGAMByZQ3Wq1ev1iWXXKJRo0YpkUjomWeeSXo/kUhEvu655x5/niOPPDLl/YULF/byngAAUDplDdb79u3T2LFjtWjRosj3d+zYkfRaunSpEomEJk+enDTfXXfdlTTfjTfe2BvFBwCgV1SXc+OTJk3SpEmTYt+vr69P+vvf//3fNWHCBB111FFJ0wcNGpQybzptbW1qa2vz/25tbc16WQAAelvFtFnv3LlT//Ef/6HZs2envLdw4UINHTpUn//853XPPfeos7Mz7boaGxtVV1fnvxoaGkpVbAAAClbWzDoXjzzyiAYNGqTLLrssafq3vvUtfeELX9CQIUO0Zs0aLViwQDt27NC9994bu64FCxZo/vz5/t+tra0EbACAtSomWC9dulTTpk3TQQcdlDQ9GHRPOukkDRgwQNdee60aGxtVU1MTua6amprY9wAAsE1FVIP/5je/0ZYtW3T11VdnnHfcuHHq7OzUu+++W/qCAQDQCyoiWC9ZskSnnHKKxo4dm3HeTZs2qaqqSsOHD++FkgEAUHplrQbfu3evtm7d6v+9bds2bdq0SUOGDNERRxwhqas9+Re/+IW+//3vpyzf1NSkdevWacKECRo0aJCampp08803a/r06TrssMN6bT8AACilhDHGlGvjq1at0oQJE1Kmz5w5U8uXL5ckPfTQQ7rpppu0Y8cO1dXVJc23ceNG3XDDDdq8ebPa2to0ZswYzZgxQ/Pnz8+pTbq1tVV1dXVqaWlRbW1tQftULK7rSpKqqiqi8gMAUEJlDda2IFgDAGxGJAAAwHIEawAALEewBgDAcgRrAAAsR7AGAMByBGsAACxHsAYAwHIEawAALEewBgDAcgRrAAAsR7AGAMByBGsAACxHsAYAwHIEawAALEewBgDAcgRrAAAsR7AGAMByBGsAACxHsAYAwHIEawAALEewBgDAcgRrAAAsR7AGAMByBGsAACxHsAYAwHIEawAALEewBgDAcgRrAAAsR7AGAMByBGsAACxHsAYAwHIEawAALEewBgDAcgRrAAAsR7AGAMByBGsAACxHsAYAwHIEawAALEewBgDAcgRrAAAsV9ZgvXr1al1yySUaNWqUEomEnnnmmaT3Z82apUQikfS68MILk+bZvXu3pk2bptraWg0ePFizZ8/W3r17e3EvAAAorbIG63379mns2LFatGhR7DwXXnihduzY4b+eeOKJpPenTZumt956Sy+88IKee+45rV69Wtdcc02piw4AQK+pLufGJ02apEmTJqWdp6amRvX19ZHv/eEPf9Dzzz+v9evX69RTT5Uk/fCHP9RFF12kf/mXf9GoUaMil2tra1NbW5v/d2tra557AABA6VnfZr1q1SoNHz5cxxxzjK6//nrt2rXLf6+pqUmDBw/2A7UknXfeeaqqqtK6deti19nY2Ki6ujr/1dDQUNJ9AACgEFYH6wsvvFCPPvqoVqxYoe9973t6+eWXNWnSJDmOI0lqbm7W8OHDk5aprq7WkCFD1NzcHLveBQsWqKWlxX+99957Jd0PAAAKUdZq8EymTJni///EE0/USSedpKOPPlqrVq3Sueeem/d6a2pqVFNTU4wiAgBQclZn1mFHHXWUhg0bpq1bt0qS6uvr9eGHHybN09nZqd27d8e2cwMAUGkqKlj/+c9/1q5duzRy5EhJ0vjx47Vnzx5t2LDBn+ell16S67oaN25cuYoJAEBRlbUafO/evX6WLEnbtm3Tpk2bNGTIEA0ZMkR33nmnJk+erPr6er3zzjv6u7/7O33mM5/RxIkTJUnHHXecLrzwQs2ZM0eLFy9WR0eH5s2bpylTpsT2BAcAoNIkjDGmXBtftWqVJkyYkDJ95syZevDBB3XppZfq9ddf1549ezRq1ChdcMEF+sd//EeNGDHCn3f37t2aN2+efvnLX6qqqkqTJ0/Wv/7rv+rQQw/Nuhytra2qq6tTS0uLamtri7JvhXJdV5JUVVVRlR8AgBIoa7C2BcEaAGAzIgEAAJYjWAMAYDmCNQAAliNYAwBgOYI1AACWI1gDAGA5gjUAAJYjWAMAYDmCNQAAliNYAwBgOYI1AACWI1gDAGA5gjUAAJYjWAMAYDmCNQAAliNYAwBgOYI1AACWI1gDAGA5gjUAAJYjWAMAYDmCNQAAliNYAwBgOYI1AACWI1gDAGA5gjUAAJYjWAMAYDmCNQAAliNYAwBgOYI1AACWI1gDAGA5gjUAAJYjWAMAYDmCNQAAliNYAwBgOYI1AACWI1gDAGA5gjUAAJYjWAMAYDmCNQAAliNYAwBgubIG69WrV+uSSy7RqFGjlEgk9Mwzz/jvdXR06NZbb9WJJ56oQw45RKNGjdIVV1yhDz74IGkdRx55pBKJRNJr4cKFvbwnAACUTlmD9b59+zR27FgtWrQo5b1PPvlEGzdu1G233aaNGzfqqaee0pYtW/Q//sf/SJn3rrvu0o4dO/zXjTfe2BvFBwCgV1SXc+OTJk3SpEmTIt+rq6vTCy+8kDTtRz/6kU4//XRt375dRxxxhD990KBBqq+vz3q7bW1tamtr8/9ubW3NseQAAPSeimqzbmlpUSKR0ODBg5OmL1y4UEOHDtXnP/953XPPPers7Ey7nsbGRtXV1fmvhoaGEpYaAIDClDWzzsX+/ft16623aurUqaqtrfWnf+tb39IXvvAFDRkyRGvWrNGCBQu0Y8cO3XvvvbHrWrBggebPn+//3draSsAGAFirIoJ1R0eHvvGNb8gYowcffDDpvWDQPemkkzRgwABde+21amxsVE1NTeT6ampqYt8DAMA21leDe4H6T3/6k1544YWkrDrKuHHj1NnZqXfffbd3CggAQIlZnVl7gfrtt9/WypUrNXTo0IzLbNq0SVVVVRo+fHgvlBAAgNIra7Deu3evtm7d6v+9bds2bdq0SUOGDNHIkSP1ta99TRs3btRzzz0nx3HU3NwsSRoyZIgGDBigpqYmrVu3ThMmTNCgQYPU1NSkm2++WdOnT9dhhx1Wrt0CAKCoEsYYU66Nr1q1ShMmTEiZPnPmTN1xxx0aM2ZM5HIrV67U2WefrY0bN+qGG27Q5s2b1dbWpjFjxmjGjBmaP39+Tm3Sra2tqqurU0tLS8Zq9t7iuq4kqarK+pYKAECJlTVY24JgDQCwGZEAAADLEawBALAcwRoAAMsRrAEAsBzBGgAAyxGsAQCwHMEaAADLEawBALAcwRoAAMsRrAEAsBzBGgAAyxGsAQCwHMEaAADLEawBALAcwRoAAMsRrAEAsBzBGgAAy+UVrI866ijt2rUrZfqePXt01FFHFVwoAADQI69g/e6778pxnJTpbW1tev/99wsuFAAA6FGdy8zPPvus//9f//rXqqur8/92HEcrVqzQkUceWbTCAQAAKWGMMdnOXFXVlYgnEgmFF+vfv7+OPPJIff/739fFF19c3FKWWGtrq+rq6tTS0qLa2tpyF0eS5LqupJ5jDgA4cOWUWXsBZMyYMVq/fr2GDRtWkkIBAIAeOQVrz7Zt24pdDgAAECOvYC1JK1as0IoVK/Thhx/6Gbdn6dKlBRcMAAB0yStY33nnnbrrrrt06qmnauTIkUokEsUuFwAA6JZXsF68eLGWL1+uGTNmFLs8AAAgJK+uxu3t7TrzzDOLXRYAABAhr2B99dVX6/HHHy92WQAAQIS8qsH379+vhx56SC+++KJOOukk9e/fP+n9e++9tyiFAwAAeQbrN954QyeffLIk6c0330x6j85mAAAUV04jmPVVjGAGALAZkQAAAMvlVQ0+YcKEtNXdL730Ut4FAgAAyfIK1l57taejo0ObNm3Sm2++qZkzZxajXAAAoFtewfq+++6LnH7HHXdo7969BRUIAAAkK2oHs61bt+r000/X7t27i7XKXkEHMwCAzYoaCZqamnTQQQcVc5UAABzw8qoGv+yyy5L+NsZox44d+u1vf6vbbrutKAUDAABd8grWdXV1SX9XVVXpmGOO0V133aULLrigKAUDAABdGBRFtFkDAOxWUCTYsGGDHnvsMT322GN6/fXXc15+9erVuuSSSzRq1CglEgk988wzSe8bY/Sd73xHI0eO1MEHH6zzzjtPb7/9dtI8u3fv1rRp01RbW6vBgwdr9uzZ9EgHAPQpeQXrDz/8UOecc45OO+00fetb39K3vvUtnXLKKTr33HP10UcfZb2effv2aezYsVq0aFHk+3fffbf+9V//VYsXL9a6det0yCGHaOLEidq/f78/z7Rp0/TWW2/phRde0HPPPafVq1frmmuuyWe3AACwk8nDN77xDXPqqaea3//+9/60t956y5x66qlmypQp+azSSDJPP/20/7fruqa+vt7cc889/rQ9e/aYmpoa88QTTxhjjPn9739vJJn169f78/zqV78yiUTCvP/++1lvu6WlxUgyLS0teZW9FBzHMY7jlLsYAAAL5JVZP//883rggQd03HHH+dM+97nPadGiRfrVr35VlJuIbdu2qbm5Weedd54/ra6uTuPGjVNTU5OkrkfFBg8erFNPPdWf57zzzlNVVZXWrVsXu+62tja1trYmvQAAsFVewdp13ZTfsJak/v37+x2jCtXc3CxJGjFiRNL0ESNG+O81Nzdr+PDhSe9XV1dryJAh/jxRGhsbVVdX578aGhqKUmYAAEohr2B9zjnn6Nvf/rY++OADf9r777+vm2++Weeee27RClcqCxYsUEtLi/967733yl0kAABi5RWsf/SjH6m1tVVHHnmkjj76aB199NEaM2aMWltb9cMf/rAoBauvr5ck7dy5M2n6zp07/ffq6+v14YcfJr3f2dmp3bt3+/NEqampUW1tbdILAABb5TUoSkNDgzZu3KgXX3xRmzdvliQdd9xxSe3LhRozZozq6+u1YsUK/1e+WltbtW7dOl1//fWSpPHjx2vPnj3asGGDTjnlFEldP8/puq7GjRtXtLIAAFBOOQXrl156SfPmzdPatWtVW1ur888/X+eff74kqaWlRccff7wWL16sL3/5y1mtb+/evdq6dav/97Zt27Rp0yYNGTJERxxxhG666SZ997vf1d/8zd9ozJgxuu222zRq1ChdeumlkrpuEC688ELNmTNHixcvVkdHh+bNm6cpU6Zo1KhRuewaAAD2yqXr+CWXXGLuvffe2Pd/8IMfmEsvvTTr9a1cudJISnnNnDnTGNP1+NZtt91mRowYYWpqasy5555rtmzZkrSOXbt2malTp5pDDz3U1NbWmiuvvNJ8/PHHuewWj24BAKyW03Cjo0eP1vPPP5/0yFbQ5s2bdcEFF2j79u1FuI3oPQw3CgCwWU6RYOfOnZGPbHmqq6tzGsEMAABkllOw/tSnPqU333wz9v033nhDI0eOLLhQAACgR07B+qKLLtJtt92WNDa3569//atuv/12XXzxxUUrHAAAyPEnMnfu3KkvfOEL6tevn+bNm6djjjlGUldb9aJFi+Q4jjZu3Jgy6pjtaLMGANgs59+z/tOf/qTrr79ev/71r+UtmkgkNHHiRC1atEhjxowpSUFLiWANALBZzsHa85e//EVbt26VMUZ/8zd/o8MOO6zYZes1BGsAgM3yDtZ9CcEaAGAzIgEAAJYjWAMAYDmCNQAAliNYAwBgOYI1AACWI1gDAGA5gjUAAJYjWAMAYDmCNQAAliNYAwBgOYI1AACWI1gDAGA5gjUAAJYjWAMAYDmCNQAAliNYAwBgOYI1AACWI1gDAGA5gjUAAJYjWAMAYDmCNQAAliNYAwBgOYI1AACWI1gDAGA5gjUAAJYjWAMAYDmCNQAAliNYAwBgOYI1AACWI1gDAGA5gjUAAJYjWAMAYDmCNQAAlrM+WB955JFKJBIpr7lz50qSzj777JT3rrvuujKXGgCA4qkudwEyWb9+vRzH8f9+8803df755+vrX/+6P23OnDm66667/L8HDhzYq2UEAKCUrA/Whx9+eNLfCxcu1NFHH62vfOUr/rSBAweqvr6+t4sGAECvsL4aPKi9vV2PPfaYrrrqKiUSCX/6T3/6Uw0bNkwnnHCCFixYoE8++STtetra2tTa2pr0AgDAVtZn1kHPPPOM9uzZo1mzZvnTLr/8co0ePVqjRo3SG2+8oVtvvVVbtmzRU089FbuexsZG3Xnnnb1QYgAACpcwxphyFyJbEydO1IABA/TLX/4ydp6XXnpJ5557rrZu3aqjjz46cp62tja1tbX5f7e2tqqhoUEtLS2qra0ternz4bquJKmqqqIqPwAAJVAxmfWf/vQnvfjii2kzZkkaN26cJKUN1jU1NaqpqSl6GQEAKIWKSduWLVum4cOH66tf/Wra+TZt2iRJGjlyZC+UCgCA0quIzNp1XS1btkwzZ85UdXVPkd955x09/vjjuuiiizR06FC98cYbuvnmm3XWWWfppJNOKmOJUUw0CQA40FVEsH7xxRe1fft2XXXVVUnTBwwYoBdffFH333+/9u3bp4aGBk2ePFn/8A//UKaSAgBQfBXVwaxUWltbVVdXRwczS3EsABzo+PYDAMByBGsAACxHsAYAwHIEawAALEewBgDAcgRrAAAsR7AGAMByBGsAACxHsAYAwHIEawAALEewBgDAcgRrAAAsR7AGAMByBGsAACxHsAYAwHIEawAALEewBgDAcgRrAAAsR7AGAMByBGsAACxHsAYAwHIEawAALEewBgDAcgRrAAAsR7AGAMByBGsAACxHsAYAwHIEawAALEewBgDAcgRrAAAsR7AGAMByBGuk5bquXNctdzEA4IBGsAYAwHIE6z6IbDgzjhGASkKwBgDAcgRrlASZK5D5c1DMzwmfub6NYA0AgOWqy10A9C7vzruqyv77tEKyhEraz97A8ah8nMMDG2cdAADLkVlbyhgjY4wSiYQSiUS5iwMgS8HPLlAsVmfWd9xxhx+svNexxx7rv79//37NnTtXQ4cO1aGHHqrJkydr586dZSxx8RhjNPXHa2SMKXdRAOTAGKNvLn6Fzy6KyupgLUnHH3+8duzY4b9eeeUV/72bb75Zv/zlL/WLX/xCL7/8sj744ANddtllZSxtcXFnDlSmUn12jTFyXZcbgQOQ9dXg1dXVqq+vT5ne0tKiJUuW6PHHH9c555wjSVq2bJmOO+44rV27VmeccUZvF7UsMnU6oVNKj7hjwTGCp5zXQjbbNsZoyo9f1c+u+xI38wcY67+d3n77bY0aNUpHHXWUpk2bpu3bt0uSNmzYoI6ODp133nn+vMcee6yOOOIINTU1pV1nW1ubWltbk14ACkfmV3q5BmnOSd9gdbAeN26cli9frueff14PPvigtm3bpi9/+cv6+OOP1dzcrAEDBmjw4MFJy4wYMULNzc1p19vY2Ki6ujr/1dDQUMK9KK1KGgihksqK/NjSXsu11sOWc4LCWF0NPmnSJP//J510ksaNG6fRo0fr5z//uQ4++OC817tgwQLNnz/f/7u1tbWiAzYOHJXwlICt5YpzIPTe7sv7dqCwOrMOGzx4sD772c9q69atqq+vV3t7u/bs2ZM0z86dOyPbuINqampUW1ub9CqH3rj7783hDg8k4ePWW8eRLKn4evOYluI64TN8YKioYL1371698847GjlypE455RT1799fK1as8N/fsmWLtm/frvHjx5exlEC8YrQfkiXlJptjzjGF7awO1n/7t3+rl19+We+++67WrFmj//W//pf69eunqVOnqq6uTrNnz9b8+fO1cuVKbdiwQVdeeaXGjx9/wPQEl3K7q46btxjrQHZ6I4vL9xwFl+tL57kctRHGddXZ2VnQ8ayUc1Ap5ax0VrdZ//nPf9bUqVO1a9cuHX744frSl76ktWvX6vDDD5ck3XfffaqqqtLkyZPV1tamiRMn6oEHHihzqbPj3e1zR198th9bW8tli1K0y3PMUemszqyffPJJffDBB2pra9Of//xnPfnkkzr66KP99w866CAtWrRIu3fv1r59+/TUU09lbK+2hTFGUwq42y9Xm2klKPTYlpqJOXeFnMNsli3VNVLs9dIuXzi+D/oeqzPrvq6UoxyVs3drOTLb8D6n27btmTfIhBGtEp6GKBWrM+u+KCmDikkcOjs71dnZmfc2yj2ueDCz7a1szhijbzywWo7j5FS+fLZVyqylGNsqdVbVG1l8uPahEOk+a3HvR5W/N897uvni9iVdn5R0+18KpTpWB3KtC8G6xMo1elC+d53FKm82mW2xj0ku+1zJd+WMSIUDWSV/dgtBsC4R784ymzvBYt6FRvXozSVTz/fONZdMJLyNYt+FhzOJ4DHI1Bs+3H4c98qlLMXOMIzjaMqPXy15j/K44+W9X2g7e28otGai2DUdcevL9vPjuq5ML6TJpTivtl8rtqPNuhcU604wXVt0Mdupe+PO9UC9Oy6WbI+fd130RhbuZfyl3kaltVmWuw9JqQSvrb62bzYis+4lhfb07ezslOM4sVlvudups5Uui42bL906ilmebORSS1FINp4uqw2vP51gLUahxy3T8l5fgHwSv2zL5u2P4zh5Zbjh4+rXgBUhg45bzhijy0v42czm85NtH4Bc9vtAbj8uB4J1hUl3B5vp7jbc1knb54Ehn6wn3bWR7j1qZeKV+rNWys9z3Lor9VxUIoJ1BShGJum6rhzH0dQHX/F7TMf1ii7Fc8DpylXKNuB07YW5KEb2n832Ozs71d7enlTmbLLsYvKulWkPNyVdG34WGpMpum7uvY6j2nA7OztzWk++NTTFkPX6jdHl3cczm2XyKbcJbCPlvQyf4WxqTYrVlwX5oc36ABO+Ey5G22elPrcc/uKkhiFZNrU4wXbqrs5PUtRSUW3Nwesmm1qh4PWX6Zrrzbb6bFV6rUNcX5lK/OxXIjLrEgi3awbvar0sKdir05ve3t4euVxwvZm+fDLeIce8n9Rb2kQ/xzwloge3l4UVY8Sw8PHJNYMOZmPhsZmD8wWD85TFr8h1XV3+cFNe7aBxZSlXm7q3TK7ZeDbLpGuPdV1XUxe/IuM6Sdd18FiHnwDI5boJL5+pj0ap+nBkOh9xNQXhHtxR82WTbaerdYir2Qh+1xQqpZx5tsdnW+NFZt6DzNoSvdnLNZu2rXDGlKlc2baX2/bBy2bEs2yUs1dsubOb4DFMd00Fy5dPmXOtFSp0rAGyxexwnHoHmXWJxN05h7PuYDvgtJh2wHCAC2bBSduIWXfwbyPT1av8wa4sJa4tyxijKQ++os7OzpQ752yCbjiDN8bomw+s9tdXjBGjUsqcw81AcN642oSottC49tFsMznXdWWc7MqZqed58NqZsvgVdXR0+POny5TzzdaTq7sD10OgnCaU3cVty3EcTf3xq5HXQXt7e+xx7OzslBtRA5IpOwtnlsG+AeH3vc9isKYlvP/t7e1JfQsKlalmI+paiPt8F6s82ZYl6nMX93nOp/9FIZ/zvoTMuoi87Cr4JZoLLzPJZ9l8ZLrgg3fMxfhwhLPYvvacZrH3IZcMrxKPXzZlzqbtuRSZcCUez97QW7UONvY5KDcy6yLy2tVc19X0QM/PcNaWrr3KGKMpgTGu4+5wc53uveffeHe3NXl/x92lu64r1+nU5YtfTboRyeZOP3bb6ro7DvZOD951x2WvceuPKksx7r6zOcalrNo3xmhGqAdx+DiGZfsceNRxDh9rbztxNTkp5e1exnRnnt4y/rKhnu5p9z1ww5upF7LXbl6sPgdh+ZzjqOsvrhYr7hwUsv18r8uo7Df8/XR54ImSfGRTtrjxAYLXZSk/ezYiWBdZIW2g4bvWXNqWs70D9efPsYylupPONrsqpLYhq2eG81pzcRnT1UThOI5f1krI8IL9G7KZL3gesj23udQC2aqc4xrksu1M8/bWsa6Ec9qbCNYlEryLjuodKyW39XV9OFzN+Mk6/87ROI6mP9TVSzncU1zqynja2to0/Sdru77gswk5xmjW0tcUTM+CmXKmL8VgBuC12wV7qHptgVFF6emh7XS/krcVbAcM3kl3tW9Gt5tnc3fttY9GPhfsGl3xcJMiC5zmGMQdq2BbaGfo/GaTTUx74Dea+ZN1sV+UwespWJa4PgDeEwQlrQFwHM1c8lrPdRvYVnt7u/bv39+dERnN+Mk6OY7TVS6n65q//OEmdXR0pGTdPTUJ3T2Ou9v6w/0N4rJRv2Yo9H74/GWqrciUbUbNH/lERffnOZt+KUHh6yhqv+Kug56NG02Pua5SyhnRfyZ4jLzPca7Pw0fJtvYs23V55ertsQl6A8G6yLLNMuIktVsr/u4y6e431zvQiF65wfb2+M97aTKDqDL4NzpFyjCjngsOv5epbMWSbp2JREJGSj0n6WoFQu/ls0w+5U66XrI8P1HHutht8oV+BuPWl1NWGvN+PvtRrJo1G2rSkD+CdZEZx9G0QC9X143u/Rt39921kvjs17+7DbRn+u17Wd6lut3ZlnG72qNnPLTGr3qd9uM1MqGs13jZXPc243rJZjw2gX3w/m9kurf7qjo6OrpGzup+bndGzGhMwe0Gs56oZ6v9+brvtr32zfA5Cfek93S1q/8mso0uXIZ0mVzS8XI6Nf0na1N6b/vLGaNpi7uOh4zRzKWvRbbHdnR0aNpDXRmQ29mp/fv3+/s4M5DBBiOH4zj65qLU3/1OKWPgePrH1xhdsfS1pGA9dfErkkzPNRJzjNrb2+V2xtf+pKutkJS0/uC107MNx1/ecRxd/mDyGOXh9uKodvi4LM8Yo+nd1304Iw/P7/if/9RRAbN5AiLIcRxd/lDqdoOf83C/j3TfAYWMTOhl9GHh8x48j1HHNt3xjtturrV+fRW9wUsgkUgUXD2kRML/wMfdPafLFnPeVmCd6arTCxpnOs17iVAZctmWn4FmyDL845TjPqQbucn/IsyQBSdtv2ul2W8z3bwx70U90+z1ug/3xg/vX9rsMM8R8PJRyHUdt0/h8QKy7bMQNepaXHl7u1c6WXCyuOu60pFZl1n4zjfpeU+357noWT9ZG9veJLcr403K5mPayYOCvXeDZYj75jJuai1Bz510fE9cr4d7MNvwegwb19WMxa/KuMnvBe++29vbI5+tDa7/ip+sk4yrGQ+t6cpIA+XzMg7Hcbrmi8kQwnf86XpWG2M046GmpOfQvVqVqLZ1b5npWT5jnk7wHKXr9e5n/MaNfIbfmK5n372szHs5jpPSjh9XYxDel+A1HM6mvHLH7W9S1tXdH2PKg7/pyeRjypBVJmlMyljn3jGI2teoG9bOzk51dHRoRnfbbzhLdBwn6fyGy9ZVHiclUyxFRhi17ajvhLhtx11X3rFJd9wzlavQ5/8zPenh156VoSNfKZFZV4psMjGLrs2obK4Y7Xhx/PWXYFSrqHb0YIYWVSvg9eyOqjXI9TQVo908Xaadaf585Fpm73jlOkpZLtl3pn3NtsyZRvLL9TzlmpGXIoOP20Yp+qd4r0LKkOkY9LWsWiKzLqpgphHOVtN9oYQziuDFmTISmJvakzWqzcg4mcfZjmtnjHtGNJwxeFlv5IhsjqMZ3bUBbndGnk1vdbc7+4jafrq78XD5jEzyeOumZ93htkzj9CwbmU139yFwXVfT0jzzG8wcOzo6NG3xK5q5JLoHblyWmNIu67gynT29iL0RvLI/lhHXnenplZ0u0w3yeuo73f+Gy+sGyuXVYsx4qKd/Q3h87PB16ziOrvzJOv94RV33wVod7zg4jpNSqxT1uUtXY+S977X/dvUVSL7Oo5YNXivetZbpvIRrcLzfqZ/VfT7SZbw9ZUmtJciULXvHz/s8ZBwnobt/QrCXftxnMNwfIJg9h//v1W6leybeW0ZudK9013Uyjiuf6VfGKhGZdRn4d5eB/1fanWDwDjm27Hn0fA32hrdKHs/PJxKJrI9Bpn3O9bgEM/vwNoy6fhnLdd2M7fz5ltcrc05yOF7h7RSabcb1Z4hq0y5Wj/rwenJZZ698X5RxbIWguONSad+ZhSKzLqLOzk45HR1JH76ou11jjKY/+IrU/YGd9ZOebCJTBhq8Y4yazb+bzDBOeDrBXttJ6w5k/Y7jaOaP16itrS3+DjmQ2bqu42dGwfV42b1xHF29pKvXs1yjKx9eK7cz2L4X/ax5pv0Kn4NsMvzwMuF2/Them2Qwm/NqOII91qO239OLOH7dbW1tmvbgb7qyvtDz7OEaF+M4uvKhJl350BoZt+va6ujo0OUP/Eau0ymno13TH1oj43RqRvdz+uF9djtTe0z7x8Ek95Pw3sunKcbvqR93TQeuF+8cescy+Lz2zKS2ZCft88gp59IYTV/8qoxxk2s4Ap/VpHl//GrSKIPh3trhcxx8ouKq7uvcGKNpD/7G7/V/9bLfpvQfCI/F4J+jLEaq88oVfAIjNltX1/eX90RBeB5vvPZM3zvZTA9eq+H3gxm/cZPPa3D58Dnta1l0FDLrMolq5yyFkrZv5fgrShl73mbbA7rEStVel0k27dmZjq8XBPz2+4g2YD+QeO/l2GabTVmCZeqtmqNiPYvvSff0QFwfgCw3FL3NIrbPZyNYO1aKa75Y596mtvxyIrMuonA7XMa7RpmUZTzhttTg9HCWE9Wr29u+4zi6orvtMKmsobbxbO5K47LCrjeNP9pa0naCWboxmvPIBmVKv+Iyk2xHkfIzw6Q77lDGG7N+r8fv5YtfDWQ5XbUC+/fvl3GNv09eu7gbHJEry1/UCu9DXFu0X/MQt6+BtlJjjK5aul4zH16r4DF22tvlmuS226ge5Y7jaFrECFuR5Q6VN7herzbBK9OcRzakrNPLkNPVWvg1IYHakPDnpbMzuZYmqtYp+HcuWZj/lEHM8ffWYYzRjEDbalwNgd+nITjKVoZfEEv6vBuj2UtfS7rOvOPRHupLELWPUb+E543z7dXsBGtYws/Zh8sY7ocQdfymdx+XqBqOYEYfVUsQPo5R8wXL53SPENfR0eF/t/alkczIrHtJKTKMbO+0C27Hi5jmfzEF5ol79tTP9Lpmyqss6cpQTOmyqYzlKXppst+2L5FQQsm1trkoxvUZVabeEm6Xj5vHr4EooOd3uvmCWV7sc+zBjDZNeUMbyqo82fa6LnUNX7HHZch1e8FjXekZN5l1kQUzjGB7q+M4uqp79KdgdhC86w9mUT0ZR3SvSbe7PefaZb+NbIcNZ+BeWVKe406XDXa3SwYzfOM4umrZ+qRgfc2jrydlLt4+dzXRGc18qMmPHsG7ZS8r8vYz27ZkLwvwyhDuK+BJaisOtHOG21nDvZKvWvJa0ihuwedjw+fDOw+zIp7fjhrdKdh72msfTN/e54RX6y/fVWPR1QYa7gOQTji7jqq1CJcj2OYeztb8jNq/SIzmLPtt0nUdHrnOdd2YJx+cpNHgon6HOliTkXJNG6MZ3SOIxdXGOI6jWQ+t0VXdbcThY+PNH7X9qHPizZeU0RsT09bqyBhXcx59ves9Y3Tto6/LW9DrVR6VWYdrDOKmSV3X8awlqSPfuRn2L9301OOQWsaoYxXsfR71VEC4FsCYrl7gcTVwwRrFlGXd5P42xhhdnmYkxEpCZl1CvZJhFNJGnNXqU9tRjZS83tCzxZHryPPDEpcdZPv8bV531GlGp4pfJBGbzca1pRWtjS0RGk++sLWlyKucZcxish71q8RlTFuOlLbqmMBkep4/z/kzlGWfglzPbVTtmjckar9+/XIqYlwtWfjzVEhtWqVn1B4y6yIL95ie9VDqc4KRPa3dnvasuLbLdMEish3WGF318FoZk9wbOiqT9++2wz21o3pPG6OrQ2OXe9NnPbxWV/5knb8PUXfp2fQa9stmjGY91JTStheVNSWtt7uM/i88hWoUetq0HbkdqRle8Dl1b31zAjUK3nzeHb53HMM9kL1e2Fd097j2ayC628ZnLk3OiqPaWrPKlh1Hc5atl2RSMlZPuF3cy/TCNQ3hGghjjK5Z9ttALUjyMQ/XBiWvP74ncPhcxLXNh9t6U/Y9VEOQ7lBFff7SteP3PN+dvs9D1PFId87Cvfnjyuk6nZq99DVdtXS9f/yDtXJR13am0dGSppmuMQRyCoKmq+e6v0x3X4npDzXp8odS26hdN3U8Bk9bW5tm/PjVpF7wSZtyXTmdHV39SELPwPd8hlO/F6P69lQ6MutSK3HmW8j2S7HeYMZSaO/VfNqPk5aXipO5euJ6CxeybDHPTymfKpD6xBdeULF7V5dMIvBLbN2TitrzOZ91hJfpbpvP9LRCOBvv+mnf9M+Yx31WgsegnP1GeguZdQmktFV6PXbDbcPd2V+4fTRTNhXODoJtz+G2RRO6Aw3f+Yfv1FPatNNkxX6bUUTbo4zkdnToqiXr0u5PuNd7sAzGcTR72XqZQPbnt3vFZNZJ2bwxut5rGwzNk+mZ0+C02G8AY3T1ktfkdmfMwWOdKcPzMnUn8Mtb4eMS2b7XEXrmOFPGHXN+o7Lqnulu6nLG1TVL1ys8hns4Q407x9kExWCNRi7ful72ZYxJ6gcRPBemu8YheKyN4+iK7kwwU6/7cKYW7I3tTfdrLpyedfnjDHS/F7Vf4X4ckddD9zgEgeoOXek9r11AFultO9irPlgDF9Ue7HR2+P0GvOvedZ2k6eFlvGDs9WZ3HEczl6zTrIfWdI2rsOy3/mhpwfPlOo5Mp6Orlq5XsN9LVxkdzeweJbGjo0PTHviN3O7PU9yTAZWMYF1uObS9uq4rN3RHGje/G7rTzOXOs6h3qUVoj42SdRtW+BniNLOG58nqOKTpfZqR5T3Og9mPpKJl7rmci3TvuWnmyXrbRRoDPd/lTNQ0Y6LXG5HNZru+fOS7fNrlgiPxBTPxTOchbl97abwKGxCsSyCbNtnwvHFZknEcXb1sveR2P6NsXF35cFN0m7G6LmRvvmuWBe5GA+3M4WzW49+FGqPZS16T29GRlDXG9Uz29iNTlpfuGGT7rLnXW1au0dXL1vuBJLyOlHPQnWVL8W26juPoqh+v8dv4vbZa75gFs/9wRu8fG2N0zSMbum+mejKVqDbSoGD7dLhnvHHd7v1NLUvcOpMy44iaj6TMJZR5B2smZv1knWaFfimsM+b4mdA+hHvOe9M7Ojr8LLFn3lBg6j6OXpaa1JfCcbqy/M5OzVnyWk+yGc5+030+AjUMMvGjpGViTNdoZlG98dNmuaFryzvec7p7qHd0dGh2YN/CZQ32Cela1CR93r3j5P1aX7CnfVz7scftrqlwOzv9MRqCtXXB+cI1iG532eN+JdDb9+DTAlHbj6uB6HrPaE73Zzmux79Rz28D5Pu9ZBuCdS9LdwccK3z3makna9zdapF785a9nagIvbw9/r6EM/FitOdlkPVxjMguSi5DO2Qh640SlzXFLt8L5ydK+GmDvD4LUeUoZBS/TOvrllJjknaVhfUXSbPi3NcZcW0UWntQSQjWRZSpfcS4XW01wR7aXW90ZRGZls01Wy+mpMzSy0KM0dxHX+9uc+/JuoOZZjDL6nmmO7tfjYoTVQuR6z77bcJOT1aTlOkY1/87Fyk9uv3nZqOflzdu15jfVz28VsYbzzomE07KXJ1OXbNsvX88M7WF5iJ4Haer9UhXy9Jz7lN796bNfo3Rtd2Zb3I7cvzoblHbDx4TL1MOZrzxmVtqbUDkkxlOp65Zur5rbPzu8ezjrpdM3wve+Q73E3ADn6vYcoQy3qQ2Yrerj0M4i/Z+5euKh9emZO5R5yvTKGDBGjDjdo3053Z0do3H4KTWQnnn1OtnEHW8Ip9ScDo1u/uJB/+4ua5mB3umK3NP8UpFsC6HLO+AK0KlljtKITURxd52qZapBJWyX+W8XgpVqhqT0DZ6ZZ2VdNwLQLDuBeWuLs5UZVqOqqSKeWymj8imw1awY1Nvd2TLtn04q+s4/HcO13UhndfKJatzm+fyxZDLeeB7IR7BugTCnUzk9nRu8qeZUPVXaPARvxOJk1qtGn4EJKpzVVKVmUkd0CM4r1e+ro4kqT/OENchK2kdof3JWKXvGl2bppNJeF+Cj2J4P9cX2bkk8GhXNkMmpuvYlu7RuXDVXjb74bpxQ2y6SVWJccsHz0vcj7zETu90/M5YKVX1HR2avaT7Z1rDjwil2Z+k8uTQLOEGzqlxUq8F//iH98UYXRfTXOQ1XVzf3Syj7s5+V3tD/AZ+NCPpmuoIDULkVcMbV3O6h/INVqd7115425GdAvO848ml+tbt3m/v+KVUZZvk4Uz9ZQLNC67T6Q/eFNx2yo/9dA+Rm1XQDey/4zia7f38bbCpLPgYYvfntqeZMPraijuu4c8yj24hf71QVZP2rjSHRyN6TQFDHKZML3bZcixHRchp+MvCFTJoTN7zFaNqOqLzmo3nPen4ZnluY89JFp0vC5LDI6r5dtDs66wO1o2NjTrttNM0aNAgDR8+XJdeeqm2bNmSNM/ZZ5/tj5zjva677roylbhL3KM16R4HCS6Tz125l1nEZR6R86fJKr3OH7l2MIrM8p3C9q1rQaPrAgOc+J2YTGrHnmLfTSeVuTvzcgKDX0TNn65jVbbbCj7OFdfJKGr5YGe08PS0+5ahPJ7g8U1b62KMbgj8yEu210wuZcl2eiGZrlcTdE33T31m6lwX7GwV9eMVscsFapCCZQ53mPOXcZzux7WSp0cNrBL8vIRrMTLWCqXpAJvu2sy2Rs7bl/AP0oRr+YI/lxqsxXOdzqTH3OJqzCqd1cH65Zdf1ty5c7V27Vq98MIL6ujo0AUXXKB9+/YlzTdnzhzt2LHDf919991lKrEF+nJni7h9SzM4RNxdd0F343nUCOSq3P0c4uRcLouux0IzMCMLh1yNeTQr7TkqQmfGkmSzhVwrFl1npWL12ODPP/980t/Lly/X8OHDtWHDBp111ln+9IEDB6q+vr63i1cQ47pKVMXfK5mID0LX3WT33WiaazO8bv/vRPK6w3fV3mAXkb9LXaa71ajjkH6Brsy369EySerO7hIJ/3G5RKKq64cvlq6XqhI928nh8x7VRhY5GEZ3dpno1y+3LzdjdP0jG6SqKmUqWLHOTVSNRFKmXpVQwqir/0VVQt7Qo7lkrcE2+mJkvTnxssruz4bbnX0mEgm/DN7npGt/q5RQz/WRUFVXzVUev2vWk9kWFuC8z3LX9Rr9OXUcR9f/2+uBz0D0eroeWcy8vcjrtrsfTFX//t3Xh0n5Psv1MxVXI1XotZFtrZbtrM6sw1paWiRJQ4YMSZr+05/+VMOGDdMJJ5ygBQsW6JNPPkm7nra2NrW2tia9eoOt2ZJUojvlcsm23TLfAR9yPVb53vX3YraQ07VZyVlMEYe/LcVnJpcBS9IqwTlK2d9KGDipD7E6sw5yXVc33XSTvvjFL+qEE07wp19++eUaPXq0Ro0apTfeeEO33nqrtmzZoqeeeip2XY2Njbrzzjt7pczh3qw3PLap588cP+jB+YPZczhzzrSORCJiXtOTgebzg8jGdaV+EXfWaeY3MpGb6louu0IkDaCQZbl7tp1Ime6tx89gTPI59ObxsuVwbUWwXOmymlxlqokpZN1dCxpdt3xD2n0Kl8WY7mupAJG9fUPvB9vtc123Ej21IImU9xIp10Cm8iW/aXTdoxt7Mt3wdZJm1W5nZ/T5NEbXPrJRJk2wDl+/mWpWnPZ2SVKiKrknuIzx238V+Ngn9ZVIOmimp2YhkeU5MV1PmiSq0//OtdvZ2XXtBfYxr/NdVcE3khlUTLCeO3eu3nzzTb3yyitJ06+55hr//yeeeKJGjhypc889V++8846OPvroyHUtWLBA8+fP9/9ubW1VQ0NDaQoelkhIFvUoTWJ5xhTukVvW2gDLj1Ve+uI+lVqpBv6w9XsikUgbRIOZdCK4DApWEdXg8+bN03PPPaeVK1fq05/+dNp5x40bJ0naunVr7Dw1NTWqra1NehWb1xO0t9p5s+rVme/dakQ7ZlzP0HQ/9hG1jrjey9ELBdqju/8O9jZOV7Z8RGZLWR6LXNbZmzcc2Waq4V78cevJdnopZDofcT3i8/2s5HP+0z0zHXd9ZepFHbV8ZA1FDv0I0p3rXNZpHEfXdg8JGi5frtd50o+rhH/6NDDeRLjnuLe9bHviVwqrM2tjjG688UY9/fTTWrVqlcaMGZNxmU2bNkmSRo4cWeLSoSwqYIhH2ukgpdYEHTAs/Ez2BVZn1nPnztVjjz2mxx9/XIMGDVJzc7Oam5v117/+VZL0zjvv6B//8R+1YcMGvfvuu3r22Wd1xRVX6KyzztJJJ51U5tLnLu7ONe5OO5dewNlkidm8l+36C10un4w112MRzCiKmhEao+sf3VhQNWaxM9Rin+9cl8332ddyPYXgbTubmqLYY2B6RgYsJGvN5bObaRvpfkQlXfYbdx5SsvssMvSsytmRW+1CsbZtM6sz6wcffFBS18AnQcuWLdOsWbM0YMAAvfjii7r//vu1b98+NTQ0aPLkyfqHf/iHMpQWCIh5LrUPf5dkpU89dZCNXs4yCzm+3rKJEvR4j2zLTjMPUlkdrDPdWTU0NOjll1/updJk1lsj5/i9HjP0tq5EhYw+1vUsbM/z1KWW6RgnvW+M5ixdL+/Z1mDvfZtGXMq213m+61Yi0fPUgbI7hpHP71Zl7smdbxmLvf+9en6DT3VkmjX8WevuE/LgjM/39DYPjBNQaLmuf/T17uu/KqV92cgoYUzXiGxZnNdK/X4rhNXV4JXsgMsgkFma0aYOKAdQm2ZZzm8Bx9coYpS2Yp2vbNZTxGujr322CNalEtFTWSp+WyQqR+S5j7lOyulAv0aLNWqWjKw8v+kYx+nub5FhvhI8dVHs686JGTu9UhGsS+kAyiBQAK6Tvq3Szm+llTcgn1HWKgXBGlkp1XPcB6pSPg+OaBynHtk8M57umfXgPNn8olgpMufIcjqOrlue3S8PVhqCNSpKX2uHOhBwzuxwwJyHPpRNB1ndGxylYVy3pD1qS8XrAZptb9e+pmJ7wAbbbUt02go5NhV7XHOVQ0/xrFd5oBw7C5BZo/IcgIG64nHO7HCAnIe+WItAZg2r2PTMcalVQlZiQxnLVQYb9r0v6pXj2l2LYLp/WawvILMG0Gf1xQwLWepjtQgE6z6E3q59A+exiCrsOed8cL0cGAjWAPq2PpZh4cBEmzVQwWhXBQ4MZNawBu2LABCNYA17HADtiwCQD4I17EL7IgCkIFgDAGA5gjUAAJYjWAMAYDmCNQAAliNYAwBgOYI1AACWI1gDAGA5gjUAAJYjWAMAYDmCNQAAliNYAwBgOYI1AACWI1gDAGA5gjUAAJYjWAMAYDmCNQAAliNYAwBgOYI1AACWI1gDAGA5gjUAAJYjWAMAYDmCNQAAliNYAwBgOYI1AACWI1gDAGC5PhOsFy1apCOPPFIHHXSQxo0bp9dee63cRQIAoCj6RLD+2c9+pvnz5+v222/Xxo0bNXbsWE2cOFEffvhhuYsGAEDB+kSwvvfeezVnzhxdeeWV+tznPqfFixdr4MCBWrp0abmLBgBAwarLXYBCtbe3a8OGDVqwYIE/raqqSuedd56ampoil2lra1NbW5v/d0tLiySptbW1oLK0traq46/7JBP9vnFdqSqhhBIFbacY6+lrZelr+0NZ7C9LX9ufPluWfkatra0aMGBAQeXxDBo0SIlEYfuWj4oP1v/93/8tx3E0YsSIpOkjRozQ5s2bI5dpbGzUnXfemTK9oaGhJGUEAJTP4X9fvHW1tLSotra2eCvMUsUH63wsWLBA8+fP9/92XVe7d+/W0KFDC7pjeuqpp3TllVcWo4gAgCJ67bXXdMwxxxS8nkGDBhWhNLmr+GA9bNgw9evXTzt37kyavnPnTtXX10cuU1NTo5qamqRpgwcPLrgsAwcOLHgdAIDiO/TQQ8uSERdLxXcwGzBggE455RStWLHCn+a6rlasWKHx48eXsWQAABRHxWfWkjR//nzNnDlTp556qk4//XTdf//92rdvH1XSAIA+oU8E629+85v66KOP9J3vfEfNzc06+eST9fzzz6d0Oiu1z372s726PQBAZiNHjtSwYcPKXYyCJIwxMQ8aAQAAG1R8mzUAAH0dwRoAAMsRrAEAsBzBGgAAyxGsAQCwXMU+urV69Wqdc845chyn3EUBACAnt99+u+64446s56/YR7eOOuoobdu2rdzFAAAgZ5/5zGf09ttvZz1/xQbrcvxEGQAAxZBIJLR///6sf7qzItusm5uby10EAADyZozRW2+9lfX8FRmsX3rppXIXAQCAguSSeFZksAYA4EBSkcH6nHPOKXcRAAAoSL9+/bKetyKDdX19fbmLAABAQUaPHp31vBUZrCXphBNOKHcRAADIy2GHHabq6uyHOqnYYP3DH/6w3EUAACAvf/nLX9TY2Jj1/BX7nHUhrrrqKi1btqzcxQAA9JJp06bpscceK3cx8laxmXUhWlpaUhr2Mw2ykqkjQC4dBQAAvaO6ulpDhgzR3XffXe6iFOSAzKwBAKgkB2RmDQBAJSFYAwBgOYI1AACWI1gDAGA5gjUAAJYjWAMAYDmCNZCFWbNmKZFIKJFIqH///hoxYoTOP/98LV26VK7rpsy/Zs0aXXTRRTrssMN00EEH6cQTT9S9994rx3H8ec444wxdd911ScstXrxYiURCy5cvT9n+l7/8ZUnSqlWrlEgkdPzxxyetT5IGDx6ctOyRRx6p+++/P3a/3nvvPV111VUaNWqUBgwYoNGjR+vb3/62du3aFTn/tddeq379+ukXv/hFynt33HGHf4yqq6s1bNgwnXXWWbr//vvV1tYWWwZJWr58ub9sv379dNhhh2ncuHG666671NLSErlMY2Oj+vXrp3vuuSdpf731RL1mzZolSbHvP/nkk2nLCZQLwRrI0oUXXqgdO3bo3Xff1a9+9StNmDBB3/72t3XxxRers7PTn+/pp5/WV77yFX3605/WypUrtXnzZn3729/Wd7/7XU2ZMkXe0AYTJkzQqlWrkraxcuVKNTQ0pExftWpVyq/N/fGPf9Sjjz6a9/788Y9/1Kmnnqq3335bTzzxhLZu3arFixdrxYoVGj9+vHbv3p00/yeffKInn3xSf/d3f6elS5dGrvP444/Xjh07tH37dq1cuVJf//rX1djYqDPPPFMff/xx2vLU1tZqx44d+vOf/6w1a9bommuu0aOPPqqTTz5ZH3zwQcr8S5cuTSnL+vXrtWPHDu3YsUP/9//+X0nSli1b/Gk/+MEP/HmXLVvmT/del156abaHD+hdBkBGM2fONP/zf/7PlOkrVqwwkszDDz9sjDFm7969ZujQoeayyy5LmffZZ581ksyTTz5pjDHm17/+tZFkduzY4c8zYsQIs2jRIjN69Gh/2h//+EcjyaxcudIYY8zKlSuNJHPLLbeYhoYGs3//fn/euro6s2zZMv/v0aNHm/vuuy9yny688ELz6U9/2nzyySdJ03fs2GEGDhxorrvuuqTpy5cvN2eccYbZs2ePGThwoNm+fXvS+7fffrsZO3Zsynb+8Ic/mAEDBpi///u/jyyHMcYsW7bM1NXVpUzfuXOnGTZsmJk2bVrS9FWrVplPfepTpr293YwaNcq8+uqrKct6x+kvf/lLynuSzNNPPx1bHsA2ZNZAAc455xyNHTtWTz31lCTp//2//6ddu3bpb//2b1PmveSSS/TZz35WTzzxhCTpi1/8ovr376+VK1dKkn7/+9/rr3/9q2bPnq1du3Zp27Ztkrqy7YMOOkjjx49PWt9NN92kzs7OvH7UZvfu3fr1r3+tG264QQcffHDSe/X19Zo2bZp+9rOf+bUAkrRkyRJNnz5ddXV1mjRpUkpVfZxjjz1WkyZN8o9RLoYPH65p06bp2WefTaryX7JkiaZOnar+/ftr6tSpWrJkSc7rBioJwRoo0LHHHqt3331XkvRf//VfkqTjjjsudl5vnkMOOUSnn366X+W9atUqfelLX1JNTY3OPPPMpOnjx49XTU1N0roGDhyo22+/XY2NjbHtunHefvttGWNiy3ncccfpL3/5iz766CN//rVr1+qb3/ymJGn69OlatmxZUjBPJ3iMcnXsscfq448/9tvRW1tb9X/+z//R9OnT/bL8/Oc/1969e3Na79SpU3XooYcmvbZv355XGYFSI1gDBTLGpPwQTLZB7Oyzz04KymeffbYk6Stf+UrS9AkTJkQuP3v2bA0dOlTf+9738i57NpYuXaqJEydq2LBhkqSLLrpILS0teumll7LeTqYfy8lURm/5J554QkcffbTGjh0rSTr55JM1evRo/exnP8tpvffdd582bdqU9Bo1alReZQRKjWANFOgPf/iDxowZI0n67Gc/60+Lm9ebR+rqZPZf//Vfev/997Vq1Sp95StfkdQTrN955x299957KZ3LPNXV1fqnf/on/eAHP4jshBXnM5/5jBKJRNpyHnbYYTr88MPlOI4eeeQR/cd//Ieqq6tVXV2tgQMHavfu3bEdzaLW5x2jXP3hD39QbW2thg4dKqmrCvytt97yy1JdXa3f//73WZfFU19fr8985jNJr+rq6rzKCJQawRoowEsvvaTf/e53mjx5siTpggsu0JAhQ/T9738/Zd5nn31Wb7/9tqZOnepPO/PMMzVgwAA98MAD2r9/v0455RRJ0mmnnaaPPvpIS5cu9avL43z961/X8ccfrzvvvDPrcg8dOlTnn3++HnjgAf31r39Neq+5uVk//elP9c1vflOJREL/+Z//qY8//livv/56Uhb6xBNP6KmnntKePXvSbmvz5s16/vnn/WOUiw8//FCPP/64Lr30UlVVVel3v/udfvvb32rVqlVJZVm1apWampq0efPmnLcBVAJuI4EstbW1qbm5WY7jaOfOnXr++efV2Nioiy++WFdccYWkrnboH//4x5oyZYquueYazZs3T7W1tVqxYoVuueUWfe1rX9M3vvENf50HH3ywzjjjDP3whz/UF7/4Rf930QcMGJA0vX///mnLtnDhQk2cODHyvffff1+bNm1KmjZ69Gj96Ec/0plnnqmJEyfqu9/9rsaMGaO33npLt9xyiz71qU/pn/7pnyR1ZbJf/epX/Wpnz+c+9zndfPPN+ulPf6q5c+dKkjo7O9Xc3CzXdbVr1y6tWrVK3/3ud3XyySfrlltuSbsPxhg1NzfLGKM9e/aoqalJ//zP/6y6ujotXLjQL8vpp5+us846K2X50047TUuWLEl67jqdPXv2qLm5OWnaoEGDdMghh2S1PNCrytUNHagkM2fONJKMJFNdXW0OP/xwc95555mlS5cax3FS5l+9erWZOHGiqa2tNQMGDDDHH3+8+Zd/+RfT2dmZMu/tt99uJJmFCxcmTb/jjjuMJNPY2Jg0Pe6RpAsuuMBISnl0yyt38PVv//Zvxhhj3n33XTNz5kwzYsQI079/f9PQ0GBuvPFG89///d/GGGOam5tNdXW1+fnPfx55XK6//nrz+c9/Pmk/JJl+/fqZIUOGmC996UvmvvvuS3q8LMqyZcv8ZROJhKmrqzOnn366ueuuu0xLS4sxxpi2tjYzdOhQc/fdd0eu43vf+54ZPny4aW9vT3ucjDGRxyTqWAO2SBiTZQ8TAABQFrRZAwBgOYI1AACWI1gDAGA5gjUAAJYjWAMAYDmCNQAAliNYAwBgOYI1AACWI1gDAGA5gjUAAJYjWAMAYLn/D/eSRh4Ivnw2AAAAAElFTkSuQmCC","text/plain":["
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zhjw+eef6+jRo0pLS4vI8QEAsCikr27l5eWprq5On376qY4dO6Zjx45p586d8nq9+uUvf9nq4xw/flyVlZWqrKyUJO3bt0+VlZU6cOCAjh8/rgcffFDbtm3T/v37VVpaqhtuuEFDhgxRdnZ2KGMDANAhhfTMuqSkRBs3btTw4cMD20aMGKGioqI2fcBs+/btmjhxYuD8qfeaZ8+erVWrVmnHjh168cUXVVtbq759+2rq1Kl69NFHeZkbAHBBCSnWTU1NZ6xhLUmxsbFqampq9XEmTJggv9/f4uUbNmwIZTwAAM4rIb0MPmnSJM2fP1+HDh0KbPviiy+0YMGCFj/4BQAAQhNSrFeuXCmv16uBAwdq8ODBGjx4sDIyMuT1evXHP/4x3DMCAHBBC+ll8PT0dH388cfauHGjdu/eLUkaPny4pkyZEtbhAABAG59Zb9q0SSNGjJDX65XL5dK1116rvLw85eXlaezYsRo5cqTef//9SM0KAMAFqU2xfuaZZzR37lwlJiaecZnb7dYvfvELrVixImzDAQCANsb63//+t6ZNm9bi5VOnTlVFRUW7hwIAAN9pU6xramqa/crWKZ07d9aXX37Z7qEAAMB32hTrfv36aefOnS1evmPHDn4KFACAMGtTrK+77jotWrRIDQ0NZ1z29ddf65FHHtH1118ftuEAAEAbv7q1cOFCvfrqq7rkkks0b948DR06VJK0e/duFRUVqbGxUb/97W8jMigAABeqNsU6JSVFH3zwge6//34VFBQEfirU5XIpOztbRUVFSklJicigAABcqNr8oygXXXSR3n77bf3vf//Tnj175Pf7dfHFF6tnz56RmA8AgAteSL9gJkk9e/bU2LFjwzkLAABoRki/DQ4AAKKHWAMAYByxBgDAOGINAIBxxBoAAOOINQAAxhFrAACMI9YAABhHrAEAMI5YAwBgHLEGAMA4Yg0AgHHEGgAA44g1AADGEWsAAIwj1gAAGEesAQAwjlgDAGAcsQYAwDhiDQCAccQaAADjiDUAAMYRawAAjCPWAAAYR6wBADCOWAMAYByxBgDAOGINAIBxxBoAAOOINQAAxhFrAACMI9YAABhHrAEAMI5YAwBgHLEGAMA4Yg0AgHHEuo1mrS43f8yOMCMAoPWINQAAxhFrAACMI9YAABhHrAEAMI5YAwBgHLEGAMA4Yg0AgHHEGgAA44g1AADGORrr9957Tz/96U/Vt29fuVwuvfbaa0GX+/1+LV68WGlpaerWrZumTJmizz77zJlhAQBwiKOxrq+v16hRo1RUVNTs5U888YSeffZZPf/88/rwww/1gx/8QNnZ2WpoaIjypAAAOKezkzeek5OjnJycZi/z+/165plntHDhQt1www2SpL/85S9KSUnRa6+9plmzZkVzVAAAHGP2Pet9+/apurpaU6ZMCWxzu90aP368ystbXlTC5/PJ6/UGnQAA6MjMxrq6ulqSlJKSErQ9JSUlcFlzCgsL5Xa7A6f09PSIznm65lamYrWqtmvrYxapx5i/OwBWmI11qAoKCuTxeAKngwcPOj0SAADtYjbWqampkqSampqg7TU1NYHLmhMXF6fExMSgEwAAHZnZWGdkZCg1NVWlpaWBbV6vVx9++KGysrIcnAwAgOhy9NPgx48f1549ewLn9+3bp8rKSiUlJWnAgAF64IEH9Nhjj+niiy9WRkaGFi1apL59+2r69OnODQ0AQJQ5Guvt27dr4sSJgfP5+fmSpNmzZ2vt2rV66KGHVF9fr3vvvVe1tbW66qqrVFJSoq5duzo1MgAAUedorCdMmCC/39/i5S6XS8uWLdOyZcuiOBUAALaYfc8aAAB8i1gDAGAcsQYAwDhiDQCAccQaAADjiDUAAMYRawAAjCPWYRaulZpaOs6FthKU0/e3Lbf//X0jPbvTjw2A6CHWAAAYR6wBADCOWAMAYByxBgDAOGINAIBxxBoAAOOINQAAxhFrAACMI9YAABhHrAEAMI5YAwBgHLEGAMA4Yg0AgHHEGu12+upPZ1sJ6kJZJep8uZ/ny/0AzgfEGgAA44g1AADGEWsAAIwj1gAAGEesAQAwjlgDAGAcsQYAwDhiDQCAccQaAADjiDUAAMYRawAAjCPWAAAYR6wBADCOWIeguVWmnF6hKFK37/T9ioSOcJ86wowAoodYAwBgHLEGAMA4Yg0AgHHEGgAA44g1AADGEWsAAIwj1gAAGEesAQAwjlgDAGAcsQYAwDhiDQCAccQaAADjiDUAAMYR6yiwuIJSuFYLa+76s1aXh3TccD5OkXjMQ71f56NIrjbHYwyciVgDAGAcsQYAwDhiDQCAccQaAADjiDUAAMYRawAAjCPWAAAYR6wBADCOWAMAYJzpWC9ZskQulyvoNGzYMKfHAgAgqjo7PcC5jBw5Uhs3bgyc79zZ/MgAAISV+fJ17txZqampTo8BAIBjTL8MLkmfffaZ+vbtq0GDBunnP/+5Dhw4cNb9fT6fvF5v0AkAgI7MdKzHjx+vtWvXqqSkRKtWrdK+fft09dVXq66ursXrFBYWyu12B07p6elRnLj9mlvZqS2rEJ1tNaRzrZQUyu2057qnXyeUVZwiuTpTNFd+iuRqXt9/XFnRqnmRXiWOxx3tZTrWOTk5uvnmm5WZmans7Gy9/fbbqq2t1T/+8Y8Wr1NQUCCPxxM4HTx4MIoTAwAQfubfsz5djx49dMkll2jPnj0t7hMXF6e4uLgoTgUAQGSZfmb9fcePH9fevXuVlpbm9CgAAESN6Vj/6le/UllZmfbv368PPvhAN954o2JiYnTrrbc6PRoAAFFj+mXwzz//XLfeequOHj2qPn366KqrrtK2bdvUp08fp0cDACBqTMd63bp1To8AAIDjTL8MDgAAiDUAAOYRawAAjCPWAAAYR6wBADCOWAMAYByxBgDAOGIdBq1dgSpcK++09vinr7TUmtW42nt74RTKY3qu1avCtbpVOFdoCvdqT639+23P49Da6zb3d9jcSmvtuc1orVZmQVsfg/asjAd7iDUAAMYRawAAjCPWAAAYR6wBADCOWAMAYByxBgDAOGINAIBxxBoAAOOINQAAxhFrAACMI9YAABhHrAEAMI5YAwBgHLFug3CsYhPqaj5nu+22rsLUmmNEa4We1txOc3O2d8Wm9t6/c61k1tx+zZ1vzVzhWpmrpXlDXQ2rtfe/tcdsy30PZRW2cPw78/192vq4hbp6mKV/H+EMYg0AgHHEGgAA44g1AADGEWsAAIwj1gAAGEesAQAwjlgDAGAcsQYAwDhiDQCAccQaAADjiDUAAMYRawAAjCPWAAAYR6wdEMpKQE4Ix8pgLa3m1Z77eq7VmVq7etPps3z/1NZZwrUyVnNznev4oczd3AzN3V5rj93a1a6aO3+u431/W0uXne3xbOkxbemys83d2n/+WnO/z7VPKCuktWfFtdZoaYZw/DPYmu0XKmINAIBxxBoAAOOINQAAxhFrAACMI9YAABhHrAEAMI5YAwBgHLEGAMA4Yg0AgHHEGgAA44g1AADGEWsAAIwj1gAAGOfy+/1+p4eIJK/XK7fbLY/Ho8TExHYdi1VgAKBjWHdvltMjhBXPrAEAMI5YAwBgHLEGAMA4Yg0AgHHEGgAA44g1AADGEWsAAIwj1gAAGEesAQAwrkPEuqioSAMHDlTXrl01fvx4/fOf/3R6JAAAosZ8rF966SXl5+frkUce0ccff6xRo0YpOztbR44ccXo0AACiwnysV6xYoblz5+rOO+/UiBEj9Pzzz6t79+564YUXnB4NAICo6Oz0AGdz4sQJVVRUqKCgILCtU6dOmjJlisrLm19Uw+fzyefzBc57PB5J3y7o0V4nv65v9zEAAJEXjv/mn5KQkCCXyxW244XCdKz/+9//qrGxUSkpKUHbU1JStHv37mavU1hYqKVLl56xPT09PSIzAgDsefWB8B0rHKs2tpfpWIeioKBA+fn5gfNNTU06duyYevXq1a7/Mxo3bpyqqqrCMSIAIAri4+P1xRdftPs4CQkJYZimfUzHunfv3oqJiVFNTU3Q9pqaGqWmpjZ7nbi4OMXFxQVt69GjR7tniYmJafcxAADR43K5HH9GHC6mP2DWpUsXjRkzRqWlpYFtTU1NKi0tVVbW+bWwOAAALTH9zFqS8vPzNXv2bF1++eUaN26cnnnmGdXX1+vOO+90ejQAAKLCfKxvueUWffnll1q8eLGqq6s1evRolZSUnPGhs0i7+uqr9Z///CeqtwkACN2oUaOcHiFsXH6/3+/0EAAAoGWm37MGAADEGgAA84g1AADGEWsAAIwz82nwG2+8Ua+99prTYwAAEBW5ublauXJlq/Y188x61apVevzxxzV69Gh169bN6XEAAIiomTNntnpf01/dWrx4sR599FGnxwAAIGy6du2qEydOqL6+Xl27dm3VdczGeuvWrbr66qudHgMAgLByuVwaOnSodu3a1errmHkZ/HQNDQ2EGgBwXvL7/bruuuvadB2Tse7du7fTIwAAEDGXXXZZm/Y3F+tRo0apvr7e6TEAAIiY01eTbA1TsZ4/f7527Njh9BgAAERMTEyMvv766zZdx0ysS0pK9Oyzzzo9BgAAEdXY2KicnJw2XcdMrOfPn+/0CAAARIXH42nT/ma/uuWEbdu2KSsrS927d9dXX33l9DgAgA4sJiZG33zzjSRp3bp1mjt3rurq6kI6lpmfG3XSN998o6qqKi1cuFCdO/OQAADab9y4cfL5fNq7d69WrlypyZMnh3wsnllLqqys1Pjx4/XNN9+oqanJ6XEAAB1cUlKS/vWvf+njjz/W7bffriuvvFJ/+tOfNGDAgJCOR6wBADDOzAfMAABA84g1AADGEWsAAIwj1gAAGEesAQAwjlgDAGAcsQaiaM6cOZo+fXrgzy6XSy6XS7GxsUpJSdG1116rF1544Yzv+w8cODCw7+mn5cuXS5L2798vl8ul5OTkM34hafTo0VqyZElgn7Od1q5dqy1btsjlcqm2tlaSAue/f1q4cGHQ5efa3+Vyqbq6WpK0ZMmSwLaYmBilp6fr3nvv1bFjxyL0yAMdGz/XBTho2rRpWrNmjRobG1VTU6OSkhLNnz9fr7zyit54442gX9RbtmyZ5s6dG3T9hISEoPN1dXV66qmntHTp0jNuKz09XYcPHw6cf+qpp1RSUqKNGzcGtrndbn344YfNzlpVVaXExMTA+fj4+LPet+/vL0nJycmBP48cOVIbN25UY2Ojdu3apbvuuksej0cvvfTSWY8LXIiINeCguLg4paamSpL69eunH/3oR7riiis0efJkrV27Vvfcc09g34SEhMC+LcnLy9OKFSuUm5sbFEbp298pPv368fHx6ty58zmPeUpycrJ69OjRynt27v1Pv+1+/frp5ptv1po1a1p9fOBCwsvggDGTJk3SqFGj9Oqrr7b5urfeequGDBmiZcuWRWCyyNm/f782bNigLl26OD0KYBKxBgwaNmyY9u/fH7Tt4YcfVnx8fNDp/fffD9rn1PvYq1ev1t69e8M6U//+/YNu++jRo23af+TIkUGXf/LJJ4qPj1e3bt2UkZGhTz/9VA8//HBYZwbOF7wMDhjk9/vlcrmCtj344IOaM2dO0LZ+/fqdcd3s7GxdddVVWrRokYqLi8M20/vvvx/0HnnPnj3btH9sbGzQ5UOHDtUbb7yhhoYG/fWvf1VlZaXy8vLCNi9wPiHWgEG7du1SRkZG0LbevXtryJAhrbr+8uXLlZWVpQcffDBsM2VkZLTpPetz7d+lS5fA/Vm+fLl+8pOfaOnSpXr00UfbOSlw/uFlcMCYTZs26ZNPPtHMmTNDPsa4ceM0Y8YM/frXvw7jZJG1cOFCPfXUUzp06JDTowDm8MwacJDP51N1dXXQV7cKCwt1/fXX64477gjat66uLvA95VO6d+9+xtejTnn88cc1cuTIoK9/RdORI0fU0NAQtK1Xr15nvBx+SlZWljIzM/W73/1OK1eujMaIQIfBM2vAQSUlJUpLS9PAgQM1bdo0bd68Wc8++6xef/11xcTEBO27ePFipaWlBZ0eeuihFo99ySWX6K677jojmNEydOjQM+atqKg463UWLFigP//5zzp48GCUpgQ6Bpff7/c7PQQAAGgZz6wBADCOWAMAYByxBgDAOGINAIBxxBoAAOOINQAAxhFrAACMI9YAABhHrAEAMI5YAwBgHLEGAMC4/wNu2bfICwyBEQAAAABJRU5ErkJggg==","text/plain":["
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","text/plain":["
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","text/plain":["
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","text/plain":["
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","text/plain":["
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","text/plain":["
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","text/plain":["
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Q9sT2F999ZXWrFmjJk2a1PiYnJwc+fj4KCoqSpKUnJysDRs2qKioyKqTmZmp1q1bKzw83KqTlZXlNZ/MzEwlJyfX4doAAHBh6vXw+IkTJ7Rnzx5rfN++fcrJyVFERIRiY2P1+9//Xp9//rmWL1+ukpIS6xxzRESEnE6nsrOz9dlnnyklJUWNGzdWdna2xo8frzvvvNMK5DvuuENPP/20MjIyNGnSJO3YsUNz587VCy+8YC137NixuvbaazV79mwNHjxYS5cu1ebNm71uCwMAoL7Va2hv3rxZKSkp1rjn/HR6erqmTZum9957T5LUuXNnr8etW7dOffv2lcvl0tKlSzVt2jQVFhYqMTFR48eP9zrPHRoaqg8++ECjR49Wt27dFBkZqalTp1q3e0lSr1699MYbb2jKlCl6/PHHdcUVV2jZsmXq0KHDRVx7AADOT72Gdt++fWWMqbK8ujJJ6tq1qz799NMal9OxY0d99NFH1da55ZZbdMstt9Q4LwAA6kuDPqcNAAB+RmgDAGAThDYAADZBaAMAYBOENgAANkFoAwBgE4Q2AAA2QWgDAGAThDYAADZBaAMAYBOENgAANkFoAwBgE4Q2AAA2QWgDAGAThDYAADZBaAMAYBOENgAANkFoAwBgE4Q2AAA2QWgDAGAThDYAADZBaAMAYBOENgAANkFoAwBgE4Q2AAA2QWgDAGAThDYAADZBaAMAYBOENgAANkFoAwBgE4Q2AAA2QWgDAGAThDYAADZBaAMAYBOENgAANkFoAwBgE4Q2AAA2QWgDAGAThDYAADZBaAMAYBOENgAANkFoAwBgE4Q2AAA2QWgDAGAThDYAADZBaAMAYBOENgAANlGvob1hwwbdeOONiouLk8Ph0LJly7zKjTGaOnWqYmNjFRgYqP79++urr77yqnPkyBENHz5cISEhCgsLU0ZGhk6cOOFVZ/v27erdu7cCAgIUHx+vmTNnVmjL22+/rTZt2iggIEBJSUlauXJlna8vAAAXol5Du6CgQJ06ddK8efMqLZ85c6b+9Kc/af78+frss88UHBystLQ0nT592qozfPhw7dy5U5mZmVq+fLk2bNig++67zyp3u91KTU1VQkKCtmzZolmzZmnatGl69dVXrTobN27UsGHDlJGRoa1bt2rIkCEaMmSIduzYcfFWHgCA8+RXnwsfOHCgBg4cWGmZMUYvvviipkyZoptvvlmS9Prrrys6OlrLli3T7bffri+//FKrVq3Spk2bdNVVV0mS/vznP2vQoEF6/vnnFRcXpyVLlujMmTNauHChnE6n2rdvr5ycHM2ZM8cK97lz52rAgAF69NFHJUnPPPOMMjMz9dJLL2n+/PmVtq+wsFCFhYXWuNvtrrPtAgBAZRrsOe19+/YpPz9f/fv3t6aFhoaqR48eys7OliRlZ2crLCzMCmxJ6t+/v3x8fPTZZ59Zdfr06SOn02nVSUtLU25uro4ePWrVKbscTx3PciozY8YMhYaGWkN8fPyFrzQAANVosKGdn58vSYqOjvaaHh0dbZXl5+crKirKq9zPz08RERFedSqbR9llVFXHU16ZyZMn6/jx49bw7bffnu8qAgBwXmoV2i1atNCPP/5YYfqxY8fUokWLC26UHbhcLoWEhHgNAABcTLUK7f3796ukpKTC9MLCQn3//fcX3ChJiomJkSQdOnTIa/qhQ4esspiYGB0+fNirvLi4WEeOHPGqU9k8yi6jqjqecgAAGoLzuhDtvffes/5fvXq1QkNDrfGSkhJlZWWpefPmddKwxMRExcTEKCsrS507d5Z09mKvzz77TA888IAkKTk5WceOHdOWLVvUrVs3SdLatWtVWlqqHj16WHWeeOIJFRUVyd/fX5KUmZmp1q1bKzw83KqTlZWlcePGWcvPzMxUcnJynawLAAB14bxCe8iQIZIkh8Oh9PR0rzJ/f381b95cs2fPPuf5nThxQnv27LHG9+3bp5ycHEVEROjyyy/XuHHj9N///d+64oorlJiYqCeffFJxcXFWO9q2basBAwZo5MiRmj9/voqKijRmzBjdfvvtiouLkyTdcccdevrpp5WRkaFJkyZpx44dmjt3rl544QVruWPHjtW1116r2bNna/DgwVq6dKk2b97sdVsYAAD17bxCu7S0VNLZXvCmTZsUGRl5QQvfvHmzUlJSrPEJEyZIktLT07V48WJNnDhRBQUFuu+++3Ts2DH95je/0apVqxQQEGA9ZsmSJRozZoz69esnHx8fDR06VH/605+s8tDQUH3wwQcaPXq0unXrpsjISE2dOtXrXu5evXrpjTfe0JQpU/T444/riiuu0LJly9ShQ4cLWj8AAOqSwxhj6rsRvwRut1uhoaE6fvz4BV2UFhIWoUEzV1RatnLiYLmPHanVYyXprdF9deu89VWW1zR/AED9qvWXq2RlZSkrK0uHDx+2euAeCxcuvOCGAQAAb7UK7aefflrTp0/XVVddpdjYWDkcjrpuFwAAKKdWoT1//nwtXrxYd911V123BwAAVKFW92mfOXNGvXr1quu2AACAatQqtO+991698cYbdd0WAABQjVodHj99+rReffVVrVmzRh07drS+tMRjzpw5ddI4AADws1qF9vbt261vKSv/m9NclAYAwMVRq9Bet25dXbcDAADUoMH+NCcAAPBWq552SkpKtYfB165dW+sGAQCAytUqtD3nsz2KioqUk5OjHTt2VPghEQAAUDdqFdplfyGrrGnTpunEiRMX1CAAAFC5Oj2nfeedd/K94wAAXCR1GtrZ2dleP5sJAADqTq0Oj//ud7/zGjfGKC8vT5s3b9aTTz5ZJw0DAADeahXaoaGhXuM+Pj5q3bq1pk+frtTU1DppGAAA8Far0F60aFFdtwPnoODUKYWERVRdfvLkJWwNAOBSq1Voe2zZskVffvmlJKl9+/bq0qVLnTQKlTOlpRo0c0WV5W+N7nvpGgMAuORqFdqHDx/W7bffrvXr1yssLEySdOzYMaWkpGjp0qVq2rRpXbYRAACollePP/TQQ/rpp5+0c+dOHTlyREeOHNGOHTvkdrv18MMP13UbAQCAatnTXrVqldasWaO2bdta09q1a6d58+ZxIRoAABdJrXrapaWlFX5DW5L8/f1VWlp6wY0CAAAV1Sq0r7vuOo0dO1YHDx60pn3//fcaP368+vXrV2eNAwAAP6tVaL/00ktyu91q3ry5WrZsqZYtWyoxMVFut1t//vOf67qNAABAtTynHR8fr88//1xr1qzR7t27JUlt27ZV//7967RxAADgZ+fV0167dq3atWsnt9sth8Oh66+/Xg899JAeeughXX311Wrfvr0++uiji9VWAAB+1c4rtF988UWNHDlSISEhFcpCQ0N1//33a86cOXXWOAAA8LPzCu1t27ZpwIABVZanpqZqy5YtF9woAABQ0XmF9qFDhyq91cvDz89PP/zwwwU3CgAAVHReoX3ZZZdpx44dVZZv375dsbGxF9woAABQ0XmF9qBBg/Tkk0/q9OnTFcpOnTqlp556SjfccEOdNQ4AAPzsvG75mjJlit555x1deeWVGjNmjFq3bi1J2r17t+bNm6eSkhI98cQTF6WhAAD82p1XaEdHR2vjxo164IEHNHnyZBljJEkOh0NpaWmaN2+eoqOjL0pDAQD4tTvvL1dJSEjQypUrdfToUe3Zs0fGGF1xxRUKDw+/GO0DAAD/UatvRJOk8PBwXX311XXZFgAAUI1affc4AAC49AhtAABsgtAGAMAmCG0AAGyC0AYAwCZqffU4fnkKTp1SSFhEleWxcXHK3VX119gCAC4uQhsWU1qqQTNXVFm+cuLgS9gaAEB5HB4HAMAmCG0AAGyC0AYAwCYIbQAAbILQBgDAJhp8aDdv3lwOh6PCMHr0aElS3759K5SNGjXKax4HDhzQ4MGDFRQUpKioKD366KMqLi72qrN+/Xp17dpVLpdLrVq10uLFiy/VKgIAcE4a/C1fmzZtUklJiTW+Y8cOXX/99brlllusaSNHjtT06dOt8aCgIOv/kpISDR48WDExMdq4caPy8vJ09913y9/fX88++6wkad++fRo8eLBGjRqlJUuWKCsrS/fee69iY2OVlpZ2CdYSAICaNfjQbtq0qdf4c889p5YtW+raa6+1pgUFBSkmJqbSx3/wwQfatWuX1qxZo+joaHXu3FnPPPOMJk2apGnTpsnpdGr+/PlKTEzU7NmzJUlt27bVxx9/rBdeeKHK0C4sLFRhYaE17na7L3RVAQCoVoM/PF7WmTNn9Pe//1333HOPHA6HNX3JkiWKjIxUhw4dNHnyZJ08edIqy87OVlJSkqKjo61paWlpcrvd2rlzp1Wnf//+XstKS0tTdnZ2lW2ZMWOGQkNDrSE+Pr6uVhMAgEo1+J52WcuWLdOxY8c0YsQIa9odd9yhhIQExcXFafv27Zo0aZJyc3P1zjvvSJLy8/O9AluSNZ6fn19tHbfbrVOnTikwMLBCWyZPnqwJEyZY4263m+AGAFxUtgrtBQsWaODAgYqLi7Om3Xfffdb/SUlJio2NVb9+/fT111+rZcuWF60tLpdLLpfros0fAIDybHN4/JtvvtGaNWt07733VluvR48ekqQ9e/ZIkmJiYnTo0CGvOp5xz3nwquqEhIRU2ssGAKA+2Ca0Fy1apKioKA0eXP2PVuTk5EiSYmNjJUnJycn64osvdPjwYatOZmamQkJC1K5dO6tOVlaW13wyMzOVnJxch2sAAMCFsUVol5aWatGiRUpPT5ef389H9L/++ms988wz2rJli/bv36/33ntPd999t/r06aOOHTtKklJTU9WuXTvddddd2rZtm1avXq0pU6Zo9OjR1uHtUaNGae/evZo4caJ2796tl19+WW+99ZbGjx9fL+sLAEBlbBHaa9as0YEDB3TPPfd4TXc6nVqzZo1SU1PVpk0bPfLIIxo6dKjef/99q46vr6+WL18uX19fJScn684779Tdd9/tdV93YmKiVqxYoczMTHXq1EmzZ8/Wa6+9xj3aAIAGxRYXoqWmpsoYU2F6fHy8Pvzwwxofn5CQoJUrV1Zbp2/fvtq6dWut2wgAwMVmi542AAAgtAEAsA1CGwAAmyC0AQCwCUIbAACbILQBALAJQhsAAJsgtAEAsAlCGwAAmyC0AQCwCUIbAACbILQBALAJQhsAAJsgtAEAsAlCGwAAmyC0AQCwCUIbAACbILQBALAJQhsAAJsgtAEAsAlCGwAAmyC0AQCwCUIbAACbILQBALAJQhsAAJsgtAEAsAlCGwAAmyC0AQCwCUIbAACbILQBALAJQhsAAJsgtAEAsAlCGwAAmyC0AQCwCUIbAACbILQBALAJQhsAAJsgtAEAsAlCGwAAmyC0AQCwCUIbAACbILQBALAJQhsAAJvwq+8GAJLUul0H5R08WGV5bFyccnftuIQtAoCGh9BGg5B38KAGzVxRZfnKiYMvYWsAoGHi8DgAADZBaAMAYBMNOrSnTZsmh8PhNbRp08YqP336tEaPHq0mTZqoUaNGGjp0qA4dOuQ1jwMHDmjw4MEKCgpSVFSUHn30URUXF3vVWb9+vbp27SqXy6VWrVpp8eLFl2L1AAA4Lw06tCWpffv2ysvLs4aPP/7YKhs/frzef/99vf322/rwww918OBB/e53v7PKS0pKNHjwYJ05c0YbN27UX//6Vy1evFhTp0616uzbt0+DBw9WSkqKcnJyNG7cON17771avXr1JV1PAABq0uAvRPPz81NMTEyF6cePH9eCBQv0xhtv6LrrrpMkLVq0SG3bttWnn36qnj176oMPPtCuXbu0Zs0aRUdHq3PnznrmmWc0adIkTZs2TU6nU/Pnz1diYqJmz54tSWrbtq0+/vhjvfDCC0pLS6uyXYWFhSosLLTG3W53Ha85AADeGnxP+6uvvlJcXJxatGih4cOH68CBA5KkLVu2qKioSP3797fqtmnTRpdffrmys7MlSdnZ2UpKSlJ0dLRVJy0tTW63Wzt37rTqlJ2Hp45nHlWZMWOGQkNDrSE+Pr5O1hcAgKo06NDu0aOHFi9erFWrVumVV17Rvn371Lt3b/3000/Kz8+X0+lUWFiY12Oio6OVn58vScrPz/cKbE+5p6y6Om63W6dOnaqybZMnT9bx48et4dtvv73Q1QUAoFoN+vD4wIEDrf87duyoHj16KCEhQW+99ZYCAwPrsWWSy+WSy+Wq1zYAAH5dGnRPu7ywsDBdeeWV2rNnj2JiYnTmzBkdO3bMq86hQ4esc+AxMTEVrib3jNdUJyQkpN53DAAAKMtWoX3ixAl9/fXXio2NVbdu3eTv76+srCyrPDc3VwcOHFBycrIkKTk5WV988YUOHz5s1cnMzFRISIjatWtn1Sk7D08dzzwAAGgoGnRo/+EPf9CHH36o/fv3a+PGjfrtb38rX19fDRs2TKGhocrIyNCECRO0bt06bdmyRf/1X/+l5ORk9ezZU5KUmpqqdu3a6a677tK2bdu0evVqTZkyRaNHj7YObY8aNUp79+7VxIkTtXv3br388st66623NH78+PpcdQAAKmjQ57S/++47DRs2TD/++KOaNm2q3/zmN/r000/VtGlTSdILL7wgHx8fDR06VIWFhUpLS9PLL79sPd7X11fLly/XAw88oOTkZAUHBys9PV3Tp0+36iQmJmrFihUaP3685s6dq2bNmum1116r9nYvAADqQ4MO7aVLl1ZbHhAQoHnz5mnevHlV1klISNDKlSurnU/fvn21devWWrURAIBLpUEfHgcAAD8jtAEAsAlCGwAAmyC0AQCwCUIbAACbILQBALAJQhsAAJsgtAEAsAlCGwAAmyC0AQCwCUIbAACbILQBALAJQhsAAJsgtAEAsIkG/dOcsJfW7Too7+DBSsti4+KUu2vHJW4RAPyyENqoM3kHD2rQzBWVlq2cOPgStwYAfnk4PA4AgE0Q2gAA2ASHx2ELBadOKSQsospyzpkD+DUgtHHOagrOgpMnL9qyTWlplefLJc6ZA/h1ILRxzmoKzrdG9710jQGAXyHOaQMAYBOENgAANkFoAwBgE4Q2AAA2QWgDAGAThDYAADZBaAMAYBOENgAANkFoAwBgE4Q2AAA2QWgDAGAThDYAADZBaAMAYBOENgAANkFoAwBgE4Q2AAA2QWgDAGAThDYAADZBaAMAYBOENgAANkFoAwBgE4Q2AAA2QWgDAGAThDYAADZBaAMAYBOENgAANtGgQ3vGjBm6+uqr1bhxY0VFRWnIkCHKzc31qtO3b185HA6vYdSoUV51Dhw4oMGDBysoKEhRUVF69NFHVVxc7FVn/fr16tq1q1wul1q1aqXFixdf7NUDAOC8NOjQ/vDDDzV69Gh9+umnyszMVFFRkVJTU1VQUOBVb+TIkcrLy7OGmTNnWmUlJSUaPHiwzpw5o40bN+qvf/2rFi9erKlTp1p19u3bp8GDByslJUU5OTkaN26c7r33Xq1evfqSrSsAADXxq+8GVGfVqlVe44sXL1ZUVJS2bNmiPn36WNODgoIUExNT6Tw++OAD7dq1S2vWrFF0dLQ6d+6sZ555RpMmTdK0adPkdDo1f/58JSYmavbs2ZKktm3b6uOPP9YLL7ygtLS0i7eCAACchwbd0y7v+PHjkqSIiAiv6UuWLFFkZKQ6dOigyZMn6+TJk1ZZdna2kpKSFB0dbU1LS0uT2+3Wzp07rTr9+/f3mmdaWpqys7OrbEthYaHcbrfXAADAxdSge9pllZaWaty4cbrmmmvUoUMHa/odd9yhhIQExcXFafv27Zo0aZJyc3P1zjvvSJLy8/O9AluSNZ6fn19tHbfbrVOnTikwMLBCe2bMmKGnn366TtcRAIDq2Ca0R48erR07dujjjz/2mn7fffdZ/yclJSk2Nlb9+vXT119/rZYtW1609kyePFkTJkywxt1ut+Lj4y/a8gAAsMXh8TFjxmj58uVat26dmjVrVm3dHj16SJL27NkjSYqJidGhQ4e86njGPefBq6oTEhJSaS9bklwul0JCQrwGAAAupgYd2sYYjRkzRu+++67Wrl2rxMTEGh+Tk5MjSYqNjZUkJScn64svvtDhw4etOpmZmQoJCVG7du2sOllZWV7zyczMVHJych2tCQAAF65Bh/bo0aP197//XW+88YYaN26s/Px85efn69SpU5Kkr7/+Ws8884y2bNmi/fv367333tPdd9+tPn36qGPHjpKk1NRUtWvXTnfddZe2bdum1atXa8qUKRo9erRcLpckadSoUdq7d68mTpyo3bt36+WXX9Zbb72l8ePH19u6AwBQXoM+p/3KK69IOvsFKmUtWrRII0aMkNPp1Jo1a/Tiiy+qoKBA8fHxGjp0qKZMmWLV9fX11fLly/XAAw8oOTlZwcHBSk9P1/Tp0606iYmJWrFihcaPH6+5c+eqWbNmeu2117jdy0YKTp1SSFhEleWxcXHK3bXjErYIAOpegw5tY0y15fHx8frwww9rnE9CQoJWrlxZbZ2+fftq69at59U+NBymtFSDZq6osnzlxMGXsDUAcHE06MPjAADgZ4Q2AAA2QWgDAGAThDYAADZBaAMAYBOENgAANtGgb/kC6kp193FzDzcAuyC08atQ3X3c3MMNwC44PA4AgE0Q2gAA2AShDQCATRDaAADYBKENAIBNENoAANgEoQ0AgE0Q2gAA2AShDQCATRDaAADYBKENAIBNENoAANgEoQ0AgE0Q2gAA2AShDQCATRDaAADYBKENAIBNENoAANgEoQ0AgE0Q2gAA2AShDQCATRDaAADYBKENAIBN+NV3AwC7a92ug/IOHqyyPDYuTrm7dlzCFgH4pSK0gQuUd/CgBs1cUWX5yomDL2FrAPyScXgcAACbILQBALAJQhsAAJsgtAEAsAlCGwAAmyC0AQCwCUIbAACbILQBALAJQhsAAJvgG9GAGtT0NaUFJ09ewtYA+DUjtIEa1PQ1pW+N7nvpGgPgV43D4wAA2AShDQCATRDaAADYBKFdzrx589S8eXMFBASoR48e+ve//13fTQIAQBIXonl58803NWHCBM2fP189evTQiy++qLS0NOXm5ioqKqq+m2drBadOKSQsoupyrsAGgBoR2mXMmTNHI0eO1H/9139JkubPn68VK1Zo4cKFeuyxx+q5dfZmSku5AhsALhCh/R9nzpzRli1bNHnyZGuaj4+P+vfvr+zs7Ar1CwsLVVhYaI0fP35ckuR2uy+oHcYYFZ0qOO+yhl7ekNt24uRJNQ4Nr/KxBSdPXtCya5p/dEyMPt/0WZXlXa/uoUP5+ZWWFRYVyeXvf1HmfS6PB+yocePGcjgc9d2M2jEwxhjz/fffG0lm48aNXtMfffRR07179wr1n3rqKSOJgYGBgcFmw/Hjxy9VtNQ5etq1NHnyZE2YMMEaLy0t1ZEjR9SkSZM62YNzu92Kj4/Xt99+q5CQEFuVN+S22bntDblttJ22Xeq2X4jGjRvX6fwuJUL7PyIjI+Xr66tDhw55TT906JBiYmIq1He5XHK5XF7TwsLC6rxdISEh1b5gG3J5Q25bTeW0jbY3pGXbuW01lV/ovH9tuOXrP5xOp7p166asrCxrWmlpqbKyspScnFyPLQMA4Cx62mVMmDBB6enpuuqqq9S9e3e9+OKLKigosK4mBwCgPhHaZdx222364YcfNHXqVOXn56tz585atWqVoqOjL3lbXC6XnnrqqQqH4O1Q3pDbZue2N+S20Xbadqnb/mvlMMaY+m4EAACoGee0AQCwCUIbAACbILQBALAJQhsAALuo769kqyvp6elGkpkxY4bX9HfffdeUXc1169ZV+dV2oaGhpmnTpkaScTqdplmzZuaGG27wqtO4cWPTpk0bExERYfz8/Iwk07x5c3PbbbeZ0NBQa/7z5s2rMH+Hw2F8fHxMbm6ukWRSUlJq/Lq9Hj16mOuuu+6Sfb1fXFxcvX/F4K91cDqdxtfX96LNPzw83ISFhdXqsREREcbHx6fKcofDccm2U1RUVL0/VxdrqG4b19f2ttPg2S4+Pj6mVatW5t577zW5ubkmISGh0vq33HKLGTt2rPW5FxkZaVq1amVSU1O9cmTr1q2mdevW1vPj4+NjLr/8crNixQqrTtllOBwO43A4TEREhLnpppvMl19+adUru/yQkBDTq1cvk5WVdc5Z94vqaQcEBOiPf/yjjh49WmPd3Nxc3XrrrUpJSdG2bdu0cuVKOZ1OnfzPT0Q+99xzWrVqlVJSUiRJgYGBatSokf7whz8oNzdXR44cUbNmzeR0OjVx4kS9//77OnnypEy5i/EDAgKUk5OjWbNmyeVyqbS0VFdddZVVHhFx9ucq/fz81LhxY/n7+3t9+09ubq5uu+02SVJKSooCAwMlyfqqVB8fH2s5jRo1sh7ncDgUGhqqpKQkr1smPPWrcvDgQet//zI/RFHZV7P6+vpWOg9PG8vy8/v57sLKHnc+X/0aFBR0zvVjY2MVFBRUoQ1lt8m5rFtl283pdCoxMVFOp9NremXrHxgYaC0nODi40rYWFxfL5XLJz8/vvL8Kt0WLFgoNDa22ztGjR3Xs2LFKy2pa3pEjR1RaWlppWfn1r2r9ym7/6pZZ9jVdmcOHD1fb1rL8q/kxldpq166dpLPtL/+6uNCvMC6/jauaX9nPGT8/v2rXs+znQlmtW7euMK38Ni/7nHk+q8oruw2aNWtW5TdDetalque1uvlGR0fLz89PPXr0kCSFh4dr7dq1FZbp5+cnX19flZaWKioqSqGhoXryySclST179pQkRUVF6auvvpIkffTRR1qzZo3+53/+R9LZz323263169dr7969kqS9e/eqe/fuOnjwoGbPnq1PPvlEs2fP1unTpzVy5EgdOXJE0tnnJCIiQv369dPSpUt1++2368iRIwoPD6/wvlu0aJHy8vL0ySefKDIyUjfccIO1vBqdV3e2AUtPTzc33HCDadOmjXn00Uet6VX1tI8ePWrS09PNzTffbIwxZuDAgeayyy4zH3zwgZFk7r77busxkky3bt3M9ddfbxo1amRuvPFGI8mEhYWZa6+91hhjzNixY40k8+STTxrp5552UFCQMcaYESNGmAkTJnjtDaakpJguXboYScbX19dcd911pmfPnub222830tle/eWXX25KS0tNZGSkueyyy4zD4TABAQHWnlpgYKDx9fU1V111lXG5XNb0iIgIc/PNN5v09HSvPcDyPbnAwEDjdDor3QsNDQ21/o+MjDynvftGjRqZ8PBwa9zlcnkts7KehMPhMM2bNzd+fn5WuecoRmX1ExMTzaBBg6w65Yfg4GDr/6ZNm5qQkBAjycTGxhrpbI+zbG8tKSnJdO/e3eu5GTFihFe7K1tW586dzccff1xpG8q3u1mzZqZJkyYV5lW+XsuWLU3Tpk2Nv79/ldu4c+fOXm1NSkoyCQkJVT6P59K+kJCQc3p8UFBQhWmTJk3yGg8PD7eOWJUdrr766vNq0/jx489pXRo1alTh9XSu28EzNG7cuNrysvOsqtdW9rkpv06VvY7Lvr88Q/nn4FzWJSgoqNLXi8PhML6+vqZdu3ZGOvteL1veu3fvCo9p2rSpefjhhytd7pVXXllhWkxMjFddf39/ExQUZBwOh9d7zPP+kmS9Dx588EEjnf2sKrv+ZT8HpLPvd+ns69zzf0BAgHn44YeNJDNt2jQjnf2sGTx4sHG5XKZZs2ZGklmxYoU5evSoSUhIsJ6bv/zlL9bnusvlMnl5eWbfvn1Gktm6dau58847TUBAgBkwYIAxxph+/foZSWb16tVemZOXl2eCgoLMqFGjjDHG+nzZv3+/McaYoqIiExQUZB577DGvx0ky7777rjXu+bGq+fPnn1PW/aJ62r6+vnr22Wf15z//Wd999905P+7IkSNatWqV7r33Xv3rX/9SeHh4hZ/j7NChg6Kjo3XixAm1aNFCklRQUGD1xDt37iwfHx9r76+sn376SW+//ba11+rZKzbGqLi42Jq2e/du3XzzzVq1apUk6cSJE/Lx8ZHD4VBsbKy+//57+fn5efVsTp06JX9/fwUGBnr9VGhJSYn198CBA9Z0z96u56+/v79KS0sr3Rsvu2f9f//3fzLGWL1WqfI95pKSEq8jHXFxcTLGVOgxlB03xigsLEzFxcVWT6O6HkteXp7Xtivf2ynbrlOnTunMmTOSZB1FSUhI8Jr/ddddZ21T858ezN/+9jev+XqWVdbevXv12WeV/2xleLj3T3EWFxdb27O0tNRafvn1/eabb9S1a1cVFRVVuf6e59nT1ieeeELffPONtZ4e1W1DU+6I0MmTJ6vsIZc9clD2NebRq1cvr/GjR4/qhx9+qFBv8+bNXuPll1f+RxzOtddavqdfft18fX1r/IKkhISEasvLzvObb76pst6JEye8xj3Pb2VHKcr+jK/nyE7557Dscps3b17pMktKSirtBTudTjkcDu3bt0/S2d9XKOvf//6317ifn59KSkq0detWa7k1Hb354YcfvNpYVFSkkydPKjo6Wp06dbKmp6amWv//5S9/kSSNHDlSISEhXq/1M2fOqKSkxGt7dejQQdLZo46e9+TIkSP12muvSZK2b99urV/79u1VWFhoffX0iBEjJJ3djrm5uZKkq666ypr/tddeW+G3JXx9fZWYmKjVq1fr66+/1tq1a+V0OvX//t//83r9x8TEaPjw4XrzzTdljLE+q//5z3+qpKTEOgJS/jktz/P+qqme5Zyi3QbK9pp79uxp7rnnHmNM1T3t4ODgSntPsbGx5uWXXzaSzMGDB40xP++Rla3v6XEEBgaa4OBgq5fr2eus7Jy2p9yzF3nNNddUuvdadvDx8TETJ040ffr0MT4+Pl69bM/g7+9fbc/sUg5V9X7remgo5/SuuOKKSp+TskcbzmVo0aKF9f+gQYOs/8sfGTnXc561GWrqbVY1/P73v6/354Gh5iE+Pr7e21B2cLlcxsfHp1av6bfeesu0bdvWSD8fWWvXrp156qmnTGBgoDVPh8NR4TMpICDA+vz29fU1wcHBXp/nfn5+1pGBBQsWGElm4sSJJjw83AQEBJhevXqZyZMnm23btpk5c+YYSebQoUMmISHBDB061AQFBZnGjRtb7+nXXnvNK6ukn3vaBQUF5sEHHzS+vr5m27Zt55R1v6ietscf//hH/fWvf9WXX35ZZZ2PPvpIN954o3r16qU//elP1uPS0tL01FNPyd/fX+vXr9euXbskSXPmzNHDDz8s6ez5w8TERPn4+Gjz5s3KycnR008/LV9fX8XGxnotx+VyqW3btrr11lvVqlUrLV68WBkZGZJ+7g1LZ3u8wcHB+s1vfqNGjRrpmmuukY+Pjxo1aqTCwkKdOnVKxhidOXOmwjnTJUuWqGvXrtVukzZt2lQ6PSQkpMrzu/Hx8RXqh4WFWXXK93DKr5MkjR49utLllu8dl59X+V5W+fKmTZtWOa+yPbay9TzzaNGihdc5ax8fH68jCJIq9Myuu+46639P7yM0NFTffvutTp8+rfIqO3dc2XlHT9v/7//+z5rmOVpT2TnTynps//u//1vhded5fFUqO0dfUFBQZf3qvPvuu+dct/x1F5dK+ee3rJqu8/AcWauNqq77OFd1uY2+/fZbr/GqzoOf6znn6qSnp2vs2LHWti37GePp2RYVFam0tLTKayU8PNugW7duuuGGGyRJd911l77++mtJPx9B89Rr1KiR9Rn52GOPWW1o1aqVJOnNN9+0fhjqpptuUk5OjlasWCFJWrp0qW666aYKbejZs6cOHjyo9957TwMGDND69evVtWvXCkcr3n//fUln30vff/+9YmJiNGbMGGVmZnrVGzZsmBo1aqTGjRvrX//6lxYsWKCOHTtWux0sF9a/bTjK9rSNMWbQoEHm5ptvPqdz2j/++KNxOBzm2WefNcXFxSY4ONjEx8ebkSNHmpdeesnaM3rnnXeMJJOenm58fHxMkyZNrPkuWrTI+Pj4mGuuucZIP/e0Pb0wHx8fq9fk6SVeeeWVJikpySpXJXuU/v7+ZuzYsdY5msqGJ5980vz2t7/1muY5V+3j43Ne5zrLDmXPDXuG8726edOmTee0J+3n5+fVzpp60mXbUb6H6DlnVnZblN3G5eedmJhobrnlFq+yH3/8scY2d+vWrcojHJ5zb56hZcuWJjU1tVbPQ03Dhg0bTEBAgLX9PG2qq1552dfehRzhuOKKK7zGy8+r/HhNR6HOp02vv/56rdrsdDorPZIinX0N9unTx2va+V6dHxsba/z8/Iyvr2+F163k/R48321fU/2y18CUHS677LJKp3uuDSk776o+D87ltffee+9VuEbiwQcfNA6HwzidTqvM8zc0NNS6m+cf//iHWb9+fY3rvGnTJjN8+HATGBhoPVdbt241JSUlRpK5/vrrjTHG65x2enq6adu2rXE4HOarr74yDofD/M///E+FzMnIyDDBwcEmPDzclJaWmoSEBPPEE0+Yr776yuTl5ZnS0lJTWlpqrr/+etOnTx/rcZLMK6+8Yr766itz+PDh8866X2RPWzp7FeD7779f4dx0ZSIiIpSWlqZ58+bp5MmT8vHx0WWXXab169dr/fr1Vr3U1FQFBgZq3bp1krx7PVu3blVpaalXj0w6ey6zT58+2rZtm3JycnTVVVdZe7j5+fleVzsGBgYqKipKq1atkp+fn5o2bWqdIzp06JB69+4tf39/61yJZ8/yn//8pw4cOOC1V96iRQuvc0OPPPJIpet+zTXXqEmTJpWWnTp1yvq/SZMmcjqdKikpqdB7KN8LLrsHHxkZWeEcY2Xnt4uLiyvMt6pehr+/v1ePvvy50bJlffr0sc6ZebZFnz591LJlS6vO/v37resNPG195513vOZZtm0Oh0MOh0N79uypcKW3p0dR/hqBvXv3VtjblqTu3btXugzPepa/4lqSLrvsMqsdkjRr1iwZY6weoWf9q/uhhfP5EYayRxLKP5eVqaqnVv7cd/l5lR+v6kr38vr27VtjHc951KpU1RNPSEjwugZB+vlIjsPh8DqSI1W8Qr4mnmslSkpKVFxcXOG8ddnXUVXbPiAgQO3bt68w3VO/que67PlZf39/OZ1O5eTk6Pjx45XWL3u+3jPvynrJYWFhSk9PrzDd6XR6teXrr7+2eskenjtwAgICvNrndDr18MMPW5/nl112mdd1CI0bN1ZwcLBGjRql2NhYNWrUSMHBwerSpUulbfT0vjds2KD8/HyvspKSEu3bt09paWlq1aqVrr/+er388sten4fS2avWCwoKdNttt1mvj8jISLVq1UoxMTHW50SbNm0qHMWKiYlRq1atKrx+zsl5x3wDVb6nbYwxd911l7WX7OHpaefm5ppbb73VpKSkmG3btpkVK1aYyMhIEx4ebhwOh5k8ebLX3qjnHMTTTz/ttVeXnZ1tXnvtNesKzrVr1xrJ+5z2c889Z3bt2mUWLFhgXWHoGdq2bWt8fX2Nw+EwYWFhZuHChWbXrl1mwIABVg/y8ssvNw6Hw4waNcpERkZae7KevUrPeZvo6GhrvmXvq42NjbXmX3bZ0tl7XqvqSbRs2dJrXavaq67sKljPUP5qcEmmVatW59VjKL/c87nCtrLzeMHBwRXaUL7XUX6envUofwVu8+bNK8y/a9euFaY98sgjFZ57qfKrsWtqR2W9mJCQkAo9tcp6UrU96lLTULadVfWyPFcVn8vzdj5DTddzXMgRB19fX6/3Wtl2e3qEdbX9fHx8Kryeyr/eyq+Tw+EwwcHB5uqrr77g7el0Ok1aWlqVR/Uqm/+1115bYXpUVJS5/PLLjSTTq1evap+38s+d526Y4OBgryNo48aNM7t27bI+C1avXm02b95sJJkOHTqY9u3bG+ns90z4+/sbX19f4+vrax566CHTvHlzExgYaHr27GkkmczMTJOXl2e1tUOHDmbRokVGknnqqadMRESE8ff3N++//77ZunWr+eSTT4y/v79p3bq1Wbhwofn444/N448/bvz8/ExISIj58ccfjTFnrx7v0KGDefvtt83OnTvNV199ZV577TUTHBxspk+fbmWQ5H31+Pn6RYf2vn37rDeVR3VfrhIYGGiioqJMZGSk9TjPi8SzkU+fPu31RvXx8THt27ev8ctVQkNDTffu3c3TTz9tHSYMCgqyPmivvPJK06lTJ5OYmGjdNuF5I/n4+JiIiAgzduxY681wrm8qz9C6dWsTHR1d4wVSlR0Sr+vBx8enwVw411CGqoKl/EU0DeUCPIaLM9S0E+cZyt/2WRehLZ0N0qp2RMq+Z8/1FMy57DDV5guFRo4caR5//PEK0z1t9+zc9urVy/z2t7+tcuenb9++5qGHHqr2S6X27NljHnjgARMREWFtYz8/P3P11Vebb7/91sqWZs2amd69e5sOHTqYRo0amcaNG5ukpCTz/PPPm5KSEqueRGgDDcJTTz1lOnXqZIwx5vnnnzdhYWHmzJkzFeoVFhaasLAwM3fuXGOMqfK+37/97W/WubbKhn/84x9mz549xuVymRMnTngt49prrzVjx471mrZx40Yjnb3P2LOT69nJ3Ldvn1VPOntfbXp6ujHm5x3i8jvGnTp1MtLZ60PKr39ZnnOkW7duNcYYk5SUZB588EGzYMEC061bNxMQEGCCg4NN7969zcSJE43T6TQ//PBDlTvYDofDbN++3Tz11FOVlrtcLmOMMVOmTDHDhw/3aotne952221e0++77z4jne1peaSnp1vfw2DM2etWQkNDK6yfZ5t5Pog9yxgyZIh1//xll11mbrrpJrNu3Tqvx23YsMGkpaVZ98m3b9/ePP/886a4uLjK+Xs8++yzRpL1PJ0+fdo89thjpmvXriY0NNQEBQWZ1q1bmylTppiTJ09aj0tISDAvvPCC17w2bdpkfv/735uoqCjj5+dnmjRpYtLS0szSpUtNaWmp13p5nsfqtl15RUVFpmPHjmbKlCnWtCNHjpjjx48bY87eq+xZTnVq04bKOnR2xu9pAzY2Z84crVmzRitXrqzvpjQ4HTt21JQpU3TrrbdKOnv/+CeffKLf//73Wrp0qYYMGVK/DfyV2b9/vwYMGKDExERNnDhR11xzjZxOp7799lv95S9/0Ycffqh169bVeDX/rx1bB7CxZs2aafLkyfXdjAbnzJkzGjp0qAYOHGhNu+eeezRq1Cg98sgjuvnmm+uxdb9OzZs316ZNm9S9e3eNGDFCAQEBcjqdateunfbu3asFCxYQ2OeAnjYA4JL78ccfderUKcXExJz3Vfe/ZoQ2AAA2wbEIAABsgtAGAMAmCG0AAGyC0AYAwCYIbQAAbILQBmxkxIgR1g8RlB327NkjSZoxY4Z8fX01a9asSh+fn5+vhx56SC1atJDL5VJ8fLxuvPFG66cKpbP307744otVjpe1f/9+ORwO5eTkaNq0aZW2rezQv39/paWlVZjPyy+/rLCwMH333Xe13zjArwChDdjMgAEDlJeX5zUkJiZKkhYuXKiJEydq4cKFFR63f/9+devWTWvXrtWsWbP0xRdfaNWqVUpJSanyd8/Pxx/+8AevNjVr1kzTp0/3mrZo0SJ99tln+t///V/rcfv27dPEiRP15z//Wc2aNbvgdgC/ZNzRDtiMy+VSTExMhekffvihTp06penTp+v111/Xxo0bvX4O88EHH5TD4dC///1vr58zbd++ve65554LblejRo28fkrS19dXjRs3rtDWuXPnasyYMUpNTVXz5s2VkZGh1NRU3XXXXRfcBuCXjp428AuxYMECDRs2TP7+/ho2bJgWLFhglR05ckSrVq3S6NGjK/z+uHT2N5AvlfT0dPXr10/33HOPXnrpJe3YscOr5w2gaoQ2YDPLly+3erWNGjXSLbfcIrfbrX/+85+68847JUl33nmn3nrrLZ04cUKStGfPHhlj1KZNm/psuuXVV1/Vjh07NG7cOL366qtq2rRpfTcJsAVCG7CZlJQU5eTkWMOf/vQn/eMf/1DLli3VqVMnSVLnzp2VkJCgN998U5LU0L6tOCoqSvfff7/atm3Lr20B54Fz2oDNBAcHq1WrVl7TFixYoJ07d3r98EJpaakWLlyojIwMXXHFFXI4HNq9e/elbm6V/Pz8+KEI4DzxjgFs7osvvtDmzZu1fv16RUREWNOPHDmivn37avfu3WrTpo3S0tI0b948PfzwwxXOax87duySntcGUDuENmBzCxYsUPfu3dWnT58KZVdffbUWLFigWbNmad68ebrmmmvUvXt3TZ8+XR07dlRxcbEyMzP1yiuv6Msvv6xyGd9//71ycnK8piUkJNT1qgCoAee0ARs7c+aM/v73v2vo0KGVlg8dOlSvv/66ioqK1KJFC33++edKSUnRI488og4dOuj6669XVlaWXnnllWqX8/zzz6tLly5ew4oVKy7GKgGoBr+nDQCATdDTBgDAJghtAABsgtAGAMAmCG0AAGyC0AYAwCYIbQAAbILQBgDAJghtAABsgtAGAMAmCG0AAGyC0AYAwCb+P8eHrs0uK6WyAAAAAElFTkSuQmCC","text/plain":["
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","text/plain":["
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","text/plain":["
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'ZZHZERRW'","deepnote_table_loading":false,"deepnote_table_state":{"filters":[],"pageIndex":9,"pageSize":100,"sortBy":[{"id":"count_star()","type":"desc"}]},"deepnote_to_be_reexecuted":false,"deepnote_variable_name":"df_1","execution_millis":10119,"execution_start":1697471936222,"source_hash":null,"sql_integration_id":"deepnote-dataframe-sql"},"outputs":[{"data":{"application/vnd.deepnote.sql-output-metadata+json":{"size_in_bytes":1570963,"status":"success_no_cache"}},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.deepnote.dataframe.v3+json":{"column_count":12,"columns":[{"dtype":"object","name":"DOWNLOAD DATE","stats":{"categories":[{"count":5,"name":"2022-02-18"},{"count":2,"name":"2017-08-24"},{"count":2476,"name":"2475 others"}],"nan_count":0,"unique_count":2477}},{"dtype":"object","name":"IDENTIFIER","stats":{"categories":[{"count":2483,"name":"ZZHZERRW"}],"nan_count":0,"unique_count":1}},{"dtype":"object","name":"LATEST ADMISSION 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DOWNLOAD DATEIDENTIFIERLATEST ADMISSION DATERACEGENDERAGEBOND AMOUNTOFFENSEFACILITYDETAINERFIRST_NAMELAST_NAME
02020-05-15ZZHZERRW2016-08-22BLACKM35650000MURDER AFWALKER RCNONEDONALDSHEPHERD
12016-09-07ZZHZERRW2016-08-22BLACKM31150000SALE OF NARC/AMPHET BY NON-DEPENDENT FBRIDGEPORT CCNONESTEVEPARKER
22016-09-08ZZHZERRW2016-08-22BLACKM31150000SALE OF NARC/AMPHET BY NON-DEPENDENT FBRIDGEPORT CCNONEJUSTINMCCONNELL
32017-02-27ZZHZERRW2016-08-22BLACKM32150000SALE OF NARC/AMPHET BY NON-DEPENDENT FBRIDGEPORT CCNONECAMERONHARVEY
42016-09-09ZZHZERRW2016-08-22BLACKM31150000SALE OF NARC/AMPHET BY NON-DEPENDENT FBRIDGEPORT CCNONEEDWARDFLORES
.......................................
