{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# What is a gravity wave?\n![](data:image/png;base64,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)\n\nA rotating neutron star emits. Neutron stars are not perfectly spherical and their centers of gravity are unbalanced. Therefore, rotating neutron stars emit gravitational waves.\n\n## something called a pulsar\n\n![]( data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAz0AAAD1CAIAAAD1WQxmAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAJM3SURBVHhe7Z0HWFXH08alSxeQIiAoKjbAhqjYEcHee9fYjRqN0SSmmZjEGjXJ3xgTu2JX7CCKFSlSFUGkSRGk91783rArHwEklHsvXJjf44O7c849dXfmnVP2SLx7964ZQRAEQRAE0eCR5P8TBEEQBEEQDRvSbQRBEARBEOIB6TaCIAiCIAjxgHQbQRAEQRCEeEC6jSAIgiAIQjwg3UYQBEEQBCEekG4jCIIgCIIQD0i3EQRBEARBiAek2wiCIAiCIMQD0m0EQRAEQRDiAek2giAIgiAI8YB0G0EQBEEQhHhAuo0gCIIgCEI8IN1GEARBEAQhHpBuIwiCIAiCEA9ItxEEQRAEQYgHpNsIgiAIgiDEA9JtBEEQBEEQ4gHpNoIgCIIgCPGAdBtBEARBEIR4QLqNIAiCIAhCPCDdRhAEQRAEIR6QbiMIgiAIghAPSLcRBEEQBEGIB6TbCIIgCIIgxAPSbQRBEARBEOIB6TaCIAiCIAjxgHQbQRAE0WiRkJB4/PgxK+/YsaNslSDEEdJtBEEQRJPA0tJy+/bturq6vE4QYojEu3fveJEgCIIgGhcSEhKPHj0aMGAArxOEmEPX2wiCIAiCIMQD0m0EQRBEQ+Tx48cjRoxAYceOHazAQLV9+/YSEhLq6uqbN2/m1vf8+eefbOqMGTOSkpK4tQQsEHZeKcHPzw9L/tCiSsFs5X6IVfTu3ZtXSsAMYWFhrIz5sXZYAGZDldkxAyxltwoWrJpXKmxP2TlRXrlyJezllkk0NUi3EYKkuLjYzs5u0aJF48aNGzhwYLdu3YyMjLS1tVVVVZs3by4tLf2PG5OQkJSUlJGRgUVFRUVDQ6N169aYrW/fvosXLz58+PCbN2/44giCaPJAh3l7e2/fvp1VIdrOnz9/9OjRd+/eeXp63r59GzOwSeDmzZtffPEFm/rxxx+vWrWKT6gMaKahQ4dOnDiRLcrLywvCiE/7N3Bl7dq1K32hAT+EbkOhVFdhEmaAH2NGLLZnz56JiYlYMtwaqmw2zIDZ3N3dWRU4OTnZ2NiwMvsh257g4OCUlJSy2//111/jL+ylyyyr6oimAz3fRtSJ9PT0c+fO3b1719fXNyoqKisrC2pMT0+vRYsW0GpIDSHLoNuArq4u7AYGBjBGRES8ffs2vgS4tuQSIiMj4Q0TEhKys7Oh8PBz6LmuXbuOHTt2+vTpfH1ElaA7FxUVSUlJsTL0MbMThDgCMbR161Y4kzNnznBTs2YzZsyYN2/eqFGjWBWToNIcHBxYFXIKP8E8rIolIIEsfb6NVUujHlNp+/fvZ1XIoA4dOty7dw8qjVnKgpnbtGmzceNGlKEOXVxcoKvGjBnDtgRqMi0t7ccff0SZbXbpJgFsFTaSbcPmzZvxw9KVYlMnTJjANrjsKhj44cWLF9n2jBgx4quvvip9UA8/gTCl5/aaIKTbiNqAzA/uMjo6Ojc3V0FBAQILnsXKymrq1Kllr/nXmocPH8J7Iv199epVeHh4cXFx27ZtbW1tV69ebWxszGciSigoKIBQi4mJkZeXx+FC7NHR0cERy8/PRxCCHdIZkQ/6mF3mVFJSwiTMjAJfBEE0SJjMCg0NZdexKqWsSPLz8+vevXu5oCZR5r2EcrqtrJxiQAz17NmzrHIqpaxAhPYyNTWFUEO2ybQaRNWaNWtK1WQ5ykoubMOCBQtCQkLYJGwe+iY6KcplVRoDK0IGy7YH2wbvWir4iCYL6TaiBvz0008nT558+fKliorKsGHDkCZOnz5dVlaWTxYayG6PHDkCf/f27VuoDbjmZcuWzZkzh09uwkRERFy7dg05OiIWAkBhYSGMKOAvujb0HAQc5BosKLBJioqK2dnZKJuYmBgaGnbs2LF58+aoEkRDo5zMKgUSyt7ePjU11dHREVVkdExOVTo/2vyHdFvZSYwdO3bgb6W6DRlRy5Yt2W979+599uzZjIyMxYsXP336FBYsqlR+gbCwsEOHDiHzhD6D7oSl7Iogv9hVPXi2X3/9tfTKHBbCCmXZvn072x708U2bNnl4eFhYWEycOHHKlCmlqyOaFHQbhfhv9uzZAxcDEfDzzz+3a9cOHgceE3nh3LlzRSDaALLY8+fPx8bGwml+++23+fn5CxcuVFNTW758OZ+jKZGVlRUfHw8JC8WG4AFZFhQU1KlTJ6i30aNHQ9GOHz8e+T3curGxsZWVFQLV2LFjzc3NTU1Nu3btqqmpaWRkpKuri9CCgLF3797Tp0/fvXs3PDw8OTk5Ly+Pr4YgGiQzZszYvXs3ksavvvoKDgF6iE8QMhBJ6ERQfhBwKSkp6ETwitBkqMII7ViqolDFnChgC2/fvo2NxFQ2iYFdcHNzQ8HFxQUKjBkZ7JG4spSKSKwOHdbT0xM/gRPu0KEDvZrQNKHrbURVTJ8+/dKlSxBn/fv3/+STTz50F6BeWL9+/fHjx+FAkX1u2bKl9NneRkxhYSHSdyTcaWlpKKPzIu/v0qVLZmamjo4Ouw3KZy2hoKAAFsyGPB5/i4qKmBESHGWoNPwwIiLizZs3OIywq6qqmpmZYYHKysrsITmCqEcqXj+DUhk6dGhwcHBZkVR6nxSpCBLLste9QBXX22p0nxRs3rzZwMAAyY+dnR27X4n5P/744ydPnqBc+quKCyn3aFrpLVdsAIRd6V3gittTBdgYqEYsiteJJgNdbyMqZ8GCBXJycg8fPty3b19WVhacS4MSbeCXX36Bg4aszM/Ph1uEcDl58iSf1hjJy8vDzl68eBEyS19f39jYePny5dDTCFGGhoY4WeVEG2AWdvMFf6VLkJeXhxDHXz09vY4dO1pbW8+ZM2fFihW2trYqKip37949duzY4cOHAwICEhISShZDEA2FjIwM5GllZdmtW7d46f3bmk5OTrxeovN4qTKQ7EGB8UrJnVA4unLXxsqC7nbv3j2otEGDBjELRCSqzs7OZX+VmppqaWnJKyVqErkWr5QwfPhwR0dH2FEu++heue0BkIA3b95EAdsGVYe/zA5MTU2xIl4hmhTIPAiiLKtWrUJQh2fcvn07N4kD48aNgzQxMzMLCQnhpkZBcXExvLOLi8uuXbsgVZGjp6enwwj4HIIDOhhBApHjt99+27Zt2+7du9PS0oqKivhkghAt7B4or5TAnhU7ffo0K3/55Zcs5WBTwY0bN9TU1PBDlPGXvYrOqsxSdoFYAmY+cOAAK2M5WBqb9CGgCzEbZmZVX19frAJGVmVgIebm5uyOJ7YHM6Baug0Mti5sP6+XUHZ78HN4YFTZcgB+guWwVWO9KLPjQDQ1SLcR/8+mTZuUlJRUVVXZgyNiBxQbdBvU2/jx47lJzMnPz/fy8vr7779//vlne3v7yMhIdntUeGD5OTk58fHxHh4ekG6HDx++c+cODmxp8CAIkVFOZjGghKCTYMdflKFjys0DNcNmgNBBu0XhQ7oNQABhNhihkKqTqWLmcioNP4Qy45USsFJoMiwToMCq5RYOcYap5cQcYEKQ/RYFVPmE94vF6jAJ2yBeeTUhQOj5NoJjamoKD7h8+fJffvmFm8STkydPfvrppxkZGevXr9+6dSu3iiFv3769fv06/rZs2XL06NGtWrWSlpbm04RPcXHxzZs3X79+nZycLCUlpaWlNWXKFGh6GhOOIAiiHiHdRjS7cuXK3LlztbW1nz9/3miGhNiwYcOePXuGDx/OHlgWI4qKitLS0ry9vZFqy8rK9ujRo1evXgoKCnyyCMGWQL1FRkaGh4d7eHhgG6ZNm6arq8snEwRBECKHdFtTZ+XKlX/88cekSZMuXrzITY2FBw8ejB8/vmXLliHvh7hs+OTm5np6ekJAp6am9u7d29TUFHqaT6s/8vPz/f39nz59Clk/Z84cetWUIAiiviDd1qSxsLDw8/PbtWvX6tWruanR0bVr14iIiLNnz44ePZqbGirBwcEODg5QbEZGRra2tmpqag1HIcFRQLc5OTkNHToUh1RVVZVPIAiCIEQI6bYmSmZmpqGhoaKioqurq56eHrc2UiZOnHjlypVvS+CmhkRhYWFiYqK7u3tgYGCLFi369OljYmJScVCPegdt5vTp08nJyShjC0eNGsVGGCEIgiBEBum2JoqWlhbkmo+PD683dr7++usff/zx5MmTs2bN4qaGQUpKioeHR1BQEHqihYVFp06d2PtiDZO3b9+mp6cHBARgawcNGlR2GC2CIAhCBJBua4q0atVKXV39xYsXvN40mD9//unTp93d3Xv06MFN9Q1086NHjyDdunfvPnDgQCg2sbiChQ0+ceKEgoLCzJkzFRUVuZUgCIIQPqTbmhympqa5ubnBwcG83pQYNmyYp6dnQkKCaD6r+iEKCgqioqJcXV1fv36tr69vaWnZoUMHPk1MgOJ0cHCA3LSwsIDcpMFBCIIgRAPptqbFvHnzLly4kJyc3GjG+6gpkK0pKSnR0dG8LnJycnIcHR1DQkJUVFR69+7dvn17ZWVlPk18KCoqQkOKjIxUVVXt2LFjnz59mmyLIgiCECWUJTchTp8+feLEiT///LMph9jnz58XFhZOnjyZ10VOXFxcYGBg586dZ86c2aNHD3EUbUBKSmrixIlDhw4tKChwdXUNDw+nDJAgCEIE0PW2JoSOjk6/fv0uX77M602VI0eOLF68ODY2VktLi5uET3FxcXJyspeXl4+Pj5KSEhtNg08TW4qKivD34MGDGRkZRkZGUHI0tBtBEIRQId3WVFi3bh3ia1ZWFq83bTp37tyiRQtXV1deFzKQNb6+vs+fP8/JyenevbupqakoJaOwiYyM9PT0fPHixaRJkxqBGCUIgmjI0H3SpsIff/yxYcMGXmnyHD161M3N7e7du7wuZBISEtzd3dXU1GbNmjVkyJDGJNqAgYHB8OHDFRQUHj9+HBcXV1xczCcQBEEQgoautzUJtmzZsmvXroyMDF5vMhw4cABKIiUlpeKgaJBukFOfffYZr1cAAmvw4MG8Ugdw2E+cOJGVlbVo0aJGPODZixcvvLy8cLSnTp3apk0bbiUIgiAESlPRbTt27LC0tBwwYACvNzFat249cOBAOzs7Xm9KQJnt3bu3qKioRk39ypUr48aN45U6AGno4+Pj6ek5bNiwPn36cGsjBQr14sWLOTk58+bNo3HdCIIghIFY3if18/ODDmNlFEaMGMHK1QEzl/62iRAaGhodHX3gwAFeb2Ls3LnT0dFRU1Ozmte6ZGRkTp06VXfRVlxc7OHhcfr0afzt1q2biYkJn9B4UVJS6tGjB3v9gpsIgiAIgVL/uu3MmTM1El6g7P2+jRs3Ojg48Eo1wMz4Ca80DY4cOdKyZUsVFRVeb3pYWVmFhIR07979P6WbvLz8b7/9VvdvYSUlJZ08edLJyUlVVXXmzJmjR49uCtefJCQkunTp0qpVKx8fn4iIiMLCQj6BIAiCEBCC0W29e/deuXIlvDYKN2/ebN++Pcp//vknm+rn58cs0GeIZ8y4efNmWNTV1e3t7Znwevz48YwZMzDPh+aEwmOzDRw4cNOmTTCyaun1s3JzVgpmxk9QqObqAAqowohJzCJeQD1069aNV5oqysrKd+7cWbNmjZSUlJycHLf+GyUlpR9++GHZsmW8Xluys7M9PT0hXPr37z99+vR27drxCU0AGRmZUaNGQa3evXvX19eXWwmCIAgBIRjdhigFZfDu3TuEqG+//fb27duJiYk7d+5kU3/++eeLFy9iqpWV1aFDh2CBkgsNDcU8wcHBKLDZAH741VdfYc4WLVoEBgbCAs1kYGAAC+Z8+PBhWFjYgAEDHj16tH379nKPK0EmpqSkwIiNOXr0KLdWSXVWByOWhiqMWD7TfOJFUFAQ4iivNG2++eYbNB4tLS1NTU1ueg8O0YYNGz799FNery2RkZHnzp17+vSpvr6+qalpExziWEdHZ9KkSRDKhYWFBQUF3EoQBEEIAsHoNltbW3aVomfPnnv27DEyMtLQ0Gjfvj2bCjHErvdYWloyy4f4/PPP2asDWA6z2NvbL1++XEJComXLln/88YeTkxOzV+Ty5cs//PADClh7Ne+cVnN1vXr1GjFixObNm8eMGSOObzbk5uYuWrSIV5o8/fr1gxzHX3V1dW5q1kxNTW3p0qVIOXi9tkRERBw5ciQjI8Pa2nrhwoXi8vZocXFxXl4eG0RXILBPePn6+t68eZPulhIEQQgQUTzfxl4dgBgaOHAgs0DGhYaGQht16NDhP69wJCYmvntP3e9h/ScVV/fjjz/+/fffqqqqv/76a+nNXzGioKCgSd2q+0+kpaWvXLmC04o2KSkpCZEBsV73t1XS09N9fHykpKQGDx7ckF8dhUpDk4C4hKLCXzT4R48eXb9+3cXFBZbU1FQIfT5rHTAwMGjdunVQUND9+/fLPpBKEARB1AWh6zY/Pz9nZ+f9+/dDBm3fvp0ZHz9+jHQc0eLq1aszZsxgxkqZMGHC119/nVQC5mQ3LkFaWhorlDJx4kTMiQLmqXqZVVDp6iA6lZWVN27cuGDBgorrbfggThsbG/MK8R5oNW9v7y5duixcuPCnn37i1tqC5h0SEvLq1auhQ4d26tQJipBPaBiwYVAgy5KTk+3t7c+ePXvs2LHTp08fP378xIkTUGxRUVHoj6dOnUL1zJkz6KF1vE4GQWxtba2oqOjh4QHpxq0EQRBE3RC6btPX18ffdu3asdumTPdABrGROZ88ebJ58+YqLnVAPKmpqbErcz179jQyMoKxc+fOCDwIjRCFbDawbNkyzAmjubk55Be31pBKV7dmzRobGxss+ejRox999BGbU4xAwEYQ5RWiDN27d3/+/PnevXt5vbakp6efP3/+5s2baNiGhoaysrJ8QgMgIyPD3d0d/QUcOXLkzz//RDaCtERaWhoaTkZGRklJacqUKXPmzEGXRPdUUFDIysqCbnNwcGBzvn79OjY2Nicnp6Y3UuXk5ObOnYt+BH1c9jFWgiAIotbUz7i7K1eunDVrFntWDBFi69atNRrLgyAaDrm5uUg/XFxcLC0tLSwsIN34hPomOzs7ODjYy8sL+kxdXT0/Px+aDJsHIYUC5FpmZqaioiIEHBOakGXQbfLy8pjzxo0bKSVgtry8PCkpKSRg2tra/fr1q+mbFlj7iRMnsAGTJk2iwXgJgiDqSP3oNj8/v8WLF3t6eqJsbm7+999/00AVhJgSHh5ub28vJyc3derUiq+p1hcxMTF3796NiorS0NAYNmyYrq5ucXEx5BeozuVAiNGCgoI3b95gOWpqauzRvZycHCsrK3TVmkq3p0+f3r59e8yYMdTNCYIg6gh9n5QgakleXl5iYqKjo6OWlhZECbfWK+jOaWlp0ElPnjxRVVVFUiSoN6Ch5JydnYOCgrDXWOzAgQM/NAxepRw7dgxCcObMmXTJjSAIoi5Ifffdd7xIEET1gDx68+bNo0eP7ty5AyFiYWGhVuG79aInPT3d19fXyckpJiamW7duNjY2nTt3FtQbEtLS0kZGRpqamtBtUG8qKio4CJBuUlJSfI4qUVJSYi/bGhoachNBEARRc+h6G0HUmKKiohs3bkC+WFpadu/eXUFBoX5fIEUvDgkJuXv3bnJyctu2bYcOHdqyZUsoLT5ZcGBFGRkZUIeenp7FxcWmpqYDBw7E7vPJHyY/P//69esvX75ctGiRjo4OtxIEQRA1hK63EUSNyczMhEgyMzMbMmSIrKxs/Yo2gO15/PhxTEzM6NGjhw0bpqysLKQ3iLGncnJyhoaGMjIy0G2BgYFYb4cOHaSkpJhM/NChwAxKSkoBAQHYVGNjY3rBmSAIonY0Kt22ZcuW3r17N6hRGIhGxrt378LDw+/duweZMnDgwHp/WquwsDA4ONjZ2Tk6Otrc3Lxfv37/KSJTU1NzcnISExMLCgoSEhKg9rKzs2GHIEMBAqs6oqpVq1Zt2rRBX4Ma8/LyCgkJgSDDAst+haIcqqqqWD6km6amZsuWLbmVIAiCqAmN6j7punXrEE1Lv4tKEAIHOunmzZsvXrwYP368AJ8eqzUZGRmnTp2CDrO1tTUyMqriTU/oqvz8fAgsV1dXVKGxINGwOwC9RkZGBjrszZs3+vr6bLxc9quqKS4uxs/d3NxevnwZHx+vrKyMxAlgaXyOf5OXl3fkyBEVFZUpU6ZQfkUQBFELGpVui4qKatu2bXR0ND1AQwgc9JSkpCRHR8eIiAg1NbWPPvqo3pVHamrqlStXILYGDBgwaNAgbv032OysrCxfX9+HDx9CZaKqqqoKeaehoZGQkKCpqYlqbm7u27dvExMTobeys7Nbt25taGiIv5gKC/jPK3AQcM+fP3d2dsaiunbtam5urqury6f9m2fPnl29enXChAkmJibcRBAEQVSbxvZewtq1axGTSj+oRRCCAurn6NGjECi9e/c2NTWt9zukOTk50EkvX76EaMP2VPpyAEQYZvD29k5JSWnfvr2cnJy2tnanTp2kSkZxKygokJaWhiaDEygqKoJ0U1FRCQgICAwMTE9PhwKDtmvRokXnzp2NjY2rI1IhBD08PKDMIPusrKy0tLQqCj5skr29PTTivHnzsHBuJQiCIKpHY9Ntr1+/Rox58+ZNwxkBlWgE5OfnX7t2DYIGImbs2LH1fqUtKSkJ2xMbGwuFNGHChIqiDf06MjLy9u3bUEg6Ojo2NjboERISEhBqVd/bhYCDIszLy4uLi/P398fPse9qamomJiZt27aF0qri2htWCi3o6ur68OFDZWXlOXPmVPocG6TkxYsXe/XqNWLECG4iCIIgqkdje58UcQXxxsfHZ9iwYdxEEHWjsLAQGgiNCiqkITyYVVxcjPzE19fXwsJi5MiR5Z5pg/DKzMx0dna+c+cOyoMHD7a1tUW/kCrhPx/IgyzDDkIIQud16dKlTZs2YWFhaWlp0KwBAQGtW7eG8vvQEcDCsQr8REZGJj4+HrISS9PQ0Cgn9XAY3759Gxoaqqenp6qqyq0EQRBENWhs19sA4oGJicmbN2+qeLWNIKpPTEyMnZ0dmpO5ubmpqel/Sh9hk5iYePbsWUVFxREjRpR7lDM9Pf3Zs2eQdPn5+WZmZt26davjhWf4h5ycnOzsbKg3LJaND9e7d2/oLRUVFegzPt+/gV50d3fHlkBBjh07Fj8pJ/WwnGPHjrVr127UqFEQgtxKEARB/BeNcPw2xFeINsQMKysrbiKIOuDl5RUbGwuF0alTp3oXbQUFBcePH09JSRkwYECHDh24tURgvXjx4vLly69evWrTps3EiRM7duxY94/cY38hzhQUFFq1aoXVQcP5+/sHBwcHloAZoAsr3jmFRVtbu3Pnzs+fP3/58mVWVpaRkVHZQycvL4+/0HbYVHrKjSAIovo0wuttAKGrZ8+eUG90F4aoC4WFhQEBAbdv34ZqmTBhQr2/i5CXlwfZ5OTkZGFhMXjwYKn335hKSkq6c+dOaGioiooK0hUottJJgiU/Pz81NdXX1zcxMTEmJgbeg71e8KFvlULbubi4vH37VkNDY/z48TiMpeotLS3t4sWLRUVFCxYs+NB1O4IgCKIcjfN7CQgSr1+/fvHixZAhQ7iJIGoOWtGNGzf09PSgk1q2bFnxwpIoKS4u9vLycnZ2NjIyMjMzg1piGgha6sKFC8nJyZaWltbW1q1btxbedkIOQry2bdu2a9eubdq0iYqKgq6FajQ0NKz0dqempqaBgYG6ujo0ZVZWFnZBS0uLTYLUg+yDBJSVlcU8zEgQBEFUTX3GIaGyfv36PXv2ZGRk8DpB1JycnJzc3Nw+ffq0b9++3h/Dcnd3f/LkCbTa6NGjIXSYaCssLLxy5UpMTAzkmrm5uWguMEMXQsBBzg4dOlRfX9/f3x/blpeXxyf/G+hdqN5evXohj7p+/XpcXByzY/tNTU2xED8/v4SEBGYkCIIgqqbRfp8U0QIp/suXLwcPHsxNBFFtiouLIZLu3bunrKxsa2vLrfXEu3fvEhMTz5w5o6mpOXHiRLRtZi8oKIiOjvbx8WF3Iav4WIIwgPDCejt06PDs2bOgoKC0tDRZWVkIx0qv9kFWAvasW+n3SSH+sEdubm7QxG3atGFKlCAIgqiCRqvbQLt27ZYuXbp69Wp6eoaoKW/fvj1//ryhoeHw4cPr/SnJjIyMmzdv5ubmWltbo1UzI5Slg4PDo0ePlJSU2PcJ6uU2LsSWgYEBFFh4eLi/v39eXp6Ojk7FHodtU1dXz8zMDAwM1NLSKpWeKioq7F0H7Bd2hBkJgiCID9Fo75OCrl27jh8/fs+ePbxOENUmLi4OiqRLly71/uhVQUGBm5tbREREt27dSkUbCA0NffbsWefOnadOnYpJQnoRoTpAqA0aNGj27NkQZ0+fPnV3d+cTKjB48GBlZWUfHx/sFDc1a9ajRw85OTlnZ2deJwiCID5MY77eBoyMjFasWLF27VoaI4qoPpBEN2/etLS07Nu3LzfVE8XFxampqU+ePNHU1GSihxmh5G7cuKGgoADRo6+vX+93GGVkZOTl5c3MzDIzM6HbOnbsqKSkVHGr0A1lZWVdXV3z8vIgNNXU1GBUVFRE1cvLS11dXVtbm81JEARBVEoj120IA/7+/gjDAwYM4CaCqJL8/Px79+4lJiaizdT70GIvX768du0aNom9AcCMCQkJ0Ea5ublz584tNy5a/QJNpqKiEhgY+ObNGyi5St/AhfQsKCgICAjAXnTo0IGNx4vjjE4aFxcHy4eGFCEIgiBAY75Pyli3bt2ePXsKCwt5nSCqJCgoKCwsbNCgQXp6etxUTyQnJ0Of5eTkTJo0CfqMW5s1CwkJSUpKYp8c5aYGg46OTu/evaEpHRwcUlJSuLUMioqK0KBWVlZRUVFQeGz8SFVVVTZKdr08okcQBCFGNH4v2aNHD2tra3rKjagOiYmJFy9eVFJSat26db2/zhIaGvrmzZuuXbtCn7HH16ByvL29nZycNDQ0GtSVtlKwSUOGDJkxY4aCgsLdu3eLior4hDI0b94cvbJt27bYF6hSZoyLi0tNTY2Pj2dVgiAIolKaRHbLLrmxzJ4gqiAqKkpSUtLS0rIhvI4QERHRokWLUaNGsa9CAYgbf39/VIcNG1b3b1gJD1VVVRMTk1evXrFvYVVK375909LSSl9i6N69u7S0tI+PD10aJwiCqIImodvMzc0HDx5Ml9yI/yQ+Pl5CQqJ0lIp6BPIlJSWl7HP6xcXFYWFh0dHR48aNa+DP70tJSaHTQXS6urqmpqZy67+BMu7UqRN025s3b1CF1Ovfv39QUFB4eDibgSAIgqhIU3mahF1y45VGiq2tLTRHKatWrUKkZ5NQKAezE2VJT0+PjIyEgCh9A6C+yM/PP3XqVPPmzUeOHMlNJScxIyODfeK94Q9JqKioOGzYsISEhODg4EovdWMXevbsKSkp6eLiwm6nGhsbY9du375N7ZMgCOJDNBXdZmFh0a9fv7179/J6E2D//v1SUlJXrlx59epV586dUS7Ltm3b+HzEe6DbsrKyOnXqVL9PxxcWFj5//jwlJUVJSamsPoOmDAsLa9mypYqKCjc1bLp06dKhQwcPDw8cWG76N3p6er179w4PDw8KCoK2w3517NgRu5+bm8vnIAiCIP5NU9FtoClccqvIihUrbt26BenG6+/54osveIl4T0ZGRps2bep9zDZsxtOnT1VVVZFslNVtnp6eeXl5PXv2lBDC6whFRUVQTpCtvC4gkCzl5+e7urryegUsLS3V1dW9vb3ZY23YjOzsbHo7gSAI4kM0Id2GEGJubv7bb7/xeqPD0dExOTmZX097j6SkJMI8K3/22WeIzWFhYfwHRBkSExMfPnyoo6NTv1ezCgoK2OhxrVq10tXVLZVo6enpAQEBbdu27dChA7PUmuLiYogkyKlnz57dvXv39evXKJw6derEiRN//vmng4PDmTNnoPX9/f0zMzOhFPE3NzcXLQfwRVQbHE9ssLu7e0REBDf9Gzk5uf79+4eHh7948QJVzIyG+vjx47IfVCAIgiBKkaiFLxZfEA/mzZvXxIULYiQbDKxJnfr/xMvLy8nJadKkScbGxtxUH+Tk5Jw8eVJDQwNbwk3NmrHh0KBsRo0a1aNHD26tFVCEWE5aWlpGRgZagoKCAmQclg+BVVRUhPVGR0fLyMigbWAGRUVFNTW1rKwszAZFhWrnzp1r+mhdfHw8RKG2tvbEiRNLX4wtC3b52rVrMTEx8+fPx+pu3LiRkJAwffr0SmcmGh83b978/fffQ0NDef3fWFlZrV69ukuXLrxONHjgWHBCr1+/zuvN/hmX8eOPP161ahWvE3Wjaek2MGHCBBsbm5UrV/J60wNh2NHREYWyyqCJk5+ff+LECU1NTQSJ+v26OXTVwYMHhw0b1qdPH2aBnPL29nZ2doagtLW1hYRi9ppSUFDg4+MDeZqSkoKFQBWxe5TJycmYamBgAAEHO6pMmaWmpgYHB0NCwQidB10lKytrYWFhaGhYdhDg6uDq6nr//v0xY8aYmppy07/Bis6dO9etWzcIU7j7iIiIqVOnQkryyUTjBed6+/btR44c+dBDjS1atBg0aNDatWvZyMxEQyYpKQnuy87ODu6COZZS9PT0li5dumTJklatWnGTOPDXX39hj3jlPWZmZtiRenyipsnptgcPHixevBhxgtcbEYi71TmbUvX3AfKGCQ4a1Al8zcSJEyFKuFXkYDPevn3r5OQE6TZ37tzSbyEgnp05cwZqe8SIEWW/K199oPyw5Dt37kRGRkIMWVtb4y9EWHVaAraK3avFEh4/fhwUFKSoqAj5qKGhgS2s/pN2hw4dQs49Y8aMD11Fu3z5MtJ05BLY0ytXrowePRrOURhP8hENisOHD3/33XdRUVG8XhlIpdq2bQvdNm/evJ49e3Ir0fC4cOHCli1b/P39ef3fQLHhPM6aNWv+/Pn1mx5XB3i848ePHzhwAH6Jm96jqqqKHYEPnD59OvaFfaxPlDQ53QbGjRuHqLBs2TJebxSEhYVNmDDh+fPnvP5hmuAZr5qMjIxz587l5eUtX768Ht8kxQacP38eCau5uXmvXr2aN2/O7KGhoVevXkXQ6tatG7PUCCS+z5498/b2btmyJZQQwl6tvUx6evrr168fPnyYk5OTn5+PINq6dWs+7b+A4LO3tx88ePCHktTU1NSjR49CN8MbPnnyZNSoUaampqTbGjc+Pj4//PADmjdSi+HDhyMKqqur82klXL9+/ezZs+x1GTQMS0vL1atXY042VRwJCQlBGob+yOvvGTNmDHYfSRGviyHo49u2bTt9+jRcmbS0NHZn8uTJbJKbmxvOI3vIFcknXMecOXNqetleZLCIAG8cGBiIXJdbK8PY2Pijjz6CEhXx0FGN/LvylaKjo/PVV199/PHHvN4ouHTp0t9//80rVdIEz3jVpKSk+Pn56erqdu7cuR6FApwdEjuoaiQVpdIqOzsbXl5PTw9yp6YPlmVmZrq4uNy7dy8+Pr5Pnz5QfvCY8Kd8cs2Rk5OD+IO3hVxD+ElISEDSCWN1DpqysjI2A8e5a9eupZK0LDBCND99+hTKEhoOya6JiQldG27EpKWl2dnZIUDm5uZOmjTpyy+/tLa2RmqBblgK4iKa/Zs3bzAzsoXo6Gh0jS5duqipqfGliAnIqA8ePHjkyBE46hs3bnh4eLz8N8HBwWj8ELIqKirwRfxn4gMSuQsXLiD1QudFhIUs++STT5CnlZ5HyG50agB/i1w0MTFRW1u7Ye7po0eP9u7dC8+JVoeqvLz81KlTN2zYMOE9cE3JyclwaEiz4QnZLgP2c1GAONEEQTaPXsQr4k/Z8bFWrFjBrf+mNG/gdeI9L168+PHHH9nwsPUIXPkPP/xw7NgxnE1uevcO7h5GT09PXq82hYWFDg4OW7ZswQLj4uK4VXBAEX7//fe//PIL1BsCalHJMCJV4+/vj05XxZzwkocPH/7999+R5u7atUsYm000HGJjY9lND3Nz82vXrnFrBcLCwk6fPj137lz2FZM2bdogQUXz5pPFAeiA2bNnVyeuQ9wgNq1evRpyNiMjg/9eHHB1dR0xYgTbiylTpqCz8wnvgcrBTg0ZMoTNgz2dOHHi3bt3+eSGAbzT9evXy35FEDt14MABb29vPkcJMTExx48f79+/P5sHAnTmzJkPHz7kk4VPU7zeBnCgseON5vWWU6dOXb16FQWkoWg9zFgOqAE2fF3Pnj07duzIjARAphgVFdWrV69aP/JfdxCHkGoXFBQsXLiw7OUo6DZkqAhsNb3AgJ3y9fWVlZVdsGCBMB4ladWqVXZ2NkKLn58fsmcJCQktLa0qLrzB1yAxxe6oqqp+6MFkdukOC8zNzcUBQZre8B+CESNu3LhRv+9KlwVppL29PTYJbX7OnDmTJk36UO9DyzcxMWnfvr2UlBT6Qnh4eFJSkqKiIlpRPXbY6oM9/euvvyA9U1JSmAXdGTJu5MiRViVYWlqitUdHR2MSUiAkkEjhEhMT4aXr8XHbGgFNduLEiStXrmD7O3TosHjx4qFDh5Z75gSnrF27dgYGBgi+eXl5kOMAB0ddXV1PT0/0j4hVCuTm3r170SzhP9HwoKE3bNgwefLkci4Lqg77grODXAK7zPZFWloaDbVFixZ8JmHSRHWbkZERMjycmzqOqtAQ2Lp16yeffMLKFy5c+JBrLtVt7u7ua9euZUYCvH79OiAgAE2iHm++QLS9fPkSeR7S0FL1A4+ATB0+ffDgwTX1a/itm5ubvr4+2kN17mPWFDhlHDEdHR1EU6i3oKAguDYozg/dh8U2wMchJsXGxuKHH3o7AafgzZs3UHgQhZB6lGAIEBzM3r17NwTpBq12/fr1Xbt2IUxCuCxZsgSyjE/7AAj2EATQbS9evECMZKkCml899tnqwPb0yJEj2HIIFHTw8ePHT58+HdmUjY3NoBL69eunq6vbunVr7CO0HXuYLzk5GdINfQpduB4fuq0mcKGXLl3y8vKCOJsyZQpUaaXnBc4BcgeNEL0bpx4hCSo8IiICBwf7WO97ioNvZ2eHGJqZmQl9Cbm2Zs0anB0++d/AIaNBIttHvgqfhh3Bz1HAGYcPF/Zzik1UtwFNTc3vv/9e3AcEOXbsWKkIc3R0LPs5y3IguP7666+IhUgI0ByFEcvFERwQxAD4jgEDBtTXgGFFRUUPHjxAoU+fPmUvtkESIZFVUVGp6RP68B1IS+Tk5JAv1vSpuOoDJ4u2BEcMhwsvDJmIg9m2bdsPbSq2RFVV1cPDA3uEn1Q6Gzw7Yhi7K4GcCok4n0DUmS1btiAsNQTplpeXd+7cOTRRlCdNmjRt2rTqPHbJrmQEBgbGxMQg0qelpbHrcGxqwwT5zMGDB5F9QZZNnDgRCTYUW5cuXcp2c/QLSOrhw4d37twZ+9imTRs4hMjISOxpbm6umZkZu0HckHn69Km9vT02e8SIEfPnz8c28wmVATcLP8DOJns+DN6vIezp48ePcbJw2Dt16oS9WLx4MRoYn/YBoM+6du0KV+bn5wfp5unp+erVKzRmSHC2g0Ki6eo2dPjLly8jPHTv3p2bxA144SdPnqCtoIzwiXaG5sImVQTBEo3s9u3bqampPj4+6GD1JVMaFIWFhXfu3EGGZGFhwU0iB2Hs3r17yNvgAsoGMAhKeHwUytmrBh4QMtTFxQVNAhmhCFJYpJ5oeMiew8LCsPZWrVphpZWuF8eZPVpehbxDK8VJYQNDVB0AiBoB3Xb9+vUxY8bUu3RDhLt69SqaN6Q51AyadzVfQEGyDa8FfYAuA9TV1ZEAlHsFtUHh5OR05syZ7OxsyNPPPvvsQ+MXMtCJBg0aZGtrix2EDsjMzEQBGuI/1UP9Ahn9999/37hxA9sJYWpVjWH22J4aGhpC17KXTFNSUuA6oG5Lxz8SMcgEDh8+jFwCmnLhwoUff/wxmhafViVwdJBoEN/wbMnJyVjOy5cvsRdo1TW9SVJ9GvoFWKEi1l8s/emnn2bPnn3gwAGU4cvOnz9ftVMA48ePRyBHAa0TrpMZmzhQD+hd9fscCbstaGBgULafw2UjsOHv2LFjq3/NrLi42NvbGwkJghnSd9G8jwlNaWRkNGHCBLg8Ly+vK1euIDHg0yqAPYKPxl9erwzINTi+/Px8XicExOjRo5l0Q5TlpvoAqSZAtPvoo49mzJhR/eatpqa2ZMmSXbt29evXD71m3759J0+eRHPikxsYGRkZiOJZWVnQXjjy1bw02Lx5cxsbG/h2iBhIt7i4OHbntGECsXXr1q27d++irKOjU6MLZn379p07dy7TN2/evPnf//63c+fO58+fQ5HzOUQF1giFDeBzsFU4Wcgw+bRqgAx57dq169evh8uFM3z9+rW9vT2OSVJSEp9D0DRp3TZq1CicHvR8Xhcfjh07tnnzZlZGQ0HUr86DenAcpdlMr169WIGoX6C04NwRuuD1yl6CguTCySosLIRHq/TaVaUUFBRERkZCPy1duhRCkFtFAlY6f/58eGG4rTt37kAfsGfU+OT3YAZEXPaC/Ydg2WpUVBQyV24iBES9Sze0CsSznJwcpBY1io6lKCgo4LdIVlNTU11dXRtsI0H2goMM7YWjPXjwYG6tBl26dIEIGDFiBLoATpafnx+f0PBITEzEKQgODtbV1UXTqtGoyFCoEO7ff/+9ubm5srIyNNPRo0e3bNmCpfE5RAXcEfYiMDBQS0sLJ6sW915UVFSWL1/+zTffIOdUVFSEA/z222/v378PB87nEChNWrcBcbzkdvz48QULFrCykZERu09aTVxcXHip5KobLzVh4CwSEhKQNfK6yIHSiomJgQsrd7sH7t7b2xsuoEa3