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a/_images/cba7bffb2c9de7eecdd6f63fb7c144b98e7e4c531174abc0ea0e74feecbab616.png b/_images/cba7bffb2c9de7eecdd6f63fb7c144b98e7e4c531174abc0ea0e74feecbab616.png new file mode 100644 index 000000000..62ccd9f63 Binary files /dev/null and b/_images/cba7bffb2c9de7eecdd6f63fb7c144b98e7e4c531174abc0ea0e74feecbab616.png differ diff --git a/_sources/notebooks/Kepler/lightkurve_custom_aperture_photometry/lightkurve_custom_aperture_photometry.ipynb b/_sources/notebooks/Kepler/lightkurve_custom_aperture_photometry/lightkurve_custom_aperture_photometry.ipynb index bfe6a67cb..d153583b8 100644 --- a/_sources/notebooks/Kepler/lightkurve_custom_aperture_photometry/lightkurve_custom_aperture_photometry.ipynb +++ b/_sources/notebooks/Kepler/lightkurve_custom_aperture_photometry/lightkurve_custom_aperture_photometry.ipynb @@ -52,8 +52,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2024-05-01T13:48:45.651998Z", + "iopub.status.busy": "2024-05-01T13:48:45.651802Z", + "iopub.status.idle": "2024-05-01T13:48:47.405548Z", + "shell.execute_reply": "2024-05-01T13:48:47.404993Z" + } + }, "outputs": [], "source": [ "import lightkurve as lk\n", @@ -105,8 +112,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2024-05-01T13:48:47.408273Z", + "iopub.status.busy": "2024-05-01T13:48:47.407838Z", + "iopub.status.idle": "2024-05-01T13:48:49.756847Z", + "shell.execute_reply": "2024-05-01T13:48:49.756272Z" + } + }, "outputs": [], "source": [ "tpf_example = lk.search_targetpixelfile(\"KIC 7461601\", mission=\"Kepler\", cadence='long', quarter=10).download()" @@ -114,9 +128,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2024-05-01T13:48:49.759180Z", + "iopub.status.busy": "2024-05-01T13:48:49.759001Z", + "iopub.status.idle": "2024-05-01T13:48:50.196987Z", + "shell.execute_reply": "2024-05-01T13:48:50.196406Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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99hsSEhJw4MABvPXWW1ItdadOncLff/8NAPDz88PUqVPZv6Wamhqkpqbi/PnzUkGbKsqsSgzDoLy8HNnZ2Thw4ACKiooANLQWNOXF1rDGhEIhmyYvLw/u7u4AGrr/SktLATT/mhGfE1/fqVMnqTRnz57F3bt30aNHD6nWFnk0V37xuYcPHyqc7+tOrUFIly5dZE7bMjc3R2BgIGxtbfHDDz+8NAhp27Ytli9fLvHGIn5xHzhwALW1tbCyssLq1avZFggOh4MePXpg1apVmD9/fpN5h4eHo6ysDIMGDcJXX30lcc7KygrTpk2DmZkZ/vzzT+zdu/eV33w1wdXVFfr6+qivr8eTJ080dt9z584BaHhDeNm0zPDwcDx+/BjvvvsuvL295b6HOPi0sLDAunXrEBsbC+B/M2PEo+UjIyPxn//8R+ab0avIz88H0NDa9s033yAlJYWdGVNdXY2srCxkZWUhMjISq1atkvpkKJ4B1NQsAKChC0M8+r64uFil5X8Vjd9gxQ9OZf373/+WOmZoaIj+/fujS5cumD17Nq5du4bCwkKJFqOMjAxcvXoVAPDJJ59g2LBh7DkHBwcsXrwYy5Yta3KcTEpKCi5evAgrKyt8//33sLe3Z8/xeDz4+fnB3d0dn3zyCa5du4bs7GyZda2srMSaNWvQrVs39pidnR2WLFmCWbNmoaSkBLGxsZg0aZLENTt37gTQMMDxq6++kvhkb2RkBF9fX/j6+qqlzKrw66+/sq1hjZmbm+OTTz6R+sBjbm4OY2NjVFdXswG4LI3/tkpKStggpPGMueZeM43PyZplV1xcjL/++gs8Hk+ucWdijT9Y5ubmynw/EQgE7PtSVVUVampqpLpQSdNadLEy8RTEx48fs9GuLEFBQTI/2TAMgytXrgAARowYIbMLxNXVFQMGDJCZb11dHS5dugQAGD9+fJP3F0fNOTk5zZazteBwOGyTrKwZIn/++SeOHz+u0q6YvLw8dmZL4weDLOnp6Th27BhsbGzw4YcfKnSfyspKAA1daLGxsXjzzTfx559/Yt++fTh48CAWL14MMzMzlJWV4bvvvgOfz1eqPi+7/40bN5CSkoIxY8Zg165d2Lt3L/bv349PP/0UPB4PBQUFWLt2rdSnJ3FL0cumrYvPi9O3Bo3/ltQ5Hd3W1hYdOnQAwzBSgwbF3