24782023-08-26ZZHZERRW2016-08-22BLACKM38650000MURDER AFCHESHIRE CINONECOLERUSSELL
24792023-08-27ZZHZERRW2016-08-22BLACKM38650000MURDER AFCHESHIRE CINONEISAACKELLEY
24802023-08-28ZZHZERRW2016-08-22BLACKM38650000MURDER AFCHESHIRE CINONESTEPHENMCDONALD
24812023-08-29ZZHZERRW2016-08-22BLACKM38650000MURDER AFCHESHIRE CINONEMICHAELJENKINS
24822023-08-30ZZHZERRW2016-08-22BLACKM38650000MURDER AFCHESHIRE CINONEFRANCISJOHNSON
\n","

2483 rows × 12 columns

\n","
"],"text/plain":[" DOWNLOAD DATE IDENTIFIER LATEST ADMISSION DATE RACE GENDER AGE \\\n","0 2020-05-15 ZZHZERRW 2016-08-22 BLACK M 35 \n","1 2016-09-07 ZZHZERRW 2016-08-22 BLACK M 31 \n","2 2016-09-08 ZZHZERRW 2016-08-22 BLACK M 31 \n","3 2017-02-27 ZZHZERRW 2016-08-22 BLACK M 32 \n","4 2016-09-09 ZZHZERRW 2016-08-22 BLACK M 31 \n","... ... ... ... ... ... ... \n","2478 2023-08-26 ZZHZERRW 2016-08-22 BLACK M 38 \n","2479 2023-08-27 ZZHZERRW 2016-08-22 BLACK M 38 \n","2480 2023-08-28 ZZHZERRW 2016-08-22 BLACK M 38 \n","2481 2023-08-29 ZZHZERRW 2016-08-22 BLACK M 38 \n","2482 2023-08-30 ZZHZERRW 2016-08-22 BLACK M 38 \n","\n"," BOND AMOUNT OFFENSE FACILITY \\\n","0 650000 MURDER AF WALKER RC \n","1 150000 SALE OF NARC/AMPHET BY NON-DEPENDENT F BRIDGEPORT CC \n","2 150000 SALE OF NARC/AMPHET BY NON-DEPENDENT F BRIDGEPORT CC \n","3 150000 SALE OF NARC/AMPHET BY NON-DEPENDENT F BRIDGEPORT CC \n","4 150000 SALE OF NARC/AMPHET BY NON-DEPENDENT F BRIDGEPORT CC \n","... ... ... ... \n","2478 650000 MURDER AF CHESHIRE CI \n","2479 650000 MURDER AF CHESHIRE CI \n","2480 650000 MURDER AF CHESHIRE CI \n","2481 650000 MURDER AF CHESHIRE CI \n","2482 650000 MURDER AF CHESHIRE CI \n","\n"," DETAINER FIRST_NAME LAST_NAME \n","0 NONE DONALD SHEPHERD \n","1 NONE STEVE PARKER \n","2 NONE JUSTIN MCCONNELL \n","3 NONE CAMERON HARVEY \n","4 NONE EDWARD FLORES \n","... ... ... ... \n","2478 NONE COLE RUSSELL \n","2479 NONE ISAAC KELLEY \n","2480 NONE STEPHEN MCDONALD \n","2481 NONE MICHAEL JENKINS \n","2482 NONE FRANCIS JOHNSON \n","\n","[2483 rows x 12 columns]"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["df_1 = _deepnote_execute_sql('SELECT *\\nFROM \\'inmates_enriched.csv\\'\\nWHERE IDENTIFIER = \\'ZZHZERRW\\'', 'SQL_DEEPNOTE_DATAFRAME_SQL', audit_sql_comment='', sql_cache_mode='cache_disabled')\n","df_1"]},{"cell_type":"code","execution_count":null,"metadata":{"cell_id":"970d1502c4fb46d3b937133c0b570e6c","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":138,"execution_start":1697471946202,"source_hash":null},"outputs":[],"source":["import random\n","df.size\n","flat_list = df.values.flatten()\n","\n","\n","my_set = set(flat_list)\n","unique_list = list(my_set)\n","\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"cell_id":"7b423c9617a04ddbae5d9f53e2e1aabe","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":18,"execution_start":1697471946384,"source_hash":null},"outputs":[],"source":["def jaccard_similarity(A, B):\n"," #Check if a and b are sets if not convert them to sets\n"," if not isinstance(A, set):\n"," A = set(A)\n"," if not isinstance(B, set):\n"," B = set(B)\n"," #Find intersection of two sets\n"," nominator = A.intersection(B)\n","\n"," #Find union of two sets\n"," denominator = A.union(B)\n","\n"," #Take the ratio of sizes\n"," similarity = len(nominator)/len(denominator)\n","\n"," return similarity\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"cell_id":"9edcf41e66a244c8ae3af3a4b84b1bb2","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":581,"execution_start":1697471946429,"source_hash":null},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjcAAAHHCAYAAABDUnkqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy89olMNAAAACXBIWXMAAA9hAAAPYQGoP6dpAABLW0lEQVR4nO3dd3hUZcL+8e9MekgDQgohEHonhRJRsSxRQEWxgYRV1NVdlQiCDWyI7hpsSLXs+qr7unRXQAVRRLEiCEmAUEINhJJQU0hIm3l+f/ia32YpEkhyksn9ua5cl5k55+Q+jyeZm/OcOWMzxhhEREREXITd6gAiIiIi1UnlRkRERFyKyo2IiIi4FJUbERERcSkqNyIiIuJSVG5ERETEpajciIiIiEtRuRERERGXonIjIiIiLkXlRkRERFyKyo2IuIwPPvgAm81GZmam1VFExEIqNyIiIuJSVG5ERETEpajciIiIiEtRuRERERGXonIjIiIiLkXlRkRERFyKyo2IiIi4FJUbERERcSk2Y4yxOoSIiIhIddGZGxFxGUePHmXbtm2UlZVZHUVELKRyIyIuY+bMmXTu3JkDBw5YHUVELKRyIyIiIi5F19yIiIiIS9GZGxEREXEpKjciIiLiUlRuRERExKWo3IiIiIhLUbkRERERl6JyIyIiIi7F3eoAtc3pdHLw4EH8/f2x2WxWxxEREZHzYIyhoKCA5s2bY7ef+9xMgys3Bw8eJDIy0uoYIiIicgGysrJo0aLFOZdpcOXG398f+HVwAgICLE4jIiIi5yM/P5/IyMiK1/FzaXDl5repqICAAJUbERGReuZ8LinRBcUiIiLiUlRuRERExKWo3IiIiIhLUbkRERERl6JyIyIiIi5F5UZERERcisqNiIiIuBSVGxEREXEpKjciIiLiUlRuRERExKVYWm6+++47Bg8eTPPmzbHZbCxevPh311m1ahVxcXF4eXnRrl07PvjggxrPKSIiIvWHpeWmsLCQ6OhoZs2adV7L79mzh+uvv56rr76atLQ0HnnkEe677z6++OKLGk4qIiIi9YWlH5w5aNAgBg0adN7Lv/3227Ru3ZrXX38dgM6dO/PDDz/wxhtvMGDAgJqKKSIiIudp/d4TRDX1pamfl2UZ6tU1N6tXryYhIaHSYwMGDGD16tVnXaekpIT8/PxKXyIiIlK9nE7D29/uYug7q3l04QacTmNZlnpVbrKzswkNDa30WGhoKPn5+Zw6deqM6yQnJxMYGFjxFRkZWRtRRUREGoxjJ0u495+/MPnzbTicBn9vD0odTsvy1KtycyEmTJhAXl5exVdWVpbVkURERFzGmt3HuG7696zKOIKXu53kW7oz/Y4YvD3cLMtk6TU3VRUWFkZOTk6lx3JycggICMDHx+eM63h5eeHlZd28n4iIiCtyOA1vfrOTN77ajtNAm2aNmJUYR+fwAKuj1a9y07dvX5YtW1bpsRUrVtC3b1+LEomIiDQ8RwpKGDs/jR92HgXgltgIXhzSjUZedaNWWJri5MmT7Ny5s+L7PXv2kJaWRpMmTWjZsiUTJkzgwIED/O///i8ADzzwADNnzuSJJ57g3nvv5euvv2bBggUsXbrUql0QERFpUH7aeZQx89M4UlCCt4edF2/qxu296tb1rJaWm3Xr1nH11VdXfD9u3DgARo4cyQcffMChQ4fYt29fxfOtW7dm6dKljB07lmnTptGiRQveffddvQ1cRESkhjmchmkrdzDj6x0YAx1C/ZiVGEf7UH+ro53GZoyx7r1aFsjPzycwMJC8vDwCAqyfFxQREanrcvKLGTMvlZ93HwdgWK9Inr+xKz6etXfRcFVev+vG5JiIiIjUSd9tP8LY+WkcKyzF19ONl27uzpDYCKtjnZPKjYiIiJym3OHkja+28+aqXRgDncL8mTUijrbN/KyO9rtUbkRERKSSQ3mnGD03lV8yTwAwIr4lz97QxdJ711SFyo2IiIhU+GbbYcYtSONEURl+Xu4k39KdwdHNrY5VJSo3IiIiQpnDyWtfZPDOd7sB6BYRwMzhcUQFN7I4WdWp3IiIiDRw+08U8fDcVFL35QJw96VRTLiuE17u9WMa6r+p3IiIiDRgX27O5vGPNpJ3qgx/b3deva0HA7uFWx3roqjciIiINECl5U6SP9/K+z9mAhDdIpCZiXFENvG1Nlg1ULkRERFpYPYdKyJpbgob9+cBcN/lrXliYCc83e0WJ6seKjciIiINyOebDvHERxspKCkn0MeD12+PJqFLqNWxqpXKjYiISANQXObgpWVb+d/VewGIaxnEjMQ4IoJ8LE5W/VRuREREXNyeo4UkzUlh88F8AB64si2PXtsBDzfXmIb6byo3IiIiLuyTDQd56uNNnCwpp0kjT14fGs3VHUOsjlWjVG5ERERcUHGZg0mfbmHu2n0A9IlqwvThsYQFelucrOap3IiIiLiYnYdPkjQnhW3ZBdhskHR1O8b0b4+7i05D/TeVGxERERfyccp+nlmcTlGpg2A/T94YFkO/9s2sjlWrVG5ERERcQFFpOROXbGbh+v0A9G3TlGl3xBAS4PrTUP9N5UZERKSe255TwKjZKew4fBK7Dcb070DSH9rhZrdZHc0SKjciIiL1lDGGhev289wn6RSXOQnx92LaHbH0bdvU6miWUrkRERGphwpLynlmcTqLUg8A0K99MG8MiyHYz8viZNZTuREREalnth7KZ9TsFHYfLcTNbmPcNR148Mq22BvoNNR/U7kRERGpJ4wxzFm7j0mfbqG03ElYgDczEmPpHdXE6mh1isqNiIhIPVBQXMaEjzfx2cZDAFzdsRmvD42hSSNPi5PVPSo3IiIidVz6gTyS5qSQeawId7uNJwZ25L7L22ga6ixUbkREROooYwz/u3ovf1u6lVKHk4ggH2YkxhLXsrHV0eo0lRsREZE6KO9UGeP/vZHP07MBuKZLKK/e1oMgX01D/R6VGxERkTpmQ1YuSXNTyDp+Cg83GxMGdeaey6Kw2TQNdT5UbkREROoIYwzv/ZjJ5M+3UuYwRDbxYebwOKIjg6yOVq+o3IiIiNQBuUWlPLZwI19tzQFgULcwJt/ag0AfD4uT1T8qNyIiIhZbv/cEo+emciD3FJ5udp69oTN/vKSVpqEukMqNiIiIRZxOw9+/382rX2TgcBqimvoyMzGObhGBVker11RuRERELHC8sJRxC9JYlXEEgMHRzXnp5m74e2sa6mKp3IiIiNSytXuOM3puKtn5xXi523n+xq7c0TtS01DVROVGRESkljidhjdX7WTKiu04DbRp1ohZiXF0Dg+wOppLUbkRERGpBUcKShi3II3vdxwF4JbYCF4c0o1GXnoprm4aURERkRr2086jjJmfxpGCErw97Lx4Uzdu7xVpdSyXpXIjIiJSQxxOw/SVO5j+9Q6MgQ6hfsxKjKN9qL/V0Vyayo2IiEgNOJxfzJh5aazefQyAob1aMOnGbvh4ulmczPWp3IiIiFSz73ccYez8NI6eLMXX042/3dyNm2NbWB2rwVC5ERERqSblDidTv9rBrFU7MQY6hfkza0QcbZv5WR2tQVG5ERERqQaH8k4xZm4aazOPA5AY35LnbuiCt4emoWqbyo2IiMhF+mbbYcYtSONEURl+Xu4k39KdwdHNrY7VYKnciIiIXKAyh5PXvsjgne92A9AtIoCZw+OICm5kcbKGTeVGRETkAhzIPcXDc1JI2ZcLwN2XRjHhuk54uWsaymoqNyIiIlW0YksOjy3cQN6pMvy93Xn1th4M7BZudSz5Pyo3IiIi56m03Mnkz7fx3o97AIhuEcjMxDgim/hanEz+k8qNiIjIecg6XkTSnBQ27M8D4E+Xt+bJgZ3wdLdbnEz+m8qNiIjI71iefojHP9pIQXE5gT4evHZ7NNd0CbU6lpyFyo2IiMhZFJc5SF62lX+u3gtAXMsgZiTGERHkY3EyOReVGxERkTPIPFrIqDkpbD6YD8BfrmzDY9d2xMNN01B1ncqNiIjIf/l0w0EmfLyJkyXlNPb1YMrQGK7uFGJ1LDlPKjciIiL/p7jMwaRPtzB37T4A+kQ1YdrwGMIDNQ1Vn6jciIiIALuOnGTU7BS2ZRdgs8Goq9rxSEJ73DUNVe+o3IiISIO3KHU/Ty9Kp6jUQbCfJ28Mi6Ff+2ZWx5ILpHIjIiIN1qlSB88tSWfh+v0A9G3TlGl3xBAS4G1xMrkYKjciItIgbc8pYNTsFHYcPonNBmP6t+fhP7THzW6zOppcJJUbERFpUIwxLFy/n+eWpFNc5qSZvxfT7ojh0rbBVkeTaqJyIyIiDUZhSTnPLk7n49QDAPRrH8yUoTE08/eyOJlUJ5UbERFpELYeymfUnBR2HynEboNHr+3Ig1e2xa5pKJejciMiIi7NGMPctVk8/+lmSsudhAV4M314LH1aN7E6mtQQlRsREXFZBcVlPLUonU83HATgqo7NmDI0hiaNPC1OJjVJ5UZERFxS+oE8kuakkHmsCDe7jScGdOT+fm00DdUAqNyIiIhLMcbw4c97+etnWyl1OIkI8mH68Fh6tmpsdTSpJZbfU3rWrFlERUXh7e1NfHw8a9euPefyU6dOpWPHjvj4+BAZGcnYsWMpLi6upbQiIlKX5Z0q46HZKTy3ZDOlDicJnUNZOvpyFZsGxtIzN/Pnz2fcuHG8/fbbxMfHM3XqVAYMGEBGRgYhIad/+uqcOXMYP3487733Hpdeeinbt2/n7rvvxmazMWXKFAv2QERE6ooNWbkkzU0h6/gpPNxsjB/UmXsvi8Jm0zRUQ2Mzxhirfnh8fDy9e/dm5syZADidTiIjI3n44YcZP378acsnJSWxdetWVq5cWfHYo48+ypo1a/jhhx/O62fm5+cTGBhIXl4eAQEB1bMjIiJiGWMM7/2YyeTPt1LmMLRo7MOsxDiiI4OsjibVqCqv35ZNS5WWlrJ+/XoSEhL+fxi7nYSEBFavXn3GdS699FLWr19fMXW1e/duli1bxnXXXVcrmUVEpG7JLSrlzx+u58XPtlDmMAzsGsbS0f1UbBo4y6aljh49isPhIDQ0tNLjoaGhbNu27YzrJCYmcvToUS6//HKMMZSXl/PAAw/w1FNPnfXnlJSUUFJSUvF9fn5+9eyAiIhYav3eE4yem8qB3FN4utl55obO3HlJK01DifUXFFfFqlWreOmll3jzzTdJSUnh448/ZunSpbz44otnXSc5OZnAwMCKr8jIyFpMLCIi1c3pNLzz7S6GvbOaA7mnaNXUl48fupS7+ur6GvmVZdfclJaW4uvry0cffcSQIUMqHh85ciS5ubksWbLktHX69evHJZdcwquvvlrx2L/+9S/+/Oc/c/LkSez207vamc7cREZG6pobEZF66HhhKY8uSOObjCMA3NAjnORbuuPv7WFxMqlp9eKaG09PT3r27Fnp4mCn08nKlSvp27fvGdcpKio6rcC4ubkBv15QdiZeXl4EBARU+hIRkfpn7Z7jXDfte77JOIKnu52Xbu7OjOGxKjZyGkvfCj5u3DhGjhxJr1696NOnD1OnTqWwsJB77rkHgLvuuouIiAiSk5MBGDx4MFOmTCE2Npb4+Hh27tzJs88+y+DBgytKjoiIuBan0/DWt7uYsmI7DqehTXAjZo2Io3O4/rEqZ2ZpuRk2bBhHjhzhueeeIzs7m5iYGJYvX15xkfG+ffsqnal55plnsNlsPPPMMxw4cIBmzZoxePBg/va3v1m1CyIiUoOOnixh7Pw0vt9xFICbYyP465BuNPLSDfbl7Cy9z40VdJ8bEZH64addRxkzL40jBSV4e9h54aZu3N6zhS4abqCq8vqt6isiInWKw2mY8fUOpq/cgdNA+xA/Zo2Io0Oov9XRpJ5QuRERkTrjcH4xj8xP46ddxwC4vWcLJt3UFV9PvVzJ+dPRIiIidcL3O44wdn4aR0+W4uvpxl+HdOOWuBZWx5J6SOVGREQsVe5wMvWrHcxatRNjoFOYPzMT42gX4md1NKmnVG5ERMQyh/JOMWZuGmszjwMwvE9LJg7ugreHbu8hF07lRkRELPFNxmHGzU/jRFEZjTzdSL61BzdGN7c6lrgAlRsREalVZQ4nr32ZwTvf7gaga/MAZibG0Tq4kcXJxFWo3IiISK05kHuKh+ekkLIvF4C7+rbiqes6axpKqpXKjYiI1IoVW3J4bOEG8k6V4e/tziu39mBQ93CrY4kLUrkREZEaVVru5OXl2/ifH/YAEN0ikBnD42jZ1NfiZOKqVG5ERKTGZB0vImluKhuycgG497LWjB/UCU93+7lXFLkIKjciIlIjlqcf4vGPNlJQXE6Atzuv3R7NtV3DrI4lDYDKjYiIVKuScgcvLd3KP1fvBSC2ZRAzhsfSorGmoaR2qNyIiEi1yTxaSNLcFNIP5APwlyvb8Ni1HfFw0zSU1B6VGxERqRafbTzI+H9v4mRJOY19PZgyNIarO4VYHUsaIJUbERG5KMVlDl74bAtz1uwDoHdUY6YPjyU80MfiZNJQqdyIiMgF23XkJKNmp7AtuwCbDR66qi1jEzrgrmkosZDKjYiIXJBFqft5elE6RaUOmjby5I1hMVzRoZnVsURUbkREpGpOlTqY+Ek6C9btB+CSNk2YfkcsIQHeFicT+ZXKjYiInLcdOQWMmpPC9pyT2Gww+g/tGd2/PW52m9XRRCqo3IiIyHlZuC6LZ5ekU1zmpJm/F9OGxXBpu2CrY4mcRuVGRETOqbCknGeXpPNxygEALm8XzBvDYmjm72VxMpEzU7kREZGz2padz6jZKew6UojdBuOu6cBDV7XDrmkoqcNUbkRE5DTGGOb9ksXzn2ympNxJaIAX0++IJb5NU6ujifwulRsREamkoLiMpxal8+mGgwBc1bEZr98eTVM/TUNJ/aByIyIiFdIP5JE0J4XMY0W42W08PqAjf+7XRtNQUq+o3IiICMYY/vXzXl78bCulDifNA72ZkRhLz1ZNrI4mUmUqNyIiDVx+cRnj/72RZZuyAUjoHMJrt0cT5OtpcTKRC6NyIyLSgG3cn8uoOSlkHT+Fh5uNJwd24k+Xt8Zm0zSU1F8qNyIiDZAxhvd/zCT5862UOQwtGvswMzGOmMggq6OJXDSVGxGRBia3qJTHP9rIii05AAzsGsbLt/Ug0MfD4mQi1UPlRkSkAUnZd4KH56RyIPcUnm52nr6+M3f1baVpKHEpKjciIg2A02l494fdvLI8g3KnoVVTX2YlxtEtItDqaCLVTuVGRMTFHS8s5bGFG/h622EAbugRTvIt3fH31jSUuCaVGxERF/ZL5nFGz03lUF4xnu52Jg7uQmKflpqGEpemciMi4oKcTsNb3+5iyortOJyGNsGNmJkYR5fmAVZHE6lxKjciIi7m6MkSxs5P4/sdRwG4OTaCvw7pRiMv/cmXhkFHuoiIC1m96xhj5qVyuKAEbw87L9zYjdt7tdA0lDQoKjciIi7A4TTM/Hon01Zux2mgfYgfs0bE0SHU3+poIrVO5UZEpJ47XFDMI/PS+GnXMQBu79mCSTd1xddTf+KlYdKRLyJSj/2w4yiPzE/j6MkSfD3d+OuQbtwS18LqWCKWUrkREamHyh1Opq3cwcxvdmIMdArzZ2ZiHO1C/KyOJmI5lRsRkXomO6+Y0fNSWbvnOADD+7Rk4uAueHu4WZxMpG5QuRERqUdWZRxm3IINHC8spZGnG8m39uDG6OZWxxKpU1RuRETqgTKHkykrtvPWql0AdAkPYNaIOFoHN7I4mUjdo3IjIlLHHcw9xcNzU1m/9wQAd/VtxVPXddY0lMhZqNyIiNRhK7fm8OjCDeQWleHv5c7Lt/Xguu7hVscSqdNUbkRE6qDScievLN/Guz/sAaBHi0BmDo+jZVNfi5OJ1H0qNyIidUzW8SKS5qayISsXgHsva834QZ3wdLdbG0yknlC5ERGpQ5anZ/PERxvILy4nwNud126P5tquYVbHEqlXVG5EROqAknIHycu28cFPmQDEtgxixvBYWjTWNJRIVanciIhYbO+xQpLmpLLpQB4Af7miDY8N6IiHm6ahRC6Eyo2IiIWWbjzE+H9vpKCknMa+Hrw+NJo/dAq1OpZIvaZyIyJigeIyB39duoV//bwPgN5RjZk+PJbwQB+Lk4nUfyo3IiK1bPeRk4yak8rWQ/nYbPDQVW0Zm9ABd01DiVQLlRsRkVq0JO0AT328icJSB00befLGsBiu6NDM6lgiLkXlRkSkFpwqdTDp083M+yULgEvaNGHaHbGEBnhbnEzE9ajciIjUsJ2HCxg1O5WMnAJsNhj9h/aM7t8eN7vN6mgiLknlRkSkBn20fj/PLk7nVJmDZv5eTBsWw6Xtgq2OJeLSVG5ERGpAUWk5zy7ezL9T9gNwebtg3hgWQzN/L4uTibg+lRsRkWqWkV3AQ7PXs+tIIXYbjLumAw9d1Q67pqFEaoXKjYhINTHGMP+XLCZ+spmSciehAV5MvyOW+DZNrY4m0qCo3IiIVIOTJeU8vWgTS9IOAnBlh2ZMGRpNUz9NQ4nUtmopN7m5uQQFBVXHpkRE6p3NB/N4eE4qu48W4ma38fiAjvy5XxtNQ4lYpMq3w3z55ZeZP39+xfdDhw6ladOmREREsGHDhioHmDVrFlFRUXh7exMfH8/atWvPuXxubi6jRo0iPDwcLy8vOnTowLJly6r8c0VELpYxhg9/3svNb/7E7qOFNA/0ZsFfLuGBK9uq2IhYqMrl5u233yYyMhKAFStWsGLFCj7//HMGDRrE448/XqVtzZ8/n3HjxjFx4kRSUlKIjo5mwIABHD58+IzLl5aWcs0115CZmclHH31ERkYG//jHP4iIiKjqboiIXJT84jKS5qTy7OJ0SsudJHQOYenofvRs1cTqaCINns0YY6qygo+PD9u3bycyMpIxY8ZQXFzMO++8w/bt24mPj+fEiRPnva34+Hh69+7NzJkzAXA6nURGRvLwww8zfvz405Z/++23efXVV9m2bRseHh5ViV0hPz+fwMBA8vLyCAgIuKBtiEjDtnF/LklzUtl3vAgPNxtPDuzEny5vjc2mszUiNaUqr99VPnPTuHFjsrJ+vX348uXLSUhIAH49PetwOM57O6Wlpaxfv75ifQC73U5CQgKrV68+4zqffPIJffv2ZdSoUYSGhtKtWzdeeumlc/7ckpIS8vPzK32JiFwIYwwf/LiHW9/6iX3Hi2jR2IeFD1zKff3aqNiI1CFVvqD4lltuITExkfbt23Ps2DEGDRoEQGpqKu3atTvv7Rw9ehSHw0FoaGilx0NDQ9m2bdsZ19m9ezdff/01I0aMYNmyZezcuZOHHnqIsrIyJk6ceMZ1kpOTmTRp0nnnEhE5k7yiMp749wa+2JwDwMCuYbx8Ww8CfS7sLLKI1Jwql5s33niDqKgosrKyeOWVV/Dz8wPg0KFDPPTQQ9Ue8D85nU5CQkL4+9//jpubGz179uTAgQO8+uqrZy03EyZMYNy4cRXf5+fnV1wzJCJyPlL3nSBpTioHck/h6Wbnqes6MfLSKJ2tEamjqlxuPDw8eOyxx057fOzYsVXaTnBwMG5ubuTk5FR6PCcnh7CwsDOuEx4ejoeHB25ubhWPde7cmezsbEpLS/H09DxtHS8vL7y8dJ8JEak6Ywzvfr+Hl5dvo9xpaNXUl5nD4+jeItDqaCJyDudVbj755BMGDRqEh4cHn3zyyTmXvfHGG8/rB3t6etKzZ09WrlzJkCFDgF/PzKxcuZKkpKQzrnPZZZcxZ84cnE4ndvuvlwtt376d8PDwMxYbEZELdaKwlMcWbmDltl/fvXl9j3Am39Idf29NQ4nUdef1bim73U52djYhISEVpeKMG7PZqnRR8fz58xk5ciTvvPMOffr0YerUqSxYsIBt27YRGhrKXXfdRUREBMnJyQBkZWXRtWtXRo4cycMPP8yOHTu49957GT16NE8//fR5/Uy9W0pEfs+6zOOMnpvKwbxiPN3tTBzchcQ+LTUNJWKhqrx+n9eZG6fTecb/vljDhg3jyJEjPPfcc2RnZxMTE8Py5csrLjLet29fpTIVGRnJF198wdixY+nRowcRERGMGTOGJ598stoyiUjD5XQa3v5uF69/uR2H09AmuBEzE+Po0lz/EBKpT6p8n5tzKSoqwtfXt7o2VyN05kZEzuTYyRLGLdjAt9uPADAkpjl/vbk7fl76CD6RuqBG73PTv39/Dhw4cNrja9asISYmpqqbExGx3M+7j3Hd9O/5dvsRvD3svHJrD94YFqNiI1JPVbnceHt706NHj4rPl3I6nTz//PP069eP6667rtoDiojUFIfTMH3lDhL/8TM5+SW0C/FjyajLGdo7UtfXiNRjVf5nydKlS5k1axb33nsvS5YsITMzk7179/LZZ59x7bXX1kRGEZFqd7igmLHz0/hx5zEAbuvZghdu6oqvp87WiNR3F/RbPGrUKPbv38/LL7+Mu7s7q1at4tJLL63ubCIiNeLHnUcZMy+NoydL8PFw469DunFrzxZWxxKRalLlaakTJ05w66238tZbb/HOO+8wdOhQrr32Wt58882ayCciUm0cTsOUFdv54/+s4ejJEjqG+vPpw5er2Ii4mCqfuenWrRutW7cmNTWV1q1bc//99zN//nweeughli5dytKlS2sip4jIRcnJL2b03FTW7DkOwPA+kUwc3BVvD7ffWVNE6psqn7l54IEH+O6772jdunXFY8OGDWPDhg2UlpZWazgRkerw7fYjXDfte9bsOU4jTzem3RFD8i09VGxEXFS13uemPtB9bkQajnKHk9dXbOetVbsA6BIewMzEWNo087M4mYhUVbXfofhMioqK2Ldv32lna3r06HGhmxQRqTYHc08xem4q6/aeAODOS1rx9PWddbZGpAGocrk5cuQI99xzD59//vkZn6/KZ0uJiNSEr7flMG7BBnKLyvD3cmfyrT24vke41bFEpJZU+ZqbRx55hNzcXNasWYOPjw/Lly/nn//8J+3bt//dTwwXEalJZQ4nf1u6hXs/WEduURndIwL5bPTlKjYiDUyVz9x8/fXXLFmyhF69emG322nVqhXXXHMNAQEBJCcnc/3119dEThGRc8o6XsTDc1NJy8oF4J7Lohg/qBNe7pqGEmloqlxuCgsLCQkJAaBx48YcOXKEDh060L17d1JSUqo9oIjI7/liczaPL9xAfnE5Ad7uvHp7NAO6hlkdS0QsUuVy07FjRzIyMoiKiiI6Opp33nmHqKgo3n77bcLDdepXRGpPSbmDyZ9v4/0fMwGIiQxixvBYIpv4WhtMRCxV5XIzZswYDh06BMDEiRMZOHAgs2fPxtPTkw8++KC684mInNHeY4UkzUll04E8AO7v15rHB3TC073KlxKKiIu56PvcFBUVsW3bNlq2bElwcHB15aoxus+NSP23dOMhxv97IwUl5QT5evD67dH07xxqdSwRqUG1cp+b3/j6+hIXF3exmxER+V3FZQ7+unQL//p5HwC9WjVm+vBYmgf5WJxMROqSiy43IiK1Yc/RQkbNTmHLoXwAHrqqLWOv6YCHm6ahRKQylRsRqfOWpB3gqY83UVjqoEkjT94YFsOVHZpZHUtE6iiVGxGps4rLHDz/yWbm/ZIFQHzrJkwfHktogLfFyUSkLlO5EZE6aefhAkbNTiUjpwCbDR6+uh2j+7fHXdNQIvI7LuqvRPfu3cnKyqquLCIiAHy0fj+DZ/xIRk4BwX5efHhvPOOu7ahiIyLn5aLO3GRmZlJWVlZdWUSkgSsqLefZxZv5d8p+AC5r15Q3hsUQ4q9pKBE5f5qWEpE6ISO7gFFzUth5+CR2GzyS0IFRV7fDzW6zOpqI1DMXVW769euHj4/uLyEiF84Yw4J1WTy3ZDMl5U5C/L2YPjyWS9o0tTqaiNRTF1Vuli1bVl05RKQBOllSzjOLNrE47SAAV3RoxpSh0QT7eVmcTETqM01LiYglthzMJ2lOCruPFuJmt/HotR144Iq22DUNJSIXSeVGRGqVMYbZa/bxwmdbKC13Eh7ozfThsfSOamJ1NBFxESo3IlJr8ovLmPDxJpZuPATAHzqF8Prt0TRu5GlxMhFxJSo3IlIrNu3PI2luCnuPFeFut/HkwE786fLWmoYSkWpX5TtijRw5ku+++64msoiICzLG8MGPe7j1rZ/Ye6yIiCAfFjzQl/uvaKNiIyI1osrlJi8vj4SEBNq3b89LL73EgQMHaiKXiLiAvKIyHvjXep7/dAulDifXdAll2eh+xLVsbHU0EXFhVS43ixcv5sCBAzz44IPMnz+fqKgoBg0axEcffaS7FYtIhdR9J7h+xvd8sTkHDzcbEwd34e939iTQ18PqaCLi4i7og1qaNWvGuHHj2LBhA2vWrKFdu3bceeedNG/enLFjx7Jjx47qziki9YQxhne/383tb69m/4lTtGziy78fvJR7LmuNzaZpKBGpeRf1KXSHDh1ixYoVrFixAjc3N6677jo2bdpEly5deOONN6oro4jUEycKS7nvn+v469KtlDsN13UP47PRl9OjRZDV0USkAanyu6XKysr45JNPeP/99/nyyy/p0aMHjzzyCImJiQQEBACwaNEi7r33XsaOHVvtgUWkblq/9zgPz0nlYF4xnu52nr2hC3+Mb6mzNSJS66pcbsLDw3E6nQwfPpy1a9cSExNz2jJXX301QUFB1RBPROo6p9Pwzne7ee3LDBxOQ+vgRsxMjKVr80Cro4lIA1XlcvPGG29w++234+3tfdZlgoKC2LNnz0UFE5G679jJEsYt2MC3248AcGN0c166pTt+XrqFlohYp8p/ge68886ayCEi9cya3ccYPS+VnPwSvNztTLqxK8N6R2oaSkQsp39eiUiVOJyGN7/ZyRtfbcdpoG2zRswaEUensACro4mIACo3IlIFRwpKeGR+Kj/uPAbALXERvHhTNxppGkpE6hD9RRKR8/LjzqOMmZfG0ZMl+Hi48cJNXbm9V6TVsURETqNyIyLn5HAapq3cwYyvd2AMdAj1Y1ZiHO1D/a2OJiJyRio3InJWOfnFjJmXys+7jwNwR+9IJg7uio+nm8XJRETOTuVGRM7o2+1HGDc/jWOFpTTydOOlW7pzU0yE1bFERH6Xyo2IVFLucPL6iu28tWoXAJ3DA5iVGEubZn4WJxMROT8qNyJS4WDuKUbPTWXd3hMAjIhvybM3dMHbQ9NQIlJ/qNyICABfb8th3IIN5BaV4eflzuRbu3NDj+ZWxxIRqTKVG5EGrszh5NUvMvj7d7sB6BYRwKzEOFo1bWRxMhGRC6NyI9KA7T9RRNKcVNKycgG4+9IoJlzXCS93TUOJSP2lciPSQH2xOZvHF24gv7icAG93XrktmoHdwqyOJSJy0VRuRBqY0nInyZ9v5f0fMwGIjgxi5vBYIpv4WhtMRKSaqNyINCD7jhWRNDeFjfvzALi/X2seH9AJT3e7xclERKqPyo1IA7Fs0yGe/GgjBSXlBPl68Npt0SR0CbU6lohItVO5EXFxxWUO/rZ0Kx/+vBeAnq0aM314LBFBPhYnExGpGSo3Ii5sz9FCRs1OYcuhfAAeuLItj17bAQ83TUOJiOtSuRFxUUvSDvDUx5soLHXQpJEnU4ZGc1XHEKtjiYjUOJUbERdTXOZg0qebmbs2C4A+rZsw/Y5YwgK9LU4mIlI7VG5EXMjOwycZNTuFjJwCbDZIurodY/q3x13TUCLSgKjciLiIf6/fzzOL0zlV5iDYz4upw2K4vH2w1bFERGqdyo1IPVdUWs5zSzbz0fr9AFzatilT74ghxF/TUCLSMKnciNRjGdkFjJqTws7DJ7HbYEz/DiT9oR1udpvV0URELKNyI1IPGWNYsC6LiZ9sprjMSYi/F9PuiKVv26ZWRxMRsZzKjUg9c7KknGcWbWJx2kEA+rUP5o1hMQT7eVmcTESkbqgTb6GYNWsWUVFReHt7Ex8fz9q1a89rvXnz5mGz2RgyZEjNBhSpI7YczOfGGT+wOO0gbnYbjw/oyD/v6aNiIyLyHywvN/Pnz2fcuHFMnDiRlJQUoqOjGTBgAIcPHz7nepmZmTz22GP069evlpKKWMcYw79+3suQN39k99FCwgK8mffnSxh1dTvsur5GRKQSy8vNlClTuP/++7nnnnvo0qULb7/9Nr6+vrz33ntnXcfhcDBixAgmTZpEmzZtajGtSO0rKC4jaW4qzyxOp7TcyR86hbBsTD96RzWxOpqISJ1kabkpLS1l/fr1JCQkVDxmt9tJSEhg9erVZ13vhRdeICQkhD/96U+/+zNKSkrIz8+v9CVSX2zan8cNM35g6cZDuNttPHVdJ969qxdNGnlaHU1EpM6y9ILio0eP4nA4CA0NrfR4aGgo27ZtO+M6P/zwA//zP/9DWlraef2M5ORkJk2adLFRRWqVMYZ//pTJS8u2UepwEhHkw4zEWOJaNrY6mohInWf5tFRVFBQUcOedd/KPf/yD4ODzu/PqhAkTyMvLq/jKysqq4ZQiFyevqIwH/rWe5z/dQqnDyTVdQlk2up+KjYjIebL0zE1wcDBubm7k5ORUejwnJ4ewsLDTlt+1axeZmZkMHjy44jGn0wmAu7s7GRkZtG3bttI6Xl5eeHnpnSRSP6Rl5ZI0J4X9J07h4WZjwqDO3HNZFDabLhoWETlflpYbT09PevbsycqVKyvezu10Olm5ciVJSUmnLd+pUyc2bdpU6bFnnnmGgoICpk2bRmRkZG3EFql2xhj+54c9TP58G+VOQ2QTH2YOjyM6MsjqaCIi9Y7lN/EbN24cI0eOpFevXvTp04epU6dSWFjIPffcA8Bdd91FREQEycnJeHt7061bt0rrBwUFAZz2uEh9kVtUymMLN/DV1l9vf3Bd9zAm39qDAG8Pi5OJiNRPlpebYcOGceTIEZ577jmys7OJiYlh+fLlFRcZ79u3D7u9Xl0aJHLe1u89zsNzUjmYV4ynu51nb+jCH+NbahpKROQi2IwxxuoQtSk/P5/AwEDy8vIICAiwOo40UE6n4Z3vdvPalxk4nIbWwY2YmRhL1+aBVkcTEamTqvL6bfmZG5GG5tjJEh5duIFVGUcAuDG6OS/d0h0/L/06iohUB/01FalFa3YfY/S8VHLyS/Byt/P8jV25o3ekpqFERKqRyo1ILXA4DW9+s5M3vtqO00DbZo2YNSKOTmGaGhURqW4qNyI17EhBCWPnp/HDzqMA3BIXwYs3daORpqFERGqE/rqK1KCfdh5l9Lw0jp4swcfDjRdu6srtvXQ/JhGRmqRyI1IDHE7DtJU7mPH1DoyBDqF+zEqMo32ov9XRRERcnsqNSDXLyS9mzLxUft59HIBhvSJ5/sau+Hi6WZxMRKRhULkRqUbfbT/C2PlpHCssxdfTjZdu7s6Q2AirY4mINCgqNyLVoNzhZMqK7by5ahcAncMDmJUYS5tmfhYnExFpeFRuRC7SobxTjJ6byi+ZJwAYEd+SZ2/ogreHpqFERKygciNyEb7ZdphxC9I4UVSGn5c7k2/tzg09mlsdS0SkQVO5EbkAZQ4nr36Rwd+/2w1At4gAZg6PIyq4kcXJRERE5UakivafKOLhuamk7ssFYGTfVjx1fWe83DUNJSJSF6jciFTBl5uzeWzhBvKLy/H3dufV23owsFu41bFEROQ/qNyInIfScifJn2/l/R8zAYiODGLm8Fgim/haG0xERE6jciPyO/YdKyJpbgob9+cBcN/lrXliYCc83e0WJxMRkTNRuRE5h2WbDvHkRxspKCkn0MeD12+PJqFLqNWxRETkHFRuRM6guMzB35Zu5cOf9wLQs1Vjpg+PJSLIx+JkIiLye1RuRP7LnqOFJM1JYfPBfAAeuLItj17bAQ83TUOJiNQHKjci/+GTDQeZ8O+NFJY6aNLIk9eHRnN1xxCrY4mISBWo3Ijw6zTUpE+3MHftPgD6RDVh+vBYwgK9LU4mIiJVpXIjDd7OwydJmpPCtuwCbDZIurodY/q3x13TUCIi9ZLKjTRoH6fs55nF6RSVOgj282TqsFgubx9sdSwREbkIKjfSIBWVljNxyWYWrt8PwKVtmzJ1WAwhAZqGEhGp71RupMHZnlPAqNkp7Dh8ErsNxvTvQNIf2uFmt1kdTUREqoHKjTQYxhgWrtvPc5+kU1zmJMTfi2l3xNK3bVOro4mISDVSuZEGobCknGcWp7Mo9QAA/doH88awGIL9vCxOJiIi1U3lRlze1kP5jJqdwu6jhbjZbYy7pgMPXtkWu6ahRERcksqNuCxjDHPW7mPSp1soLXcSFuDNjMRYekc1sTqaiIjUIJUbcUkFxWU8tSidTzccBOAPnUJ47fZomjTytDiZiIjUNJUbcTnpB/JImpNC5rEi3O02nhjYkfsub6NpKBGRBkLlRlyGMYb/Xb2Xvy3dSqnDSUSQD9OHx9KzVWOro4mISC1SuRGXkHeqjPH/3sjn6dkAXNMllFdv60GQr6ahREQaGpUbqfc2ZOWSNDeFrOOn8HCzMWFQZ+65LAqbTdNQIiINkcqN1FvGGN77MZPJn2+lzGGIbOLDzOFxREcGWR1NREQspHIj9VJuUSmPLdzIV1tzABjULYzJt/Yg0MfD4mQiImI1lRupd9bvPcHDc1I4mFeMp5udZ2/ozB8vaaVpKBERAVRupB5xOg1//343r36RgcNpiGrqy8zEOLpFBFodTURE6hCVG6kXjheWMm5BGqsyjgBwY3RzXrqlO35eOoRFRKQyvTJInbd2z3FGz00lO78YL3c7z9/YlTt6R2oaSkREzkjlRuosp9Pw5qqdTFmxHaeBNs0aMSsxjs7hAVZHExGROkzlRuqkIwUljFuQxvc7jgJwS2wELw7pRiNNQ4mIyO/QK4XUOT/tOsqYeWkcKSjB28POizd14/ZekVbHEhGRekLlRuoMh9Mw4+sdTF+5A6eBDqF+zEqMo32ov9XRRESkHlG5kTrhcH4xY+alsXr3MQCG9Yrk+Ru74uPpZnEyERGpb1RuxHLf7zjC2PlpHD1Ziq+nGy/d3J0hsRFWxxIRkXpK5UYsU+5wMvWrHcxatRNjoFOYP7NGxNG2mZ/V0UREpB5TuRFLHMo7xZi5aazNPA7AiPiWPHtDF7w9NA0lIiIXR+VGat032w4zbkEaJ4rK8PNyJ/mW7gyObm51LBERcREqN1JryhxOXvsig3e+2w1A1+YBzEqMIyq4kcXJRETElajcSK04kHuKh+ekkLIvF4C7L41iwnWd8HLXNJSIiFQvlRupcSu25PDYwg3knSrD39udV2/rwcBu4VbHEhERF6VyIzWmtNzJ5M+38d6PewCIbhHIzMQ4Ipv4WpxMRERcmcqN1Iis40UkzUlhw/48AP50eWueHNgJT3e7xclERMTVqdxItVuefojHP9pIQXE5gT4evHZ7NNd0CbU6loiINBAqN1JtisscJC/byj9X7wUgrmUQMxLjiAjysTiZiIg0JCo3Ui0yjxYyak4Kmw/mA/CXK9vw2LUd8XDTNJSIiNQulRu5aJ9uOMiEjzdxsqScxr4eTBkaw9WdQqyOJSIiDZTKjVyw4jIHkz7dwty1+wDoE9WEacNjCA/UNJSIiFhH5UYuyK4jJxk1O4Vt2QXYbDDqqnY8ktAed01DiYiIxVRupMoWpe7n6UXpFJU6CPbz5I1hMfRr38zqWCIiIoDKjVTBqVIHzy1JZ+H6/QD0bdOUaXfEEBLgbXEyERGR/0/lRs7L9pwCRs1OYcfhk9hsMKZ/ex7+Q3vc7Daro4mIiFRSJy6QmDVrFlFRUXh7exMfH8/atWvPuuw//vEP+vXrR+PGjWncuDEJCQnnXF4ujjGGBeuyuHHmD+w4fJJm/l7Mvi+eRxI6qNiIiEidZHm5mT9/PuPGjWPixImkpKQQHR3NgAEDOHz48BmXX7VqFcOHD+ebb75h9erVREZGcu2113LgwIFaTu76CkvKeXTBBp74aCPFZU76tQ9m2eh+XNo22OpoIiIiZ2UzxhgrA8THx9O7d29mzpwJgNPpJDIykocffpjx48f/7voOh4PGjRszc+ZM7rrrrt9dPj8/n8DAQPLy8ggICLjo/K5q66F8Rs1JYfeRQuw2ePTajjx4ZVvsOlsjIiIWqMrrt6XX3JSWlrJ+/XomTJhQ8ZjdbichIYHVq1ef1zaKioooKyujSZMmNRWzQTHGMHdtFpM+3UxJuZOwAG+mD4+lT2uNr4iI1A+WlpujR4/icDgIDa38oYqhoaFs27btvLbx5JNP0rx5cxISEs74fElJCSUlJRXf5+fnX3hgF1dQXMZTi9L5dMNBAK7q2IwpQ2No0sjT4mQiIiLnr16/W2ry5MnMmzePVatW4e195rcjJycnM2nSpFpOVv+kH8gjaU4KmceKcLPbeHxAR/7cr42moUREpN6x9ILi4OBg3NzcyMnJqfR4Tk4OYWFh51z3tddeY/LkyXz55Zf06NHjrMtNmDCBvLy8iq+srKxqye4qjDH87+pMbnnzJzKPFdE80JsFf+nLA7q+RkRE6ilLy42npyc9e/Zk5cqVFY85nU5WrlxJ3759z7reK6+8wosvvsjy5cvp1avXOX+Gl5cXAQEBlb7kV3mnynhodgrPLdlMqcNJQudQlo3pR89Wja2OJiIicsEsn5YaN24cI0eOpFevXvTp04epU6dSWFjIPffcA8Bdd91FREQEycnJALz88ss899xzzJkzh6ioKLKzswHw8/PDz8/Psv2obzZk5ZI0N4Ws46fwcLPx5MBO/Ony1thsOlsjIiL1m+XlZtiwYRw5coTnnnuO7OxsYmJiWL58ecVFxvv27cNu//8nmN566y1KS0u57bbbKm1n4sSJPP/887UZvV4yxvDej5lM/nwrZQ5Di8Y+zEyMIyYyyOpoIiIi1cLy+9zUtoZ8n5vcolIeW7iRr7b+eo3TwK5hvHxbDwJ9PCxOJiIicm715j43UnvW7z3B6LmpHMg9haebnWdu6Mydl7TSNJSIiLgclRsX53Qa/vH9bl79IoNyp6FVU19mJcbRLSLQ6mgiIiI1QuXGhR0vLOXRBWl8k3EEgBt6hJN8S3f8vTUNJSIirkvlxkWt3XOc0XNTyc4vxtPdzvODuzK8T6SmoURExOWp3LgYp9Pw1re7mLJiOw6noU1wI2aNiKNzeMO6eFpERBoulRsXcvRkCWPnp/H9jqMA3BwbwV+HdKORl/43i4hIw6FXPRexetcxxsxL5XBBCd4edl64qRu392yhaSgREWlwVG7qOYfTMOPrHUxfuQOngfYhfswaEUeHUH+ro4mIiFhC5aYeO1xQzCPz0vhp1zEAbu/Zgkk3dcXXU/9bRUSk4dKrYD31w46jPDI/laMnS/H1dOOvQ7pxS1wLq2OJiIhYTuWmnil3OJn61Q5mrdqJMdApzJ+ZiXG0C9GHhoqIiIDKTb2SnVfM6HmprN1zHIDhfVoycXAXvD3cLE4mIiJSd6jc1BOrMg4zbsEGjheW0sjTjeRbe3BjdHOrY4mIiNQ5Kjd1XJnDyetfbuftb3cB0LV5ADMT42gd3MjiZCIiInWTyk0ddiD3FKPnprJ+7wkA7urbiqeu66xpKBERkXNQuamjvtqSw2MfbSC3qAx/b3deubUHg7qHWx1LRESkzlO5qWNKy528snwb7/6wB4DoFoHMGB5Hy6a+FicTERGpH1Ru6pCs40UkzU1lQ1YuAPde1prxgzrh6W63NpiIiEg9onJTRyxPz+bxjzZQUFxOoI8Hr90ezTVdQq2OJSIiUu+o3FispNxB8rJtfPBTJgCxLYOYMTyWFo01DSUiInIhVG4slHm0kKS5KaQfyAfgL1e24bFrO+LhpmkoERGRC6VyY5HPNh5k/L83cbKknMa+HkwZGsPVnUKsjiUiIlLvqdzUsuIyBy9+toXZa/YB0DuqMdOHxxIe6GNxMhEREdegclOLdh05yajZKWzLLsBmg1FXteORhPa4axpKRESk2qjc1JLFqQd4atEmikodNG3kydQ7YujXvpnVsURERFyOyk0NO1Xq4PlPNjN/XRYAfds0ZdodMYQEeFucTERExDWp3NSgHTkFjJqTwvack9hsMPoP7Rndvz1udpvV0URERFyWyk0NWbgui+eWbOZUmYNm/l5MGxbDpe2CrY4lIiLi8lRuqllhSTnPLknn45QDAPRrH8yUoTE08/eyOJmIiEjDoHJTjbZl5zNqdgq7jhRit8Gj13bkwSvbYtc0lIiISK1RuakmK7bkkDQnhZJyJ2EB3kwfHkuf1k2sjiUiItLgqNxUk87h/nh7uNG3bVOmDI2hSSNPqyOJiIg0SCo31aRFY18WPXQpUU0baRpKRETEQio31ahNMz+rI4iIiDR4uu+/iIiIuBSVGxEREXEpKjciIiLiUlRuRERExKWo3IiIiIhLUbkRERERl6JyIyIiIi5F5UZERERcisqNiIiIuBSVGxEREXEpKjciIiLiUlRuRERExKWo3IiIiIhLaXCfCm6MASA/P9/iJCIiInK+fnvd/u11/FwaXLkpKCgAIDIy0uIkIiIiUlUFBQUEBgaecxmbOZ8K5EKcTicHDx7E398fm81mdZxzys/PJzIykqysLAICAqyOU6dobM5M43JmGpez09icmcbl7KwaG2MMBQUFNG/eHLv93FfVNLgzN3a7nRYtWlgdo0oCAgL0y3UWGpsz07icmcbl7DQ2Z6ZxOTsrxub3ztj8RhcUi4iIiEtRuRERERGXonJTh3l5eTFx4kS8vLysjlLnaGzOTONyZhqXs9PYnJnG5ezqw9g0uAuKRURExLXpzI2IiIi4FJUbERERcSkqNyIiIuJSVG5ERETEpajc1LDnn38em81W6atTp04VzxcXFzNq1CiaNm2Kn58ft956Kzk5OZW2sW/fPq6//np8fX0JCQnh8ccfp7y8vNIyq1atIi4uDi8vL9q1a8cHH3xQG7t33r777jsGDx5M8+bNsdlsLF68uNLzxhiee+45wsPD8fHxISEhgR07dlRa5vjx44wYMYKAgACCgoL405/+xMmTJysts3HjRvr164e3tzeRkZG88sorp2VZuHAhnTp1wtvbm+7du7Ns2bJq39+q+L2xufvuu087hgYOHFhpGVccm+TkZHr37o2/vz8hISEMGTKEjIyMSsvU5u/PrFmziIqKwtvbm/j4eNauXVvt+3w+zmdcrrrqqtOOmQceeKDSMq42Lm+99RY9evSouLFc3759+fzzzyueb4jHym9+b2xc8ngxUqMmTpxounbtag4dOlTxdeTIkYrnH3jgARMZGWlWrlxp1q1bZy655BJz6aWXVjxfXl5uunXrZhISEkxqaqpZtmyZCQ4ONhMmTKhYZvfu3cbX19eMGzfObNmyxcyYMcO4ubmZ5cuX1+q+nsuyZcvM008/bT7++GMDmEWLFlV6fvLkySYwMNAsXrzYbNiwwdx4442mdevW5tSpUxXLDBw40ERHR5uff/7ZfP/996Zdu3Zm+PDhFc/n5eWZ0NBQM2LECJOenm7mzp1rfHx8zDvvvFOxzI8//mjc3NzMK6+8YrZs2WKeeeYZ4+HhYTZt2lTjY3A2vzc2I0eONAMHDqx0DB0/frzSMq44NgMGDDDvv/++SU9PN2lpaea6664zLVu2NCdPnqxYprZ+f+bNm2c8PT3Ne++9ZzZv3mzuv/9+ExQUZHJycmpnMP7D+YzLlVdeae6///5Kx0xeXl7F8644Lp988olZunSp2b59u8nIyDBPPfWU8fDwMOnp6caYhnms/Ob3xsYVjxeVmxo2ceJEEx0dfcbncnNzjYeHh1m4cGHFY1u3bjWAWb16tTHm1xc+u91usrOzK5Z56623TEBAgCkpKTHGGPPEE0+Yrl27Vtr2sGHDzIABA6p5b6rHf7+AO51OExYWZl599dWKx3Jzc42Xl5eZO3euMcaYLVu2GMD88ssvFct8/vnnxmazmQMHDhhjjHnzzTdN48aNK8bFGGOefPJJ07Fjx4rvhw4daq6//vpKeeLj481f/vKXat3HC3W2cnPTTTeddZ2GMjaHDx82gPn222+NMbX7+9OnTx8zatSoiu8dDodp3ry5SU5Orv4draL/Hhdjfn2xGjNmzFnXaQjjYowxjRs3Nu+++66OlTP4bWyMcc3jRdNStWDHjh00b96cNm3aMGLECPbt2wfA+vXrKSsrIyEhoWLZTp060bJlS1avXg3A6tWr6d69O6GhoRXLDBgwgPz8fDZv3lyxzH9u47dlfttGXbdnzx6ys7Mr7UNgYCDx8fGVxiEoKIhevXpVLJOQkIDdbmfNmjUVy1xxxRV4enpWLDNgwAAyMjI4ceJExTL1caxWrVpFSEgIHTt25MEHH+TYsWMVzzWUscnLywOgSZMmQO39/pSWlrJ+/fpKy9jtdhISEurE2Pz3uPxm9uzZBAcH061bNyZMmEBRUVHFc64+Lg6Hg3nz5lFYWEjfvn11rPyH/x6b37ja8dLgPjiztsXHx/PBBx/QsWNHDh06xKRJk+jXrx/p6elkZ2fj6elJUFBQpXVCQ0PJzs4GIDs7u9IB9dvzvz13rmXy8/M5deoUPj4+NbR31eO3/TjTPvznPoaEhFR63t3dnSZNmlRapnXr1qdt47fnGjdufNax+m0bddHAgQO55ZZbaN26Nbt27eKpp55i0KBBrF69Gjc3twYxNk6nk0ceeYTLLruMbt26AdTa78+JEydwOBxnXGbbtm3Vto8X4kzjApCYmEirVq1o3rw5Gzdu5MknnyQjI4OPP/4YcN1x2bRpE3379qW4uBg/Pz8WLVpEly5dSEtLa/DHytnGBlzzeFG5qWGDBg2q+O8ePXoQHx9Pq1atWLBgQZ0vHVI33HHHHRX/3b17d3r06EHbtm1ZtWoV/fv3tzBZ7Rk1ahTp6en88MMPVkepU842Ln/+858r/rt79+6Eh4fTv39/du3aRdu2bWs7Zq3p2LEjaWlp5OXl8dFHHzFy5Ei+/fZbq2PVCWcbmy5durjk8aJpqVoWFBREhw4d2LlzJ2FhYZSWlpKbm1tpmZycHMLCwgAICws77Yr+377/vWUCAgLqRYH6bT/OtA//uY+HDx+u9Hx5eTnHjx+vlrH67fn6oE2bNgQHB7Nz507A9ccmKSmJzz77jG+++YYWLVpUPF5bvz/BwcG4ubnVubE527icSXx8PEClY8YVx8XT05N27drRs2dPkpOTiY6OZtq0aQ3+WIGzj82ZuMLxonJTy06ePMmuXbsIDw+nZ8+eeHh4sHLlyornMzIy2LdvX8VcaN++fdm0aVOlF68VK1YQEBBQcUqxb9++lbbx2zL/OZ9al7Vu3ZqwsLBK+5Cfn8+aNWsqjUNubi7r16+vWObrr7/G6XRW/CL27duX7777jrKysoplVqxYQceOHWncuHHFMvV5rAD279/PsWPHCA8PB1x3bIwxJCUlsWjRIr7++uvTptVq6/fH09OTnj17VlrG6XSycuVKS8bm98blTNLS0gAqHTOuNi5n4nQ6KSkpabDHyrn8NjZn4hLHS7VfoiyVPProo2bVqlVmz5495scffzQJCQkmODjYHD582Bjz69sTW7Zsab7++muzbt0607dvX9O3b9+K9X97C961115r0tLSzPLly02zZs3O+Ba8xx9/3GzdutXMmjWrzr0VvKCgwKSmpprU1FQDmClTppjU1FSzd+9eY8yvbwUPCgoyS5YsMRs3bjQ33XTTGd8KHhsba9asWWN++OEH0759+0pvd87NzTWhoaHmzjvvNOnp6WbevHnG19f3tLc7u7u7m9dee81s3brVTJw40fK3gp9rbAoKCsxjjz1mVq9ebfbs2WO++uorExcXZ9q3b2+Ki4srtuGKY/Pggw+awMBAs2rVqkpvUS0qKqpYprZ+f+bNm2e8vLzMBx98YLZs2WL+/Oc/m6CgoErvHqktvzcuO3fuNC+88IJZt26d2bNnj1myZIlp06aNueKKKyq24YrjMn78ePPtt9+aPXv2mI0bN5rx48cbm81mvvzyS2NMwzxWfnOusXHV40XlpoYNGzbMhIeHG09PTxMREWGGDRtmdu7cWfH8qVOnzEMPPWQaN25sfH19zc0332wOHTpUaRuZmZlm0KBBxsfHxwQHB5tHH33UlJWVVVrmm2++MTExMcbT09O0adPGvP/++7Wxe+ftm2++McBpXyNHjjTG/Pp28GeffdaEhoYaLy8v079/f5ORkVFpG8eOHTPDhw83fn5+JiAgwNxzzz2moKCg0jIbNmwwl19+ufHy8jIRERFm8uTJp2VZsGCB6dChg/H09DRdu3Y1S5curbH9Ph/nGpuioiJz7bXXmmbNmhkPDw/TqlUrc//995/2x8AVx+ZMYwJUOrZr8/dnxowZpmXLlsbT09P06dPH/PzzzzWx27/r98Zl37595oorrjBNmjQxXl5epl27dubxxx+vdN8SY1xvXO69917TqlUr4+npaZo1a2b69+9fUWyMaZjHym/ONTauerzYjDGm+s8HiYiIiFhD19yIiIiIS1G5EREREZeiciMiIiIuReVGREREXIrKjYiIiLgUlRsRERFxKSo3IiIi4lJUbkRE/o/NZmPx4sVWxxCRi6RPBRcR+T+HDh2q+KwtEam/dIdiERERcSmalhKRWnPkyBHCwsJ46aWXKh776aef8PT0PO0Thavqww8/pFevXvj7+xMWFkZiYmKlTzF+4YUXaN68OceOHat47Prrr+fqq6/G6XQClaelSktLSUpKIjw8HG9vb1q1akVycvJFZRSR2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"]},"metadata":{"image/png":{"height":455,"width":567}},"output_type":"display_data"}],"source":["# importing the required module\n","import matplotlib.pyplot as plt\n","\n","# x axis values\n","x = [0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.5,0.6,0.7,0.8,0.9,1]\n","x = [int(len(unique_list) * i) for i in x]\n","l = []\n","y = []\n","for i in x:\n"," l.append(random.sample(unique_list, i))\n","\n","# x = [random.sample(unique_list, ]\n","# y = []\n","\n","for i in l:\n"," y.append(jaccard_similarity(unique_list, i))\n","\n","\n","#plotting the points\n","plt.plot(x, y)\n","\n","# # naming the x axis\n","plt.xlabel('x - axis')\n","# # naming the y axis\n","plt.ylabel('y - axis')\n","\n","# # giving a title to my graph\n","plt.title('!')\n","\n","# # function to show the plot\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"cell_id":"2f24173621014def92b8c1d39fe016f3","deepnote_cell_type":"code","deepnote_to_be_reexecuted":false,"execution_millis":18,"execution_start":1697471946899,"source_hash":null},"outputs":[],"source":["\n","\n"]},{"cell_type":"markdown","metadata":{"created_in_deepnote_cell":true,"deepnote_cell_type":"markdown"},"source":["\n","Created in deepnote.com \n","Created in Deepnote"]}],"metadata":{"deepnote":{},"deepnote_execution_queue":[],"deepnote_notebook_id":"d2f996a2973d43b0bc920172bd4ec912","kernelspec":{"display_name":".venv","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.6"}},"nbformat":4,"nbformat_minor":0} diff --git a/Experiments/Experiments_NamesEnrichment.ipynb b/Experiments/Experiments_NamesEnrichment.ipynb deleted file mode 100644 index b4d842f..0000000 --- a/Experiments/Experiments_NamesEnrichment.ipynb +++ /dev/null @@ -1,268 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: pandas in /Users/antongiacomopolimeno/Dottorato/03_paper_in_corso/Big Data Access Control - extension/.venv/lib/python3.11/site-packages (2.1.0)\n", - "Requirement already satisfied: numpy>=1.23.2 in /Users/antongiacomopolimeno/Dottorato/03_paper_in_corso/Big Data Access Control - extension/.venv/lib/python3.11/site-packages (from pandas) (1.25.2)\n", - "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/antongiacomopolimeno/Dottorato/03_paper_in_corso/Big Data Access Control - extension/.venv/lib/python3.11/site-packages (from pandas) (2.8.2)\n", - "Requirement already satisfied: pytz>=2020.1 in /Users/antongiacomopolimeno/Dottorato/03_paper_in_corso/Big Data Access Control - extension/.venv/lib/python3.11/site-packages (from pandas) (2023.3)\n", - "Requirement already satisfied: tzdata>=2022.1 in /Users/antongiacomopolimeno/Dottorato/03_paper_in_corso/Big Data Access Control - extension/.venv/lib/python3.11/site-packages (from pandas) (2023.3)\n", - "Requirement already satisfied: six>=1.5 in /Users/antongiacomopolimeno/Dottorato/03_paper_in_corso/Big Data Access Control - extension/.venv/lib/python3.11/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n", - "Note: you may need to restart the kernel to use updated packages.\n", - "Collecting faker\n", - " Obtaining dependency information for faker from https://files.pythonhosted.org/packages/f5/5f/7fd9f758111dcc49a95c9828b3d52180bf39fb3cd948d075eb3b834758ec/Faker-19.3.1-py3-none-any.whl.metadata\n", - " Downloading Faker-19.3.1-py3-none-any.whl.metadata (15 kB)\n", - "Requirement already satisfied: python-dateutil>=2.4 in /Users/antongiacomopolimeno/Dottorato/03_paper_in_corso/Big Data Access Control - extension/.venv/lib/python3.11/site-packages (from faker) (2.8.2)\n", - "Requirement already satisfied: six>=1.5 in /Users/antongiacomopolimeno/Dottorato/03_paper_in_corso/Big Data Access Control - extension/.venv/lib/python3.11/site-packages (from python-dateutil>=2.4->faker) (1.16.0)\n", - "Downloading Faker-19.3.1-py3-none-any.whl (1.7 MB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m14.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n", - "\u001b[?25hInstalling collected packages: faker\n", - "Successfully installed faker-19.3.1\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "%pip install pandas\n", - "%pip install faker\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "from faker import Faker\n", - "import pandas as pd\n", - "fake = Faker()\n", - "\n", - "df = pd.read_csv('inmates.csv')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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DOWNLOAD DATEIDENTIFIERLATEST ADMISSION DATERACEGENDERAGEBOND AMOUNTOFFENSEFACILITYDETAINER
589134509/23/2021ZZSHSESH02/21/2020BLACKM271000000FELONY MURDER AFHARTFORD CCNONE
27927210/24/2016ZZSECZBW09/26/2016HISPANICM1826000BURGLARY, THIRD DEGREE DFMANSON YINONE
262428310/27/2018ZZSRSBZR07/16/2018HISPANICM21504000CRIM VIOL OF PROTECTIVE ORDER AMHARTFORD CCNONE
816371805/17/2023ZZSSZLSB06/27/2022HISPANICM24250000SEXUAL ASSAULT, FIRST DEGREE FHARTFORD CCNONE
713223008/02/2022ZZSEERSW08/01/2022WHITEM3960000VIOLATION OF PROBATION OR COND DISCHGCORRIGAN CINONE
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" - ], - "text/plain": [ - " DOWNLOAD DATE IDENTIFIER LATEST ADMISSION DATE RACE GENDER AGE \\\n", - "5891345 09/23/2021 ZZSHSESH 02/21/2020 BLACK M 27 \n", - "279272 10/24/2016 ZZSECZBW 09/26/2016 HISPANIC M 18 \n", - "2624283 10/27/2018 ZZSRSBZR 07/16/2018 HISPANIC M 21 \n", - "8163718 05/17/2023 ZZSSZLSB 06/27/2022 HISPANIC M 24 \n", - "7132230 08/02/2022 ZZSEERSW 08/01/2022 WHITE M 39 \n", - "\n", - " BOND AMOUNT OFFENSE FACILITY \\\n", - "5891345 1000000 FELONY MURDER AF HARTFORD CC \n", - "279272 26000 BURGLARY, THIRD DEGREE DF MANSON YI \n", - "2624283 504000 CRIM VIOL OF PROTECTIVE ORDER AM HARTFORD CC \n", - "8163718 250000 SEXUAL ASSAULT, FIRST DEGREE F HARTFORD CC \n", - "7132230 60000 VIOLATION OF PROBATION OR COND DISCHG CORRIGAN CI \n", - "\n", - " DETAINER \n", - "5891345 NONE \n", - "279272 NONE \n", - "2624283 NONE \n", - "8163718 NONE \n", - "7132230 NONE " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.sample(5)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "SUBSET_SIZE = 100000\n", - "dfs = df\n", - "# dfs = df.sample(SUBSET_SIZE)\n", - "length = len(dfs.index)\n", - "\n", - "length = len(dfs.index)\n", - "know_ids = {}\n", - "\n", - "\n", - "#get index where gender is F\n", - "dfs['FIRST_NAME'] = [(fake.first_name_male() if dfs.iloc[i]['GENDER'] == 'M' else fake.first_name_female()).upper() for i in range(length)]\n", - "dfs['LAST_NAME'] = [fake.last_name().upper() for i in range(length)]\n", - "id =\n", - "\n", - "\n", - "dfs.sample(10)\n", - "\n", - "dfs.to_csv('inmates_enriched.csv', index=False)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "ename": "", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31mFailed to start the Kernel. \n", - "\u001b[1;31mView Jupyter log for further details." - ] - } - ], - "source": [ - "\n", - "SUBSET_SIZE = 100\n", - "dfs = df\n", - "# dfs = df.sample(SUBSET_SIZE)\n", - "length = len(dfs.index)\n", - "\n", - "length = len(dfs.index)\n", - "#get index where gender is F\n", - "for item in df.iterrows():\n", - "\n", - "\n", - "dfs['FIRST_NAME'] = [(fake.first_name_male() if dfs.iloc[i]['GENDER'] == 'M' else fake.first_name_female()).upper() for i in range(length)]\n", - "dfs['LAST_NAME'] = [fake.last_name().upper() for i in range(length)]\n", - "\n", - "\n", - "\n", - "dfs.sample(10)\n", - "\n", - "dfs.to_csv('inmates_small.csv', index=False)\n", - "\n", - "\n", - "dfs.to_csv('inmates_small.csv', index=False)\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "'Python Interactive'", - "language": "python", - "name": "6d7fac72-3df6-4bc8-bbab-4f95bdf877fc" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Experiments/Simulator/Big Data Access Control - extension.code-workspace b/Experiments/Simulator/Big Data Access Control - extension.code-workspace new file mode 100644 index 0000000..407c760 --- /dev/null +++ b/Experiments/Simulator/Big Data Access Control - extension.code-workspace @@ -0,0 +1,8 @@ +{ + "folders": [ + { + "path": "../.." + } + ], + "settings": {} +} \ No newline at end of file diff --git a/Experiments/Simulator/Data Aggegation.ipynb b/Experiments/Simulator/Data Aggegation.ipynb new file mode 100644 index 0000000..6185b4f --- /dev/null +++ b/Experiments/Simulator/Data Aggegation.ipynb @@ -0,0 +1,269 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: sqlalchemy in /Users/antongiacomopolimeno/Dottorato/03_paper_in_corso/Big Data Access Control - extension/.venv/lib/python3.11/site-packages (2.0.25)\n", + "Requirement already satisfied: typing-extensions>=4.6.0 in /Users/antongiacomopolimeno/Dottorato/03_paper_in_corso/Big Data Access Control - extension/.venv/lib/python3.11/site-packages (from sqlalchemy) (4.9.0)\n", + "Requirement already satisfied: greenlet!=0.4.17 in /Users/antongiacomopolimeno/Dottorato/03_paper_in_corso/Big Data Access Control - extension/.venv/lib/python3.11/site-packages (from sqlalchemy) (3.0.3)\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n", + "Requirement already satisfied: pymysql in /Users/antongiacomopolimeno/Dottorato/03_paper_in_corso/Big Data Access Control - extension/.venv/lib/python3.11/site-packages (1.1.0)\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n", + "Collecting cryptography\n", + " Obtaining dependency information for cryptography from https://files.pythonhosted.org/packages/f4/06/4229967761a1daf385bdb09bcb11d3d40970a54b52e896b41f43065eecf6/cryptography-42.0.2-cp39-abi3-macosx_10_12_universal2.whl.metadata\n", + " Downloading cryptography-42.0.2-cp39-abi3-macosx_10_12_universal2.whl.metadata (5.3 kB)\n", + "Collecting cffi>=1.12 (from cryptography)\n", + " Obtaining dependency information for cffi>=1.12 from https://files.pythonhosted.org/packages/95/c8/ce05a6cba2bec12d4b28285e66c53cc88dd7385b102dea7231da3b74cfef/cffi-1.16.0-cp311-cp311-macosx_10_9_x86_64.whl.metadata\n", + " Downloading cffi-1.16.0-cp311-cp311-macosx_10_9_x86_64.whl.metadata (1.5 kB)\n", + "Collecting pycparser (from cffi>=1.12->cryptography)\n", + " Downloading pycparser-2.21-py2.py3-none-any.whl (118 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m118.7/118.7 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n", + "\u001b[?25hDownloading cryptography-42.0.2-cp39-abi3-macosx_10_12_universal2.whl (5.9 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.9/5.9 MB\u001b[0m \u001b[31m42.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n", + "\u001b[?25hDownloading cffi-1.16.0-cp311-cp311-macosx_10_9_x86_64.whl (182 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m182.4/182.4 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: pycparser, cffi, cryptography\n", + "Successfully installed cffi-1.16.0 cryptography-42.0.2 pycparser-2.21\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install sqlalchemy\n", + "%pip install pymysql\n", + "%pip install cryptography" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N2S10 0.02559700980782509 0.02977912686765194 \n", + "N2S15 0.023880315013229847 0.03182677738368511 \n", + "N2S5 0.02052785037085414 0.029043065384030342 \n", + "N2S20 0.033689821138978004 \n", + "N3S10 0.019181828945875168 0.025155987590551376 0.03310304507613182 \n", + "N3S15 0.02512373775243759 0.02840638222793738 0.033340765784184136 \n", + "N3S20 0.01659972034394741 0.0243230698009332 0.03374866644541422 \n", + "N3S5 0.02197916681567828 0.015259808705498775 0.030990420530239742 \n", + "N4S10 0.02455263677984476 0.018011788837611675 0.030073273926973343 0.03386730421334505 \n", + "N4S15 0.028003688901662827 0.02190727461129427 0.02712459582835436 0.03377068229019642 \n", + "N4S5 0.028514925856143236 0.02033175784163177 0.02257567341439426 0.03298728261142969 \n", + "N6S5 0.021926400096466143 0.019953267260765035 0.024053960107266903 0.028724052322407562 0.025251608341932297 0.03227434307336807 \n" + ] + } + ], + "source": [ + "from pandas import pandas\n", + "from sqlalchemy import create_engine\n", + "\n", + "import pymysql\n", + "\n", + "\n", + "def convert_strings(input_list):\n", + " output_list = []\n", + " for i, s in enumerate(input_list):\n", + " if(i > 0):\n", + " new_s = s.replace(\"s\",\"\").replace(str(i),\"\",1)\n", + " else:\n", + " new_s = s.replace(\"s\",\"\")\n", + " output_list.append(int(new_s))\n", + " return str(output_list)\n", + "\n", + "pymysql.install_as_MySQLdb()\n", + "\n", + "my_conn = create_engine(\"mysql+mysqldb://root:root@localhost:3008/ACL_Extension\") # fill details\n", + "my_conn = my_conn.connect()\n", + "\n", + "\n", + "# print(df.query('node == '))\n", + "\n", + "from pathlib import Path\n", + "results = {}\n", + "tables = {}\n", + "pathlist = Path(\"Performance/\").rglob('*.txt')\n", + "for path in sorted(pathlist):\n", + "\n", + "\n", + "\n", + " # because path is object not string\n", + " nodes = path.parts[1].lower().replace(\"n\",\"\")\n", + " services = path.parts[2].lower().replace(\".txt\",\"\").replace(\"results_\",\"\").split(\"s\")[1]\n", + " table_name = f\"w{nodes}s{services}n{nodes}\"\n", + " #print(path)\n", + " try:\n", + " if table_name not in tables:\n", + " df = pandas.read_sql(f\"SELECT node,count(service),avg(metric) as avg,stddev(metric) as dev,(avg(metric) / stddev(metric)) as coef FROM stats_{table_name} GROUP BY node ORDER BY avg DESC\", my_conn)\n", + " tables[table_name] = df\n", + " else:\n", + " df = tables[table_name]\n", + "\n", + " open_file = open(path, \"r\")\n", + " window_size = str(path).split(\"_\")[1].split(\"n\")[0].replace(\"w\",\"\")\n", + "\n", + " read_file = open_file.read()\n", + " if len(read_file) > 0:\n", + " #remove last comma\n", + " cleaned_file = read_file[:-1].split(\",\")\n", + " # print(cleaned_file)\n", + " result_array = convert_strings(cleaned_file)\n", + "\n", + " # print(result_array)\n", + "#\n", + " cleaned_file = f\"[{result_array}]\"\n", + " # print(cleaned_file)\n", + " filtered_df = df[df['node'] == result_array]\n", + " selected_index = filtered_df.index[0]\n", + " metric_value = filtered_df['avg'].values[0]\n", + "\n", + " if nodes not in results:\n", + " results[nodes] = {}\n", + " if services not in results[nodes]:\n", + " results[nodes][services] = {}\n", + " results[nodes][services][window_size] = {\n", + " \"selected_index\": selected_index,\n", + " \"metric_value\": metric_value,\n", + " \"total\": len(df),\n", + " \"dev\": filtered_df['dev'].values[0],\n", + " \"coef\": filtered_df['coef'].values[0],\n", + " }\n", + "\n", + " #print(f\"{path.parts[2].lower()[:-4][8:]} {result_array} {selected_index}/{len(df)} => {selected_index/len(df):.4f} {metric_value:.4f} {filtered_df['avg'].values[0]:.4f} {filtered_df['dev'].values[0]:.4f} {filtered_df['coef'].values[0]:.4f} {metric_value}\")\n", + "\n", + " open_file.close()\n", + " except Exception as e:\n", + " #print(e)\n", + " pass\n", + "\n", + "# for N in results:\n", + "# print(f\"Node {N}\", end=\" \")\n", + "# for S in results[N]:\n", + "# print(f\"\\tService {S}\", end=\"\\n\")\n", + "# for W in results[N][S]:\n", + "# print(f\"\\t\\tWindow {W} => {results[N][S][W]}\")\n", + "\n", + "for N in results:\n", + " for S in results[N]:\n", + " print(f\"N{N}S{S}\", end=\"\")\n", + " for W in results[N][S]:\n", + " value = results[N][S][W].get(\"metric_value\")\n", + " print(f\" {value}\",end=\" \")\n", + " print()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "N2S5 0.525999922997585 0.9223083288982994\n", + "N2S10 0.7146094649919829 0.8512835934736874\n", + "N2S15 0.3527476726431038 0.5598453306665571\n", + "N2S20 0.5310426357196792 0.06307014991629034\n", + "N3S5 0.028457272416839374 0.27914129415971356 0.7643158095731485\n", + "N3S10 0.17471550492538124 0.7149735259786957 0.03417352603374568\n", + "N3S15 0.005335226805612048 0.5390987597968014 0.5492557913559589\n", + "N3S20 0.801295604909027 0.6526771566312796 0.772545706153225\n", + "N4S5 0.8303239999469701 0.20864298648147372 0.3547860352104477 0.2807566539587585\n", + "N4S10 0.29562131833128114 0.2729654532488399 0.43164738231579713 0.36996335096591293\n", + "N4S15 0.46139405842571324 0.28335425075788223 0.33838940408491613 0.1964768810119345\n", + "N4S20 0.842035435573991 0.3973329306062281 0.8998315571783574 0.4798850588800757\n", + "N5S5 0.44678428639814083 0.5033300947997191 0.7651502669497279 0.8299409563036589 0.14901640054182674\n", + "N5S10 0.49674295746091557 0.3588535144917916 0.8576798866843331 0.050230473630653805 0.21001897303213568\n", + "N5S15 0.09769806617837484 0.20247618802530165 0.4670725213113255 0.19680302097238334 0.4307661462364786\n", + "N5S20 0.27725417013777576 0.8384701506951161 0.7760784207546361 0.43437217324383537 0.8777692870408613\n" + ] + } + ], + "source": [ + "import random\n", + "\n", + "data = \"\"\"N2S5\n", + "N2S10\n", + "N2S15\n", + "N2S20\n", + "N3S5\n", + "N3S10\n", + "N3S15\n", + "N3S20\n", + "N4S5\n", + "N4S10\n", + "N4S15\n", + "N4S20\n", + "N5S5\n", + "N5S10\n", + "N5S15\n", + "N5S20\"\"\"\n", + "\n", + "lines = data.split(\"\\n\")\n", + "for i, line in enumerate(lines):\n", + " parts = line.split()\n", + " if len(parts) < 3:\n", + " n = parts[0].split(\"S\")[0].split(\"N\")[1]\n", + " n = int(n)\n", + " for s in range(1,n+1):\n", + " lines[i] += \" \" + str(random.random())\n", + "\n", + "data = \"\\n\".join(lines)\n", + "print(data)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Experiments/Simulator/MatrixGenerator.py b/Experiments/Simulator/MatrixGenerator.py index a276b5b..efbbbf0 100644 --- a/Experiments/Simulator/MatrixGenerator.py +++ b/Experiments/Simulator/MatrixGenerator.py @@ -1,29 +1,24 @@ import numpy.random - +import configuration class MatrixGenerator: weights = [] def print_combinations(self, current_combination, depth, max_depth, services): if depth == max_depth: - #print(*current_combination) self.weights.append(current_combination) return for i in range(services): - self.print_combinations(current_combination + [round(numpy.random.uniform(0.8, 1), 3)], depth + 1, + self.print_combinations(current_combination + [round(numpy.random.uniform(0.95, 1), 3)], depth + 1, max_depth, services) def __init__(self): - numpy.random.seed(50) - from configuration import NUMBER_OF_NODES, NUMBER_OF_SERVICES - self.NUMBER_OF_COMBINATION = NUMBER_OF_SERVICES ** NUMBER_OF_NODES + self.weights = [] + numpy.random.seed(50*configuration.EXPERIMENT_ID) + + self.NUMBER_OF_COMBINATION = configuration.NUMBER_OF_SERVICES ** configuration.NUMBER_OF_NODES # self.weights = numpy.random.uniform(0.8, 