tKD24EGSS+AmEYK8eeTIkTNmzNDX13/x4sXly5fxt9xlMzU1NUTrqpUonF1WVhb2pYE/dS6m1K90Cw8Pf/ToEdonNqA699QqYmhoOHbs2P79+yPYZ2RkIOgi+eHTGgzp6ek4yNhTS0vLUaNG1bQlIxtHR4ZCxRI+NERAQwAHH3sK94WzWbvxkCdNmrRt2zb8lj0Qdu/evTNnzsTGxorynLK9kJOTQ9eoy6jOcH07duxg11B8fX3hAKEFkaKwqQKkceq28+fP49xXB0RHHNbVq1fz+oep4taPKNm+ffv8+fNZGWESe1rTYfT//PNPVpg5c+apU6dYuckCcQDB1KZNG14XOXFxcUlJSe3atSsdMYjBbuBaW1vDm3BTNYCzy8vLk5eXr68hEiDIcDDHjRs3ePDgt2/fOjo6IumENuWTSwb7QI978uQJr1cGFoKj8a7k2/bc1LjAyRU9fN0l1KN0yy6he/fuEG3lcpXqg/Rg69atkG6urq729vbR0dFl21hDAGILEkRVVRWH+kPvJFaBlpYWfti7d2+U0V8AszcokO6iFTk5OZmammJra32Bf+DAgYhrs2fPxuGCoP+xBJFlnnCY6AXYi06dOqFH1PFNl6FDh2IhzP0ivK5fvx7KGw2eTRUU4qrbEJ+KyhAcHPzdd9+hceMvQJYPSzWBOoYu5pUPc+3aNbZwxr59+9iqRZkWnD179vPPP2dlJGRocLX4Wt/SpUt37twJP56VlTVnzpyKX1xpUjCfCH/B6yIHmUN+fn5FlwefGBkZiUSwRs+oYV/wK6jA+n3pREVFpW/fvra2tvr6+m5ubsie+YSSgNS2bVsko9g1bqoAdBvaJ3oWdoebGh1QpaKHr7uE+pJut0uQKYGbagX6BZaApoJEdM2aNfD5fEIDAE0XOrKwsNDGxga9gFtrCBQAfotF3bx5E6pClIGmOqA5ubi4YNtSU1PrspsMqKXdu3fjPLJWcevWLTROhFc2VXhgLzw8PLCu+Ph47AXgE2oLGuSoUaMmTJjAXhm+c+fOunXrrl69KtjTJ07vk0K0BgYGxpWAnn/lyhXkNIzXr19DLIeEhGi/Z/ny5cj4q8PkyZOh25DArVy5kpsqQ1dXF7GEL11bG2fl0qVLWDV0j5KSEjYJRr6hQgMeCo0MBUQ+9ORygwFWH0tLS7gV9q7iwYMHxagNCJy3b98+f/7csOSzmNwkQtCZsXbImmHDhrF+Xkp4CX369KnRHZaUlBRIoi5dulTzZSihAomGAxsdHY20BwkoNgwuHnoUXcbIyKhrlW8RJiYm4ldo5w3zSzh1ZMuWLQ2h0xkbG/fu3RvSTWRvmCJMwmfCiS1evHj27NncWivQMDIyMjw9PZH5hIaG6unpDRo0iE+rb9Dm4VcdHBzMzMyGDBlSa98CYfTgwYOoqCgkluwhMD6hAYCD/9dff509exZhEUceuwnJwqfVCmlpaXgJ7CziOPwYqvBj8CHlLhULFrgj7MWJEyew8f3798de1DGdAFAC8OeQK2jnaPAJCQnIort3717rq8sVEQ/d5uPjg+YLv4/cHdINwO/jEJu+BwcF+X3nMvBfVg8sTU5Orl27drxeGeh7fNElQCYiNGLVOjo6UNPYJCgA/xKE8dr2zp07d+zYgeaFMqIdZGsdXyywsrJC8GBlxEikCKzc1IBaCgoKwt96eaUUvRp+Cp4CTpmb3oM2n5aW1rNnT8WaDOGIFB9NET+p49X+uoNoevjwYfy9f/9+Xl4e8nI0YPQUhG0cbWTSZ86cQacDqFYUcIgKAQEBLVq0QN8UquOuFxqIbgMilm6QMgiTyHLhvUs/E1RrkLjGxMQgDUDQxQIbjm6zs7ODbjMwMFiyZMnAgQNr3YDR/rGD6NHwD9jBBqXb0KnRtRGRR44cKai3oKDF09PTcULhN5C2IeLDwsZAEBL29vZokChs3bp106ZN5ZLnWgO3hgiL8/X06VNIQybfBajbGuJ90uKSe6AIaf/chiwqgoO7cuUKziKSGMgLVMHYsWM7lqF169b8x7UCx7SmTyMi1rJVw99he6DesIUMtoUQQ9h4PnfdyM/Pd3d3Z1+yQhaC2CyQt0GxqSxe/u9//8MGM2NTQ1ZWVklJqb6UARp5Tk5OpXcMu3Xrpqenp6KiwuvVAwvMysqql2uHpaB9omnZ2tquWLECPn3//v2IPexOdFhYGPQoNNmTJ09gt7GxQfAeOnTo9evXy72+gNAOP4BuVccknvhPRHnDlLl0XqkzaB7YbGgaNLnt27d/+umnaGl8Wv0BX43jCf+PpgsHW5cGzJ64ateuHfQu4NaGAdsk6Cq0n1o8rlMpOFYLFizYt28fO6cQPTiSQj2naI1YEdzU+PHjEQu4tc4goGBpK1euxL6YmppevHhx7dq1SFn55DrT4K63QZxeuHDh0qVLaPHHjh3DriooKHTu3Hn+/PnDhg0T5fjpoaGhUIr/3JStBhCO6GMMpEf4uYeHx7Nnz7DxmFrrW6iPHz/GEUCvYMs0NDS8ffu2oO4cQf7jIDs7O7PyiBEjBJVtiBFob8gakdfWywUqdO/k5OSkpCT07XL+/eXLl5A4yGLZa1bVBOnv27dvBw8eXKOrdIKioKDg+fPnO3fuXL169atXr7AN0JHINZE9Ywfl5eURgaCS0R2QiWIGOM2UlJSoqCiEusjISDhQSFgEYxTQd7AjUHjGxsZIXvkKGgsN53obQ2RX3XDSHzx4gJxk0aJFtX7MoxR0H319fThYBHikKwjwqFZnRCThgaaOsGVnZ4c8ZNasWdjNujxpCp+AkPfmzRs/P78BAwbg7PAJ9Q2806FDhxCb0JGtra0r3i6oNTIyMm3atImPj0cAzcvLQ0F45xRpJE5WamrqwoUL+/TpUxeFXSmQbhEREY8ePYKLg5ZAkO3Vq5dAnjyWgGPlxXrl9OnTrIBT9fr1a3iQ0mvCormHheBd9ulpgGMtqNdAOnbsiJYHScfr/8XevXvv3buHgpeXFzotMwL04VpfaWvZsiV6Gq+8B27U1tYWBxzloUOHMg3XpHjx4sW1a9cmT54s1KvxH6K4uNjFxQVRZ8mSJeVugvj7+yPXnDFjRvXfdYUMOnnyJLzPpEmTRK/boLHQbrEBISEhCKiDBg3asGFDQEDAuHHj0I/QudCYJ06cKC0t7evrGxwcjLANlXb8+PHS4amgUOHafv31V2QpkLPwenDZM2fOrBcNKlRwfBqI4y0LpCQEpYODQx2fMf8QEFi7du36448/JkyY8O233wqqxyHAY7Nv3rypoaGBxSJn4BPqg/T0dBzGPXv2QGZ988030DQ413xarUBiib3bvXs3OvXmzZuR//AJ9Qr6LLbq8uXLc+bMwTEXeNJbek7hLuBAvvrqK2FItwMHDmAtUBc4ZSNGjOBWgQJRiERl3759COhdu3bFiqZMmcKn1YF6dh9YO/YKmgmNu/QZlyFDhqDRs7JQwdoR6rZu3cqqaCKsUAqaCy/Vgd9++w0nj90dwAIRVj+k6wsLCzGbUC96PX78uOJjJaWeBU7h2bNnrNxEgJ44ePCglZXVwIEDuUmEICk/f/48FM+yZcvK+XckmnCLEJQ1GoIcosfAwGD06NF1f7q2+qDdQvEvWrSIDRGMVSM8L126tOx9B+iwM2fOqKqqQok6OTm5u7vPnTvXyMgIDfKXX36BU8vJySkoKICQxRKwC5BrcAhQcghXbdu25UtpLDRA3Xbjxg1ky0gV0Hi4SdCgASBMvnz5cvbs2QLUbcgKkAbs2LEDXanexc2tW7cQm8PDw9evX79y5cqaPudQEabbtm3bZmNjg4NmaWnJJ9Qr+/fvx1Zpa2t/+eWX8FEC9zY4p8eOHcM5ff36tZqa2meffbZmzRrB5m+Qht9//z3OF3vSSUi6DWRmZv71119wiUlJSfB+X3zxRceOHfm02lLP90ldXV3v3r1rbGzcuXPnBQsWIEcHAnnCsWrQGiIjIz09PeFKECfQz9EEETvZBpTC564bffr0wfIRlhC6oFCh4RCfcC7LPqV49uxZX1/ftWvXLlmyhJvKgJzGrA4gfJY+InD48GHkRhBn+vr6pRdsfXx8goKCUJCTk0OCWOu7uuIIDgJaQkpKSr18phbxG505NDQUqVi56+ewBwYGooeXuw5XNSEhIRoaGhA6H8oNBAta9dOnT7/++mt4wMTERD09vfnz56ONjRo1qjQNY6BpIQKhkcMLY6devXqVlZWFQps2beD30cInTJiALce5SE9Px/bDCaCzoID5W7duXU7UijuIefXreMshAtEGoM5v3ryJPGTq1KnwrmVlfV2AaMAy4dPQYJDt6OjooC+LMm8pBWHl6NGjSEuGDRsGTy6QQIaWj2CB/oKuje7Qo0ePitcXRA/OI84mkitkXzVyUNUEpw9hCN4AsQl+A1EMDUawVzT8/PywF3FxcePHj582bZqSkhKfIGjQzuHZIiIi4P2wOiS3WlpadW0bSPtET0ZGhl0JSCDOnTuHE8MnCB+23p07d2LVwNnZGV2CTxMy6HiPHj1i692+fTvbEpzLSj9AefHixavv4b+vLZAFWAiiKV90CegGfHLJ6UAPZPZOnTqhYfEJTYDCwkIkdjg4vC5aoHuOHz+ODeD1MkDlI0uLjY3l9WqQkJCwdevWO3fuFJW81iNsoqOjV65cyV4JQgOePn26m5sbYgyfXAFMQpp+8OBBSFL0+gsXLuDg82klIMnGEr755hvsBZaMdG7jxo3oHVAVSHX4TI0CHDFeagBArmF78JfXhcavv/6KAIZID0/ITQICDR4Ok41Avm7durS0ND5BtMCZI/3ANqxevRppDLfWGXQKdjXos88+q6J/iQwvL69x48apqKj88ssv3CQEyp7TwYMHQybyCYIAiTqcDOS+paWlo6MjtwoNRNgNGzZgR4CqqircWo0ce0VEkZSXA+dj165dwcHB0BMQnsi9hP3cMdaICIEEFwQFBWG9UIqsOnToUJE92NSuXbsBAwawsbMRogIDA7Exe/bsYWFJUlIS+QRgJ2bSpEmIWAz281pjZGSEhSxcuBDrwvLZ0X748CEyuc8//xzrQqrRtWtXdknj5cuXUAwlv2sSSElJITvHvuMUcJMIwXqLi4srvcCJFovkDEkFr1cDuCH2iBg7lUIFvtvU1BQ6DCpfX18fHhZJSJ8+faq4l4FJ6G5wWOj7EKxRUVHYQT6tBHl5eSwBTg0nBekp2i3cq4uLy+zZs5ETp6am8vkIwSGaK21lMSyBVwQEnCdc66hRo9D+nz9/Xl8Pe2C9WDv8PLqGAG/q4XBhgdBJzs7O6GVv377lE+oDJF1oM66urrwuNNg5RbPEOfXx8UETLfuodx1B/IWHgZ/B8kXwtgdiLpTulClTEGqRVOAAssFTa42odVtCQsLvv/8uKys7efLkr776Clk1nyA0/Pz8jhw5AnGN0w/xjiCN9QI+WeTY2tqixaCAHo7miLANv2lmZrZs2TIEM8BmEwaffPIJi5elr21v3779559/vnr16ubNmxEvmRE+ghWaCFBIyGKzsrJ4XYTAN8G/V3pPB80VYrpGYgUnFykKZDf+cpMQwMKhJi9dugQHhC2H9IcTR2JQnTuz0tLScGH4CwGHAx4SEoL2z6eVkJ+f7+HhgUVt2rTpyy+/hAVnBwfhyZMnf/75J7ubTwgK0Ys24aGlpdW+fXtlZWVPT0/sl4jFDRIM9IhDhw6Fhobq6upCuiFD5tPqjI6ODhYIR4Fk6fbt21BOfEJ9wEY5QRwvGca0ZuOk1hScU+w4zml6ejoOLFbKJ9SNyMhInCwcTKS4WH5NPx1bC+AnBw4cuGLFChZ54cdwDJG+sqm1QKS67fTp0+fOnUMTnzlzJmIStwqTM2fOXL58OTo6GmucNm3axIkT2XXs+uXWrVt3796FiEQKhdA1depUbFjbtm1xfErfqxUempqaZ8+ehVZjVSi22bNnIxtYtWrVb7/9xoxQeKzQFNDW1oYWcXR05HURAoGiqqpa7lGwUt6VXM/nlWogXzLQBrLScmOhCRDIrP/9739osTt27EC5R48e69ev16/2txmQRMGFoQVi11CFgCt3aRAbD4mGvoDkAecFjnvWrFnW1taQbki9PvroI/g7iGw+tziDHRc9fN0liF60obXAyfTv3786Er8WQLcBNBXs1OPHj7lVJGCl8KgPHjxAWtKhQweBP6KNZWLXUEC8qEu8ryNQPGg2z549QxdG47GwsOAThIaJicnIkSPhYbDjgFvrRlRU1JUrV0T/gUdzc/PVq1dDwMH73b9/38HBodYXC0Sk2+Di//rrL8hMxKFly5bBL/MJQgNp+oULF16+fIkT/91333Xs2FFQw57VBWdnZ3jPuLg4uBW0G0RZHA1EJmzhpEmTkFIAiCo+t9CACxg7dixaz7Zt25AXIhBeu3YNvqb0Ffp9+/YhTObm5rJq40ZPT69Lly5onGyQPFECvYgkstKbodA3TISFh4dzUzWAP/2QCqwjaC0eHh7IF9euXfv8+XNUv/nmGycnJ42ajPGLbK1ly5YQZJ6enlCZ5V44yMjIgDtDz0VThJw1NjbGefnss88OHz4MhQcf5+LiAun2xx9/CE+YigYcPdHD111CvVxpGzBgwJYtW0aMGCEk3darVy/slI6Ojr+/P3bwdcnwRqIB62KrQ6DBNjCNJUAQ8rFYdJySL/L4c6vIQXjC2hEXLC0t0XLKvlonJCANkRlaWVnBSaK5CvacGhoaVn+UpbqjoqIyZcqUFStWwLFDAWN3an1DXxS6raCg4PLly4hAffv2/eqrr+o+3GLV4IggrYfsSE9P79atm0CGSxEI0JHDhg1jZfRAxL/evXsjKvv4+MCCc8lu4E6fPp3NIwI2bdqUk5Mzfvx4Xi/D119/jch68+ZNXm/U4EQoKytDl4j4Wg7kCNIJSBle/zdKSkrYqhoNJRAWFgZ9I4xn9SBq586di54FZz1v3jxXV1fE4BptG4BIxRFOS0tDeINKCwgIKHtLF0eDPczQp08f6Lns7GxkOFgjDtGtW7cmTJgAY3x8/MaNGxcuXFgvzyM2DupFtIkARUVFGxuboUOHIufxLYFPEDJokzikT58+RevFga3797sqgo4GvTto0CBIXkTSevksBJKl6OhodGEkuthNYQyoVinwFd27d0cwwgllsbIuIC3EAYRjgQ7BXgjky0M1AvvCDl3pdzWYvUaIQredPn0aCTp6lPCGSCkFDRryCDIRbQuKbeLEiXxCNUAkKHnFs7ogovBf/hdBQUFjx45F2GPVEydOYAuh4ZjfhFyo3yB08uTJa9euVfqtsNmzZ2NTeaXxAodra2sLr1Sj9wDqDpzI27dvkWCUuxzCkJOTwwyQztVvHvDvyCAF/k67o6Pj6tWrcXxQZh+iqd3DvPBZI0eOxNFGGY5YU1Oz7PU2LB89pbi4mB0NBQWFwYMHs8uH1tbWn376KRIMhGRUz507B69S7rUGojo0VtHGMDExQctEy0GfQvMQ6uPCpfj7+0O0Ic2wsLDAsdXS0uITBIqZmRl2De3/7t27SGNEfzMEGRd2E/kbNkOAH0ioDuyRCWR6dT+nERERaPwQJBBP2BHkinyCqIBzxqk0MjJC+urg4HDx4sVaqHDhjt8GF3z48GEcKaQgQ4YM4VYhAEd/5cqVM2fOeHt7Y6VffvklTgl7cbIKMCfS/e+///5+CY8ePUpJScHRTK0eaMH37t1jvwUDBgzAAhGHyoYigA4GaQ9BgBiMbOn333//6KOPSkWSgYHBkydP+vTpg/gkpNsH/4msrKyxsfG6det27NiBKvaC2QE2Hl20gQz2KFQ0NDRiY2NxmtByyp1B4YEmAfWfl5dXqR+ECEMwCAgIgL6pZjB4/Pgx/BrOJtM3AuHs2bNIOaCosLVIP+AxdHR0+LQagi6ppqaGpo7dQQ+C/0KI/WfCu3fFiUkySkpZ2dmjRo0qdaZQeNh3eDqcEfSUGTNmREVFsT5ub2+P2AwjNgbdX2SnTHzZsmUL2nYjFm2MyMjIly9fIugg2+natauwH49Bp/Dx8XFycsLqli5dOnnyZD5BCGC/kOSHh4e3bNkSGpF981dkIK2CzggODsaqraysavSARB2BBwgLC0OQxUrhKmv9JgHcKbzZyZMnmctFNgj3wqeJCpw1hHsID09PT5xK+FVDQ0N4Qj65eghRt6FBs+/B9+3bV0gfTgFoTNh5yGekAqampkjoZ82aVR0BhHiJA4eziJlx1OD9ATZ12rRpg6oH1Bh+y34Izp8///DhQ8RLiD+kBWhbCD9eXl69evVCc2Er/eGHHz799FNWZuAo5efnX716FTtS05MncDZv3gxPh86JZs1NzZrZ2Ng0Bd2GwA8N8ezZM5wsJEMi0wEIM2gwOPWVZn5INF+8eAGJ0759++qMU4qUFM69S5cuAhmGAHrowYMHR44cgXbEMvft2/ftt98Kyl+3a9cOB/xdQUHhE7ecn3bm/LpfystXV6OlYtfOku/3FL2JiTZWBfBxt2/fZq9roAsfOHBAT0/P0dER2TO2NjMzE4exFvkP+iBysKysLBxwbmp0QLfZ2dk1btEGTExMkBUgIkDfQ99AqgpqgN9KQf89ePAgGs+kSZMWLFjwoWceBAK6Xnx8PHatbdu2/fr1E9KFvUpBNnj69OkTJ04grs2fPx+6jU8