X12dnYy1x7S09OTePC/SBwoDxo0SOJh3pidnR26du0KAE3OOvLy8pIIQMQMDAzYlqsHDx5InIuLi0N1dTX09fUxc+ZMuQfrvmqZHR0dcfz4cRw/fvyVu2JMTU1hZWUl8V5rbm6OmTNnol+/flLp9fT02KAqJiZG5oeizMxMdqo7AInXbOO//+ZeM43PyXrNbNmyBVVVVZg6dapCA0fd3d3ZLsFDhw6xLTaNnThxQqLMqn7P0XVqH7HI5/Nx5swZJCQk4NGjR6iqqmLHbjT27Nkztln2RV5eXjKPP3nyhG0ebq5/umvXrhLrSYhlZWWx61msWLHipXUBgKKioibL+ToTD0g1MDBodlCeQCDApk2bIBKJ8NFHHyn8MBM/1EUiETp27IivvvqKbbrW19fHgAEDwOFw8MMPP6C4uBjnzp1rtolYUQzDsPf38/PDRx99xJ4zNDTEiBEjUFNTgx07diA7Oxvx8fFa0XqmaSKRCDExMYiNjUV2djaeP3/OvhYbe3Ggo3jsQdeuXZt8iPv4+LBTSF8kDpTPnz/PfgCRRfwgaTwmoLHmWvpsbGwA/C9gffHe7u7ubBp5qKrMqvDhhx+yHxxqamqQlpaGXbt2YePGjTh16hSWL18u9f44adIkJCQkQCAQYMWKFfjoo4/g4+MDkUiE27dv47///S87rgeQHj/0qqKjo3Hjxg107NhR4fcCPT09TJ48Gdu2bUNeXh6+/fZbBAcHo3379qisrER0dDR27drFtjwDoJlgClJrEJKfn4/ly5dLvJEYGhrC1NSU/UWJRx6LBy/J0lTffnl5Ofv/5l7UTTXjNW62E5fjZcSL7bRmDMOwwZkmBsjW19ezQV6/fv0kFlR70b59+5CXlwc/Pz+lFlRrPNh13LhxMt+wAgIC4OTkhMePH+P27dsqDUIa3z8oKEhmmtGjRyM8PJztr28chBgbG6OiouKlf0eNF3VqLRr/Lb34gFVETU0NVq9ejcTERPaYvr4+zM3N2RbPyspK1NfXS/2cxK/T5prmeTwezM3NZb6mxa95Pp8v1yfWpn5Pzf1exHV48cOWuBVV3gW1xFRVZlUzMjJCjx494O3tja+//hoZGRnYtm0bli5dKpGuY8eO+PLLL7FhwwY8fvwYq1atkjhvbGyMmTNn4vfffwfQ0NrS+JxYc/VqfK7xNaWlpfjjjz/A5XIxd+5cpWaWjRo1Ck+fPkVERARu3bol1dLk7OyMAQMGsLP21L1opa5RaxCyadMmPHv2DA4ODpgxYwa6desm8UYmFArlekDI84ejTPTZuKn80KFDTS4upW0ePXoEgUAAoGGOv7olJCSwb/jNDUgtKCjAoUOHYGRkhA8//LDZrob6+nr2fOM3lcYPH1dX1yavb9u2LR4/fswOmFMVW1tb3L9/n72HLAYGBnB2dsaDBw+kPpXa2NigoqKi2bEeNTU1bBDZ3MNW0xoPmM3Ozm6yhfJlDhw4gMTERPB4PEyfPh39+/eHvb29xGt48eLFuHfvHtvypCri1/ynn36KESNGqDTvl1H2E3JLllkeBgYGGDVqFDZt2oQrV66goqJC6sPPgAED0LlzZ5w4cQLJycnssufe3t4YN24cnj59yqZ1cXFh/9/4w2Vzr5nG5xpf8/fff6OiogIjRoyAq6ur1HtO40BRfE5fX19qoPyMGTPQr18/nDt3DpmZmeDz+bCxsUHfvn3x3nvvsYuZOTg4tMg0aW2mtiCkqKiIbUZctGiRzOWr5W19aErjFpKSkhKJP97GmvrjbdxsWFhY2OxDTZtcv36d/b+4n1idxF0xjo6Ozc7EKS4uhlAohFAoxCeffNJsngcPHsTBgwcBQGK6opubm1xlUvXDS8zNzQ0JCQly3//FB0/79u3ZwbNNaTxIT561UzSlc+fO7IDZq1evYtSoUUrlIx5MPHnyZLz33nsy0zQ19srKygr5+fnNPpAEAkGTq+VaW1ujsLBQrV0WTRG/3yh675Yss7waP/gfP34sswXW0dERM2fOlHm9eOqxjY2NxGBQCwsLWFtbo7S0tNnXjPgcl8uV+HAgDm5Onz6N06dPN1sH8TIBgYGBmD17ttT5N954A2+88YbMa8XdhM3tFUVkU9vA1MZdMOKRzi8Sz/9WVps2bdimu6SkpCbTNXWuc+fO7EJe8jxYXtT4AaOuh56iGi+m5eTk1OSLRlWePXvGNk++/fbbau8PbTxz4NGjR02mE59T9bTpxvcXL1D1IoFAgMePH8u8f48ePdjyNfVQEa8OyePx1P77U4Senh7effddAA2rWCqy/07jVkfxe0NT7wtPnz5lf34vEs9OSE5ObvI1l5ycLHM8CPC/8WWNA3VNET+gsrKyFNonqSXLLK/GLRmKdiEyDIOLFy8CgMzxZOLXzO3bt5v8nYvfg7y8vDS+V1lpaSn7LFNm6u/rTm1BSOM1EnJycqTO8/l8dvlqZXE4HPj7+wNoiHRl9VMXFBTg8uXLMq83MjJidz88dOjQSz9pvPjpqnEdxc3nLamiogJr165l3+SDg4PVvrrmhQsXIBKJwOVyX7pTcteuXdlR+k19iU2ZMoU91riP1cHBgZ2VEBERIfNNKS4ujn2I9e3bVxXVZL3xxhvs9PDDhw/LTHP8+HF2kOWL9+/fvz+MjY3BMAz++ecfqWsrKyvZKZD+/v6tbqpfUFAQ+6k3NDS02U+nQENf/datWyXSiV83st4XALDraMgiXjGzqKiIXUmzMZFI1Oz7ijiIys3NxalTp5ote01NDdutqQoDBgyAiYkJhEIhtm/fLvcHl5YsM4AmAzqx6upq9rVrbW3dZIt0U44ePYqcnByYmprK3K1dvAv348ePZb6Xp6ens+OLXgwC1q5d2+z7TeOFxcTHZLWCNEUoFGLr1q2or69Hly5dWs2aPtpEbUFI27Zt2elkmzZtkthtMy0tDcuWLXulwW1i//rXv8Dj8VBWVoZ///vfbH+9eLGyFStWNBsZT58+HTY2Nnj+/DkWLVqEqKgoicFf5eXliIuLw5o1axAaGipxrZmZGdtnHxkZ+dIXqzrU19cjKysLe/fuxaeffsq+GCdNmtTksukzZ87EmDFjpAaQKYphGLYrpmfPnrCzs3ul/OQ1Y8YM6OvrIzs7G+vXr2fHfdTX1yMuLg5btmwB0NC