1, [self.NUMBER_OF_COMBINATION, NUMBER_OF_NODES]) - self.print_combinations([], 0, NUMBER_OF_NODES, NUMBER_OF_SERVICES) + self.print_combinations([], 0, configuration.NUMBER_OF_NODES, configuration.NUMBER_OF_SERVICES) def get_weights(self): return self.weights def get_weight(self, combination, service): - return self.weights.search_combination(combination)[0][service.parent.id] - -#if __name__ == "__main__": -# matrix = MatrixGenerator() -# print(matrix) -# print(matrix.get_weight(10, Service(0, parent=Node(0)))) + return self.weights.search_combination(combination)[0][service.parent.id] \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N2/results_1_w1n2s10.txt b/Experiments/Simulator/Performance/N2/results_1_w1n2s10.txt new file mode 100644 index 0000000..8fdf271 --- /dev/null +++ b/Experiments/Simulator/Performance/N2/results_1_w1n2s10.txt @@ -0,0 +1 @@ +s5,s10, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N2/results_1_w1n2s15.txt b/Experiments/Simulator/Performance/N2/results_1_w1n2s15.txt new file mode 100644 index 0000000..f22f1e9 --- /dev/null +++ b/Experiments/Simulator/Performance/N2/results_1_w1n2s15.txt @@ -0,0 +1 @@ +s14,s111, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N2/results_1_w1n2s20.txt b/Experiments/Simulator/Performance/N2/results_1_w1n2s20.txt new file mode 100644 index 0000000..f22f1e9 --- /dev/null +++ b/Experiments/Simulator/Performance/N2/results_1_w1n2s20.txt @@ -0,0 +1 @@ +s14,s111, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N2/results_1_w1n2s5.txt b/Experiments/Simulator/Performance/N2/results_1_w1n2s5.txt new file mode 100644 index 0000000..a8d2b8c --- /dev/null +++ b/Experiments/Simulator/Performance/N2/results_1_w1n2s5.txt @@ -0,0 +1 @@ +s4,s10, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N2/results_1_w2n2s10.txt b/Experiments/Simulator/Performance/N2/results_1_w2n2s10.txt new file mode 100644 index 0000000..4941909 --- /dev/null +++ b/Experiments/Simulator/Performance/N2/results_1_w2n2s10.txt @@ -0,0 +1 @@ +s5,s14, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N2/results_1_w2n2s15.txt b/Experiments/Simulator/Performance/N2/results_1_w2n2s15.txt new file mode 100644 index 0000000..cff4b15 --- /dev/null +++ b/Experiments/Simulator/Performance/N2/results_1_w2n2s15.txt @@ -0,0 +1 @@ +s5,s12, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N2/results_1_w2n2s20.txt b/Experiments/Simulator/Performance/N2/results_1_w2n2s20.txt new file mode 100644 index 0000000..1d8057c --- /dev/null +++ b/Experiments/Simulator/Performance/N2/results_1_w2n2s20.txt @@ -0,0 +1 @@ +s11,s16, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N2/results_1_w2n2s5.txt b/Experiments/Simulator/Performance/N2/results_1_w2n2s5.txt new file mode 100644 index 0000000..9bcbd90 --- /dev/null +++ b/Experiments/Simulator/Performance/N2/results_1_w2n2s5.txt @@ -0,0 +1 @@ +s2,s13, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N2/results_2_w1n2s5.txt b/Experiments/Simulator/Performance/N2/results_2_w1n2s5.txt new file mode 100644 index 0000000..e69de29 diff --git a/Experiments/Simulator/Performance/N2/results_3_w1n2s10.txt b/Experiments/Simulator/Performance/N2/results_3_w1n2s10.txt new file mode 100644 index 0000000..fbdd46b --- /dev/null +++ b/Experiments/Simulator/Performance/N2/results_3_w1n2s10.txt @@ -0,0 +1 @@ +s7,s17, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N2/results_3_w1n2s15.txt b/Experiments/Simulator/Performance/N2/results_3_w1n2s15.txt new file mode 100644 index 0000000..f22f1e9 --- /dev/null +++ b/Experiments/Simulator/Performance/N2/results_3_w1n2s15.txt @@ -0,0 +1 @@ +s14,s111, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N2/results_3_w1n2s20.txt b/Experiments/Simulator/Performance/N2/results_3_w1n2s20.txt new file mode 100644 index 0000000..f22f1e9 --- /dev/null +++ b/Experiments/Simulator/Performance/N2/results_3_w1n2s20.txt @@ -0,0 +1 @@ +s14,s111, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N2/results_3_w1n2s5.txt b/Experiments/Simulator/Performance/N2/results_3_w1n2s5.txt new file mode 100644 index 0000000..a8d2b8c --- /dev/null +++ b/Experiments/Simulator/Performance/N2/results_3_w1n2s5.txt @@ -0,0 +1 @@ +s4,s10, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N2/results_3_w2n2s10.txt b/Experiments/Simulator/Performance/N2/results_3_w2n2s10.txt new file mode 100644 index 0000000..a7db987 --- /dev/null +++ b/Experiments/Simulator/Performance/N2/results_3_w2n2s10.txt @@ -0,0 +1 @@ +s6,s15, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N2/results_3_w2n2s15.txt b/Experiments/Simulator/Performance/N2/results_3_w2n2s15.txt new file mode 100644 index 0000000..e69de29 diff --git a/Experiments/Simulator/Performance/N2/results_3_w2n2s5.txt b/Experiments/Simulator/Performance/N2/results_3_w2n2s5.txt new file mode 100644 index 0000000..1d5d9b1 --- /dev/null +++ b/Experiments/Simulator/Performance/N2/results_3_w2n2s5.txt @@ -0,0 +1 @@ +s2,s10, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N2/results_4_w1n2s10.txt b/Experiments/Simulator/Performance/N2/results_4_w1n2s10.txt new file mode 100644 index 0000000..4ed4a44 --- /dev/null +++ b/Experiments/Simulator/Performance/N2/results_4_w1n2s10.txt @@ -0,0 +1 @@ +s7,s14, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N2/results_4_w1n2s15.txt b/Experiments/Simulator/Performance/N2/results_4_w1n2s15.txt new file mode 100644 index 0000000..9f4117b --- /dev/null +++ b/Experiments/Simulator/Performance/N2/results_4_w1n2s15.txt @@ -0,0 +1 @@ +s14,s14, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N2/results_4_w1n2s20.txt b/Experiments/Simulator/Performance/N2/results_4_w1n2s20.txt new file mode 100644 index 0000000..9f4117b --- /dev/null +++ b/Experiments/Simulator/Performance/N2/results_4_w1n2s20.txt @@ -0,0 +1 @@ +s14,s14, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N2/results_4_w1n2s5.txt b/Experiments/Simulator/Performance/N2/results_4_w1n2s5.txt new file mode 100644 index 0000000..82ffb03 --- /dev/null +++ b/Experiments/Simulator/Performance/N2/results_4_w1n2s5.txt @@ -0,0 +1 @@ +s2,s14, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N2/results_4_w2n2s10.txt b/Experiments/Simulator/Performance/N2/results_4_w2n2s10.txt new file mode 100644 index 0000000..e69de29 diff --git a/Experiments/Simulator/Performance/N2/results_4_w2n2s5.txt b/Experiments/Simulator/Performance/N2/results_4_w2n2s5.txt new file mode 100644 index 0000000..46529b8 --- /dev/null +++ b/Experiments/Simulator/Performance/N2/results_4_w2n2s5.txt @@ -0,0 +1 @@ +s4,s14, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N3/results_1_w1n3s10.txt b/Experiments/Simulator/Performance/N3/results_1_w1n3s10.txt new file mode 100644 index 0000000..f681bcb --- /dev/null +++ b/Experiments/Simulator/Performance/N3/results_1_w1n3s10.txt @@ -0,0 +1 @@ +s5,s10,s20, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N3/results_1_w1n3s15.txt b/Experiments/Simulator/Performance/N3/results_1_w1n3s15.txt new file mode 100644 index 0000000..3a11418 --- /dev/null +++ b/Experiments/Simulator/Performance/N3/results_1_w1n3s15.txt @@ -0,0 +1 @@ +s14,s10,s210, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N3/results_1_w1n3s20.txt b/Experiments/Simulator/Performance/N3/results_1_w1n3s20.txt new file mode 100644 index 0000000..3a11418 --- /dev/null +++ b/Experiments/Simulator/Performance/N3/results_1_w1n3s20.txt @@ -0,0 +1 @@ +s14,s10,s210, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N3/results_w3n3s5.txt b/Experiments/Simulator/Performance/N3/results_1_w1n3s5.txt similarity index 100% rename from Experiments/Simulator/Performance/N3/results_w3n3s5.txt rename to Experiments/Simulator/Performance/N3/results_1_w1n3s5.txt diff --git a/Experiments/Simulator/Performance/N3/results_w2n3s10.txt b/Experiments/Simulator/Performance/N3/results_1_w2n3s10.txt similarity index 100% rename from Experiments/Simulator/Performance/N3/results_w2n3s10.txt rename to Experiments/Simulator/Performance/N3/results_1_w2n3s10.txt diff --git a/Experiments/Simulator/Performance/N3/results_1_w2n3s15.txt b/Experiments/Simulator/Performance/N3/results_1_w2n3s15.txt new file mode 100644 index 0000000..e7f9300 --- /dev/null +++ b/Experiments/Simulator/Performance/N3/results_1_w2n3s15.txt @@ -0,0 +1 @@ +s9,s19,s210, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N3/results_1_w2n3s20.txt b/Experiments/Simulator/Performance/N3/results_1_w2n3s20.txt new file mode 100644 index 0000000..2d0a996 --- /dev/null +++ b/Experiments/Simulator/Performance/N3/results_1_w2n3s20.txt @@ -0,0 +1 @@ +s15,s115,s23, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N3/results_w2n3s5.txt b/Experiments/Simulator/Performance/N3/results_1_w2n3s5.txt similarity index 100% rename from Experiments/Simulator/Performance/N3/results_w2n3s5.txt rename to Experiments/Simulator/Performance/N3/results_1_w2n3s5.txt diff --git a/Experiments/Simulator/Performance/N3/results_w3n3s10.txt b/Experiments/Simulator/Performance/N3/results_1_w3n3s10.txt similarity index 100% rename from Experiments/Simulator/Performance/N3/results_w3n3s10.txt rename to Experiments/Simulator/Performance/N3/results_1_w3n3s10.txt diff --git a/Experiments/Simulator/Performance/N3/results_w3n3s15.txt b/Experiments/Simulator/Performance/N3/results_1_w3n3s15.txt similarity index 100% rename from Experiments/Simulator/Performance/N3/results_w3n3s15.txt rename to Experiments/Simulator/Performance/N3/results_1_w3n3s15.txt diff --git a/Experiments/Simulator/Performance/N3/results_1_w3n3s20.txt b/Experiments/Simulator/Performance/N3/results_1_w3n3s20.txt new file mode 100644 index 0000000..fe28026 --- /dev/null +++ b/Experiments/Simulator/Performance/N3/results_1_w3n3s20.txt @@ -0,0 +1 @@ +s14,s119,s21, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N3/results_1_w3n3s5.txt b/Experiments/Simulator/Performance/N3/results_1_w3n3s5.txt new file mode 100644 index 0000000..ff479a5 --- /dev/null +++ b/Experiments/Simulator/Performance/N3/results_1_w3n3s5.txt @@ -0,0 +1 @@ +s4,s10,s20, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N4/results_1_w1n4s10.txt b/Experiments/Simulator/Performance/N4/results_1_w1n4s10.txt new file mode 100644 index 0000000..c657554 --- /dev/null +++ b/Experiments/Simulator/Performance/N4/results_1_w1n4s10.txt @@ -0,0 +1 @@ +s5,s10,s20,s39, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N4/results_1_w1n4s15.txt b/Experiments/Simulator/Performance/N4/results_1_w1n4s15.txt new file mode 100644 index 0000000..126e432 --- /dev/null +++ b/Experiments/Simulator/Performance/N4/results_1_w1n4s15.txt @@ -0,0 +1 @@ +s14,s10,s20,s39, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N4/results_1_w1n4s20.txt b/Experiments/Simulator/Performance/N4/results_1_w1n4s20.txt new file mode 100644 index 0000000..126e432 --- /dev/null +++ b/Experiments/Simulator/Performance/N4/results_1_w1n4s20.txt @@ -0,0 +1 @@ +s14,s10,s20,s39, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N4/results_1_w1n4s5.txt b/Experiments/Simulator/Performance/N4/results_1_w1n4s5.txt new file mode 100644 index 0000000..14651df --- /dev/null +++ b/Experiments/Simulator/Performance/N4/results_1_w1n4s5.txt @@ -0,0 +1 @@ +s4,s10,s20,s31, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N4/results_1_w2n4s10.txt b/Experiments/Simulator/Performance/N4/results_1_w2n4s10.txt new file mode 100644 index 0000000..1607e57 --- /dev/null +++ b/Experiments/Simulator/Performance/N4/results_1_w2n4s10.txt @@ -0,0 +1 @@ +s5,s17,s27,s30, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N4/results_1_w2n4s15.txt b/Experiments/Simulator/Performance/N4/results_1_w2n4s15.txt new file mode 100644 index 0000000..126e432 --- /dev/null +++ b/Experiments/Simulator/Performance/N4/results_1_w2n4s15.txt @@ -0,0 +1 @@ +s14,s10,s20,s39, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N4/results_1_w2n4s20.txt b/Experiments/Simulator/Performance/N4/results_1_w2n4s20.txt new file mode 100644 index 0000000..7aebdc4 --- /dev/null +++ b/Experiments/Simulator/Performance/N4/results_1_w2n4s20.txt @@ -0,0 +1 @@ +s14,s113,s213,s312, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N4/results_1_w2n4s5.txt b/Experiments/Simulator/Performance/N4/results_1_w2n4s5.txt new file mode 100644 index 0000000..9f353f0 --- /dev/null +++ b/Experiments/Simulator/Performance/N4/results_1_w2n4s5.txt @@ -0,0 +1 @@ +s4,s10,s24,s30, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N4/results_w3n4s10.txt b/Experiments/Simulator/Performance/N4/results_1_w3n4s10.txt similarity index 100% rename from Experiments/Simulator/Performance/N4/results_w3n4s10.txt rename to Experiments/Simulator/Performance/N4/results_1_w3n4s10.txt diff --git a/Experiments/Simulator/Performance/N4/results_1_w3n4s15.txt b/Experiments/Simulator/Performance/N4/results_1_w3n4s15.txt new file mode 100644 index 0000000..3d0358f --- /dev/null +++ b/Experiments/Simulator/Performance/N4/results_1_w3n4s15.txt @@ -0,0 +1 @@ +s4,s14,s20,s34, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N4/results_1_w3n4s20.txt b/Experiments/Simulator/Performance/N4/results_1_w3n4s20.txt new file mode 100644 index 0000000..b689a04 --- /dev/null +++ b/Experiments/Simulator/Performance/N4/results_1_w3n4s20.txt @@ -0,0 +1 @@ +s13,s14,s219,s39, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N4/results_w3n4s5.txt b/Experiments/Simulator/Performance/N4/results_1_w3n4s5.txt similarity index 100% rename from Experiments/Simulator/Performance/N4/results_w3n4s5.txt rename to Experiments/Simulator/Performance/N4/results_1_w3n4s5.txt diff --git a/Experiments/Simulator/Performance/N4/results_w4n4s10.txt b/Experiments/Simulator/Performance/N4/results_1_w4n4s10.txt similarity index 100% rename from Experiments/Simulator/Performance/N4/results_w4n4s10.txt rename to Experiments/Simulator/Performance/N4/results_1_w4n4s10.txt diff --git a/Experiments/Simulator/Performance/N4/results_w4n4s15.txt b/Experiments/Simulator/Performance/N4/results_1_w4n4s15.txt similarity index 100% rename from Experiments/Simulator/Performance/N4/results_w4n4s15.txt rename to Experiments/Simulator/Performance/N4/results_1_w4n4s15.txt diff --git a/Experiments/Simulator/Performance/N4/results_1_w4n4s20.txt b/Experiments/Simulator/Performance/N4/results_1_w4n4s20.txt new file mode 100644 index 0000000..e69de29 diff --git a/Experiments/Simulator/Performance/N4/results_w4n4s5.txt b/Experiments/Simulator/Performance/N4/results_1_w4n4s5.txt similarity index 100% rename from Experiments/Simulator/Performance/N4/results_w4n4s5.txt rename to Experiments/Simulator/Performance/N4/results_1_w4n4s5.txt diff --git a/Experiments/Simulator/Performance/N5/results_w1n5s10.txt b/Experiments/Simulator/Performance/N5/results_w1n5s10.txt deleted file mode 100644 index bcab543..0000000 --- a/Experiments/Simulator/Performance/N5/results_w1n5s10.txt +++ /dev/null @@ -1 +0,0 @@ -s7,s14,s28,s37,s48, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N5/results_w2n5s10.txt b/Experiments/Simulator/Performance/N5/results_w2n5s10.txt deleted file mode 100644 index 790b9f8..0000000 --- a/Experiments/Simulator/Performance/N5/results_w2n5s10.txt +++ /dev/null @@ -1 +0,0 @@ -s7,s14,s27,s37,s42, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N5/results_w3n5s10.txt b/Experiments/Simulator/Performance/N5/results_w3n5s10.txt deleted file mode 100644 index 3ce27ed..0000000 --- a/Experiments/Simulator/Performance/N5/results_w3n5s10.txt +++ /dev/null @@ -1 +0,0 @@ -s7,s10,s27,s37,s49, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N5/results_w4n5s10.txt b/Experiments/Simulator/Performance/N5/results_w4n5s10.txt deleted file mode 100644 index 3c6edce..0000000 --- a/Experiments/Simulator/Performance/N5/results_w4n5s10.txt +++ /dev/null @@ -1 +0,0 @@ -s6,s16,s26,s33,s43, \ No newline at end of file diff --git a/Experiments/Simulator/Performance1/N2/results_w1n2s10.txt b/Experiments/Simulator/Performance1/N2/results_w1n2s10.txt new file mode 100644 index 0000000..757b648 --- /dev/null +++ b/Experiments/Simulator/Performance1/N2/results_w1n2s10.txt @@ -0,0 +1 @@ +s7,s10, \ No newline at end of file diff --git a/Experiments/Simulator/Performance1/N2/results_w1n2s15.txt b/Experiments/Simulator/Performance1/N2/results_w1n2s15.txt new file mode 100644 index 0000000..5152e34 --- /dev/null +++ b/Experiments/Simulator/Performance1/N2/results_w1n2s15.txt @@ -0,0 +1 @@ +s12,s10, \ No newline at end of file diff --git a/Experiments/Simulator/Performance1/N2/results_w1n2s20.txt b/Experiments/Simulator/Performance1/N2/results_w1n2s20.txt new file mode 100644 index 0000000..7d0dbaa --- /dev/null +++ b/Experiments/Simulator/Performance1/N2/results_w1n2s20.txt @@ -0,0 +1 @@ +s7,s111, \ No newline at end of file diff --git a/Experiments/Simulator/Performance1/N2/results_w1n2s5.txt b/Experiments/Simulator/Performance1/N2/results_w1n2s5.txt new file mode 100644 index 0000000..5bf4895 --- /dev/null +++ b/Experiments/Simulator/Performance1/N2/results_w1n2s5.txt @@ -0,0 +1 @@ +s1,s10, \ No newline at end of file diff --git a/Experiments/Simulator/Performance1/N2/results_w2n2s10.txt b/Experiments/Simulator/Performance1/N2/results_w2n2s10.txt new file mode 100644 index 0000000..22cbb33 --- /dev/null +++ b/Experiments/Simulator/Performance1/N2/results_w2n2s10.txt @@ -0,0 +1 @@ +s1,s13, \ No newline at end of file diff --git a/Experiments/Simulator/Performance1/N2/results_w2n2s15.txt b/Experiments/Simulator/Performance1/N2/results_w2n2s15.txt new file mode 100644 index 0000000..34ff531 --- /dev/null +++ b/Experiments/Simulator/Performance1/N2/results_w2n2s15.txt @@ -0,0 +1 @@ +s8,s18, \ No newline at end of file diff --git a/Experiments/Simulator/Performance1/N2/results_w2n2s20.txt b/Experiments/Simulator/Performance1/N2/results_w2n2s20.txt new file mode 100644 index 0000000..1d8057c --- /dev/null +++ b/Experiments/Simulator/Performance1/N2/results_w2n2s20.txt @@ -0,0 +1 @@ +s11,s16, \ No newline at end of file diff --git a/Experiments/Simulator/Performance1/N2/results_w2n2s5.txt b/Experiments/Simulator/Performance1/N2/results_w2n2s5.txt new file mode 100644 index 0000000..9bcbd90 --- /dev/null +++ b/Experiments/Simulator/Performance1/N2/results_w2n2s5.txt @@ -0,0 +1 @@ +s2,s13, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N3/event_newwindown_n3_w2.dat b/Experiments/Simulator/Performance1/N3/event_newwindown_n3_w2.dat similarity index 100% rename from Experiments/Simulator/Performance/N3/event_newwindown_n3_w2.dat rename to Experiments/Simulator/Performance1/N3/event_newwindown_n3_w2.dat diff --git a/Experiments/Simulator/Performance/N3/event_newwindown_n3_w3.dat b/Experiments/Simulator/Performance1/N3/event_newwindown_n3_w3.dat similarity index 100% rename from Experiments/Simulator/Performance/N3/event_newwindown_n3_w3.dat rename to Experiments/Simulator/Performance1/N3/event_newwindown_n3_w3.dat diff --git a/Experiments/Simulator/Performance/N3/results_w1n3s10.txt b/Experiments/Simulator/Performance1/N3/results_w1n3s10.txt similarity index 100% rename from Experiments/Simulator/Performance/N3/results_w1n3s10.txt rename to Experiments/Simulator/Performance1/N3/results_w1n3s10.txt diff --git a/Experiments/Simulator/Performance/N3/results_w1n3s15.txt b/Experiments/Simulator/Performance1/N3/results_w1n3s15.txt similarity index 100% rename from Experiments/Simulator/Performance/N3/results_w1n3s15.txt rename to Experiments/Simulator/Performance1/N3/results_w1n3s15.txt diff --git a/Experiments/Simulator/Performance/N3/results_w1n3s20.txt b/Experiments/Simulator/Performance1/N3/results_w1n3s20.txt similarity index 100% rename from Experiments/Simulator/Performance/N3/results_w1n3s20.txt rename to Experiments/Simulator/Performance1/N3/results_w1n3s20.txt diff --git a/Experiments/Simulator/Performance/N3/results_w1n3s5.txt b/Experiments/Simulator/Performance1/N3/results_w1n3s5.txt similarity index 100% rename from Experiments/Simulator/Performance/N3/results_w1n3s5.txt rename to Experiments/Simulator/Performance1/N3/results_w1n3s5.txt diff --git a/Experiments/Simulator/Performance1/N3/results_w2n3s10.txt b/Experiments/Simulator/Performance1/N3/results_w2n3s10.txt new file mode 100644 index 0000000..249cb92 --- /dev/null +++ b/Experiments/Simulator/Performance1/N3/results_w2n3s10.txt @@ -0,0 +1 @@ +s7,s17,s21, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N3/results_w2n3s15.txt b/Experiments/Simulator/Performance1/N3/results_w2n3s15.txt similarity index 100% rename from Experiments/Simulator/Performance/N3/results_w2n3s15.txt rename to Experiments/Simulator/Performance1/N3/results_w2n3s15.txt diff --git a/Experiments/Simulator/Performance/N3/results_w2n3s20.txt b/Experiments/Simulator/Performance1/N3/results_w2n3s20.txt similarity index 100% rename from Experiments/Simulator/Performance/N3/results_w2n3s20.txt rename to Experiments/Simulator/Performance1/N3/results_w2n3s20.txt diff --git a/Experiments/Simulator/Performance1/N3/results_w2n3s5.txt b/Experiments/Simulator/Performance1/N3/results_w2n3s5.txt new file mode 100644 index 0000000..cbfbf5f --- /dev/null +++ b/Experiments/Simulator/Performance1/N3/results_w2n3s5.txt @@ -0,0 +1 @@ +s4,s14,s21, \ No newline at end of file diff --git a/Experiments/Simulator/Performance1/N3/results_w3n3s10.txt b/Experiments/Simulator/Performance1/N3/results_w3n3s10.txt new file mode 100644 index 0000000..7624e05 --- /dev/null +++ b/Experiments/Simulator/Performance1/N3/results_w3n3s10.txt @@ -0,0 +1 @@ +s6,s13,s28, \ No newline at end of file diff --git a/Experiments/Simulator/Performance1/N3/results_w3n3s15.txt b/Experiments/Simulator/Performance1/N3/results_w3n3s15.txt new file mode 100644 index 0000000..880a29d --- /dev/null +++ b/Experiments/Simulator/Performance1/N3/results_w3n3s15.txt @@ -0,0 +1 @@ +s7,s10,s211, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N3/results_w3n3s20.txt b/Experiments/Simulator/Performance1/N3/results_w3n3s20.txt similarity index 100% rename from Experiments/Simulator/Performance/N3/results_w3n3s20.txt rename to Experiments/Simulator/Performance1/N3/results_w3n3s20.txt diff --git a/Experiments/Simulator/Performance1/N3/results_w3n3s5.txt b/Experiments/Simulator/Performance1/N3/results_w3n3s5.txt new file mode 100644 index 0000000..ff479a5 --- /dev/null +++ b/Experiments/Simulator/Performance1/N3/results_w3n3s5.txt @@ -0,0 +1 @@ +s4,s10,s20, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N4/event_newwindown_n4_w2.dat b/Experiments/Simulator/Performance1/N4/event_newwindown_n4_w2.dat similarity index 100% rename from Experiments/Simulator/Performance/N4/event_newwindown_n4_w2.dat rename to Experiments/Simulator/Performance1/N4/event_newwindown_n4_w2.dat diff --git a/Experiments/Simulator/Performance/N4/event_newwindown_n4_w3.dat b/Experiments/Simulator/Performance1/N4/event_newwindown_n4_w3.dat similarity index 100% rename from Experiments/Simulator/Performance/N4/event_newwindown_n4_w3.dat rename to Experiments/Simulator/Performance1/N4/event_newwindown_n4_w3.dat diff --git a/Experiments/Simulator/Performance/N4/event_newwindown_n4_w4.dat b/Experiments/Simulator/Performance1/N4/event_newwindown_n4_w4.dat similarity index 100% rename from Experiments/Simulator/Performance/N4/event_newwindown_n4_w4.dat rename to Experiments/Simulator/Performance1/N4/event_newwindown_n4_w4.dat diff --git a/Experiments/Simulator/Performance/N4/results_w1n4s10.txt b/Experiments/Simulator/Performance1/N4/results_w1n4s10.txt similarity index 100% rename from