QCfDJCNZwzujsOL84yLU7p1C9f/31l6+vb4cOHSCyhXotqQqQuCorK7958waiDT6wYwk18thC1G2nTp1CCBk+fLjwzjHkEYQXTgPUz7hx4/r374/8m0/7AHDu7L6ti4tLbGwszhx+BdhbzZBffL5qgF7EfsVAYgfhiA6McIIdx1lByJkwYQIKmHnx4sXwm/jLflsKzhZ+6+rqipgBIYhekbFgab79tbL/iqPfyFiI6Lo0wjM8wrFjx1gV7fvHH3+sdX4jXuBcQBDg9EEciCaXRWtE84iKiurWrVulAwfAPamrq8PdYHuq82BoREQEEgAsDW2Pm+rGRx99dOvWreLi4o8//njlypUCvK0AdVUU9Cp72668v468y86RHWVbFBAocftukX+AVGs9yVaV98QWLVrgNGF7kBDiLw7g3bt30eng09Gjjx8/fvPmTabb4NyrKb6hjNevX79nzx5kgEjbuLXRAf/T6EUbA70AjSEsLAwtBAmPUF+Du3TpEhIbNMuFCxeWPr4sJLAWNOnAwECIGIg2Ud7mc3Nzg+JB3EQsQNDsLtqvy6ioqOTl5aGfIo0MCQlBVKrFOYX0/PvvvyE94VTnzp0L9SkviE+81w7kh7kl3495/fr1/fv34bIsLS1rcJMEjk8YFBQUIDW/ceMGrwsBtorvv/8e54ObqgTx+J8vFXz7LfzX1q1bf/75Zz5BCDg7O0MQY10DBw7EyYAu5BOqoKgo85PPkvTbJxkaJ3cwKSl0ROGfslEXVIuzq7WbAgHtqbQN/fTTTyxANgWQCeCsQdnDTXCTMMnPzz9z5szvv//O65WBLUFysmvXLl6vEpy43bt3wxfweh1AeLC2tmZtYO3atdwqOHIOH0s2s0DDTpswrSgxCZai6DfpK9YmtemYPndRcW5Vxz8zM7OKVyLgBOXk5JBE/fHHH0+fPv3P1svGoGa/vXLlCrc2OiDaeKkJAA8PDzZo0CDIem4SAoWFheyKr7Gx8alTp7hVmCDqfVUy+Cgiy507d0TjmbOzszdt2oSVQiZ27drVzs6OTxAhaL3QqdgG6BsHB4da7Di6ds+ePdHToTuR6XFr/REfH79mzRqmvKGDkQAUVfs7N0K53paUlHT06FHEJBxo4d13QCSDd545c2Z1Lk0jnl2+fLlNCb169ZoxY8YAYX66HtkAYm1MTIy5uTk6GNbYvHnzt2/fVnE9L/vH7Xkn7GQnjpObMkn56MHc//0pazVE9c4N+dUrZYYOKnj4OOenHfLr+Dgdwgbb2alTJ3QPnEQ4PlVVVfaZh0aPoqIiOhISOxwBETzAwR66RxYLqfGhS8WYBJmCE1Gd2+gJCQkBAQHwrXW8RJqVlfXLL7+gDSMlHTt27KJFiwwFNMY9gkCB452szd/lX74mM9BSqnPnomfPpUxNpIzaSKioyI20KYqMLnR1kzJqK9WhXbMPXDBDxoxD9+jRI3g6W1tb5EVwxyx/Y74Pf+EWb926dejQIXZr1dvbG0cShxGiFkfp2bNnUH7s3gSMx48fZ88GuLm5TZ8+XVkIn1ysd6AteKkJAJcF9wvBgSAtvOttaFR//fWXl5fXvHnzlixZIsCxdj8E9MqDBw+QaURGRsJBwS2LYKVQPAcPHkSvwcHcuXPnmDFj+AQRgtaLE3r//v2oqKgWLVr06dOHPxpbbYKCgvDzjIwMnKlZs2Zxa/3BYk1ISAj2CNoAuwNZWc37PEJ5ED48PDwxMRGHRngPbCGNhp/V1NSEDuOmD/P48WN7e3vExSlTpkyYMKHSZ8AFC8IAgFJEamJtbZ2SkoINAP+8hloCn68MuX8eav7RAqU9O5ovWchN75E2M5Xubtbs3bvcPw5yk/CZOnUquisrr1+/vvTzao0eNA91dXXEb5ThJZlReKDrMj3B65WBGaKr964rE4IQLqxaOyDaFi9ejNSrXbt2f//9N1w2cg8+rc4UR8dkb9uFTVTc+ZPitq0KWzZLaGnmnT3/Li/vXWpazh9/FXp5v0tJzbM7Cwv/TWWgfTL3Ave3bds2SMzDhw/v2bNn9OjRurq6LJjh9EHArV279vPPP8dfCDKkefg7bdq0uXPn/vDDD+xAIQyUqlKout9//10E550QAU+ePLl+/bqQPtxXWFh448YNJLe8LhLgLtDCRTAyQymhoaE4hoGBgfCKUGzIkfgEkYO1I4dEAdsDmLGaQBvhJ8jWShfSEBg6dOgff/zBns04efLk8uXLXVxc2KSqEbxuQ9aLA4TmZWRkxE0CpaCgAC0JHQbBbP78+dxaGXDZmPm77767c+eOjIzMokWLBPXQT9X4+flhw5DqIRivWLFiwIAB2AYGFC1AAEaMwbYB/psSpDp3RODllX+j9MevEvLy2T/tzL91m5uED1qVp6cnu2H66NEj+Clmb9wg5MM3QSf99ttvL1684FahoaamhjWGhYXxegUkJCSQa6LpoluhUXHrB9DT05OTk4so+ZBarfH19UWXSUpKgjCaMWOGYK87Fvq/KI6IVPhyo+yYkRKqKpIaGs1XLitwvJPzv4PpU2blbNslqa0lv3lTgZtH3pUb/DeV0bZtW/Ro+JkHDx6gx+EQTZo0aeXKldeuXfPy8rp37x70GYzy8vJsmBWArufu7h4cHIzMOy4uDueXjaqP9G///v1ssaB9+/bwLbxCiDMIAULyWmhL69at2717N5YPdwEhJbJrtHDIaPYoQDKK4Csvt27dgudBJoN9hGRkmWG9AHeEDTAwMCgVYXxCNWB70aFDB+g2pKPcWt/gPML5swgLMZCbm4sWyyZVjeDvk/r4+KSkpCxYsKCmlzGrw5s3bw4cOODt7a2vrw8fXUUbSktLsyt5eUpVVbVz585Lly7lE4QGot3bEtiL6Do6On369MFxYEZGjx49JkyYAGkbEBAAJQSGvH+lJWfPb5Lq6u/S0ooCAvEv//bdZnJyEnKyrIp/MsOGILxJttSQGTqY/UQE6OrqIuwhbcWRDwkJGTlyJGtkjRsEe/So2NjY+Ph4yCChvrcFQYYeyx62ZbftKoK+LSsrCxGJDYMyK30YqyIxMTHPS4YmqvU2o+kiH4WsgZrZsmULtopPEASFQcE5W7dJtdJpvuwjCTm54oSEQj//ZhLNCj29C5zuSmq2VPh8g+LXX0h3aFccFp534XLzmdPRBfiPKwCBBXeMCAqJOXXqVBbMcHBwSOEfpkyZMnv2bPzV1tZu06YN3NHw4cMxAxxC165d4SKzsrKQluCHmMSCAbowZmjVqhXaOUk3saZFixapqanoC+gRhoaGOON8goA4e/bsH3/8kZycjOYEJz9t2rTaveFYC9TV1dHB4ZYRPrBSRDehvsSKvOju3btIgRYvXgyFWo+6DatG3Hz48CEOO4IsYlO/fv2q00+fPn36999/o3cjKFtbW5uYmPAJDQDIfegEuCP4IuSi0APVeSJFwLrN2dkZmXqXLl2E8b5JQkLCmTNnMjMz4Y579+79oSDHOH78OKTGzJkze/XqxZ5nFB7wDthriBvEPIB436kEtBJmKQXhGeGwb9++iL55eXmIN9DX7MIkdFuRfwCUGfvXrLj4XXxCaZUbEfl8nxX6B8iNF90TBq1btz537lx2dra/v/+qVasa5aM/5YA3RKRH/EZ3cnV1xSkT6rNuUIcRERHIJqu4HowNQASCtoCQYjcBKwUbDHmHLa/+N0PL4ubm9vXXX+NEo1mi7yA1EqBML4qIyt33e9GLAPlVy6W7meU73snZtS/vxKmChy7NcvPe5eXJDB6osG413LOEvPy77OxCV4/m82ZD3vHfVwDHwdLSEulZcHAwXF5FnwOZiz44dOhQGxsbSLF58+ZZWVkhcZo7d27Hjh3d3d3RHxEF2aOuCH6IT8j3MBt6KFsCIaZAxMPfQtmgZ3l4eKCpQOsISouj5UAH+Pn5vXv3btSoUUuWLKl00HIhgV1Dr8Q2wDXBIWDVCPx8mkCBPPq9BHSKWbNmLVq0qOqYKwKg1QAi++vXr+Hr4KX/87ZeRkbGiRMnTp8+jfkhr+fPny+MK0q1BmJURUVFTk4O+SfK1dRtgnyfFL7+6NGj27Zt43WBkp+fHxYW9u233+I0cFNlYBsuXrzIXhqFnOJW4VBYMvQa1gW2bt36559/Mjs7sIgZrFqWH374ATOjPxQUFHDTe5L02+eeOc8r794lG3XJWLScVxjFxZgn76Yjr4oQBDm2U4CbmgYpKSkHDhzYvn07sgU0LW4VNHC+aBX29vYVW0VZUlNT0bmcnJx4vTLgYfft23f58mVeryF79+5lF/PY13K5VUDkXb2R3ME04+N1+W4e6UtWJhkYp/QfmrX714xVnxRFRacvXpHSd3CBty+bOXv3viTDjvmPXVi1CtauXYsNRkiOjo7mpmqAzsuuwSPJhE6Fpbi4GIfOwcHh2rVr9+/fZ7MR4gvUPDL8f3xWs2ZfffVV1Z2r+ly/fn348OEtWrRAlIUIQOOp5oAGAgRxB0LE1NQUu4aNcXH5725SC+BGunXrhlVAKbKHQRsCDx8+ZCOLIcv95JNP/rPXw4czF4FkjA1pxCc0JOC3vb29nz59Cm3ATVUisHsBiGpXr16FZmSNSbBAX//yyy/Hjh3T09Njd0M+BKLas2fP0NpsbW2FejnU19cXAvHHH3+EUkaiv2bNGhYGSr8KBdXICmVZsWIFZv7f//538uRJbqqMQg9PhBFeaQAgc20Kwz5VBN7Z0tLSwMDgyJEjwvsElr6+fv/+/aGTYqr84AmSXTQeT0/PuA9/3wleCQqmigtyVbB//374QTiFHj16XLhwQbDPp77Ly8t/8OhdTo6EgkLWmk+LXgQ2X/qRyrlTCutXK/2+R1JfT+HLjc3y8vPOnm9WUFDo6ZV39oJU544ylv/9FvPixYuRV2DHd+7ciQjKrf8Fgu6qVatatmwZEBCAdBwWCNaVK1fa2dmNHTsWWS8SPzYnIaYYGhqOGTOGRQGPEpi91kD5nTp16ptvvnn06BGSqD59+syaNatr16616251QUZGRltbm70zjv2ClIyPj2eTBALbU2i1oKAgVHv37m1hYcEm1TudO3fGxkC0IZe+dOnSjh07/D7wwUm4snPnzi1YsIB9ZVurhCoeMqlHVFRU4HWr/ykIgd0nxZlm300vHfNJgLi5uUERstujVdxLOnv2rI+PT79+/SAyanef6D/Jyso6f/48NNmTJ08SEhJGjRqF1UHIQ72xGSZOnJiUlIQClJy6ujozlqKgoNCpUydsW4cOHcoNQJWz5zdZG2tpky7/lHfuKXoRoPjbbkntfz2l9M88Y0f/Mz6CyGnbtu2hQ4dQEPgDkQ0chHacMoi2sLAwZEW6urpVZw61A2sJDAxEy6ki2YAgQ+PHbEhjsEmVbga8UmRkZEREBMJJaZusDghF27dvh3BEPPjtt9/at28vWAeHjDL/wmUJWZnit29lLPsqbFwvN2WiRJkuIKnWAo427/Q56V49C1zcinyfKe3eJqmr+6GhQEqBtkZK/fjxY2TeAwYMqP4dK+yprKyso6NjcHDwyJEj4dNxhDMyMuzt7TEVnmT48OE43WxmQuyANEe3gqBBJo9yr169unT5x7vWjtjYWCTbv/76KzL2wsLCESNGbNy4EQJCvp7GboVYRKaHXYN8efv2LRu2XVMQX22Cytm1axfCXNk9hUitrz0tB2IotiQkJIQ5ZAiDqKgodN6KI0t4e3vDlV29ejU5OVlDQ2P8+PE2NjYN6iZprRGMbsPZ/emnn+D1Bg4cWMeBoyoFDRTnZvLkyR/KbPJLPhXl7+8PMY7TI3BNDeXu4uJy+PBhyDW0lfT0dHiEzz77TE9PD2GDz1TC77//juiLozFu3DhuqgAOUcVRQ//RZEMGSXf752plgYNTUeBLxW1b2aRS6lG3ISgy3ebs7LxwYfmRShoxaNVw+vAUAQEBcBY49Uh2cfpg53MIAjRsLBCaDA24Cr0F3YYE6cWLF3BDlY6MCDGHn2M7O3bsWEWGUw70X7hppD3QMYsWLZo/f36NNF91KI5LyLM7K9nGUOnwn3KjbCV1W1UUZJI62oWPXfNvOryLiZHsaNx8wVyJakhknB1TU9P9+/cjjyoqKhozZkz1Tw3i7v3793Fm4TpYqzYzM0MZwhfhEO5+7NixTeFFnMYKugCaNEu60HMR3Ws3lhs6Jpw/QFNRVlaGb9+wYcOgQYPq8XkvbAZ2Bw0eghLNFTuoo6OD1luX5orQhjAK0Xb9+nVoQVjgRlatWoX9rfcn28qCAAqdCjeIc4rC69evsftQmcjBynL58mVXV1f2Dr6VldXSpUuRjrIliDuC0W12dnY45Ti7iDrcJDhw3D09PeFDS1+9LMfz588PHjwYFxeHVjt16lRuFRxIx9HznZycjI2NDQwM+vfvDwX5oXcdmG4bNmxYTYe8KvTyyT18XFJLU9rMlOk2+fVr+LT31KNugzK+e/cuQqOhoWGT0m0MqPNu3bpBpgcFBbm7uycmJiIkAAGqNzSbN2/eoA1XrZngnRE8MDN8EGISt5YBk9AjTExMKuYGHwIn99y5c9gvJKPLly/v0aOHwDMfSWWldykpeWcvFPr6SerpSbWu5HJ48ZsYTC3y8nlXUCg3Y4pMNzOoZj6tSthl7CtXrsCVo+uhk/IJ/wV2EyfRoeRr2cjXcYphQe9WVVW9desWfA4sgn2jlhAx6urqGRkZT58+Ra+BA4f35hOqR2RkJNrVkSNH7O3toQ9g6devHzL2wYMHI2Fg89QXrVq1ghMIDw9HpoF9RHOF64B/qOmrYw8ePDh27BgSGMS4U6dOeXh4vCt5ShtaDfF01qxZwrgWUxewmzityKUR9BGSkMriILhVAFEb4gEpsbW19Zo1a5CkCeNWSb0gGN3GrtYKQzOB+Pj4O3fuTJw4ES2Sm/4NOpWmpub48eMtLS0FGG/QiHHuEQLxF9EakhTbgHCILIfPURm11m2yQwcVBbzMPXwMcq0B6jb0E8Q2HAo4iC5dupR9U6EpAH0GkdSyZUt9ff3CwsLQ0FAcDTiF1q1bC8p95+bm+vn5paSkQIJU0YzherAZXl5e8KrQkdxaBiwnMDCwa9eu1ddtCEg//fQTxChWDdUCBSNw3YYjKNWubYHzg+LXEdLmPaU7Gpe93lYcG5t79ETOrn3Fb+OQukiZdFZYv6aKN0krgu4JLwSePHkyffr06l8ewAlFH3/58iW6Lfw7FBvOtYyMDAIYzgWcfhUXzomGD2I8BI23t3dsbCwEPfKicndIPgS6AzLVEydOHD16lI2zD6c3duzYOXPmwLcL/IJ07YArgErDpkK+hIWFhYSEIFwWFxfDXp3bmj4+PpcvXz58+PChQ4ewj66urjhKcPVWVlbsKU9EVaE+Jl5rNDQ0oMIhCYyMjJC2QcBBvfFp74GoHT169MiRIxcsWICI3GhEGxCAbvP19YWvxLGDbOImgfLLL78gNI4YMaJiV0EDvXr1anR0NAIYEiBBBZv8/HzkHPfu3YMYTUtLQ/Lx9ddfo/lW58TXWrdJyMsXevkUPXte+MxfonnzirrtXXZ27u8H6ku3AZyCv/76Cw4CJ7rhPKYqStDAlJSU4Cl69eqFDJ69cVm7Oy8VgWKAUAgICIAzqnrYEYgSeGdsQO/KPiyNSS9Khguu/oUiuLwbN25ERkZC7W3evFlIFxIkFBUltFrmX7QvcL4v3bunZMuWEmzAyfsPs9ZuyL/hIGVqorR3l8xwq+LwCFlb6/98sq0c0NAODg7IvBF4qv8VO/wEgTk1NTUqKqpdu3Zo2Ldv354/fz7aOaZOmzat+osiGiYsI0LPQv9q1aoV0s7/vJmIvnDx4sW9e/deuXIF/hxNa+jQofPmzVu1ahVaSAMRbQyoE3gk5JDo+JBuSK2h3qAyX716BVlWNVClCFhI89iiEMT79OkzderUZcuWLVy4ELtc/adFRQ9cMXsBEXIc4hW+q+e/YQNxT5gwoVoja4gXCDx1BKf/22+/5RVB4+/vj4V/6D1neFhMRQfj9TqDxAWZGZYJTp48ya01gV2I+umnn3i9hiQbm6UOH5M2flqSfntuKiHfxRWW+hoHpBT2mPZvv/3G600VJAyIAWfPnt26dStEEpoNpE/dBwpJTk7etWuXnZ0dFshNHwAOeufOnZUOHQILNmz79u2vq/11efQj9si2mZkZMlduFQ5ZO/ekjhif0ntgyrBRWXt/z/h4fXK7LmljJudeqOXYJaUgy1qyZAm0dadOnZ5XYwwg+Bb2hRkA179ixQpIN9hv3rwJ3QwLnH6NxhYhGiyHDx9mwdvGxubatWtV9C+oH2TsGzduLM3H5OXl0a6Cg4P5HA0SqNLPP/+8djkkMkak4kyu3blzhy+RaMDU9XpbYWEhMpKsrKwPPXxWRxDDFBUVra2tK30jAekC8oMxY8YI5Epbfn4+FgjdhnRq5syZWHItnvGs9fU2hnQ309w/DxXHxiofPvAuLS1r01dy48dmzF+cd+zUu+wc5eOHpLubSSjW2xsxv/zyCzK5UaNGNc3rbaWgvaFBampqooV4eHi8fPkScgeolXy0is9UcxAh0No9PT0hICAdqrihg/wyNTUVzhpKWv3fry1LSkq2atXq2bNnaM9IxP/z4llOTg7CkpeXFxb12Wefod0K6rp1pcj06S3VzkhSs2WzrKz80+fe5eXKr1