Experiments/Simulator/Performance/N4/results_w1n4s10.txt rename to Experiments/Simulator/Performance1/N4/results_w1n4s10.txt diff --git a/Experiments/Simulator/Performance/N4/results_w1n4s15.txt b/Experiments/Simulator/Performance1/N4/results_w1n4s15.txt similarity index 100% rename from Experiments/Simulator/Performance/N4/results_w1n4s15.txt rename to Experiments/Simulator/Performance1/N4/results_w1n4s15.txt diff --git a/Experiments/Simulator/Performance/N4/results_w1n4s20.txt b/Experiments/Simulator/Performance1/N4/results_w1n4s20.txt similarity index 100% rename from Experiments/Simulator/Performance/N4/results_w1n4s20.txt rename to Experiments/Simulator/Performance1/N4/results_w1n4s20.txt diff --git a/Experiments/Simulator/Performance/N4/results_w1n4s5.txt b/Experiments/Simulator/Performance1/N4/results_w1n4s5.txt similarity index 100% rename from Experiments/Simulator/Performance/N4/results_w1n4s5.txt rename to Experiments/Simulator/Performance1/N4/results_w1n4s5.txt diff --git a/Experiments/Simulator/Performance/N4/results_w2n4s10.txt b/Experiments/Simulator/Performance1/N4/results_w2n4s10.txt similarity index 100% rename from Experiments/Simulator/Performance/N4/results_w2n4s10.txt rename to Experiments/Simulator/Performance1/N4/results_w2n4s10.txt diff --git a/Experiments/Simulator/Performance/N4/results_w2n4s15.txt b/Experiments/Simulator/Performance1/N4/results_w2n4s15.txt similarity index 100% rename from Experiments/Simulator/Performance/N4/results_w2n4s15.txt rename to Experiments/Simulator/Performance1/N4/results_w2n4s15.txt diff --git a/Experiments/Simulator/Performance/N4/results_w2n4s20.txt b/Experiments/Simulator/Performance1/N4/results_w2n4s20.txt similarity index 100% rename from Experiments/Simulator/Performance/N4/results_w2n4s20.txt rename to Experiments/Simulator/Performance1/N4/results_w2n4s20.txt diff --git a/Experiments/Simulator/Performance/N4/results_w2n4s5.txt b/Experiments/Simulator/Performance1/N4/results_w2n4s5.txt similarity index 100% rename from Experiments/Simulator/Performance/N4/results_w2n4s5.txt rename to Experiments/Simulator/Performance1/N4/results_w2n4s5.txt diff --git a/Experiments/Simulator/Performance1/N4/results_w3n4s10.txt b/Experiments/Simulator/Performance1/N4/results_w3n4s10.txt new file mode 100644 index 0000000..d1391ee --- /dev/null +++ b/Experiments/Simulator/Performance1/N4/results_w3n4s10.txt @@ -0,0 +1 @@ +s0,s10,s27,s30, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N4/results_w3n4s15.txt b/Experiments/Simulator/Performance1/N4/results_w3n4s15.txt similarity index 100% rename from Experiments/Simulator/Performance/N4/results_w3n4s15.txt rename to Experiments/Simulator/Performance1/N4/results_w3n4s15.txt diff --git a/Experiments/Simulator/Performance/N4/results_w3n4s20.txt b/Experiments/Simulator/Performance1/N4/results_w3n4s20.txt similarity index 100% rename from Experiments/Simulator/Performance/N4/results_w3n4s20.txt rename to Experiments/Simulator/Performance1/N4/results_w3n4s20.txt diff --git a/Experiments/Simulator/Performance1/N4/results_w3n4s5.txt b/Experiments/Simulator/Performance1/N4/results_w3n4s5.txt new file mode 100644 index 0000000..35196c1 --- /dev/null +++ b/Experiments/Simulator/Performance1/N4/results_w3n4s5.txt @@ -0,0 +1 @@ +s4,s14,s20,s32, \ No newline at end of file diff --git a/Experiments/Simulator/Performance1/N4/results_w4n4s10.txt b/Experiments/Simulator/Performance1/N4/results_w4n4s10.txt new file mode 100644 index 0000000..5b9fbeb --- /dev/null +++ b/Experiments/Simulator/Performance1/N4/results_w4n4s10.txt @@ -0,0 +1 @@ +s5,s17,s27,s34, \ No newline at end of file diff --git a/Experiments/Simulator/Performance1/N4/results_w4n4s15.txt b/Experiments/Simulator/Performance1/N4/results_w4n4s15.txt new file mode 100644 index 0000000..729592a --- /dev/null +++ b/Experiments/Simulator/Performance1/N4/results_w4n4s15.txt @@ -0,0 +1 @@ +s11,s19,s27,s313, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N4/results_w4n4s20.txt b/Experiments/Simulator/Performance1/N4/results_w4n4s20.txt similarity index 100% rename from Experiments/Simulator/Performance/N4/results_w4n4s20.txt rename to Experiments/Simulator/Performance1/N4/results_w4n4s20.txt diff --git a/Experiments/Simulator/Performance1/N4/results_w4n4s5.txt b/Experiments/Simulator/Performance1/N4/results_w4n4s5.txt new file mode 100644 index 0000000..de176ab --- /dev/null +++ b/Experiments/Simulator/Performance1/N4/results_w4n4s5.txt @@ -0,0 +1 @@ +s1,s13,s24,s31, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N5/event_newwindown_n5_w2.dat b/Experiments/Simulator/Performance1/N5/event_newwindown_n5_w2.dat similarity index 100% rename from Experiments/Simulator/Performance/N5/event_newwindown_n5_w2.dat rename to Experiments/Simulator/Performance1/N5/event_newwindown_n5_w2.dat diff --git a/Experiments/Simulator/Performance/N5/event_newwindown_n5_w3.dat b/Experiments/Simulator/Performance1/N5/event_newwindown_n5_w3.dat similarity index 100% rename from Experiments/Simulator/Performance/N5/event_newwindown_n5_w3.dat rename to Experiments/Simulator/Performance1/N5/event_newwindown_n5_w3.dat diff --git a/Experiments/Simulator/Performance/N5/event_newwindown_n5_w4.dat b/Experiments/Simulator/Performance1/N5/event_newwindown_n5_w4.dat similarity index 100% rename from Experiments/Simulator/Performance/N5/event_newwindown_n5_w4.dat rename to Experiments/Simulator/Performance1/N5/event_newwindown_n5_w4.dat diff --git a/Experiments/Simulator/Performance/N5/event_newwindown_n5_w5.dat b/Experiments/Simulator/Performance1/N5/event_newwindown_n5_w5.dat similarity index 100% rename from Experiments/Simulator/Performance/N5/event_newwindown_n5_w5.dat rename to Experiments/Simulator/Performance1/N5/event_newwindown_n5_w5.dat diff --git a/Experiments/Simulator/Performance/N5/results_s1n5s5.txt b/Experiments/Simulator/Performance1/N5/results_s1n5s5.txt similarity index 100% rename from Experiments/Simulator/Performance/N5/results_s1n5s5.txt rename to Experiments/Simulator/Performance1/N5/results_s1n5s5.txt diff --git a/Experiments/Simulator/Performance/N5/results_s2n5s5.txt b/Experiments/Simulator/Performance1/N5/results_s2n5s5.txt similarity index 100% rename from Experiments/Simulator/Performance/N5/results_s2n5s5.txt rename to Experiments/Simulator/Performance1/N5/results_s2n5s5.txt diff --git a/Experiments/Simulator/Performance/N5/results_s3n5s5.txt b/Experiments/Simulator/Performance1/N5/results_s3n5s5.txt similarity index 100% rename from Experiments/Simulator/Performance/N5/results_s3n5s5.txt rename to Experiments/Simulator/Performance1/N5/results_s3n5s5.txt diff --git a/Experiments/Simulator/Performance/N5/results_s4n5s5.txt b/Experiments/Simulator/Performance1/N5/results_s4n5s5.txt similarity index 100% rename from Experiments/Simulator/Performance/N5/results_s4n5s5.txt rename to Experiments/Simulator/Performance1/N5/results_s4n5s5.txt diff --git a/Experiments/Simulator/Performance1/N5/results_w1n5s10.txt b/Experiments/Simulator/Performance1/N5/results_w1n5s10.txt new file mode 100644 index 0000000..19518a3 --- /dev/null +++ b/Experiments/Simulator/Performance1/N5/results_w1n5s10.txt @@ -0,0 +1 @@ +s7,s14,s28,s37,s47, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N5/results_w1n5s15.txt b/Experiments/Simulator/Performance1/N5/results_w1n5s15.txt similarity index 100% rename from Experiments/Simulator/Performance/N5/results_w1n5s15.txt rename to Experiments/Simulator/Performance1/N5/results_w1n5s15.txt diff --git a/Experiments/Simulator/Performance/N5/results_w1n5s20.txt b/Experiments/Simulator/Performance1/N5/results_w1n5s20.txt similarity index 100% rename from Experiments/Simulator/Performance/N5/results_w1n5s20.txt rename to Experiments/Simulator/Performance1/N5/results_w1n5s20.txt diff --git a/Experiments/Simulator/Performance/N5/results_w1n5s5.txt b/Experiments/Simulator/Performance1/N5/results_w1n5s5.txt similarity index 100% rename from Experiments/Simulator/Performance/N5/results_w1n5s5.txt rename to Experiments/Simulator/Performance1/N5/results_w1n5s5.txt diff --git a/Experiments/Simulator/Performance1/N5/results_w2n5s10.txt b/Experiments/Simulator/Performance1/N5/results_w2n5s10.txt new file mode 100644 index 0000000..eccceca --- /dev/null +++ b/Experiments/Simulator/Performance1/N5/results_w2n5s10.txt @@ -0,0 +1 @@ +s7,s11,s22,s32,s40, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N5/results_w2n5s5.txt b/Experiments/Simulator/Performance1/N5/results_w2n5s5.txt similarity index 100% rename from Experiments/Simulator/Performance/N5/results_w2n5s5.txt rename to Experiments/Simulator/Performance1/N5/results_w2n5s5.txt diff --git a/Experiments/Simulator/Performance1/N5/results_w3n5s10.txt b/Experiments/Simulator/Performance1/N5/results_w3n5s10.txt new file mode 100644 index 0000000..fbaabb8 --- /dev/null +++ b/Experiments/Simulator/Performance1/N5/results_w3n5s10.txt @@ -0,0 +1 @@ +s1,s11,s28,s37,s41, \ No newline at end of file diff --git a/Experiments/Simulator/Performance1/N5/results_w3n5s5.txt b/Experiments/Simulator/Performance1/N5/results_w3n5s5.txt new file mode 100644 index 0000000..b22edac --- /dev/null +++ b/Experiments/Simulator/Performance1/N5/results_w3n5s5.txt @@ -0,0 +1 @@ +s4,s14,s24,s33,s42, \ No newline at end of file diff --git a/Experiments/Simulator/Performance1/N5/results_w4n5s10.txt b/Experiments/Simulator/Performance1/N5/results_w4n5s10.txt new file mode 100644 index 0000000..2341d7f --- /dev/null +++ b/Experiments/Simulator/Performance1/N5/results_w4n5s10.txt @@ -0,0 +1 @@ +s1,s13,s20,s36,s44, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N5/results_w4n5s15.txt b/Experiments/Simulator/Performance1/N5/results_w4n5s15.txt similarity index 100% rename from Experiments/Simulator/Performance/N5/results_w4n5s15.txt rename to Experiments/Simulator/Performance1/N5/results_w4n5s15.txt diff --git a/Experiments/Simulator/Performance/N5/results_w4n5s20.txt b/Experiments/Simulator/Performance1/N5/results_w4n5s20.txt similarity index 100% rename from Experiments/Simulator/Performance/N5/results_w4n5s20.txt rename to Experiments/Simulator/Performance1/N5/results_w4n5s20.txt diff --git a/Experiments/Simulator/Performance/N5/results_w4n5s5.txt b/Experiments/Simulator/Performance1/N5/results_w4n5s5.txt similarity index 100% rename from Experiments/Simulator/Performance/N5/results_w4n5s5.txt rename to Experiments/Simulator/Performance1/N5/results_w4n5s5.txt diff --git a/Experiments/Simulator/Performance1/N5/results_w5n5s10.txt b/Experiments/Simulator/Performance1/N5/results_w5n5s10.txt new file mode 100644 index 0000000..e69de29 diff --git a/Experiments/Simulator/Performance1/N5/results_w5n5s5.txt b/Experiments/Simulator/Performance1/N5/results_w5n5s5.txt new file mode 100644 index 0000000..86d8fe3 --- /dev/null +++ b/Experiments/Simulator/Performance1/N5/results_w5n5s5.txt @@ -0,0 +1 @@ +s4,s14,s22,s32,s44, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N6/event_newwindown_n6_w2.dat b/Experiments/Simulator/Performance1/N6/event_newwindown_n6_w2.dat similarity index 100% rename from Experiments/Simulator/Performance/N6/event_newwindown_n6_w2.dat rename to Experiments/Simulator/Performance1/N6/event_newwindown_n6_w2.dat diff --git a/Experiments/Simulator/Performance/N6/event_newwindown_n6_w3.dat b/Experiments/Simulator/Performance1/N6/event_newwindown_n6_w3.dat similarity index 100% rename from Experiments/Simulator/Performance/N6/event_newwindown_n6_w3.dat rename to Experiments/Simulator/Performance1/N6/event_newwindown_n6_w3.dat diff --git a/Experiments/Simulator/Performance/N6/event_newwindown_n6_w4.dat b/Experiments/Simulator/Performance1/N6/event_newwindown_n6_w4.dat similarity index 100% rename from Experiments/Simulator/Performance/N6/event_newwindown_n6_w4.dat rename to Experiments/Simulator/Performance1/N6/event_newwindown_n6_w4.dat diff --git a/Experiments/Simulator/Performance/N6/event_newwindown_n6_w5.dat b/Experiments/Simulator/Performance1/N6/event_newwindown_n6_w5.dat similarity index 100% rename from Experiments/Simulator/Performance/N6/event_newwindown_n6_w5.dat rename to Experiments/Simulator/Performance1/N6/event_newwindown_n6_w5.dat diff --git a/Experiments/Simulator/Performance/N6/event_newwindown_n6_w6.dat b/Experiments/Simulator/Performance1/N6/event_newwindown_n6_w6.dat similarity index 100% rename from Experiments/Simulator/Performance/N6/event_newwindown_n6_w6.dat rename to Experiments/Simulator/Performance1/N6/event_newwindown_n6_w6.dat diff --git a/Experiments/Simulator/Performance1/N6/results_w1n6s10.txt b/Experiments/Simulator/Performance1/N6/results_w1n6s10.txt new file mode 100644 index 0000000..c14e23b --- /dev/null +++ b/Experiments/Simulator/Performance1/N6/results_w1n6s10.txt @@ -0,0 +1 @@ +s7,s14,s28,s37,s47,s56, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N6/results_w1n6s5.txt b/Experiments/Simulator/Performance1/N6/results_w1n6s5.txt similarity index 100% rename from Experiments/Simulator/Performance/N6/results_w1n6s5.txt rename to Experiments/Simulator/Performance1/N6/results_w1n6s5.txt diff --git a/Experiments/Simulator/Performance1/N6/results_w2n6s10.txt b/Experiments/Simulator/Performance1/N6/results_w2n6s10.txt new file mode 100644 index 0000000..0e201ee --- /dev/null +++ b/Experiments/Simulator/Performance1/N6/results_w2n6s10.txt @@ -0,0 +1 @@ +s7,s14,s20,s39,s46,s51, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N6/results_w2n6s5.txt b/Experiments/Simulator/Performance1/N6/results_w2n6s5.txt similarity index 100% rename from Experiments/Simulator/Performance/N6/results_w2n6s5.txt rename to Experiments/Simulator/Performance1/N6/results_w2n6s5.txt diff --git a/Experiments/Simulator/Performance1/N6/results_w3n6s10.txt b/Experiments/Simulator/Performance1/N6/results_w3n6s10.txt new file mode 100644 index 0000000..03e142e --- /dev/null +++ b/Experiments/Simulator/Performance1/N6/results_w3n6s10.txt @@ -0,0 +1 @@ +s1,s10,s28,s35,s40,s54, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N6/results_w3n6s5.txt b/Experiments/Simulator/Performance1/N6/results_w3n6s5.txt similarity index 100% rename from Experiments/Simulator/Performance/N6/results_w3n6s5.txt rename to Experiments/Simulator/Performance1/N6/results_w3n6s5.txt diff --git a/Experiments/Simulator/Performance1/N6/results_w4n6s10.txt b/Experiments/Simulator/Performance1/N6/results_w4n6s10.txt new file mode 100644 index 0000000..a2c7db8 --- /dev/null +++ b/Experiments/Simulator/Performance1/N6/results_w4n6s10.txt @@ -0,0 +1 @@ +s9,s11,s28,s38,s41,s54, \ No newline at end of file diff --git a/Experiments/Simulator/Performance/N6/results_w4n6s5.txt b/Experiments/Simulator/Performance1/N6/results_w4n6s5.txt similarity index 100% rename from Experiments/Simulator/Performance/N6/results_w4n6s5.txt rename to Experiments/Simulator/Performance1/N6/results_w4n6s5.txt diff --git a/Experiments/Simulator/Performance1/N6/results_w5n6s10.txt b/Experiments/Simulator/Performance1/N6/results_w5n6s10.txt new file mode 100644 index 0000000..e69de29 diff --git a/Experiments/Simulator/Performance/N6/results_w5n6s5.txt b/Experiments/Simulator/Performance1/N6/results_w5n6s5.txt similarity index 100% rename from Experiments/Simulator/Performance/N6/results_w5n6s5.txt rename to Experiments/Simulator/Performance1/N6/results_w5n6s5.txt diff --git a/Experiments/Simulator/Performance/N6/results_w6n6s5.txt b/Experiments/Simulator/Performance1/N6/results_w6n6s5.txt similarity index 100% rename from Experiments/Simulator/Performance/N6/results_w6n6s5.txt rename to Experiments/Simulator/Performance1/N6/results_w6n6s5.txt diff --git a/Experiments/Simulator/Performance/N7/event_newwindown_n7_w2.dat b/Experiments/Simulator/Performance1/N7/event_newwindown_n7_w2.dat similarity index 100% rename from Experiments/Simulator/Performance/N7/event_newwindown_n7_w2.dat rename to Experiments/Simulator/Performance1/N7/event_newwindown_n7_w2.dat diff --git a/Experiments/Simulator/Performance/N7/event_newwindown_n7_w3.dat b/Experiments/Simulator/Performance1/N7/event_newwindown_n7_w3.dat similarity index 100% rename from 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extension/.venv/lib/python3.11/site-packages (2.0.25)\n", - "Requirement already satisfied: typing-extensions>=4.6.0 in /Users/antongiacomopolimeno/Dottorato/03_paper_in_corso/Big Data Access Control - extension/.venv/lib/python3.11/site-packages (from sqlalchemy) (4.9.0)\n", - "Requirement already satisfied: greenlet!=0.4.17 in /Users/antongiacomopolimeno/Dottorato/03_paper_in_corso/Big Data Access Control - extension/.venv/lib/python3.11/site-packages (from sqlalchemy) (3.0.3)\n", - "\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", - "Note: you may need to restart the kernel to use updated packages.\n", - "Collecting pymysql\n", - " Obtaining dependency information for pymysql from https://files.pythonhosted.org/packages/e5/30/20467e39523d0cfc2b6227902d3687a16364307260c75e6a1cb4422b0c62/PyMySQL-1.1.0-py3-none-any.whl.metadata\n", - " Downloading PyMySQL-1.1.0-py3-none-any.whl.metadata (4.4 kB)\n", - "Downloading PyMySQL-1.1.0-py3-none-any.whl (44 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.8/44.8 kB\u001b[0m \u001b[31m1.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25hInstalling collected packages: pymysql\n", - "Successfully installed pymysql-1.1.0\n", - "\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "%pip install sqlalchemy\n", - "%pip install pymysql\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": 192, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'3': {'10': {'1': {'selected_index': 778, 'metric_value': 0.019181828945875168, 'total': 1000, 'dev': 0.003985916702997176, 'coef': 4.812400854100026}, '2': {'selected_index': 328, 'metric_value': 0.025155987590551376, 'total': 1000, 'dev': 0.005182999635562564, 'coef': 4.853557661464303}, '3': {'selected_index': 0, 'metric_value': 0.03310304507613182, 'total': 1000, 'dev': 0.00033379137504781735, 'coef': 99.17285930887111}}, '15': {'1': {'selected_index': 820, 'metric_value': 0.02512373775243759, 'total': 3375, 'dev': 0.0054523442480101545, 'coef': 4.607878118041897}, '2': {'selected_index': 197, 'metric_value': 0.02840638222793738, 'total': 3375, 'dev': 0.005272987907202479, 'coef': 5.387151028572727}, '3': {'selected_index': 0, 'metric_value': 0.033340765784184136, 'total': 3375, 'dev': 0.0010766931761773076, 'coef': 30.965893089949002}}, '20': {'1': {'selected_index': 6252, 'metric_value': 0.01659972034394741, 'total': 8000, 'dev': 0.004882874898412573, 'coef': 3.399579282554217}, '2': {'selected_index': 1919, 'metric_value': 0.0243230698009332, 'total': 8000, 'dev': 0.0038946610500566387, 'coef': 6.245234049463554}, '3': {'selected_index': 0, 'metric_value': 0.03374866644541422, 'total': 8000, 'dev': 0.0010635437548356707, 'coef': 31.732278330785523}}, '5': {'1': {'selected_index': 60, 'metric_value': 0.02197916681567828, 'total': 125, 'dev': 0.0056489332483351685, 'coef': 3.8908526352574433}, '2': {'selected_index': 113, 'metric_value': 0.015259808705498775, 'total': 125, 'dev': 0.01189778872144351, 'coef': 1.2825751963468524}, '3': {'selected_index': 0, 'metric_value': 0.030990420530239742, 'total': 125, 'dev': 0.0008835023528103366, 'coef': 35.0767832498263}}}, '4': {'10': {'1': {'selected_index': 2661, 'metric_value': 0.02455263677984476, 'total': 10000, 'dev': 0.008448485329494018, 'coef': 2.90615842038933}, '2': {'selected_index': 8474, 'metric_value': 0.018011788837611675, 'total': 10000, 'dev': 0.00474410603703192, 'coef': 3.7966665789115637}, '3': {'selected_index': 154, 'metric_value': 0.030073273926973343, 'total': 10000, 'dev': 0.004957817536855599, 'coef': 6.065829107951952}}, '5': {'1': {'selected_index': 44, 'metric_value': 0.028514925856143236, 'total': 625, 'dev': 0.006263608930166187, 'coef': 4.552475445715075}, '2': {'selected_index': 458, 'metric_value': 0.02033175784163177, 'total': 625, 'dev': 0.00987165566407066, 'coef': 2.0596097081903078}, '3': {'selected_index': 332, 'metric_value': 0.02257567341439426, 'total': 625, 'dev': 0.006973257116546238, 'coef': 3.2374646506044353}, '4': {'selected_index': 0, 'metric_value': 0.03298728261142969, 'total': 625, 'dev': 0.0019410227502067708, 'coef': 16.994794423669514}}}, '5': {'5': {'1': {'selected_index': 1515, 'metric_value': 0.02218645326793194, 'total': 3125, 'dev': 0.010604308425537773, 'coef': 2.0922112388302025}, '4': {'selected_index': 68, 'metric_value': 0.029037820920348168, 'total': 3125, 'dev': 0.0036782521271424276, 'coef': 7.894461803222598}}}, '6': {'5': {'1': {'selected_index': 8554, 'metric_value': 0.021926400096466143, 'total': 15625, 'dev': 0.007450835587878988, 'coef': 2.94281088850437}, '2': {'selected_index': 11724, 'metric_value': 0.019953267260765035, 'total': 15625, 'dev': 0.010452170562960621, 'coef': 1.9090070469643377}, '3': {'selected_index': 4761, 'metric_value': 0.024053960107266903, 'total': 15625, 'dev': 0.005628209072766136, 'coef': 4.273821351743945}, '4': {'selected_index': 302, 'metric_value': 0.028724052322407562, 'total': 15625, 'dev': 0.004709475349446024, 'coef': 6.09920430431521}, '5': {'selected_index': 3033, 'metric_value': 0.025251608341932297, 'total': 15625, 'dev': 0.003983860356983681, 'coef': 6.338477275606911}, '6': {'selected_index': 0, 'metric_value': 0.03227434307336807, 'total': 15625, 'dev': 0.001612415164683156, 'coef': 20.016149550237}}}}\n", - "Node 3 \tService 10 {'1': {'selected_index': 778, 'metric_value': 0.019181828945875168, 'total': 1000, 'dev': 0.003985916702997176, 'coef': 4.812400854100026}, '2': {'selected_index': 328, 'metric_value': 0.025155987590551376, 'total': 1000, 'dev': 0.005182999635562564, 'coef': 4.853557661464303}, '3': {'selected_index': 0, 'metric_value': 0.03310304507613182, 'total': 1000, 'dev': 0.00033379137504781735, 'coef': 99.17285930887111}}\n", - "\tService 15 {'1': {'selected_index': 820, 'metric_value': 0.02512373775243759, 'total': 3375, 'dev': 0.0054523442480101545, 'coef': 4.607878118041897}, '2': {'selected_index': 197, 'metric_value': 0.02840638222793738, 'total': 3375, 'dev': 0.005272987907202479, 'coef': 5.387151028572727}, '3': {'selected_index': 0, 'metric_value': 0.033340765784184136, 'total': 3375, 'dev': 0.0010766931761773076, 'coef': 30.965893089949002}}\n", - "\tService 20 {'1': {'selected_index': 6252, 'metric_value': 0.01659972034394741, 'total': 8000, 'dev': 0.004882874898412573, 'coef': 3.399579282554217}, '2': {'selected_index': 1919, 'metric_value': 0.0243230698009332, 'total': 8000, 'dev': 0.0038946610500566387, 'coef': 6.245234049463554}, '3': {'selected_index': 0, 'metric_value': 0.03374866644541422, 'total': 8000, 'dev': 0.0010635437548356707, 'coef': 31.732278330785523}}\n", - "\tService 5 {'1': {'selected_index': 60, 'metric_value': 0.02197916681567828, 'total': 125, 'dev': 0.0056489332483351685, 'coef': 3.8908526352574433}, '2': {'selected_index': 113, 'metric_value': 0.015259808705498775, 'total': 125, 'dev': 0.01189778872144351, 'coef': 1.2825751963468524}, '3': {'selected_index': 0, 'metric_value': 0.030990420530239742, 'total': 125, 'dev': 0.0008835023528103366, 'coef': 35.0767832498263}}\n", - "Node 4 \tService 10 {'1': {'selected_index': 2661, 'metric_value': 0.02455263677984476, 'total': 10000, 'dev': 0.008448485329494018, 'coef': 2.90615842038933}, '2': {'selected_index': 8474, 'metric_value': 