giv36NjIV5TS+wlUNWVrZdu3ZItxB6cXyGDh1axY6kp6d///336O8oDx8+HN124cKF7Ehqa2vr6ektX74clob2ZA9RO3BmodWQYqGrQoHBkpaWFloZjo6OO3bsuHTpEvoX+o6xsbGVldX06dMRDtiiGibwRd26dYMDQUaHlJJb/wvMjyQH/vzTEhBJBTv6DyEkJKDdeLFWILn/8ccf58yZI6Q3Nfbu3YtIhrjC62VA78rNzR07dmyPHj24qQ6wHUFHnTt3bsU3iqsPukFQUNBPP/30xRdfcFMNKbj/KGPhUihiXgfS0hJycmov//kIT/2CeBYTE/Pbb799/PHH3NS0KSoq8vf3f/r0KaICchgTE5MJEybwabUiLy/PxcWFPb6GaIFU+EN356G39u3bB89ramrKBqIsi5ubG1LnefPm/edD+tjynj17Qina2NjcunVLgG9afBD4nHfviqLfFDjekZs+WUJJqZrvH/wn8GZILTZs2KCgoIBuWMVgQEePHoVXwSnDnMhdIVj5hBKwHKGKV0L0oG1v2bLF3d2d1/8LNAyIto0bNyKH5yZxAA370KFD8Eu8XiU6OjofffRR0xybU6xp0LoNEgHZM1LeiroNYQa5MmTE/Co/LV99sKLw8PBx48Yha+GmWlF33QbyTp8v9PLmFcg2MxO5eXzg7/qFdFuloBMlJycHBgbev38fvh5yAemvoaFhpe97VgeorkePHkFCDRs2rG3btlBvfMK/8fPze/LkSVJS0oIFC8pplKysrLNnz0J8zJo1q+rHcbDZQ4YMQY6OLlB631CsGTBgALTvyJEjz58/D/nLrWXAEZs8efKDBw9QvnHjxqhRo5idaMQgz9m/f//u3bsTEhKg17m1MmRkZNB/LSwsVq5cyb5pSxANCuHn1nUgICAgOzsb/pfXy/D8+fP8/HxBjfEbWjJUPbprHUWboJCbOVVx18+l/xqIaCM+BOSRhoaGiYnJwIEDIYCgui5cuODp6ckn15zu3buPHz9eXV39Zglo6pXmV2ZmZhMnTlRSUnJyckJP4dYSFBQUsJDY2Fhv7/9PACqlc+fO8+bNg0ZsNO9brV+/Hqfj1q1bOAvcVAaE7R07djDRZlAyrA+zE40beXn5Tz/9dPv27einSHKqoFevXnv27Ll8+TKJNqJhUlfdhojCS4IGIefx48eysrLoSNz0HvapYEzSq2wchJqSmZl54sSJgoKCzz//nJsIoua0aNECum3RokULFy5s1arVvXv3rl+//uzZs9zcXD5HtWnevLmxsfHcuXPNzc1DQkLs7OySk5P5tDJAL+ro6FhYWCDrCCr5Ik0pmGRqaqqtrQ0RGf9f38B58eJFXl7ezp07eV3MgVuAlkVh//798BXMWIqrq+vevXtRwDxQeB+6lkk0StCnfHx8oqoELaRxXHgmGit11W3w9Uhtqz9SVE1ZuXIlL1Xgk08+4aU6EBoaumvXLuxCpY/QEUSNYEN/qampDRkyBIoKkuvmzZtQCd7e3hBe0EZ8vuohLS1taWmJvAW/RQ4DcVbpYyt9+/bV0tLy9PTEPEg/uLXkdo+1tTUyq+fPn1fxvEtSUhLEJQoKCgrFDemruLUG7ojptuzs7HJfLMXRuHbtGv7KyclNmDBhzZryQyQSBEE0cARwn7R169ZVD0XbkHF3d0d0nD59umB34ciRI7GxsbxCND3atGkza9asKVOmTJw4sXv37m5ubufPn0cen5ubWyNtBP2B5fTu3fvVq1fnzp2D/qv46XSIRRsbm3fv3t2/fx8SjVtLMDQ07Natm6+vb9yHP0WPpGXdunUoREdHZ2RkMKNYY2Bg0KtXLwkJCei2zMxMbi0BVXb10cjI6OOPP8Y8zE4QBCEuNOjn2z4EVJFAnsXx8vJCOGzevLkARRtL9Nlg+sxCNE3k5eX19fU7duxobW09bNgwSISHDx8ePHjw8uXLfI7qIScnBxUyb9681NRUBweHJ0+esO/tlAVKhX2OBqsod0e1X79+aOEuLi68Xhns4Tk29jqziDUFBQVJSUnYqRYtWkCVcmsJ0KYvX75EAVIYMCNBEIQYIZa6DdT9w/6FhYXXrl1r167dhg0buEkQ1OVpdKJRghwD6m3UqFH9+/eHCAsMDDxz5sz9+/chsKr5uj6aura29kcffQSp4ePjc/78+djY2LLX7SAKkTCgU+Tk5JS7KwopZmpqCrESEBDATRVgkwYPHiyKQUCET3x8fFRUFAqGhoYQo8zI+Pnnn8PDw1Ho1KkTsxAEQYgXjcFN1w47Ozv8beCjKRKNBn19fSsrqxkzZgwcODArKwvy6/bt2/fu3QsNDc3IyKh6YAJG69atofzGjh2bl5d3/fr1Fy9elHsrqEOHDl26dPH19XVzcyu9Pwgp1r17dy0trcePH6elpTFjOdjHEsq92SC+tG3bdty4cdhxd3d3HA1ubdYMiu3WrVvsEcBevXo1DpFKEERTo+l6rrCwsG7duhkbG/M6QQgZCQkJVVVV6La5c+cuW7ZMRkbmyZMnZ86cOXr06OnTp9+9e5eenl7102/Kysposf369YP4uHbt2l9//VXuibRBgwZhLc7OzmVvjKqoqEDwxcXFfeiSG7QgZOWbN294XczBkUTvxpFEofR4onDy5EkoVxyfL774YtiwYcxOEAQhXjRR3RYaGspLBCFaJCUlZWVlFRQUhg8fvnDhwp49e7Zo0QLya//+/X///ffhw4erHhcUsgOaY/To0SYmJlAhT58+LftAG3ThvHnz2rVr9+zZs8jISPbgGsDMEHw+Pj7lnn5jeHl5RUdHe3p63rx5k5vEmcwSUMDRKP0ImL29/ZYtW1CA9v3444+F9O18giAIYdMUdVt4ePilS5fguIU65OaCBQt4iSAqQ0VFpXXr1ra2tpMnT54wYYK2tnbXrl1jYmLOnz8fGBhY9dNvBgYGkG7QZ+7u7pi/dIQRiEKoQBsbGxQeP35c9i7hgAEDMFvF5y+hEa9cuYICBI2QvlYnYuTl5TU1NSFwc3Jy2OtB+fn52PHi4mIoZhw3HR0dNidBEITY0RR1G3LxrKysr7/+WkjDl7BXShE1WZUgqgACS0FBQVdXF9LN2tp6zJgxaD9Xr149dOiQg4ND2fHYygJRgsRj0qRJo0aNYl9o4BNKaNmyZb9+/YKDg2/cuFE6AgjESufOnaHzoqOjmYWB5TBx06ZNGwhBZhRroERL3ydl19sgbfft2wcLhOmmTZvoyTaCIMQX8l+ChxQbUQukpaUhoXr27GljY2NhYaGvr//ixYsjR45UMaaajIxMp06doPnYA/hv3rwpvcHat2/f+fPnKyoqQrKgQRYVFWHhPXr0UFVVvXfvXk5OTuktVEhAKDYUgoKCIPWYUazBXpuZmUGc4Riy90lDQkLYOxxdu3ZtHNqUIIgmS0PXbZcuXeIlAYFwRbqKaMjo6OhYWVmNGzcOMis9Pf3KlSuhoaEpKSmV3jaVk5ODzoMmc3JyOnv2bOm4u1AthoaGAwcO1NPT8/DwYIPNamtrQxdGRkZimW/fvmVzgtatW+OvrKxsdV5rbfgUFxdDjKIQFxeXmprKPlTKdg17WvVX9gmCIBo4DVe3tWrVasCAAYgxvC4goNvgzWfMmMHrQgBBtHnz5iiYm5szC0HUCDQhRUVFqDdTU1OINgiyo0ePHjx40M/Pr6zeYkDnLVmyBE1aRkam7Li70C5QaUOHDoVSKX29tF+/frq6ulFRUc7OzswCJk2aBJGHn+tX+BawOII+HhMTg7/sZdJz5875+/ujMHXq1C+++AK7WTIXQRCEWEL3SQWPiYkJ+3BQYmIiAi0zEkRNgXobNGgQNBkSAKQxhSUjRR85cuSvv/6C8MrNzS2918lecbCxscnKyrKzs3NwcHj9+jVUi6SkJCahQQYGBnp5eSUkJMAyZ84clhH99ttvvr6+2dnZ8fHxSUlJEHyPHj1iCxRrcBBSU1NRSE9Px26WviRra2tbbhhegiAIsUMMdJv4jk0QERFx+/ZtXiGImiMvL6+rq2ttbT1p0qQpU6ZAgfXt2zcuLs7e3v7KlStBQUHskhKjY8eO0HkQcMgWbty44ebmlp+fD6HWo0cPbW3t+/fvOzk5Qb1BpZmamo4dO1ZCQgLtE/aUlBSsCHNCKZZqQfEF+8I+b5WTkwMJC0mKPe3duzeOIZuBIAhCfGnQug3hqkWLFuI4HOhPP/3U+B5/XrduXUxMDK8QIgSKSlZWVkdHZ/To0YMHDx43blz79u3Dw8MvX758+vRpd3f32NhYNqelpSXmGTp0KOaHIHv79i20i4qKysyZMwcOHBgSEgIdc+nSpYyMDEjAadOmQcB5e3s/fPhQU1MTPz958iR7iYEtTUwpLCxMSEhgd0v37t2L3cHRWLNmjZqaGp+DIAhCbBGD621ZWVmlYxkQ9QVOAXvmHeqha9euzEiIEgkJCRkZGWg4MzOzkSNHsvHbEhMTHzx4cKgEaDL21qSFhcWsWbNwps6cOWNvb495YOzRo8fSpUuh+dChnJ2dcTZRwKJgadmyJeYfMGCAqqpqdHT0hwYfERfQViMiIlCAdMvNzUVh8eLFY8aMKZlIEAQh3kh99913vFgrkNMjPAjvI826urqurq7q6urslTcGu7ODkCMtLc1N1QauHHHOxMRE2E+69OrV6/Dhw69fv542bRob0U2sQZj/6quvUED4//7775mRqEe0tLS6dOnStoRWrVqlp6cHBgZCokF45eXlKSsrs+EJX7x4ERwcHBYWBjWD/qKhoYHWiGb58uVLTIKxuLhYSkpKVlZWTk4OHU1NTa1FCegp7K1MsaOwsPDGjRvYZV5v1uzbb7+Fu+AVgiAIcaahX28zMDDAXzjiso/dLFy4EPGJDRbaYGEfY0DIhK5lFrGmdBSxhw8fsgJRv0BUSUpKonUZGxv36dNn3LhxPXv2hGjz9fW9cuXKsWPHoOSGDx8+d+5cCDuIs6dPn169evXkyZP6+vrIJaZOnTp06FAoPH9///Dw8ICAAMg+LBZy5+zZs3/99RekD7OIHWlpaWU/5wUfguPDKwRBEGKOGNwn7dy5s7Ozc9nRR5WUlLS0tH799Vdeb6iUPgdtb2/PCuKLra0tLxEND2g4FRWVkSNHLl++HFmNpaWlgoKCo6MjVBp7t5SNWwbFhvzn9OnTdnZ20GRSUlIQdtOnT7exscGk3r17Y1FQeHp6epA+L168OH/+/KNHj8qmTGIBZCh2mVeaNYOWbTSfzCcIghAD3TZgwAD8vXDhAqsCDQ0NNsJ7A+fLL79khW3btrGCuHP27FleIhokkGs6OjpDhw4dM2aMhYUFxJyqqio02bNnzzA1KSmpsLCwoKAAOi8oKOjGjRsXL16EMoOsMTAwePXqFXuQFHmRubk5lDozsjF7xYjHjx+Xvd4mWfIlMV4hCIIQc8RAtyH7t7S0jIyMLP14dikVLdVBVlb2zJkzOTk5vC40evXq9dlnn6GAYMmejxZTDA0NWYFpaKLhA/U2ZMiQCRMmTJ48GRpu4cKFEHMyMjJQchAx7JtaRUVFWVlZwcHBz58/hz5r3rw5+86Vv7//06dPYRw5ciR+rqioyJZZCvpd2fFHGhrlbu/iOBgZGfEKQRCEmCMGug3o6+tDbJ08eZLXS55ZQeyB/OL1aoPke/78+S1atNi9ezc3CR8vL68//viDV8QNNze3zMxMFKCeK4ZwosHCRg+Rk5ODXFNXVzc3N1+8ePGSJUsmTZr07t07iDZoO4mS79NDzBkbG0OdM10OTaatrf327dtnz56lpKTAkp+fj5+A9PR0FxeXo0ePsoFtGyAZGRn37t1DAfley5YtIT2/+eYb7CObShAEIe6Ih27r0qUL+3JUKSYmJohJvFJD4NBnzpyJuCWCD5UuXboUIRAF6DY2HIN4ERQUNH36dHbXaerUqaqqqsxOiB3QLvIlGBkZzZo1C6cVAg5avF+/ftA6ISEhkGVsTui8YcOGmZqaPn/+3M7O7uLFi7du3Xr06NHTp0/Pnz//4MEDXV3dBvuqqaOjY1JSEgpffvkl9CWSvYEDB7JJBEEQjQDx0G0Aui0qKsrNzY3XS0ZFDw8P9/T05PWaAC2FBd65c+fVq1fcJBzat2/P3icNDg6Oi4tLT09ndpGRnlHI/g0Z6VX2n++z/3/Powqio6PZJ2IR3T/55BNmJMQaSUlJDQ2NVq1aaWpqWllZDRkyZPbs2RBw7G441FthYaGTk1Nubu748eNHjhxZXFwcFhZ2//599vEPaL4RI0aoqallZReduxTn4paall6Yn99QbpuyFymkpaXHjBkzevRodXV1PoEgCKJRIFGaZNeO7777rnv37hMmTOB1YYJ1IWD07duX10sspqamWHst7oMgOG3duhVByNjYmJuERunFif79+4vgIh8jPCLn+YvMX37jH+aXby453Oqfj/+AkLDsgJdZX29qO2xIVVHNzs4OEZ2Vt2/fvnHjRlYmGis//PCDj48PMqIOHTogsenUqRPkHZIcBQUFNvIOOjskUWZmke/zjJNn30ZG5eblFWtpykwapzV4gJqWZi0vgQuK2NjYzp07p6WlDR061NHRkT4hTxBE40MA4+5mZWUhU1dRUeEmoVE63G6pO5aVlXV1dbW0tEQsYZbqU1xc/PDhw/j4+FevXgl7TE5NTU32ldX09HQnJ6dx48YhNLJJQiIzq2jDl8GOd5JUlKV/3tLe1lpjpI3G+DGa/fqo4p95TxU3jzSne8ka6jId2n3wVTsIYvZ4E8BmswLRiEFDPXTokLOzM+QaWml4eHhISEhAQEBqauqQIUN0dXUlJSXz8ovPXoz7/c8oQ/3ms6fpmHRWysoqcn6Y4uWTMWSgmqxsvV3Cz83NRXZx79495KLsexLYWj6NIAiisVBXv7Z58+aMjAwRvJsJPvroo6SkJPaMPENPTw+KrXajbOCHU6ZMwcYHBwdfvHhRqN9kXLVq1VdffYUokpaWhqAogns3Y6b4xsXn/++XTlfPdevZXRn/TLr8/zcbtLVkTx4ykZGR2LEnIiy88nP3xRdfsBHnFRQUoqOjmZFo3HTs2HHatGloqMiRUJ01a9aCBQv69OmTl5cHDcfm8Xia7uSc3Le36ool+sOtNKZO0t64rs2kcZphr3Nu3Epg89QLV69ePX36NPKxwYMHr1u3jt5FIAiiUSJO+aiSkpK2tvb//vc/Xi8Zn6J79+4o1O4xNRMTk40bN/bs2fP58+e3b9/28fHhE4TADz/8gHWV3lBGjGEFAYKDUHYXRttqdO1c1eufB/Z15qUKJCYmskVpamra29tDHzM70biB1oFua9euXUxMzK5du27duoUGMGjQoBkzZrRt2xYzpKQW3nJKLCp6FxSc/c0PoTGxeRISzVpqyEybpN3GoPnRU7E/7gzPyKyfz9JfunSJPYvZpk0bfX19ZiQIgmhkiJNuU1NTY8HDwcGBWUDfvn1lZWUvX77Mhp6qBWPHju3fv7+7u/u1a9dcXV25VQj8/PPPGzZsYOX58+cLXLphFxwdHXmlbowaNYotCiFw+PDhzEg0BSDaPv/8cxRu3ry5atUqLy8vlGVkZPLyiu8/SvlpZ7i3b8aYkS03rDFISSu8cz+5sPCfB2QV5KXWrzYcNlTd0zv90RN+b12U+Pn5OTs7o2Bqajpz5kxhP4dAEARRX4jZ8x8jRoxQUlKKjY3l9WbNWrZs+eWXXxYXF586dSo/P59ba4KEhASkyXfffScnJwexItRvMkIgfvHFF5KSkqmpqePHj8eqBw8enJiYWMe3QyolL//dqvUvy71GWvbfnI/8MVtm1r+ujmRnZ7dv3/7p06coQyh7eHgwO9F0gO5BMoNCSkrKZ599lpWVhfJ1h8QdeyMSk/M3b2w7ebyWeU9Vq0FqNx0T0zMKS37UrGMHhaUL9Ey6KP3vz+jYuNoMiF070tLStm/fjkyDDf8xY8YMa2trNokgCKLxUdf3EiCYHj161Lx58w4dOnCTkHny5ElRUVHbtm3LjgGrq6v77NkzWOpyf6RPnz5RUVEuLi6qqqqtWrXiVkEzbNgwaCMzM7OcnJyEhISIiIidO3eqqKj069ePz1FbcAQyMjLY0Klv4/Nv3U7q2EGxRzflTsaKlf7T12se/jonN694yEA1toTk5GSEvVKt9uLFC0g3ViaaDlJSUqampiEhITExMUgq0LU7depid/6tspLUjq0djNsryMhISkg0M9Bvfvd+SlR0nmUfVUnJf96YlpOTVFaWfuCSkpVZBGEnJSWKMd7u3LkDcRkfH9+mTZsNGzZ8/vnn9GQbQRCNmLrqNklJycLCQjc3tyFDhnCTkGndujWkm6amZtmHruTl5eG4ITiwMbX+pg3cfbt27V6/fu3t7Y2/Ojo6Skr//yy/ABk+fPjo0aOtra2PHz/OPtV1+/bthw8famlp1UX+ltVtJl2UzlyIQxz96bv2/SxUK/3Xzkjh0tX4bzYZqan9837uwoUL9+7d++DBg5KFNVuyZMnUqVNpJIWmCZriyJEjPT09AwIC0tLShtuMPnk2uV0bhQGWajLSXI01l5fMzCq6divBuINiaz0+LLaSotT9RykZmUWW/VooyAtdP717927+/PmhoaEoo/WuWrWKRBtBEI2but4nlZCQaNWqFXzlzz//zE1CBrqtefPmN2/eLPv5Aei22bNny8rKxsTEQLpxa81RVlZetmwZ0vc3b94cOHAgu4Ri4XyK0djYGBERgWf9+vU4jPfu3Rs1ahQKNjY2//ndVewj2zZ2axgLgXRG9Cr9fnYLVem1Kw2CQ7NZtWqKioo2bdp09OhRdqVNTU1t6NChBw8epIeEmjItW7b86aefUICUXzB/dvDLh2/j8wvKjK8rKyM5yqalspL0cbvY5OQCZszMLMrOLmqpLqOoIHT9hM6+du3ap0+fwgX9+uuvkydPpoE/CIJo9NT1ehtAah4VFZWeni6y78l06dIlPDwcQqfcRT7omOfPn6urq7NPFNQaGRkZAwMD/D1//ryLi0tBQUFmZmYdl1kFtra2OHrdu3fHWiBGw8LCcDxjY2N79+7N5/g3UGnXrl27ePGiq6urhoYGNgyizdHREUJw8eLFfKZmzV6+ynZ/mrZgji6vVyA9o+jS1fjcLOe7d66wCA0sLCyePHmycuVKViWaMrq6urm5uc+ePXsV/NrIeLWZicag/moyMv9/91NJSUpFRcrpXrKOllz79v/kDC7uqQ9dUkfYaHQ3VWbzCIPk5GR0gTlz5jg4OKCfLlmy5IsvvkA6xycTBEE0XgSg2wA8uyh1m3zJNxax0qysrPbt23NryUel7t+/D+nTq1evOmbeLVq0gAxq06YN5JS7u7uvr6+CgoLwhsOAdGN3Tg8dOlRYWOjn53fjxo0HDx60bt264m3fY8eOvXz5ctiwYYMHD+7YseOdO3cgYTt06DBp0qRTp05hU7W1tZWVlf9Tt236JiQxseDsqbV3nC4zC5azdOlSGvWDKAXJA9rDwwfO6emxRc16deyg0lr/X/KonZHC8xcZfs8zIekg6G46JkZG5y5fpK+qUuOhsKsDujy02tdff7179+6EhAR0+RUrVqDR0vesCIJoIojrbQUNDQ0lJSVXV9dy3yedN29eamoqewOu7kC3gZUrV8rJyTk5OUHj/vLLL9lCu3PaqVOnnJwcTU1NduUAGhRKTkJCAvHp5MmTWDtWbWdn9/r1a4hIBKojR47A+PjxY6jM6dOnnz17NiIiQldXl71UIScrIS0tMXHms9S0wsIi/r4qDg6WlpycsWhlQEBgRvir/8lKJ2GN27dvf/fu3ZAhQ9hHKgmCoaqqij61bNmyTu2LY954/3ogYtu2XaVf0QCSEs3mz9JNTik4dTZ2z++Rzg9TVJSk1dSEItrQ8iHR5s+ff/36dSkpqcWLF9++fXvz5s3opHwOgiCIxk5dv0/KgKqIior64osveF1UQLVU/D6pvb29r6/v7NmzBfuKa3h4eEBAABsgY8CAAVpaWsL7Otbx48fd3d2xxlu3bjFLjx49xo0bx8pQcqNGjYJghQLr1asXLOxW9bNnzywtLW1sbNhs4PzluP8d/OdTB9Mna7fSkUNhx44d0HZqLfu01BqSkx1VlLX1ypUrdIGN+E+CgoJmzN6mpL4oLenqpvW9Zs6a/s87pSXEvs2DYvPwSldrIT1ogNqKj/SbNxdwQvj27VukJRs3bszPz0fS0rZt2927d9va2vLJBEEQTQaB6bawsDBra2voBm4SCa6uro6Ojp9//nnZR1sSExP/+OMPOTm5KVOm1Prd0g+BjN/f3x+aSVpaWl9fH1pKeDdosCNY17Bhw1q3bj1kyJCKFxUg4DA1NzeXfaseiq3i8b97P2L5qv16BtN4/T0b1hrq6jRTU81gQxkTRNXk5OTs2fOH30vTqOh3bVvHz5pp091MVVZW4pl/5rVbiRGROekZRVu/aWfeQ6XsA3B1B70A2cvBgweRyRQUFPTs2XPBggVz5syhEWoIgmiaCEa3ge+++w4utfSakGiIjIw8fvx4YWFhOekGfv7557y8vE8//VRWVhYajlsFx86dOxFFkP1Dt3300Ufy8vICf5etqKgImmzXrl04R9gF7M61a9fGjh2blZUF1Vh2p7Aln3zyCQqpqanGxsbMWA5snoaGBgoGBgblbi4TRHXIzMx8G1+0eUtkRkbBu3cS6uoysrKScXH5erpyi+frFhe/szAX8Jht0GpfffWVm5sbVt2iRYs1a9YsW7ZMS0sL7Z/PQRAE0cQQb90G/P39L1++jOR7+vTpmpqa3NqsWVxcnJeX19OnTzt16oRJ3CpQQkJCXr16xcbOGDRokLKysp6enq7uB98DqBGQVrGxsewrQxYWFlj43bt3UW7fvv2vv/6KFXXr1q1kxn/YsmULL1WGmZnZwIEDEfa2bt3KTQRRW7KyCnf/FnTq5J+Kyr2kpJrrasefP71cWlpKgHkLMjH033379u3evbu4uFhFRaVXr15o9iYmJnwOgiCIporY6zYQGRl5+PBhrBobwE3v+f7775s3b44EfcGCBdwkaF6/fu3n58e+wg5tBOq4rqNHj+JvREQETg3koJGRkYSExLlz57KysrBkdpeWzQmgHR8/foyZef099+/f56VmzSDyyr51SxB1xNvb7+HD+w8fud2/75KWGvv999/p6+sPGDCgVatWpSMI1pqYmBi0dvRoNHVZWVlLS8slS5aMHj0a6o3PQRAE0YQRmG7bsWNHdnb2vHnzBP5IWXWAahwxYoSpqWnZj18xdu7cmZeXhwwe83CT0HBxcXny5Ekd32aVkpJi93w/++wz/MXSsAtycnJffPGFl5fXtWvXJCUlR44ceePGDVVV1XXr1pX8iCBEB5wGePPmzdKlSx0cHNijCC1btly9evXKlStRRabBZ602BQX/jNyLVv3VV1+FhYXl5OSoqan98MMPU6dO1dTUrMUCCYIgGiUC023QRj///LOFhcWoUaO4SYTcvHkzPDw8ISGhUnF24cIF5O7YsA4dOojgcWZsDC/VCi0tLXNzc1YODQ09efIkthnRKyoqquySsS+zZ8/mFYKoDwICAhwdHdHpkLMhNUJG0alTp379+llZWQ0cOFBBQQFJSFFREXIPJSUl6Lnc3FxlZT4eL36SnJyMGfbv3w8v5OHhoa+vf+7cOSxHXV19zJgxe/bsoVHZCIIgyiEw3VZcXHz37l0XFxcRXNaqlKtXr3p7e/fv33/48OHc9B7oOQSJI0eOIHFftWoVtzZsjh8/joCXmJiImIdtfvHiRemtT0TH+fPnY1/qfk+KIOoIOr6dnd3hw4cfPXoEZ4JGC6O0tLRhCZBr6HqvXr3q2rUrRFh0dHSPHj1kZGTQhp89ewbZl5+fHxsbyxYFVFRUoPmWLVs2aNAgat4EQRAVEZhuA5BNEE/1pdvAvn371NTUZs2aVenrZkFBQefPn0eY+fTTT1GteEe1gQChdvPmTQg1toUbNmzw9fW9cuUKqpjUvHlzWVnZ9evXs5kJot6BVnv79u3jx48fPny4f/9+bq2AhMQ/3gaKDaBa+P47wnJycrDr6OigsGPHjsGDB4vgojhBEISYInjdVun7ASIDqtHMzGzChAksNpQDW4jkno2dO3r06A99AFTEsDu8vPL+Niu2DVsYGhqalJTELKNGjUJh+vTpnTt3LpmRIBoWiYmJR48e9fHxCQgI0NDQQMNmD2tGRkbm5eXJyMgga5KXl4eAKygoaN++vYGBQadOnZYvX56bmwu7qqpq6V1UgiAIolIEqdsyMzPt7OxSUlI2bdrETSLHxcXFyckJ8WDRokXcVIGIiIi7d+9GRUVhtuHDh+vr6/MJIgeHC/EMygyHjpuaNWvdurW1tTW2DVt49uzZrDJvObRt23bq1Kl0/4hosMCfQJYlJydDh0VHRyspKSGDcnNzCwsL09PTgz5DG5aWloaXMDc3h1Cr3UsMBEEQTRZB6jZw/vz50NDQdevWlR0VVsTcunXL3d29Xbt2M2fOrGJ8zqKioj179uTk5KBgampqa2uL+CGMm6fZ//6eqbOzs7e3N680a4bAhr+rV68ud8Qg13bu3InCp59+unv37ubNm3/++edsEkGIHehlZT9GRxAEQdQOAes28N133yGTHjNmDK/XB46Ojq6urt26dZswYULV2bynp2d8fHxiYmJYWBhEXtmPezK0tbVr+ql1NhJvKffu3YM65JUSevbsWSooK339Fhtz8uRJVVVVSUnJpKQkWKZOndq1a1c2lSAIgiCIpknj1G3g8ePHd+7cqfqGaSmZmZmQR/n5+adOneKm9ygrK9d0MIKKo+AuXLiQl0rQ19ev4trDkSNH2GukY8eOdXZ2RgE/p7dHCYIgCIJotLoN3Lhx4+nTpx06dJg+fXoVN0yrxtvbG+KJV6qHlpbWvHnzeKV6QJyFh4dfuHABZWxq8+bN161b98MPP6CwadMmegCIIAiCIAggFN1mYGAwZcqUhvBdGgcHBzc3t+7du7dq1apPnz7c2sCAYjt16lRhYaGRkZFmCRC+UJzQnZMnTzY1NeXzEQRBEATRtBG8bgsODoYKWbBgQZs2bbipXomMjLx37x60EdSklZVVA9kq4OrqGhgYiEJycnJmZuaiRYvU1dWVlJSioqKcnJzwt3Xr1tOnT2+w48wRBEEQBCFiBK/bgKen5/Xr17+rvwF4K2Xbtm25ubmSkpIbN25sXvIBUNFTOt4HG+MDmozdAy19nxQz7Nq1C5snLS29YcOGknkJgiAIgiD+QYi6rX4H4K1IbGysr6+vu7u7rq7u1KlTRTkmO9TYixcvULh16xazgDZt2syePVtGRobXS14jPXXqlLKyMjZPT0+PWwmCIAiCIEoQim6DTIH+SE9P/+yzz7ipwRAREcE+VKqkpDR//nxuFSbnzp1LTk5++/Ytytra2qNHj2Z2dleUlcHhw4fZDdNVq1Zh87iVIAiCIAjiPULRbeDo0aMJCQkNULcx7O3tQ0JCIJImTpzYrl27svpJIOTl5eXn5+/evZtVlZWVhwwZ0qtXL1YtR25u7p49e4qKiuTl5desWVP2ChxBEARBEEQpQtRtMTExs2bNajjvAZSjuLjY0dExKCgoIyPDxsamU6dOqqqqfFrdcHd3f/XqVWhoqKKioomJCSwjR45kkyqSlpZ24cKFqKioMWPGmJubcytBEARBEEQFhKXb4uPj9+/fb2FhUen3ABoOCQkJ//vf/1DQ0tJSUVGZM2cOs9eClJSUy5cvoxAZGYm/U6dO1dDQ0NHRKZn4QXCUcKyw3vbt23MTQRAEQRBEZQhLt4Fdu3ZllgxvYWBgwE0NFX9/f0dHx4yMDFb99NNP8VdBQaE6X1TEr1xdXZ88eYKynJycrKyspqbmf467W1RUdO3aNV9fX6xi/PjxZmZmfAJBEARBEMQHEKJuY19GFwvdxnBwcMDfwMDAtLQ0FCwsLMp+4apVq1ZlP1Sam5sL1YUC+xWEF3Re+xJKpleFl5dXXFych4eHubm5trZ27969+QSCIAiCIIgPI3TdBl2yYsUKbhIH4uPj8/LyULhx4wZ7CZShrKxcduiQgoKC2NhYFFRVVadMmaKjo1PN9wnc3NycnJyKioosLS2tra0lJSX5BIIgCIIgiCoRom4DPj4+169fV1BQYHcexRovL6/79+/zSgl9+vQZMGAAr1SPly9fnjlzBoWGNigxQRAEQRANH+HqNnD48OGUlJRGoNvqjre399WrV9u0aaOjozNixAhuJQiCIAiCqB6iuEmXnZ3t7u7OK00VHIEbN25oa2tPnjyZRBtBEARBELVA6NfbAHuxdPHixfr6+tzUNCgqKsrKyvr777/T09NRVVJSok+OEgRBEARRa0Sh2zIyMnbv3t0EdZujo6OrqysKffv2xd8hQ4bU1/fsCYIgCIJoBIhOt2lpaa1cuZKbGjuenp5+fn5RUVG9e/c2MzNr3bo1n0AQBEEQBFFbRPF8m7Ky8rhx45KSkjIzM7mp8YJ9/O67765fv56SktKhQ4fRo0eTaCMIgiAIQiCI4nob4++//05MTJw5c2bZ0WsbH7///ntOTo6ZmdnAgQMVFBS4lSAIgiAIos6IbtDXcePG5ebmBgUF8XqjIyUl5dChQ6mpqcrKyra2tiTaCIIgCIIQLKLTbVpaWhA0T548iYmJ4aZGRGZm5r59++Lj41etWrV8+XJuJQiCIAiCEBwi/cjS4sWL8ffZs2es2miIjo4+cOCAgoLCtGnTyn4LiyAIgiAIQoCI7vk2UFRU5Ojo6OHh0blz5+nTp3OrmMOGZwMrVqzQ1tbmVoIgCIIgCEEjUt3GsLOze/XqlYWFha2trZSUFLeKG8XFxZmZmSdOnEhKSrK2tra0tOQTCIIgCIIghEM96DZw9epVb2/v5cuX6+jocJO4cffu3UePHqEA0VbTr8sTBEEQBEHUgvrRbYWFhY6OjpGRkStWrOAm8eHZs2ceHh7R0dHdu3c3Nzdvah+BIAiCIAiivhDpewmlSEtLjx49WkJCYt++fbm5udza4MnMzIyKirp06VJqaqqhoeGECRNItBEEQRAEITLq53obw9XV1dHRcdCgQVZWVtzUgImJiTl79ixUZq9evfr27auiosInEARBEARBiIT61G3gxYsXDg4OixYtauDDZ2RlZR08eDAtLU2sn8kjCIIgCEKsqWfdBh4+fOjs7Pzpp58qKytzU4MBB+fBgwf3799HWU5ObuHChSTaCIIgCIKoL+pft4WFhV24cEFSUnL69OkN7RPsUGzAxMRERUXF0NCwY8eOfAJBEARBEITIqX/dBuLi4v744w9oI2Vl5SVLlnBr/ZGbm3vixAkU3rx507Vr1/Hjx8vKyrJJBEEQBEEQ9UWD0G0MV1dXJycndXV1SDc5OTluFTlZWVk7d+6EUJOXl1dUVFy6dCmfQBAEQRAEUa80IN0GXFxcIN3at28/ZcqU5s2bc6sIiYuLO3v2bHp6+uTJkzt37sytBEEQBEEQDYCGpdvA8+fPL168qK2traOjM3HiRG4VMkFBQQ8fPkQhKysrNTV12bJlrVq1YpMIgiAIgiAaCA1OtzGOHj36+vXrAQMGWFlZSUoKa3Dg/Pz87OzsvXv3oqyoqCgjI2Nqajps2DA2lSAIgiAIokHRQHUbcHJygnQzMjKSl5dv06aNrq4unyAgiouLL1265O/vLyUl1bdv3549e2poaPBpBEEQBEEQDY+Gq9tAenr6X3/9lZGRoaqqqqSkJMBXTW/fvg1RGBMTM2LECAMDA4GLQoIgCIIgCIHToHVbKQ8fPrx//762tvb06dNRbdGiBbNXn9TUVFZgd0WBhobG6tWrWZkgCIIgCKLhIx66DTx69Oju3busPHz4cCMjo2q+OuDi4oK/Tk5OrNq/f39WwEJYgSAIgiAIQiwQG90GYmJi8PfkyZPZ2dmqqqqKiorMXjXsV0uWLJGQkECBbokSBEEQBCGmiJNuK8u9e/f8/Px4pUrat28/ZswYXiEIgiAIghBbxFW3EQRBEARBNDWENTQaQRAEQRAEIVhItxEEQRAEQYgHpNsIgiAIgiDEA9JtBEEQBEEQ4gHpNoIgCIIgCHGgWbP/A13fXTpQTHxXAAAAAElFTkSuQmCC)\n\nOne possible rotating neutron star is a pulsar. Neutron stars have very strong magnetic fields to confine the magnetic field lines that were present when they were stars. Therefore, rotating neutron stars emit radio waves.\n\n## Starquake\n\n![]( data:image/png;base64,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)\n\nStarquake is a neutron star change detected by a change in the frequency of the pulsar. This is thought to be a change in the period of rotation due to crustal deformation of the neutron star.\n\nWhat if we could detect the same Starquake in both pulsar and gravitational waves?\nWe can relate the object we are observing in the gravitational waves to the pulsar we are observing in the radio waves and light.\nThen we would be able to observe the same phenomenon with light, radio waves, and gravitational waves all together! That's fantastic!\n","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport numpy.fft as fft\nimport matplotlib.pyplot as plt\nimport pickle\nimport os\nimport math\nimport h5py\nimport random\nimport cv2\nimport gc\nfrom tqdm import tqdm\nfrom multiprocessing import Pool\nfrom glob import glob","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-11-27T04:48:03.666055Z","iopub.execute_input":"2022-11-27T04:48:03.666786Z","iopub.status.idle":"2022-11-27T04:48:03.671963Z","shell.execute_reply.started":"2022-11-27T04:48:03.666749Z","shell.execute_reply":"2022-11-27T04:48:03.671123Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Make temp files","metadata":{}},{"cell_type":"code","source":"!mkdir ../temp","metadata":{"execution":{"iopub.status.busy":"2022-11-27T04:48:03.675062Z","iopub.execute_input":"2022-11-27T04:48:03.675856Z","iopub.status.idle":"2022-11-27T04:48:03.964733Z","shell.execute_reply.started":"2022-11-27T04:48:03.675831Z","shell.execute_reply":"2022-11-27T04:48:03.963633Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"di = '/kaggle/input/g2net-detecting-continuous-gravitational-waves'\n# innomal = ['004f1b282', '006e25113', '008ec5560', '00948246a', '0112d6cc3', '0114979f2', '014065fac', '0153a00c9', '0159eaae7', '0177a2d23', '01b9480ca', '025517630', '027d43d14', '02efaba20', '0315b4adc', '03212f40c', '0336aa1b2', '034304bd7', '0354a13e2', '036ff1e82', '038047700', '0381b8ae7', '03ac90f05', '03bf6725b', '040b35321', '043951a69', '0453a29de', '04592c3d1', '04cef86b5', '0542c5ed2', '0559d7491', '0564deaa2', '056ab23ae', '057f4d07e', '05fa8cd9d', '06254d84b', '070d9620d', '07106d3f4', '071d02058', '073e6f222', '078ccfa8f', '079f37dac', '07b2073db', '07f958d83', '081ee0aea', '08a464736', '08bb27560', '08c9594c4', '08e3fb2f9', '0956fd807', '0958e28d2', '096b8094b', '097b1ada1', '09d51ef34', '09d7ea37a', '0a1a06b36', '0a495f928', '0a8187e8f', '0a8821a1e', '0a9ab0c9e', '0ab0bb87c', '0ae4c93c8', '0b0e4d38e', '0b269f982', '0b4c34044', '0be62c7a0', '0bf7da48d', '0c117418a', '0c3efb44a', '0c41f834e', '0c5136dfc', '0c6fda0d8', '0cac0b665', '0d6164664', '0de7c7655', '0e58736c0', '0e9c2bb33', '0f406d7bd', '0f8753c88', '0f8d5655a', '0f9fb384f', '0fb042524', '0fc3c449f', '0fd7c7cee', '0fd8589a3', '0ffbdc3d0', '100d44e76', '1031d1f36', '104b78ca2', '10aaec379', '10dbab830', '113ad4a1c', '114c6179c', '117c1ce4b', '11a183aca', '1209123fd', '120da8d2f', '125782ac2', '126790a29', '12b3ae845', '12d63203e', '12dc1453f', '12ef83f60', '12fa54de1', '130bcb45e', '136f0fd9c', '13731679d', '139216c25', '1396fff65', '13dad6fd2', '13ddf3c72', '141b4d426', '141bc8a60', '14d7c9498', '14dfefcd5', '14eb4a20c', '15dacd79b', '15fc931ea', 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'cdeb7fe4a', 'ce098e289', 'ce6d7348c', 'ce739e1bc', 'ceafe2326', 'cee2e7f4c', 'cf0ba4db5', 'cf20303b1', 'cf3c0a171', 'cf3cf15b8', 'cf55222bc', 'cf58bc8ce', 'cf69bb748', 'cf74f5267', 'cfbf329fa', 'd04508cef', 'd07399f8e', 'd0c82ed5f', 'd118e75ed', 'd121f1dc5', 'd1466f6b3', 'd19925d1e', 'd19952cda', 'd19a8e52d', 'd19d81367', 'd1b5b7051', 'd1d1158d1', 'd1da4fc07', 'd1eeb3420', 'd22485fa5', 'd25fe5c15', 'd27bd16f8', 'd28e702bd', 'd2a943911', 'd2e9a7d91', 'd31067b43', 'd31406fee', 'd37c29b9a', 'd37f93a73', 'd3a28fac6', 'd3d4cf9f5', 'd440c13e9', 'd445a6293', 'd455b021c', 'd48f9fb92', 'd4d18b9eb', 'd4e46b35f', 'd501a7590', 'd50959802', 'd51a73ed4', 'd546ba326', 'd55764ac8', 'd5b7bf6e3', 'd5f85e88d', 'd6043b353', 'd60af3b41', 'd624ac433', 'd62fb035d', 'd64a4a759', 'd65d8383c', 'd6828b59a', 'd6ffb36b7', 'd75b29ee3', 'd794f8f4e', 'd7d972e01', 'd809d13d5', 'd840eb373', 'd86124d9c', 'd86eea9ac', 'd87d58a7c', 'd8871065d', 'd901959ea', 'd90d3890a', 'd93435874', 'd94f90817', 'd95b3e50e', 'd98e85a30', 'd9ef85811', 'da1f12533', 'da3aef674', 'da6d32841', 'da86f97af', 'daf480b1a', 'db065bb55', 'db54b2271', 'db6ff787d', 'db800e410', 'db945231a', 'db965ed0a', 'dbbd4dff9', 'dbc31856a', 'dbfbacbdf', 'dc20f8235', 'dc2aaaee9', 'dc5116c0e', 'dc5fede33', 'dc8399fac', 'dc877cb55', 'dc9c34dd5', 'dc9d6570d', 'dcdf0f575', 'dce0cfaf6', 'dd17b2712', 'dd4ca64ee', 'dd64dd8bb', 'ddd8c6e90', 'ddd8feec3', 'ddf1ea317', 'ddf3c2d48', 'de0f71bf0', 'dee7c8d0f', 'deea88354', 'df036b28a', 'df0923554', 'df65f4148', 'df7cdce5c', 'df7f170d3', 'dfb0b0f94', 'dfd1f234d', 'dff5008e6', 'dff56b4f2', 'e02c28a4b', 'e0696d220', 'e08f4e117', 'e12baab36', 'e1c01fc80', 'e1c289df9', 'e1f189670', 'e2073c6e8', 'e2256cda9', 'e2411b424', 'e26f33e41', 'e2a0b4a1b', 'e2b2204dd', 'e2b58e3c5', 'e2c46e48a', 'e2fa690a6', 'e3e1319a8', 'e467390dc', 'e4d6595d9', 'e4e262fc7', 'e518ebeb0', 'e51d546f5', 'e53baf796', 'e549d8e46', 'e5afc269c', 'e5f7c1840', 'e67f75b59', 'e69899971', 'e716ea95d', 'e71ab505e', 'e72d0cb27', 'e72ec8893', 'e79ee0635', 'e812b632c', 'e83141ee0', 'e86834e4a', 'e87abad8b', 'e89d31e88', 'e8d202dda', 'e8f090670', 'e8f701133', 'e907e792c', 'e90fe12f0', 'e9372c236', 'e963d2358', 'e98acc4de', 'e99d9df73', 'e9a1da314', 'ea31213d3', 'ea37b8ec8', 'ea59736e8', 'ea662f511', 'ea720f5f1', 'ea85c9050', 'eb1fc0362', 'eb3b774f9', 'eb3b88eee', 'eb749c00e', 'eb8beefb8', 'ebae5b983', 'ebce3dbfd', 'ebeb1ca65', 'ec1a6121c', 'ec2ecfabc', 'ecaa7a6d0', 'eccaafd74', 'ecf018f65', 'ecfaccedf', 'ed72cae03', 'edb083e13', 'edc332e09', 'edce6c418', 'ee04d84f1', 'ee136acc7', 'ee23f042e', 'ee2b126c2', 'ee4863d53', 'ee630ae68', 'ee6d87dd2', 'ee7de7260', 'ee8d48b4f', 'ee98380b0', 'eecbc5c13', 'eef9690c8', 'ef1a6ac39', 'ef3b8e71d', 'ef4bfb410', 'ef559e2d9', 'ef9c0beaf', 'efb2e3891', 'efbac9284', 'efcf7e905', 'efecaf2fd', 'effe00e5c', 'f00d2044a', 'f059db589', 'f0900d441', 'f090b5877', 'f0a10ed5a', 'f113ccc2f', 'f130b46d5', 'f1685778b', 'f1b311080', 'f1b898c30', 'f1d55b314', 'f28b76446', 'f2978b544', 'f2cc75757', 'f2e091947', 'f2eeb89ff', 'f2ffd991a', 'f30a0a053', 'f32a4cf99', 'f3c23c4bf', 'f3d3ff801', 'f3dadb121', 'f3f739a5a', 'f40a2740b', 'f45d55487', 'f473e653d', 'f474d1ea1', 'f4963046e', 'f497bc8dc', 'f4c625e01', 'f55d3f1cb', 'f57956a07', 'f5860cd63', 'f59fed8e1', 'f5c7f3eb2', 'f5d25af84', 'f5e17620d', 'f5e184662', 'f63ab5d46', 'f6a989560', 'f6c0bf0f7', 'f6ccedec7', 'f6d1b538a', 'f6d48c25d', 'f74c30aeb', 'f76c95ffe', 'f7783318f', 'f8a42c2a8', 'f8ea289bd', 'f93185201', 'f9381e090', 'f96d37d25', 'f97dcf8eb', 'f9c0674bc', 'f9f17b1c6', 'fa20158b8', 'fa2f562c1', 'fa57bcae1', 'fa8dad6eb', 'faa696b45', 'faeb9f3f5', 'fb41577ad', 'fb4cb635e', 'fbefbb667', 'fc3876482', 'fc459e1e5', 'fc47ee017', 'fc4a2b9da', 'fc4b6d4a9', 'fce009635', 'fd0bde745', 'fd14d69ae', 'fd58b7eaa', 'fd8617a36', 'fd902647b', 'fdedb5b67', 'fe16f479a', 'fe3005e83', 'fe3983d5d', 'fe4c3185d', 'fe57c8226', 'fe6f5a121', 'fecaed870', 'ff771a983', 'ff7f1c8ba', 'fffa17f67']\ntrain_imgs = glob('/kaggle/input/g2net-detecting-continuous-gravitational-waves/train/*')\ntest_imgs = glob('/kaggle/input/g2net-detecting-continuous-gravitational-waves/test/*')\ntest_imgs = test_imgs[4000:]\ndef one_da(i):\n#     file_id = innomal[i]\n    data_type = \"test\"\n#     filename = '%s/%s/%s.hdf5' % (di, data_type, file_id)\n    filename = test_imgs[i]\n    file_id = filename.split('test/')[1].split('.')[0]\n    with h5py.File(filename, 'r') as f:\n        g = f[file_id]\n        hdf = []\n        for ch, s in enumerate(['H1', 'L1']):\n            a = g[s]['SFTs'][:, :4096] * 1e22  # Fourier coefficient complex64\n\n            p = a.real**2 + a.imag**2  # power\n\n            del a\n            gc.collect()\n\n            hdf.append(p)\n\n        with open(f\"../temp/{data_type}-{file_id}.pkl\", \"wb\") as f:\n            f.write(pickle.dumps(hdf))\n\n        del f, p, g, hdf\n        gc.collect()\n\n    del file_id, data_type, filename\n    gc.collect()\n\ndef da():\n    with Pool(4) as pool:\n        with tqdm(total=len(test_imgs)) as t:\n            for _ in pool.imap_unordered(one_da, list(range(len(test_imgs)))):\n                t.update(1)\n\nda()\ndel da, one_da\ngc.collect()","metadata":{"execution":{"iopub.status.busy":"2022-11-27T04:48:03.966833Z","iopub.execute_input":"2022-11-27T04:48:03.967126Z","iopub.status.idle":"2022-11-27T04:48:25.410745Z","shell.execute_reply.started":"2022-11-27T04:48:03.9671Z","shell.execute_reply":"2022-11-27T04:48:25.408823Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Load and show signal","metadata":{}},{"cell_type":"code","source":"def load_example(file_id, data_type, channel=-1):\n    with open(f\"../temp/{data_type}-{file_id}.pkl\", \"rb\") as rf:\n        x = pickle.loads(rf.read())\n    if channel<0 or channel>2:\n        return x[0].astype(np.float32) + x[1].astype(np.float32)\n    return x[channel].astype(np.float32)\n\n\ndef show_example(a, data_type=\"test\"):\n    plt.figure(figsize=(24,4))\n    for i in range(len(a)):\n        x = load_example(a[i], data_type)\n        x = x - x.mean()\n        \n#         x = np.mean(x.reshape(360, 128, 32), axis=2)\n        x = img_clipping(x)\n        \n        ax = plt.subplot(1,4,i+1)\n        ax.set_xticks(np.arange(1,16)*360)\n        ax.set_yticks([])\n        ax.set_title(a[i])\n        