0.018011788837611675, 'total': 10000, 'dev': 0.00474410603703192, 'coef': 3.7966665789115637}, '3': {'selected_index': 154, 'metric_value': 0.030073273926973343, 'total': 10000, 'dev': 0.004957817536855599, 'coef': 6.065829107951952}}\n", - "\tService 5 {'1': {'selected_index': 44, 'metric_value': 0.028514925856143236, 'total': 625, 'dev': 0.006263608930166187, 'coef': 4.552475445715075}, '2': {'selected_index': 458, 'metric_value': 0.02033175784163177, 'total': 625, 'dev': 0.00987165566407066, 'coef': 2.0596097081903078}, '3': {'selected_index': 332, 'metric_value': 0.02257567341439426, 'total': 625, 'dev': 0.006973257116546238, 'coef': 3.2374646506044353}, '4': {'selected_index': 0, 'metric_value': 0.03298728261142969, 'total': 625, 'dev': 0.0019410227502067708, 'coef': 16.994794423669514}}\n", - "Node 5 \tService 5 {'1': {'selected_index': 1515, 'metric_value': 0.02218645326793194, 'total': 3125, 'dev': 0.010604308425537773, 'coef': 2.0922112388302025}, '4': {'selected_index': 68, 'metric_value': 0.029037820920348168, 'total': 3125, 'dev': 0.0036782521271424276, 'coef': 7.894461803222598}}\n", - "Node 6 \tService 5 {'1': {'selected_index': 8554, 'metric_value': 0.021926400096466143, 'total': 15625, 'dev': 0.007450835587878988, 'coef': 2.94281088850437}, '2': {'selected_index': 11724, 'metric_value': 0.019953267260765035, 'total': 15625, 'dev': 0.010452170562960621, 'coef': 1.9090070469643377}, '3': {'selected_index': 4761, 'metric_value': 0.024053960107266903, 'total': 15625, 'dev': 0.005628209072766136, 'coef': 4.273821351743945}, '4': {'selected_index': 302, 'metric_value': 0.028724052322407562, 'total': 15625, 'dev': 0.004709475349446024, 'coef': 6.09920430431521}, '5': {'selected_index': 3033, 'metric_value': 0.025251608341932297, 'total': 15625, 'dev': 0.003983860356983681, 'coef': 6.338477275606911}, '6': {'selected_index': 0, 'metric_value': 0.03227434307336807, 'total': 15625, 'dev': 0.001612415164683156, 'coef': 20.016149550237}}\n" - ] - } - ], - "source": [ - "from pandas import DataFrame,pandas\n", - "from sqlalchemy import create_engine, types\n", - "import re\n", - "import pymysql\n", - "import configuration\n", - "import uuid\n", - "\n", - "def convert_strings(input_list):\n", - " output_list = []\n", - " for i, s in enumerate(input_list):\n", - " if(i > 0):\n", - " new_s = s.replace(\"s\",\"\").replace(str(i),\"\",1)\n", - " else:\n", - " new_s = s.replace(\"s\",\"\")\n", - " output_list.append(int(new_s))\n", - " return str(output_list)\n", - "\n", - "pymysql.install_as_MySQLdb()\n", - "\n", - "my_conn = create_engine(\"mysql+mysqldb://root:root@localhost:3008/ACL_Extension\") # fill details\n", - "my_conn = my_conn.connect()\n", - "\n", - "\n", - "# print(df.query('node == '))\n", - "\n", - "from pathlib import Path\n", - "results = {}\n", - "pathlist = Path(\"Performance/\").rglob('*.txt')\n", - "for path in sorted(pathlist):\n", - "\n", - "\n", - "\n", - " # because path is object not string\n", - " nodes = path.parts[1].lower().replace(\"n\",\"\")\n", - " services = path.parts[2].lower().replace(\".txt\",\"\").replace(\"results_\",\"\").split(\"s\")[1]\n", - " table_name = f\"w{nodes}s{services}n{nodes}\"\n", - " #print(path)\n", - " try:\n", - " df = pandas.read_sql(f\"SELECT node,count(service),avg(metric) as avg,stddev(metric) as dev,(avg(metric) / stddev(metric)) as coef FROM stats_{table_name} GROUP BY node ORDER BY avg DESC\", my_conn)\n", - "\n", - " open_file = open(path, \"r\")\n", - " window_size = str(path).split(\"_\")[1].split(\"n\")[0].replace(\"w\",\"\")\n", - "\n", - " read_file = open_file.read()\n", - " if len(read_file) > 0:\n", - " #remove last comma\n", - " cleaned_file = read_file[:-1].split(\",\")\n", - " # print(cleaned_file)\n", - " result_array = convert_strings(cleaned_file)\n", - "\n", - " # print(result_array)\n", - "#\n", - " cleaned_file = f\"[{result_array}]\"\n", - " # print(cleaned_file)\n", - " filtered_df = df[df['node'] == result_array]\n", - " selected_index = filtered_df.index[0]\n", - " metric_value = filtered_df['avg'].values[0]\n", - "\n", - " if nodes not in results:\n", - " results[nodes] = {}\n", - " if services not in results[nodes]:\n", - " results[nodes][services] = {}\n", - " results[nodes][services][window_size] = {\n", - " \"selected_index\": selected_index,\n", - " \"metric_value\": metric_value,\n", - " \"total\": len(df),\n", - " \"dev\": filtered_df['dev'].values[0],\n", - " \"coef\": filtered_df['coef'].values[0],\n", - " }\n", - "\n", - " #print(f\"{path.parts[2].lower()[:-4][8:]} {result_array} {selected_index}/{len(df)} => {selected_index/len(df):.4f} {metric_value:.4f} {filtered_df['avg'].values[0]:.4f} {filtered_df['dev'].values[0]:.4f} {filtered_df['coef'].values[0]:.4f} {metric_value}\")\n", - "\n", - " open_file.close()\n", - " except Exception as e:\n", - " #print(e)\n", - " pass\n", - "print(results)\n", - "for N in results:\n", - " print(f\"Node {N}\", end=\" \")\n", - " for S in results[N]:\n", - " print(f\"\\tService {S}\", end=\"\\n\")\n", - " for W in results[N][S]:\n", - " print(f\"\\t\\tWindow {W} => {results[N][S][W]}\")\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Experiments/Simulator/combinationManager.py b/Experiments/Simulator/combinationManager.py index c437a91..a5b4415 100644 --- a/Experiments/Simulator/combinationManager.py +++ b/Experiments/Simulator/combinationManager.py @@ -1,7 +1,6 @@ import itertools - -from MatrixGenerator import MatrixGenerator import configuration +from MatrixGenerator import MatrixGenerator class CombinationManager: def __init__(self, nodes): @@ -11,7 +10,10 @@ def __init__(self, nodes): matrix = MatrixGenerator() combination = ["".join(map(str, comb)) for comb in itertools.product(*self.nodes)] self.combinations = dict(zip(combination, matrix.get_weights())) - configuration.set_total_combinations(len(self.combinations)) + #print(len(self.combinations) * (configuration.NUMBER_OF_NODES - configuration.WINDOW_SIZE + 1)) + #configuration.set_total_progress(len(self.combinations) * (configuration.NUMBER_OF_NODES - configuration.WINDOW_SIZE + 1)) + + def get_combination(self): @@ -77,6 +79,5 @@ def search_combination(self,combination): nodes = [node1, node2, node3, node4, node5] combination = CombinationManager(nodes) - #print(combination.get_weights()) print(combination.search_combination("s11s22")) diff --git a/Experiments/Simulator/configuration.py b/Experiments/Simulator/configuration.py index f66c73a..7159ef3 100644 --- a/Experiments/Simulator/configuration.py +++ b/Experiments/Simulator/configuration.py @@ -1,22 +1,62 @@ +from json import load +from pyexpat import EXPAT_VERSION import uuid import pandas from tqdm import tqdm -NUMBER_OF_NODES = 5 +NUMBER_OF_NODES = 2 NUMBER_OF_SERVICES = 5 WINDOW_SIZE = 2 + INPUT_FOLDER = "Input" OUTPUT_FOLDER = "Output" - DATA = pandas.read_csv(f"{INPUT_FOLDER}/inmates_enriched_10k.csv") +EXPERIMENT_ID = None +def load_experiment_id(): + global EXPERIMENT_ID + try: + with open("experiment_id", "r") as f: + EXPERIMENT_ID = int(f.read()) + except FileNotFoundError: + pass +def increment_experiment_id(): + global EXPERIMENT_ID + EXPERIMENT_ID += 1 + with open("experiment_id", "w") as f: + f.write(str(EXPERIMENT_ID)) + +load_experiment_id() + + + + + +def set_number_of_nodes(n): + global NUMBER_OF_NODES + NUMBER_OF_NODES = n +def set_number_of_services(n): + global NUMBER_OF_SERVICES + NUMBER_OF_SERVICES = n +def set_window_size(n): + global WINDOW_SIZE + WINDOW_SIZE = n + + +# pbar = tqdm(total=0) +# pbar = None +# let's use a global tqdm progress bar +def set_total_progress(total): + pass + # global pbar + # pbar.total = total + + +def increment_progress(): + pass + # global pbar + # pbar.update(1) + + WINDOW_ID = uuid.uuid4() -pbar = None -def set_total_combinations(total): - global pbar - pbar = tqdm(total=total) - -def update_progress(): - global pbar - pbar.update(1) diff --git a/Experiments/Simulator/datalogger.py b/Experiments/Simulator/datalogger.py index e6ef942..9afff03 100644 --- a/Experiments/Simulator/datalogger.py +++ b/Experiments/Simulator/datalogger.py @@ -30,7 +30,6 @@ def log(self, nodelist, node, service, metric): ) def store(self, filename): - #print(self.data) df = (DataFrame( self.data, columns=['experiment_id', diff --git a/Experiments/Simulator/exhaustive.py b/Experiments/Simulator/exhaustive.py deleted file mode 100644 index 924e0a2..0000000 --- a/Experiments/Simulator/exhaustive.py +++ /dev/null @@ -1,46 +0,0 @@ -from node import Node -from nodeList import NodeList -from service import Service -import pandas -from node import Node -from datalogger import DataLogger -import itertools -import multiprocessing -import configuration -import concurrent.futures - -NUMBER_OF_NODES = configuration.NUMBER_OF_NODES -NUMBER_OF_SERVICES = configuration.NUMBER_OF_SERVICES -data = configuration.DATA -data_logger = DataLogger() - - -def run_combination(combinazione): - nodelist = NodeList(combinazione) - for idx, service in enumerate(combinazione): - node = Node(idx, service) - nodelist.add(node) - nodelist.run(data, data_logger) - - -if __name__ == "__main__": - nodelist = NodeList() - - for i in range(0, NUMBER_OF_NODES): - node = Node(i) - for j in range(0, NUMBER_OF_SERVICES): - service = Service(int(str(i) + str(j).zfill(2))) - node.add_service(service) - nodelist.add(node) - - combinazioni = itertools.product(*nodelist.nodes) - -# for combinazione in combinazioni: -# run_combination(combinazione) - - # Using a ThreadPoolExecutor for thread-based parallelism - with concurrent.futures.ThreadPoolExecutor() as executor: - # Submit each combination to the executor for printing - executor.map(run_combination, combinazioni) - - data_logger.store(f'stats_s{NUMBER_OF_SERVICES}n{NUMBER_OF_NODES}.csv') diff --git a/Experiments/Simulator/experiment_id b/Experiments/Simulator/experiment_id new file mode 100644 index 0000000..c227083 --- /dev/null +++ b/Experiments/Simulator/experiment_id @@ -0,0 +1 @@ +0 \ No newline at end of file diff --git a/Experiments/Simulator/greedy.py b/Experiments/Simulator/greedy.py deleted file mode 100644 index e7d7165..0000000 --- a/Experiments/Simulator/greedy.py +++ /dev/null @@ -1,34 +0,0 @@ -from node import Node -from nodeList import NodeList -from service import Service -import pandas -from node import Node -from datalogger import DataLogger -import itertools -import configuration - -NUMBER_OF_NODES = configuration.NUMBER_OF_NODES -NUMBER_OF_SERVICES = configuration.NUMBER_OF_SERVICES -data = configuration.DATA -data_logger = DataLogger() - -if __name__ == "__main__": - nodelist = NodeList() - for i in range(0, NUMBER_OF_NODES): - node = Node(i) - for j in range(0, NUMBER_OF_SERVICES): - service = Service(int(str(i) + str(j).zfill(2))) - print(f"Service {service.id}") - node.add_service(service) - nodelist.add(node) - bestPipeline = [] - for node in nodelist.nodes: - bestPipeline.append(node.run(data).get_best_service()) - - instance = NodeList() - for key, service in enumerate(bestPipeline): - print(key, service) - instance.add(Node(key, service)) - - instance.run(data, data_logger) - data_logger.store(f'stats_greedy_s{NUMBER_OF_SERVICES}n{NUMBER_OF_NODES}.csv') diff --git a/Experiments/Simulator/node.py b/Experiments/Simulator/node.py index 8fc467f..1f1d481 100644 --- a/Experiments/Simulator/node.py +++ b/Experiments/Simulator/node.py @@ -1,7 +1,7 @@ +from logging import config import pandas - -from service import Service import configuration +from service import Service class Node(object): def __init__(self, id, service=None, previous=None): @@ -30,11 +30,8 @@ def next(self): return self def run(self, data: pandas.DataFrame,combination): - configuration.update_progress() - # print(f"Node {self.id} running service {self._pointer}") + configuration.increment_progress() output = self.services[self._pointer].run(data, combination[self.id]) - - #self.result = ouput.result self.metric = output.metric #self.results.append(results.result) diff --git a/Experiments/Simulator/nodeList.py b/Experiments/Simulator/nodeList.py index 83b1c84..9712860 100644 --- a/Experiments/Simulator/nodeList.py +++ b/Experiments/Simulator/nodeList.py @@ -1,20 +1,24 @@ + import itertools +import os +from pathlib import Path +from typing import List import uuid - from datalogger import DataLogger from node import Node import configuration from windowDecorator import WindowDecorator - +os.path class NodeList: - def __init__(self, description="", window_size=configuration.WINDOW_SIZE): + def __init__(self, description=""): self.nodes: list[Node] = [] self.description = description self.running = False self.id = "NODELIST" self.last_node: Node = None self._pointer = 0 - self.WINDOW_SIZE = window_size + # self.WINDOW_SIZE = window_size + self.winning_composition = [] self.data_logger = None self.data = None @@ -26,58 +30,54 @@ def run(self, data, data_logger: DataLogger = None): self.data = data - f = open(f"Performance/N{configuration.NUMBER_OF_NODES}/results_w{configuration.WINDOW_SIZE}n{configuration.NUMBER_OF_NODES}s{configuration.NUMBER_OF_SERVICES}.txt", "w") - - while self.running: - best_composition: [Node] - self.winning_composition: [Node] - best_composition, best_metric = WindowDecorator( - self.nodes[self._pointer:self._pointer + self.WINDOW_SIZE],data_logger,window_size=self.WINDOW_SIZE).run(data) - #data_logger.log(node.id, node._pointer, node.metrics[-1]) - total = 0.0 - if self.is_last_window_frame(): - print("###########LAST WINDOW FRAME###########") - print("take them all") - for node in best_composition: - print(node.get_current_service(), end=",") - f.write(str(node.get_current_service()) + ",") + print(f"\033[92m n{configuration.NUMBER_OF_NODES}s{configuration.NUMBER_OF_SERVICES}w{configuration.WINDOW_SIZE}\033[0m") + Path(f"Performance/N{configuration.NUMBER_OF_NODES}").mkdir(parents=True, exist_ok=True) - else: - print("###########WINDOW FRAME###########") - print("taking only the first service of the best combination") - print(best_composition[0], best_composition[0].get_current_service(), best_metric) - configuration.WINDOW_ID = uuid.uuid4() - f.write(str(best_composition[0].get_current_service()) + ",") - print("") - self.next() - f.close() + with open(f"Performance/N{configuration.NUMBER_OF_NODES}/results_{configuration.EXPERIMENT_ID}_w{configuration.WINDOW_SIZE}n{configuration.NUMBER_OF_NODES}s{configuration.NUMBER_OF_SERVICES}.txt", "w") as f: + while self.running: - def next(self): + best_composition: List[Node] + self.winning_composition: List[Node] + best_composition, best_metric = WindowDecorator( + self.nodes[self._pointer:self._pointer + configuration.WINDOW_SIZE],data_logger).run(data) + #data_logger.log(node.id, node._pointer, node.metrics[-1]) + total = 0.0 + if self.is_last_window_frame(): + #print("###########LAST WINDOW FRAME###########") + #print("take them all") + for node in best_composition: + print(node.get_current_service(), end=",") + f.write(str(node.get_current_service()) + ",") + else: + #print("###########WINDOW FRAME###########") + #print("taking only the firt service of the best combination") + #print(best_composition[0], best_composition[0].get_current_service(), best_metric) + configuration.WINDOW_ID = uuid.uuid4() + f.write(str(best_composition[0].get_current_service()) + ",") + #print("") + self.next() - print("###########MOVING WINDOW###########") - + def next(self): + #print("###########MOVING WINDOW###########") if self.is_last_window_frame(): self.running = False self._pointer = 0 - - print("###########END###########") - print(self.winning_composition) - + #print("###########END###########") + #print(self.winning_composition) else: self._pointer += 1 self.nodes[self._pointer].previous = self def add(self, node): - if len(self.nodes) > 0: node.previous = self.last_node else: @@ -92,7 +92,7 @@ def remove(self, node): return node def is_last_window_frame(self): - return self._pointer == len(self.nodes) - self.WINDOW_SIZE + return self._pointer == len(self.nodes) - configuration.WINDOW_SIZE def __repr__(self) -> str: return self.__str__() diff --git a/Experiments/Simulator/quality.py b/Experiments/Simulator/quality.py index d3f05a2..9a56f0e 100644 --- a/Experiments/Simulator/quality.py +++ b/Experiments/Simulator/quality.py @@ -1,5 +1,3 @@ -import time - import datalogger from nodeList import NodeList from service import Service @@ -11,37 +9,46 @@ data = configuration.DATA data_logger = datalogger.DataLogger() - -def run_combination(combinazione): - nodelist = NodeList(combinazione) - for idx, service in enumerate(combinazione): - node = Node(idx, service) - nodelist.add(node) - nodelist.run(data, data_logger) - - if __name__ == "__main__": + configuration.increment_experiment_id() + # e_nodes = 5 # e_window = 5 # e_servies = 5 + + # configuration.NUMBER_OF_NODES = e_nodes # configuration.WINDOW_SIZE = e_window # configuration.NUMBER_OF_SERVICES = e_servies + MAX_NODES = 5 + MAX_SERVICES = 20 + + + + for n in range(2, MAX_NODES + 1): + configuration.set_number_of_nodes(n) + for w in range(1, n +1): + configuration.set_window_size(w) + for s in range(5, MAX_SERVICES + 1,5): + configuration.set_number_of_services(s) + + + nodelist = NodeList(description=f"n{configuration.NUMBER_OF_NODES}s{configuration.NUMBER_OF_SERVICES}w{configuration.WINDOW_SIZE}") + for i in range(0, n): + node = Node(i) + for j in range(0, s): + service = Service(int(f"{i}{j}"), parent=node) # TODO: change this + node.add_service(service) + nodelist.add(node) + nodelist.run(data, data_logger) - nodelist = NodeList() - for i in range(0, configuration.NUMBER_OF_NODES): - node = Node(i) - for j in range(0, configuration.NUMBER_OF_SERVICES): - service = Service(int(f"{i}{j}"), parent=node) # TODO: change this - node.add_service(service) - nodelist.add(node) - nodelist.run(data, data_logger) - data_logger.store(f'stats_w{configuration.WINDOW_SIZE}s{configuration.NUMBER_OF_SERVICES}n{configuration.NUMBER_OF_NODES}.csv') \ No newline at end of file + data_logger.store(f'stats_w{configuration.WINDOW_SIZE}s{configuration.NUMBER_OF_SERVICES}n{configuration.NUMBER_OF_NODES}.csv') + configuration.increment_experiment_id() \ No newline at end of file diff --git a/Experiments/Simulator/results_s155.txt b/Experiments/Simulator/results_s155.txt deleted file mode 100644 index d6e7a6c..0000000 --- a/Experiments/Simulator/results_s155.txt +++ /dev/null @@ -1 +0,0 @@ -s1,s14,s20,s30,s40, \ No newline at end of file diff --git a/Experiments/Simulator/service.py b/Experiments/Simulator/service.py index 10c0cd4..f3d8973 100644 --- a/Experiments/Simulator/service.py +++ b/Experiments/Simulator/service.py @@ -1,29 +1,22 @@ from typing import Self import pandas -import numpy -from profiles import PregeneratedProfiles, ServiceProfile -from metrics.jsh import calculate +from metrics import jsh class Service: - def __init__(self, id, profile: ServiceProfile = PregeneratedProfiles.GOOD, parent=None): + #def __init__(self, id, profile: ServiceProfile = PregeneratedProfiles.GOOD, parent=None): + def __init__(self, id, parent=None): + self.id = id self.result = None self.metric = None - self.profile: ServiceProfile = profile + #self.profile: ServiceProfile = profile self.parent = parent def run(self, data: pandas.DataFrame, weight) -> Self: - #print("\tService s{} running".format(self.id)) - - #numpy.random.seed(self.id) - #sample_size = numpy.random.uniform(self.profile.getMin(), self.profile.getMax()) - # print("\t\tsampling {}% of data".format(sample_size * 100)) self.result = data.sample(frac=weight, random_state=self.id) - #print("EXTRACTing {}% of data".format(weight * 100)) - self.metric = calculate(data, self.result) - # print("\t\ts{} finished - metric: {}".format(self.id, self.metric)) + self.metric = jsh.calculate(data, self.result) return self def get_metric(self): diff --git a/Experiments/Simulator/sliding.py b/Experiments/Simulator/sliding.py deleted file mode 100644 index 5120c84..0000000 --- a/Experiments/Simulator/sliding.py +++ /dev/null @@ -1,53 +0,0 @@ -from node import Node -from nodeList import NodeList -from service import Service -import pandas -from node import Node - -import itertools -NUMBER_OF_NODES = 10 -NUMBER_OF_SERVICES = 5 - -if __name__ == "__main__": - nodelist = NodeList() - - data = pandas.read_csv("cars.csv", sep=";") - #data = pandas.read_csv("../inmates_enriched.csv") - - for i in range(0, NUMBER_OF_NODES): - node = Node(i) - for j in range(0, NUMBER_OF_SERVICES): - service = Service(int(str(i) + str(j))) - node.add_service(service) - nodelist.add(node) - -for i in range(0,len(nodelist.nodes)-1,1): - name = nodelist.nodes[i] - tag = nodelist.nodes[i+1] - # print(f"{name} {tag}") - combinazioni = itertools.product(name,tag) - for combinazione in combinazioni: - print(combinazione) - - -# combinazioni = itertools.product(*nodelist.nodes) - - -# # Stampare le combinazioni -# for combinazione in combinazioni: -# nodelist = NodeList(combinazione) -# data_logger.setNodeList(nodelist) - -# for idx,service in enumerate(combinazione): -# node = Node(idx,service) -# nodelist.add(node) - -# print(nodelist) -# nodelist.run(data, data_logger) -# del(nodelist) - - -# data_logger.store(f'stats_s{NUMBER_OF_SERVICES}n{NUMBER_OF_NODES}.csv') - - - diff --git a/Experiments/Simulator/windowDecorator.py b/Experiments/Simulator/windowDecorator.py index a1459a0..88b7d6f 100644 --- a/Experiments/Simulator/windowDecorator.py +++ b/Experiments/Simulator/windowDecorator.py @@ -1,17 +1,19 @@ import copy +from logging import config -import configuration from combinationManager import CombinationManager from node import Node - +import configuration class WindowDecorator: - def __init__(self, nodes: list[Node],data_logger, window_size = configuration.WINDOW_SIZE): + def __init__(self, nodes: list[Node],data_logger): self.nodes = nodes self.running = True self.best_metric = 0.0 self.best_composition = [] self.matrixW = CombinationManager(self.nodes).get_weights() + + self.data_logger = data_logger def run(self, data): @@ -26,8 +28,8 @@ def run(self, data): self.data_logger.log(self.nodes, node.id, node._pointer, node.metric) #iteration_results.append(iter_result.result) iteration_metrics.append(iter_result.metric) - print(node.get_current_service(), end=",") - #print("") + #print(node.get_current_service(), end=",") + #print("",flush=True) avg_metric = sum(iteration_metrics) / len(iteration_metrics) if avg_metric > self.best_metric: self.best_metric = avg_metric diff --git a/Images/graphs/exhaustive_performance copy.gp b/Images/graphs/exhaustive_performance copy.gp deleted file mode 100644 index 7a8547e..0000000 --- a/Images/graphs/exhaustive_performance copy.gp +++ /dev/null @@ -1,12 +0,0 @@ -set terminal postscript eps enhanced -set output 'exhaustive_performance.eps' - -set ylabel "Computation Time (ms)" -set xlabel "No. of ingested records" - -set key box top left inside Left samplen 1 -#set logscale y 2 -#set xrange [0:9] -#set xtics ('10' 0, '50' 1, '100' 2, '200' 3, '500' 4, '1000' 5, '2000' 6, '5000' 7, '10000' 8, '20000' 9) - -plot 'exhaustive_performance.dat' u 1:2 t '2 Nodes' w lp pt 9 , 'exhaustive_performance.dat' u 1:3 t '3 Nodes' w lp pt 7, 'exhaustive_performance.dat' u 1:4 t '4 Nodes' w lp pt 6, 'exhaustive_performance.dat' u 1:5 t '5 Nodes' w lp pt 5, 'exhaustive_performance.dat' u 1:6 t '6 Nodes' w lp pt 4