ax.imshow(x, aspect=\"auto\", cmap=\"Greys\", norm=plt.Normalize(vmin=-256, vmax=256))\n        \ndef show_hist(x):\n    fig, ax = plt.subplots()\n    ax.hist(x.flatten(), bins=100)","metadata":{"execution":{"iopub.status.busy":"2022-11-27T04:48:25.411798Z","iopub.status.idle":"2022-11-27T04:48:25.412283Z","shell.execute_reply.started":"2022-11-27T04:48:25.412118Z","shell.execute_reply":"2022-11-27T04:48:25.412135Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def load_example(file_id, data_type, channel=0):\n    with open(f\"../temp/{data_type}-{file_id}.pkl\", \"rb\") as rf:\n        x = pickle.loads(rf.read())\n    if channel<0 or channel>2:\n        return x[0].astype(np.float32) + x[1].astype(np.float32)\n    return x[channel].astype(np.float32)\n\n\ndef show_example(a, data_type=\"test\"):\n#     plt.figure(figsize=(24,4))\n    for i in range(len(a)):\n        x = load_example(a[i], data_type)\n        x = x - x.mean()\n        \n        x = np.mean(x.reshape(360, 128, 32), axis=2)\n        x = img_clipping(x)\n        \n        cv2.imwrite(f'{a[i]}.png',x)\n#         ax = plt.subplot(1,4,i+1)\n#         ax.set_xticks(np.arange(1,16)*360)\n#         ax.set_yticks([])\n#         ax.set_title(a[i])\n#         ax.imshow(x, aspect=\"auto\", cmap=\"Greys\", norm=plt.Normalize(vmin=-256, vmax=256))\n        \ndef show_hist(x):\n    fig, ax = plt.subplots()\n    ax.hist(x.flatten(), bins=100)","metadata":{"execution":{"iopub.status.busy":"2022-11-27T04:48:25.413207Z","iopub.status.idle":"2022-11-27T04:48:25.413695Z","shell.execute_reply.started":"2022-11-27T04:48:25.413531Z","shell.execute_reply":"2022-11-27T04:48:25.413549Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def img_clipping(x, p=2, standard=256):\n    point = np.percentile(x, 100-p)\n    img = ((standard//2)*(x-x.min())/(point-x.min()))\n    img = np.clip(img, 0, standard)\n    return img.astype(np.float32)","metadata":{"execution":{"iopub.status.busy":"2022-11-27T04:48:25.414595Z","iopub.status.idle":"2022-11-27T04:48:25.415055Z","shell.execute_reply.started":"2022-11-27T04:48:25.414895Z","shell.execute_reply":"2022-11-27T04:48:25.414912Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# I found a similar signal.","metadata":{}},{"cell_type":"code","source":"train_df = pd.read_csv('/kaggle/input/g2net-detecting-continuous-gravitational-waves/sample_submission.csv')\nid_lst = train_df['id'].tolist()\nlabel_lst = train_df['target'].tolist()","metadata":{"execution":{"iopub.status.busy":"2022-11-27T04:48:25.415938Z","iopub.status.idle":"2022-11-27T04:48:25.416418Z","shell.execute_reply.started":"2022-11-27T04:48:25.416235Z","shell.execute_reply":"2022-11-27T04:48:25.416252Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for i in range(len(id_lst)):\n#     if label_lst[i] == -1:\n#         continue\n    try:\n        show_example([id_lst[i]],data_type = 'test')\n    except:\n        continue","metadata":{"execution":{"iopub.status.busy":"2022-11-27T04:48:25.417282Z","iopub.status.idle":"2022-11-27T04:48:25.417762Z","shell.execute_reply.started":"2022-11-27T04:48:25.417605Z","shell.execute_reply":"2022-11-27T04:48:25.417621Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# show_example([\"4e7400077\",\"66c0f74d0\", \"282750892\"],data_type = 'train')","metadata":{"execution":{"iopub.status.busy":"2022-11-27T04:48:25.41864Z","iopub.status.idle":"2022-11-27T04:48:25.4191Z","shell.execute_reply.started":"2022-11-27T04:48:25.418934Z","shell.execute_reply":"2022-11-27T04:48:25.418951Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# show_example([\"a1f9b8e82\", \"025517630\",\"4ca95032a\"])","metadata":{"execution":{"iopub.status.busy":"2022-11-27T04:48:25.419973Z","iopub.status.idle":"2022-11-27T04:48:25.420458Z","shell.execute_reply.started":"2022-11-27T04:48:25.420281Z","shell.execute_reply":"2022-11-27T04:48:25.420298Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# show_example([\"308417080\", \"dc2aaaee9\", \"8b180f74f\", \"698567d90\"])","metadata":{"execution":{"iopub.status.busy":"2022-11-27T04:48:25.421307Z","iopub.status.idle":"2022-11-27T04:48:25.421783Z","shell.execute_reply.started":"2022-11-27T04:48:25.421626Z","shell.execute_reply":"2022-11-27T04:48:25.421643Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# show_example([\"67e294a77\",\"fe3005e83\" ,\"34b6b7a85\"])","metadata":{"execution":{"iopub.status.busy":"2022-11-27T04:48:25.422654Z","iopub.status.idle":"2022-11-27T04:48:25.423113Z","shell.execute_reply.started":"2022-11-27T04:48:25.422953Z","shell.execute_reply":"2022-11-27T04:48:25.422971Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# show_example([\"040b35321\", \"38a84a185\"])","metadata":{"execution":{"iopub.status.busy":"2022-11-27T04:48:25.423977Z","iopub.status.idle":"2022-11-27T04:48:25.424444Z","shell.execute_reply.started":"2022-11-27T04:48:25.424269Z","shell.execute_reply":"2022-11-27T04:48:25.424286Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# show_example([\"9f37c3fde\",\"becdfa440\",\"acc728828\" ])","metadata":{"execution":{"iopub.status.busy":"2022-11-27T04:48:25.425293Z","iopub.status.idle":"2022-11-27T04:48:25.42577Z","shell.execute_reply.started":"2022-11-27T04:48:25.425614Z","shell.execute_reply":"2022-11-27T04:48:25.42563Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Eliminate noise using signal similarity\n\nThis corresponds to \"Magic#2\" that appeared in the [SETI competition](https://www.kaggle.com/competitions/seti-breakthrough-listen) a year ago.\nAt that time, this solution was considered valid. And it won the top prize.","metadata":{}},{"cell_type":"code","source":"# def denoize(a, b, offx, offy, rate, flipv=False, data_type=[\"test\",\"test\"]):\n#     plt.figure(figsize=(24,4))\n#     x = load_example(a, data_type[0])\n#     y = load_example(b, data_type[1])\n#     if flipv:\n#         x = x[::-1,:]\n    \n#     z = x.copy()\n#     z[offy:,:z.shape[1]-offx] -= y[:y.shape[0]-offy,offx:]\n\n#     x = x - x.mean()\n#     x = np.mean(x.reshape(360, 128, 32), axis=2)\n#     x = img_clipping(x)\n\n#     print(z.shape)\n#     z = z - z.mean()\n#     z = np.mean(z.reshape(360, 128, 32), axis=2)\n#     z = img_clipping(z)\n\n#     ax = plt.subplot(1,4,1)\n#     ax.set_xticks(np.arange(1,16)*360)\n#     ax.set_yticks([])\n#     ax.set_title(a)\n#     ax.imshow(x, aspect=\"auto\", cmap=\"Greys\", norm=plt.Normalize(vmin=-256, vmax=256))\n\n#     ax = plt.subplot(1,4,2)\n#     ax.set_xticks(np.arange(1,16)*360)\n#     ax.set_yticks([])\n#     ax.set_title(f\"{a} - {b}\")\n#     ax.imshow(z, aspect=\"auto\", cmap=\"Greys\", norm=plt.Normalize(vmin=-256, vmax=256))","metadata":{"execution":{"iopub.status.busy":"2022-11-27T04:48:25.426661Z","iopub.status.idle":"2022-11-27T04:48:25.427117Z","shell.execute_reply.started":"2022-11-27T04:48:25.42696Z","shell.execute_reply":"2022-11-27T04:48:25.426976Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# denoize(\"3cc6680fb\", \"575f47724\", 16, 173, 1)","metadata":{"execution":{"iopub.status.busy":"2022-11-27T04:48:25.427981Z","iopub.status.idle":"2022-11-27T04:48:25.428448Z","shell.execute_reply.started":"2022-11-27T04:48:25.428271Z","shell.execute_reply":"2022-11-27T04:48:25.428288Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# denoize(\"a1f9b8e82\", \"025517630\", 40, 21, 1, True)","metadata":{"execution":{"iopub.status.busy":"2022-11-27T04:48:25.429329Z","iopub.status.idle":"2022-11-27T04:48:25.429887Z","shell.execute_reply.started":"2022-11-27T04:48:25.429679Z","shell.execute_reply":"2022-11-27T04:48:25.429699Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# denoize(\"dc2aaaee9\", \"308417080\", 0 ,164, 1)","metadata":{"execution":{"iopub.status.busy":"2022-11-27T04:48:25.430788Z","iopub.status.idle":"2022-11-27T04:48:25.431245Z","shell.execute_reply.started":"2022-11-27T04:48:25.431087Z","shell.execute_reply":"2022-11-27T04:48:25.431104Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Thank you!","metadata":{}},{"cell_type":"code","source":"# df = pd.read_csv(\"/kaggle/input/iknowblendingisnotagoodpracticeonkaggle/submission.csv\")\n# r = []\n# for i, t in zip(df.id, df.target):\n#     if i in [\"308417080\", \"dc2aaaee9\", \"8b180f74f\", \"698567d90\"]:\n#         r.append(1.0)\n#     else:\n#         r.append(t)\n# df[\"target\"] = r\n# df.to_csv(\"submission.csv\", index=False)","metadata":{"execution":{"iopub.status.busy":"2022-11-27T04:48:25.432152Z","iopub.status.idle":"2022-11-27T04:48:25.432632Z","shell.execute_reply.started":"2022-11-27T04:48:25.432461Z","shell.execute_reply":"2022-11-27T04:48:25.432477Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## IF IT IS USEFLE, PLEASE UPVOTE!","metadata":{}}]}