3LCswEggEKC8vZ78aD4pufLy8vFxqii2Xy2WblOPj4/HHH3+wA6Tr6upw+vRp7N69G0BD0PXim5KZmRk7hfTMmTPYu3cve//8/HysXr0aJSUlMDIyanLFzerqaokyivu1hUKhxPGmXl9jxozBmDFjsGHDhqZ+xE2ytLTE0qVLYWJigpKSEnz11VcICwtjF4sDGv4u8vLycOjQIXz00Uc4ffq0xANX/DPZv38/rly5wr5unjx5gtDQUFy+fLnJwX0eHh7w8/MDAPz22284e/Ys+9AtLCzEunXrkJ6e3uRrvmvXruzfxLZt2/DHH39ITBsVCARIS0vDX3/9hRkzZkgMfn9Vpqam7MyS2NhYrFmzhl31FWgIIK5fvy41tfxVy/z06VP2dy7eB0URa9euxV9//YX09HSJGUw1NTWIj4/H119/zbYKvv/++zIHi2/fvh23b9+W+LD26NEjbN26FX/++Sc4HA7mzJkjcwxU9+7d2d2wf/31V1y+fJl9Xd69exfff/89gIauUnHAokpPnjxBWFiYxGxKkUiEe/fuYcWKFbh27RpMTU3x+eef08wYJahtTAiXy8WcOXPw/fff4+HDh/jiiy/YN4ba2loYGRlh+fLlWL58+Svdx9nZGQsXLkRoaCiysrLw+eefw9jYGCKRCLW1tbC1tcXMmTOxadMmmQOGbGxs8N1332HNmjXIz8/Hhg0b2C3JBQKBxANK3CzY2IgRIxAeHo4TJ07g7NmzsLS0BJfLhYeHB77++utXqtuLnj17JrEEfV1dHaqrqyXe4O3t7TF79myNTAtNTExkm2Hl2axOVcQbSW3YsAExMTGIiYmBmZkZamtrJQbkrlixQubv/NKlS9i0aZPMvKdNmybx/fbt26W6VPr27YtZs2Zhx44dOHbsGNtaU11dzQYEnTt3xuLFi2XeIygoCI8ePUJkZCT27NmDffv2wdjYmH2DNjQ0xOLFi5tcz2Dbtm2IioqSOp6amipRfgcHh2ZXC1aWp6cn1q9fj59++gn3799nx+/o6emxS4k3DshfDFCDg4Nx584dlJWVYe3atdDT04ORkRFb/+nTp+PWrVtNdvfMnz8fy5cvR05ODrZs2YJt27bB0NAQVVVV4HA4+Pjjj3H48OEmWzY//fRTcLlcnDt3DseOHcOxY8dgbGwMPT098Pl8icBT1Q+VESNGoLKyEuHh4YiPj0d8fDy7bHtVVZXEFPTWUuaqqiocPnwYhw8fBpfLhbGxMTgcDqqqqtj3Hn19fUybNo1ttXlRZGQkjh49CgDsbtPiB7qRkRE++eSTZrdh+Oqrr/DNN98gOzsbP/74I3g8HjgcDjsrxsHBAf/+97/V0vLL5/MlxqiZmZmhpqaGfa3b29tj2bJlzS5LT5qm1tkxffv2xQ8//ID9+/eza/pbW1uje/fuGD9+vMoGggYEBMDZ2VliAztbW1v4+flh4sSJ7OqJjad+Nda2bVts3rwZFy5cwJUrV5CTk4OKigro6+vDyckJHTt2hK+vr8wppRMmTICxsTGio6PZAXMMwyg8DU8eIpGIHczL4XBgZGQEW1tbODo6wt3dHb169UKPHj1UPs++KeJFlKysrFTe7fEyAwYMQMeOHREREYHbt2+jpKQE+vr6cHNzg7+/P0aOHClz2XRVee+99/DGG2/g2LFj7KaKhoaG8PDweOlmYhwOBwsWLEDv3r1x5swZZGdno7q6Gg4ODvD19UVQUJBSG/PJo/GAzlcZRNe2bVts3LgR169fx9WrV5GamorS0lLw+XyYmJigTZs2eOONNzB48GCpVSYdHBywYcMG7NmzBzdv3kR5eTkMDAzQp08fjB49Gj179mxykTDgf6vlRkREICYmBk+fPoWenh569uyJoKAgdO/evcmuMqBhNse8efMwbNgwnDlzBvfu3UNxcTEEAgEsLS3h6uoKb29vBAQEqGV20oQJE9C3b18cP34ciYmJKC4uRn19PZycnODu7s5227SWMs+cORM3b95EcnIynjx5gvLyctTV1cHMzAwuLi7o2rUrhg0b1uxMvODgYNy6dQsPHjxAWVkZ9PT04Obmhl69emHUqFFNLsImZmZmhvXr1+PEiROIiYlBfn4+GIZB+/bt0b9/f4wbN05tr3cHBwdMnjwZSUlJePz4MZ4/fw5jY2O4urrC398fw4cPb3XdptqEw7SWEZVqFBYWhoMHD6Jbt25Ys2ZNSxeHkBYTHR2Nn3/+GW3atMFvv/2m0R2WCSHkRS26bLsmlJeXs+MWxP2KhLyuxGOGpkyZQgEIIaTF6cS70LFjx1BbW4uAgAA4OjpCT08PAoEAd+/exZ9//omysjJYWlq+dPYGIbouMTER7dq1a7b/nRBCNEUnumP++OMPHDt2DADYQaWNB8eZmprim2++0cjCXYQQQgiRj060hAwZMgRcLhfJyckoKSnB8+fPwePx4OjoiJ49eyIwMLBVLX9NCCGEEB1pCSGEEEKI9tH5gamEEEIIaZ0oCCGEEEJIi6AghBBCCCEtQicGphJCCCHKysjIQFRUFBITE1FYWAhzc3N4eHggODhYakM+gUCA3bt3Izo6GpWVlXBzc8O0adMkdthWNK068tQW1BJCCCHktXbo0CFcuXIF3bt3x+zZszF8+HCkpKTg888/l9opeuPGjThy5AgGDRqE2bNng8vlYtWqVUhJSZHKV9606shTazCEEELIa+zevXtMXV2dxLH8/Hxm3LhxzPr169lj6enpzOjRo5lDhw6xx2pra5nZs2czX331lcT18qZVR57ahFpCCCGEvNa8vLykNpx0dnZGu3btkJeXxx6Li4sDl8vF8OHD2WM8Hg/Dhg1DWloaioqKFE6rjjy1CY0JUQORSISSkhJ2y2tCCNFWDMOguroaNjY2GtuhW5a6ujrU19crfb2+vj54PJ7c6RmGQVlZGdq1a8cey87OhouLi9SOvV26dAEA5OTksDsCy5tWHXlqEwpC1KCkpAQhISEtXQxCCFGZv/76C3Z2di1y77q6Osya8R5Ky5V/ZFlbW2P79u1yByIXL15EcXEx3n//ffZYSUkJrK2tZeYNAMXFxQqnVUee2oSCEDUwNjYGANRE8gAhtYQQQrSYHgOjt+vY97WWUF9fj9JyffzxYyZMjEUKX8+v5mL24s6or6+XKwjJy8vDtm3b4OnpiSFDhrDH6+rqpLptALB51tXVKZxWHXlqEwpC1IDtghFygHoKQggh2q81dC0bGtXD0EjxIESowPDH0tJSfPvttzAxMcGSJUugp6fHnuPxeBAIBFLXiB/+jQMcedOqI09tQkEIIYQQrSACAxEU3+5M3muqqqqwcuVKVFVV4YcffpDa+NTGxkZml0dpaSkASKSXN6068tQmNDuGEEKIVhC9wr+Xqaurw+rVq5Gfn48VK1ZIDEgV69ChA/Lz88Hn8yWOp6ens+cVTauOPLUJBSGEEEJea0KhEOvWrUNaWhqWLFkCT09PmekCAgIgEolw5swZ9phAIEBkZCQ8PDwkZqbIm1YdeWoT6o5RI55/LQDJflRhrh6EufRjJ4QQRQkZBkJG8e6Yl12zY8cOxMfHo2/fvqioqEB0dLTE+bfeegsA4OHhgYCAAISFhaG8vBxOTk6IiopCYWEh5s+fL3GNvGnVkac2oaehGtVdMaSBqYQQoiKMkmNCmJdck52dDQBISEhAQkKC1HlxEAIACxcuRHh4uMTeLStWrICPj4/UdfKmVUee2oLDMEqElaRZfD4fkyZNQs1ZCkIIIVpOn4HRu7XYv3+/1CJZmiJ+T/1t410YKzFFt7qai08+796idSCyUUsIIYQQraDu2TFE8ygIIYQQohXUNSaEtByaHUMIIYSQFkEtIYQQQrSC6P+/lLmOtE4UhBBCCNEKQjAQKjG+Q5lriGZQEEIIIUQrCJmGL2WuI60TBSGEEEK0AgPlulYoBmm9KAghhBCiFYTgQAjF115S5hqiGTQ7hhBCCCEtglpC1Ij2jiGEENURMQ1fylxHWid6GqoR7R1DCCGqQ90xuoeCEEIIIVqBghDdQ0EIIYQQrSBiOBAxigcUylxDNIOCEEIIIVqBWkJ0D82OIYQQQkiLoJYQQgghWkEELoRKXkdaJ50LQrKysrBr1y6kpqYCADw8PBASEoKOHTtKpCsoKEB4eDju3buHiooK2NvbY9CgQRg3bhyMjIzYdBs2bEBUVFST99u5cydsbW3VUxlCCCEsGhOie3QqCMnKysLixYthZ2eHKVOmgGEYnDx5EkuXLsVPP/0EV1dXAEBRUREWLlwIU1NTjBo1Cubm5khLS8OePXtw//59LF++nM1zxIgR6NGjh8R9GIbB1q1b4eDgQAEIIYRoCI0J0T06FYTs3r0bPB4PoaGhsLCwAAAMHjwYc+bMQVhYGJYtWwYAiI6ORlVVFX788Ue0b98eADB8+HAwDIOoqChUVlbCzMwMAODp6QlPT0+J+6SkpKC2thaDBw/WXOUIIeQ1J2S4Sm5gR90xrZVOBSEpKSno2bMnG4AAgI2NDby9vXH9+nVUV1fD2NgYfD4fAGBlZSVxvbW1NbhcLvT1m/+xXLp0CRwOB4MGDVJ5HQghhMgmAlepDexoTEjrpVO/GYFAAENDQ6njhoaGqK+vR25uLgCga9euAIDNmzcjOzsbRUVFiI2NxenTpzF69GiJMSEvqq+vx+XLl+Hp6QlHR8fmC6THAPpyfHFpTWFCCCGvH51qCXF1dUV6ejqEQiH09PQANAQmGRkZAIDi4mIAQK9evTBt2jQcOHAA8fHx7PUTJ05EcHBws/e4desWKioq5OqKMXq7Tq5y12fooT7TQK60hBDyuqIxIbpHp4KQkSNHYuvWrfjll18wfvx4MAyD/fv3o7S0FABQV/e/oMDBwQE+Pj7w9/eHubk5bty4gYMHD8La2hqjR49u8h6XLl2Cvr4+BgwY8NLy1ETyAKEcf/zKtC8SQshrRshwlBrfIaTZMa2WTgUhI0aMQFFRESIiIthptZ06dUJQUBAOHDjAdrPExMRgy5Yt+P3332FnZwcA8Pf3h0gkws6dOzFw4ECJcSVi1dXViI+Ph6+vr8zzUoQc2sCOEEJUhAFHqc9sDLWEtFo6FYQAwPTp0xEUFITc3FyYmprCzc0NYWFhAAAXFxcAwKlTp+Du7s4GIGJ+fn64cOECsrOzpablAsC1a9doVgwhhLQQIbgQQvExdNQd03rpXBACAGZmZvD29ma/v3PnDuzs7Nh1QsrKytgpuI3V19cDAIRC2WvyXbx4EcbGxujbt68aSk0IIaQ5QnAhZFQfhFRXV+Pw4cPIyMhARkYGKisrsWDBArz99tsS6RRZvDIpKYldFuJFoaGhEks/CAQC7N69G9HR0aisrISbmxumTZsGX19fqWsVSasNdDIIaSw2NhaZmZmYMWMGuNyGvkRnZ2fcvn0b+fn5bOsI0NBNw+Vy4ebmJpVPeXk57t69i4EDBzY7e4YQQoh2ef78Ofbt2wd7e3t06NABSUlJMtMps3jlmDFj0LlzZ4ljTk5OEt9v3LgRcXFxCAwMhLOzMy5cuIBVq1ZhzZo1Eh+oFU2rDXQqCElOTsa+ffvg6+sLc3NzpKenIzIyEj179kRgYCCbLigoCDdv3sSSJUvYFVOvX7+Omzdv4p133pH5hxQbGwuhUEhdMYQQ0kIa1glRvCVE9JKWEBsbG4SFhcHa2hqZmZlYuHChzHTKLF7p7e2NgICAJu+dkZGBmJgYhISEICgoCAAwZMgQzJ07Fzt37kRoaKhSabWFTgUhtra24HK5OHz4MKqrq+Ho6Ihp06Zh7Nix7JRdAPDx8UFoaCj27NmDU6dOoaKiAo6OjggODsb48eNl5n3x4kVYWVmhe/fumqoOIYSQRhpmxyh3XXMMDAxgbW2tVJnkWbySz+fD0NBQ4jkkFhcXBy6Xi+HDh7PHeDwehg0bhrCwMBQVFcHe3l7htNpCp4IQJycnfPvtt3Kl7dKlC1auXCl33uvXr1eyVIQQQlShtQ1MlWfxyk2bNqG6uhpcLhfe3t4ICQmR6J7Jzs6Gi4sLTExMJK7r0qULACAnJ4cNLBRJqy10KgghhBCiu0QMFyIlBqaqaxfd5hav1NfXh7+/P3r37g0LCws8fPgQERERWLJkCdatWwd3d3cAQElJicxWGPEx8SKbiqbVFhSEEEII0QoiJVtCXjYmRFnNLV7p5eUFLy8v9ns/Pz8EBARg3rx5CAsLw6pVqwA0LKJpYCC9YjaPx2PPiymSVlvo1N4xhBBCiCYovHglGmZm9uvXD4mJiexSEDweDwKBQCqtOKAQBxiKptUW1BJCCCFEKwgZjlL7fapj2XZlF6+0s7NDfX09amtrYWJiAhsbG5ndKOLtRhrP1lQkrbagIESNeP61wAvNgMJcPQhz6cdOCCGKUtcUXWUou3jlkydPwOPx2PWmOnTogMTERPD5fIkBp+np6ex5MUXSagvqjlGjuiuGqIuR/KIAhBBClCPewE7xL9UGIeLFK/v169fk4pXl5eVSx3JycpCQkABfX1928cyAgACIRCKcOXOGTScQCBAZGQkPDw+J2S6KpNUW9EQkhBCiFURKbmAnzzUnTpxAVVUV292RkJDA/n/06NEwNTVl08qzeOW6devA4/Hg6ekJKysrPHz4EGfPnoWhoSE++OADNp2HhwcCAgIQFhaG8vJyODk5ISoqCoWFhZg/f75Enoqk1RYUhBBCCHntRUREoLCwkP3+6tWruHr1KgBg8ODBEkGIPItX+vn54dKlSzh69Cj4fD4sLS3Rv39/TJkyBc7OzhJpFy5ciPDwcIn9YFasWAEfHx+pfBVJqw04DKPEpGvSLD6fj0mTJqHmrCFQT7s3EkK0mD4Do3drsX//fqlFsjRF/J767nfPYaDE1l2CGuDscosWrQORjVpCCCGEaAUhuEoNZJS9LzppDSgIIYQQohUYhgOREm331N7felEQQgghRCsIwVVqsi21hLReFIQQQgjRCg17xyhznerLQlSD1gkhhBBCSIuglhBCCCFaQQgOdcfoGApCCCGEaAXqjtE9FISoEe0dQwghqkMtIbqHnoZqVHeFFisjhBBVoZYQ3UNBCCGEEK0gYjhKbUYnooVCWi2aHUMIIYSQFkEtIYQQQrSCCBylRoU07KJLrSGtkcaCkO3bt8PMzAyTJ0/W1C0JIYToECHDBZTojhEyDGh4auukse6YkydP4sGDB5q6HSGEEB0jYjhKf5HWSWMtIba2thCJRJq6HWkhHP3XsIevq0dLl6BF1Dq+fluiG8WktHQRNE+PAVDb0qUA0LB3zIvLHsh3HXXFtFYaawnp168fkpOTwefzNXVLQgghOoRaQnSPxoKQqVOnwt7eHqtWrcL9+/c1dVtCCCGEtFIaaztfs2YNDAwMkJqaioULF8La2hr29vbg8XhNpieEEELEROAqOTuGumNaK40FIUlJSez/GYZBSUkJSkpKZKblcKjpjBBCiCQRw1FqdgytmNp6aXSK7uuG9o4hhBDVoSBE92jsaejg4KCpW7UatHcMIYSojkjJdUJo2fbWiz6SE0II0QpCcMAovWIqaY00HoQ8fPgQZ8+eRWZmJp4/fw4/Pz+EhIQAAFJTU5GZmYm33noL5ubmmi4aIYQQQjRIo0HIkSNH8Pfff0MobFg+l8Ph4Pnz5xJp/vzzTxgYGGDEiBGaLBohhJBWjsaE6B6NBSHXr1/Hjh074OjoiBkzZuCNN95AcHCwRBovLy9YWFggPj6eghBCCCESRFByTMhLpuhWV1fj8OHDyMjIQEZGBiorK7FgwQK8/fbbEumSkpKwbNkymXmEhobC09NT4phAIMDu3bsRHR2NyspKuLm5Ydq0afD19VUqnaJptYHGgpAjR47AyMgIq1evRps2bZpM17FjR+Tn52uqWIQQQrREw9gO1Qchz58/x759+2Bvb48OHTpILCkhy5gxY9C5c2eJY05OTlLpNm7ciLi4OAQGBsLZ2RkXLlzAqlWrsGbNGnh7eyucTtG02kBjQcj9+/fh4eHRbAACABYWFkhJeQ33ZyCEENIsIcMBo1R3TPPX2NjYICwsDNbW1sjMzMTChQubTe/t7Y2AgIBm02RkZCAmJgYhISEICgoCAAwZMgRz587Fzp07ERoaqlA6RdNqC40t2y4QCGBsbPzSdGVlZdDT09NAiQghhGgThuFCpMQXwzT/qDMwMIC1tbVCZeHz+ez4Rlni4uLA5XIxfPhw9hiPx8OwYcOQlpaGoqIihdIpmlZbaKwlxNHRETk5Oc2mEQgEePDgAZydnTVUKkIIIUQxmzZtQnV1NbhcLry9vRESEiLVPZOdnQ0XFxeYmEjuNt2lSxcAQE5ODuzt7eVOp0ie2kRjLSF+fn4oLCzEkSNHmkxz+PBhPH/+HP7+/poqFiGEEC3R0rvo6uvrw9/fH7Nnz8by5csxbdo0PHjwAEuWLJHamLWkpERm64r4WHFxsULpFE2rLTTWEjJ+/HhcvHgRf/31F9LT09G/f38ADd0vV69exdWrV3Hp0iU4Ojpi1KhRmioWIYQQLSECR6kN7JRZ4EwWLy8veHl5sd/7+fkhICAA8+bNQ1hYGFatWsWeq6urg4GBgVQe4k1b6+rqFEqnaFptobEgxMzMDN999x1++OEHxMXF4cqVKwCAW7du4datW2AYBm3btsU333wj1dRECCGEiBgOOEq0aigzmFVezs7O6NevH65cuQKhUMiOaeTxeBAIBFLpxYGCOHCQN52iabWFRhcrc3FxwaZNm5CQkIDbt2+jsLAQIpEIdnZ26NGjB/z9/XVqUCptYEcIIaojYrjgvGSQqSzq3jrGzs4O9fX1qK2tZT9E29jYyOweKS0tBQDY2toqlE7RtNpC409DLpeLfv36oV+/fpq+tcbRBnaEEKI6rbElBACePHkCHo8HIyMj9liHDh2QmJgIPp8v0bqfnp7OnlcknaJptYXGBqYSQggh2qy8vFzqWE5ODhISEuDr6wsu93+P1ICAAIhEIpw5c4Y9JhAIEBkZCQ8PD3YWi7zpFE2rLTTeEpKdnY1Tp04hJSUFJSUlABqamLy9vTF8+HB06tRJ00UihBCiBdQ5MPXEiROoqqpiuzsSEhLY/48ePRqmpqZYt24deDwePD09YWVlxW7IamhoiA8++EAiPw8PDwQEBCAsLAzl5eVwcnJCVFQUCgsLMX/+fIXTKZpWW2g0CNm7dy/2798PkUhyY+X8/Hzk5+fj/PnzmDRpEqZOnarJYhFCCNEC6uyOiYiIQGFhIfu9eNYmAAwePBimpqbw8/PDpUuXcPToUfD5fFhaWqJ///6YMmWKzPWtFi5ciPDwcIl9XlasWAEfHx+l0imaVhtwGEbdQ3YaREVFYePGjTAyMsKoUaMwcOBAODo6AgAKCwsRExODkydPoqamBgsWLMCQIUM0USy14PP5mDRpEmrOvn5jQjj6r+Gg264eLV2CFlHr+PrNYjOKeQ23lNBjwBtYiv3797fYzEXxeyrn87bgGCoxMLVWBGZjXovWgcimsSfGsWPHoKenh++//16qy8XNzQ1ubm7w9/fHokWLcOzYMaWDkKysLOzatQupqakAGpqvQkJC0LFjR4l0BQUFCA8Px71791BRUQF7e3sMGjQI48aNkxhc1DjfvXv34t69e6irq0ObNm3w7rvvIjAwUKlyEkIIUQyj5N4xyuy8SzRDY0FIXl4eunXr1uyYj06dOqFbt25ITk5W6h5ZWVlYvHgx7OzsMGXKFDAMg5MnT2Lp0qX46aef4OrqCgAoKirCwoULYWpqilGjRsHc3BxpaWnYs2cP7t+/j+XLl0vke+vWLaxevRru7u6YNGkSjI2N8fjxY61cnY4QQrSViOEoF1AwHJqF0UppLAgxMTGBmZnZS9OZmpoq3Vy2e/du8Hg8hIaGwsLCAkBDX96cOXMQFhaGZcuWAQCio6NRVVWFH3/8Ee3btwcADB8+HAzDICoqCpWVlWxZ+Xw+NmzYgD59+mDJkiUSo58JIYQQojyNPVF79uyJ5ORk1NbWNpmmtrYWKSkp6Nmzp1L3SElJQffu3dkABPjfzJvr16+juroaQENgAQBWVlYS11tbW4PL5UK/0biGS5cuoaysDMHBweByuaipqZEaWEsIIUT9ROAo/UVaJ40FIR9++CH09fXx/fffo6CgQOr848ePsXbtWujr6+PDDz9U6h4CgQCGhoZSxw0NDVFfX4/c3FwAQNeuXQEAmzdvRnZ2NoqKihAbG4vTp09j9OjREmNC7ty5AxMTExQXF2POnDmYMGECJk2ahK1bt2rlOv2EEKKtWnoDO6J6auuO2bRpk9SxDh06ICEhAZ9++ik6dOgABwcHAA2zY3JycsAwDPr06YNdu3YpNefZ1dUV6enpEuv3CwQCZGRkAPjfDoO9evXCtGnTcODAAcTHx7PXT5w4EcHBwRJ5FhQUQCgU4rvvvsOwYcMwffp0JCUlsXPKFy1a1HSB9OSceCQCIKIXCSGENOdVxoSQ1kltQciFCxeaPCcSiXD//n2prY+BhgViOByOUkHIyJEjsXXrVvzyyy8YP348GIbB/v372XX1G7dcODg4wMfHB/7+/jA3N8eNGzdw8OBBWFtbY/To0Wy6mpoa1NbWYsSIEfj4448BAP7+/qivr8eZM2fw/vvvy5wfDgBGb8vXUlKfoYf6TOmdEQkhhPyPiIGSQYjKi6KzBAIBHj16hPLyclRVVcHU1BSWlpZwdXWVuYPvq1JbELJmzRp1Zd2kESNGoKioCBEREYiKigLQMOMmKCgIBw4cYLtZYmJisGXLFvz++++ws7MD0BBYiEQi7Ny5EwMHDmTHlYh3JRw4cKDEvQYNGoQzZ84gLS2tySCkJpIHCOV4wdAQE0IIeSlqCVGP8vJyXLhwAdevX0dGRgbq6+ul0ujr66NLly7o06cPhg4dCktLS5XcW21BiHjchaZNnz4dQUFByM3NhampKdzc3BAWFgagYRdfADh16hTc3d3ZAETMz88PFy5cQHZ2Nnr06AGgYWDrw4cPpQaxin8BlZWVTRdGyHntFisjhBCiHQoKCrB7925cvXqVDTwsLCzg4uICc3NzGBsbg8/no7KyEo8ePUJKSgpSUlIQHh6O/v37N9sTIC+dXN7SzMwM3t7e7Pd37tyBnZ0du05IWVmZzOnC4l+CUChkj3Xq1Al37txBcXExez0Adt+bxjNxCCGEqA9DLSEqs23bNpw9exYikQjdunXDoEGD4OPjgzZt2jR5zZMnT5CYmIhLly7h8uXLuHLlCoYPH84OVVBGiwQhQqEQz58/h0AgaDKNeNDqq4qNjUVmZiZmzJjBrvHh7OyM27dvIz8/n20dARq6abhcLtzc3NhjAwYMwD///IPz58+je/fu7PFz585BT0+vxVp8CCHkddMw1VaZgIKCkBedP38eI0eORFBQEGxtbeW6pk2bNmjTpg3eeecdFBcX49ChQzh37pz2BCG3b9/GwYMHkZaWJtHaIMvRo0cVzj85ORn79u2Dr68vzM3NkZ6ejsjISPTs2VNiefWgoCDcvHkTS5YsYVdMvX79Om7evIl33nlH4hfi7u6OYcOG4fz58xAKhfDx8UFSUhLi4uIwYcIEuX95hBBCXg2NCVGd7du3w9raWunrbW1t8dFHH2HChAmvVA6NBSFxcXFYt24dGIaBhYUF7O3tYWxsrNJ72Nragsvl4vDhw6iuroajoyOmTZuGsWPHslN2AcDHxwehoaHYs2cPTp06hYqKCjg6OiI4OBjjx4+XyvfTTz+Fvb09IiMjce3aNdjb22PWrFl47733VFp+QgghTaPuGNV5lQBElfloLAjZu3cvAGDevHkYOnSoWpY/d3JywrfffitX2i5dumDlypVypdXX18eUKVMwZcqUVygdIYSQV8FAuQ3sONQd02ppLAh5/PgxfHx8MGzYME3dkhBCCCGtmMaWbbeysqKZJIQQQpTGMBylv4h8bt68iVmzZmnsfhoLQgYMGICUlBTab4UQQohSaO8Y9aupqUFRUZHG7qex7pgpU6YgKSkJq1evxqeffgonJydN3ZoQQogOYJiGL8UvVHlRtE54eLhc6R49eqTmkkjSWBBiZGSE7777DosWLcInn3wCBwcHdjaLLC2x7Luq8fxr8eL8dGGuHoS5OrlGHCGEqJUIHDBKDDKlganAgQMHYGpqChMTk2bTabq3QmNPw+LiYixfvhwFBQVgGAZPnjzBkydPZKblcHTjD6buiiEt204IISqi9PgO6o5BmzZt4O3tjQULFjSbTrychqZoLAjZvn078vPz0b17d4wZMwaOjo4qXyeEEEIIIdI8PT2RmpoqV1pGqT4v5WgsCLl79y6cnZ2xcuVKiYXDCCGEEHmIlGwJ4VBLCAIDA3Hv3r2XpvPx8dHocAiNBSEikQju7u4UgBBCCFEKDUxVXqdOndCpU6eXprO0tNTonmgaC0I8PDzw+PFjTd2OEEKIjqExIbpHY0FIcHAwFi9ejLNnz+Ldd9/V1G0JIYToCHUFIdXV1Th8+DAyMjKQkZGByspKLFiwAG+//bZEuoyMDERFRSExMRGFhYUwNzeHh4cHgoODJXZkB4CkpCQsW7ZM5v1CQ0Ph6enJfi8QCLB7925ER0ejsrISbm5umDZtGnx9faWuVSStPIqKirBx48YWm5GqsSDk4cOHGDp0KLZu3YqLFy+iR48ezU7RHTJkiKaKRgghRAuoa0zI8+fPsW/fPtjb26NDhw5ISkqSme7QoUNITU1FQEAA3NzcUFZWhhMnTuDzzz/H+vXr0b59e6lrxowZg86dO0sce3GdrI0bNyIuLg6BgYFwdnbGhQsXsGrVKqxZswbe3t5Kp5VHbW0tkpOTFb5OVTQWhGzcuBEcDgcMwyAlJaXJATIMw4DD4VAQQgghRCNsbGwQFhYGa2trZGZmYuHChTLTjR07Fl999RUMDAzYY2+++Sbmzp2Lf/75B19++aXUNd7e3ggICGjy3hkZGYiJiUFISAiCgoIANHwInzt3Lnbu3InQ0FCl0moLjQUhkydP1pn1PwghhGieugamGhgYyLUlvZeXl9QxZ2dntGvXDnl5eU1ex+fzYWhoKHNiRlxcHLhcLoYPH84e4/F4GDZsGMLCwlBUVAR7e3uF02oLjQUhU6dO1dStCCGE6KDWODCVYRiUlZWhXbt2Ms9v2rQJ1dXV4HK58Pb2RkhIiET3THZ2NlxcXKRWMu3SpQsAICcnhw0sFEmrLWj9cEIIIdqhFa4TcvHiRRQXF+P999+XOK6vrw9/f3/07t0bFhYWePjwISIiIrBkyRKsW7cO7u7uAICSkhKZrTDiY8XFxewxRdJqCwpCCCGEaAUGyi35oa5lQvLy8rBt2zZ4enpKjWP08vKS6L7x8/NDQEAA5s2bh7CwMKxatQpAw14tjceYiPF4PPa8mCJptYXGgpBvvvlGofS0gR0hhJDGlO2OUaoL5yVKS0vx7bffwsTEBEuWLJFrIU5nZ2f069cPV65cgVAohJ6eHng8HgQCgVRacUAhDjDE/5c3rbbQ2NOwqSlPjYlnz+jKAFbawI4QQnRPVVUVVq5ciaqqKvzwww+wtbWV+1o7OzvU19ejtrYWJiYmsLGxkdmNUlpaCgASeSuSVhGa3CvmRRrdwE4WkUiEZ8+e4fbt2zh+/DhGjhyJkSNHaqpYhBBCtEUr6I+pq6vD6tWrkZ+fj++++67JAalNefLkCXg8HoyMjAAAHTp0QGJiIvh8vsSA0/T0dPa8mCJp5WVtbY1PPvlE4etURfZKYWrg4OAg86tNmzbw8fFBcHAwvvnmGxw5cgT379/XVLEIIYRoiYYpuhwlvlRzf6FQiHXr1iEtLQ1LliyRWPX0ReXl5VLHcnJykJCQAF9fX3ahzoCAAIhEIpw5c4ZNJxAIEBkZCQ8PD4nZLoqklZepqSlGjBih8HWq0qoGJ3Tv3h2dOnXCP//8g/79+7d0cQghhLQiyq4TIs81J06cQFVVFdvdkZCQwP5/9OjRMDU1xY4dOxAfH4++ffuioqIC0dHREnm89dZb7P/XrVsHHo8HT09PWFlZ4eHDhzh79iwMDQ3xwQcfsOk8PDwQEBCAsLAwlJeXw8nJCVFRUSgsLMT8+fMl8lckrbZoVUEI0NBfdvPmzZYuBiGEkFZGnQNTIyIiUFhYyH5/9epVXL16FQAwePBgmJqaIjs7G0BDgJKQkCCVR+MgxM/PD5cuXcLRo0fB5/NhaWmJ/v37Y8qUKXB2dpa4buHChQgPD5fYD2bFihXw8fGRuociaRXx8OFDlJWVoUuXLmxXkSZwmJYckfKC2tpafPrpp6iursaePXtaujhK4/P5mDRpEmrOvn4DUzmGhi1dBI07kxPf0kVoEal1/JYugsZ92XtMSxdB8/REMOj9GPv375daJEtTxO+paRN7Q8RT/LMzt64engdutGgdWruNGzciOjoaoaGh7OJnQMOg1/Pnz4NhGPTt21epcSfN0VhLSOMI80U1NTXIz8/HkSNH8OzZMwwcOFBTxSKEEKI1OEqufvp6fRhURlpaGpycnCQCEIFAgEWLFqGoqAgMw2DPnj344IMP2H1rVEFjQcisWbNeOvWWYRi4uLggJCREQ6UihBCiLdQ5JuR1V1paKrULb0xMDAoLC9G5c2cMGjQIp06dwt9//w1PT0+88cYbKrmvxoIQb2/vJoMQfX19WFtbo2vXrhg4cKBWLrhCCCFEzVrBFF1dJRAIYGxsLHHsypUr4HK5+Prrr+Ho6Ij+/fvjo48+wrFjx7QvCFm7dq2mbkUIIUQHtaYVU3WNra0tnj59yn5fU1ODu3fvwtPTE46OjgAAe3t7eHt7IzU1VWX31dg6IYQQQsgrYV7hizSra9euyMzMRE5ODgAgOjoadXV16NWrl0Q6a2trPH/+XGX3bXVTdHUJ7R1DCCFEG4wbNw6XLl3CN998A29vb9y6dQtcLhdvvvmmRLrnz5+rdIaR2p6Ge/fufaXrp0yZoqKStBzaO4YQQlSHumPUp23btli2bBk2b96M+Ph4cDgcvP/++2jTpg2bRiQSITMzU6mVWZui1iBEvCGdvBoPXNWFIIQQQogK0cBUterVqxd27NiBgoICmJqawtraWuL87du3UVlZKdU68irUFoRMnz5dofTFxcU4f/486urqdGYXXUIIIarEgXJrftAzRV5cLheurq4yz3E4HLz99tvw9/dX2f3UFoT861//kitdaWkpDh48iHPnzkEgEMDExASBgYHqKhYhhBBtRS0hLapnz57o2bOnSvNssRGS5eXl+Oeff3D69GkIBAIYGRlh3LhxGDt2LMzMzFqqWIQQQlorCkJ0jsaDkOfPn+PQoUM4ffo0ampqYGRkhPHjx2PcuHEwNzfXdHEIIYSQ11ZRURE2btyINWvWtMj9NRaEVFZW4vDhwzhx4gQbfAQFBSEoKAgWFhaaKgYhhBBtxSi5dwzNjmlSbW0tkpOTW+z+ag9CqqqqEBERgRMnToDP58PQ0BBjx47F+PHjYWlpqe7bE0II0RG0d4zuUVsQwufzceTIERw7dgx8Ph88Hg/vvfcexo8fDysrK3XdlhBCiK6iMSE6R21ByIwZM1BdXQ0DAwOMGTMG//rXv6TmHBNCCCFyo+4YnaPWlhAOhwOBQIBTp07h1KlTCl0fERGhppIRQgjRRhym4UuZ60jrpNYxIeLVUoVCoTpv02rR3jGEEEJI09T2NDx27Ji6stYatHcMIYSoEI0J0Tn0kZwQQoh2oDEhaqHIHm+qRkEIIYQQ7UAtISpnbW2NTz75pMXuT0EIIYQQ7UBBiMqZmppixIgRLXZ/CkIIIYRoBwpCdA4FIYQQQghBeXk5Dh48iLt37+L58+cwMzPDsGHDMHbsWLXdU+eCkKysLOzatQupqakAAA8PD4SEhKBjx44S6QoKChAeHo579+6hoqIC9vb2GDRoEMaNGwcjIyM2XVJSEpYtWybzXqGhofD09FRfZQghhDSi5MBU0MDUlykuLsZXX32FkpISAICFhQUePXqE3NxcNk1KSgqqq6vRrVs38Hg8ldxXp4KQrKwsLF68GHZ2dpgyZQoYhsHJkyexdOlS/PTTT3B1dQXQsGvgwoULYWpqilGjRsHc3BxpaWnYs2cP7t+/j+XLl0vlPWbMGHTu3FnimJOTk0bqRQghRH2LlVVXV+Pw4cPIyMhARkYGKisrsWDBArz99ttSaQUCAXbv3o3o6GhUVlbCzc0N06ZNg6+vr9Jp1ZGnosLCwlBcXIyRI0fiww8/hJGREQIDAyXSCIVCrF69GnPnzsWwYcNe6X5iXJXk0krs3r0bPB4PoaGhGDduHIKCghAaGgqGYRAWFsami46ORlVVFVasWIEJEyZg+PDh+PzzzzFkyBDEx8ejsrJSKm9vb2+89dZbEl+0AR8hhGgQ8wpfzXj+/Dn27duHvLw8dOjQodm0GzduxJEjRzBo0CDMnj0bXC4Xq1atQkpKitJp1ZGnom7dugVnZ2d8/PHHEr0BjXXr1g1WVla4fv36K92rMY0FIVeuXMHz58/Veo+UlBR0794dFhYW7DEbGxt4e3vj+vXrqK6uBtCwpDwAqY30rK2tweVyoa8vu4GIz+e/tqu/EkKIrrKxsUFYWBh27NiBkJCQJtNlZGQgJiYG06dPx4wZMzB8+HCsWbMGDg4O2Llzp1Jp1ZGnMqqqqtChQwdwOM13XXXo0AEPHjx4pXs1prEg5IcffkBwcDDmzZuH33//HXFxcSgvL1fpPQQCAQwNDaWOGxoaor6+nu3b6tq1KwBg8+bNyM7ORlFREWJjY3H69GmMHj1aZhS4adMmTJo0CUFBQVi2bBkyMzNVWnZCCCHNE3fHKPPVHAMDA7k2WI2LiwOXy8Xw4cPZYzweD8OGDUNaWhqKiooUTquOPJVhY2OD4uLil6YzNzdX6bNbY2NC3nvvPSQnJyM7Oxu5ubnshnaurq7o2rUrunbtCh8fn1fq4nB1dUV6ejqEQiH09PQANAQmGRkZAMD+gHv16oVp06bhwIEDiI+PZ6+fOHEigoODJfLU19eHv78/evfuDQsLCzx8+BARERFYsmQJ1q1bB3d396YLpCdn56UIgIgGThFCSGuWnZ0NFxcXmJiYSBzv0qULACAnJwf29vYKpVVHnsro0aMHIiMjkZubi/bt2zeZrrKyEvX19UrdQxaNBSEzZ84E0NClkZycjKSkJCQlJSEnJwd5eXk4ffo0AMDFxQXdunXDnDlzFL7HyJEjsXXrVvzyyy8YP348GIbB/v37UVpaCgCoq6tj0zo4OMDHxwf+/v4wNzfHjRs3cPDgQVhbW2P06NFsOi8vL3h5ebHf+/n5ISAgAPPmzUNYWBhWrVrVZHmM3q5r8lxj9Rl6qM80ULS6hBDyemnhZdtLSkpktpiIjzVuSZA3rTryVMZ7772HCxcu4Mcff8TKlSvh4OAglaa2thZZWVmwtbVV+j4v0vjsGBMTE/Tt2xd9+/YF8L+g5MaNG7hw4QIePXqE/Px8pYKQESNGoKioCBEREYiKigIAdOrUCUFBQThw4ADbzRITE4MtW7bg999/h52dHQDA398fIpEIO3fuxMCBAyXGlbzI2dkZ/fr1w5UrVyRaXV5UE8kDhHL88YsUrCghhLyOWnixsrq6OhgYSH9gFE9XbfxBV9606shTGW3btsVHH32Ebdu2Yf78+VKrqNbU1ODXX3/F8+fP0b9/f6Xv86IWm6JbV1eH1NRUJCUlITk5GZmZmRAIBADABgbKmD59OoKCgpCbmwtTU1O4ubmxM2NcXFwAAKdOnYK7u7vUffz8/HDhwgVkZ2ejR48ezd7Hzs4O9fX1qK2tlWoaYwk5tIsuIYSoSgsHITwej31ONSZ++DdeO0PetOrIU1kjRoyAtbU1fv31Vxw6dAhAw4f2lJQUFBUVQSgUwtzcHBMnTnyl+zSmsSBEVtBRX18PhmFgb2+PN998kx0bIqsZSBFmZmbw9vZmv79z5w7s7OzYdULKyspgZmYmdZ24n0ueGTBPnjwBj8drcioTIYQQ1VLXOiHyamrwprjLv3E3hbxp1ZHnq+jXrx969OiBM2fO4Nq1a3jw4AH7vOvVqxc+/PDDV2ooeJHGgpApU6awD3k7OzuVBh3NiY2NRWZmJmbMmAEut2EykLOzM27fvo38/Hy2dQRoiPi4XC7c3NzYY+Xl5VKDZXNycpCQkIBevXqxeRJCCFGzFm4J6dChAxITE8Hn8yVawNPT09nziqZVR56vysjICGPHjmWXa6+vr29y6YpXpbEgRNyE1L59e7z11lvo2rUrOnXq9NI5yYpITk7Gvn374OvrC3Nzc6SnpyMyMhI9e/aUWPktKCgIN2/exJIlS9gVU69fv46bN2/inXfekYgm161bBx6PB09PT1hZWeHhw4c4e/YsDA0N8cEHH6is7IQQQlq3gIAARERE4MyZMwgKCgLQ8GyLjIyEh4eHxMwUedOqI09VU1cAAmgwCJk1axYSExNx79497Ny5ExwOB8bGxnjjjTfYFhF3d/dXCkpsbW3B5XJx+PBhVFdXw9HREdOmTcPYsWMlBo/6+PggNDQUe/bswalTp1BRUQFHR0cEBwdj/PjxEnn6+fnh0qVLOHr0KPh8PiwtLdG/f39MmTIFzs7OSpeVEEKIgtTYEnLixAlUVVWx3R0JCQns/0ePHg1TU1N4eHggICAAYWFhKC8vh5OTE6KiolBYWIj58+dL5CdvWnXkKY+XTcXVVD4chmE0uskxwzDIyclBYmIikpOTce/ePVRWVoLD4cDExIQNStS5a5+68fl8TJo0CTVnDV+7gakcGYvF6bozOfEvT6SDUuv4LV0Ejfuy95iWLoLm6Ylg0Psx9u/f3/QgfDUTv6em+78JkRKfyrn19fC4EttsHWbOnInCwkKZ57Zv3w5HR0cADeMbw8PDcfHiRYm9W3r27Cl1nbxp1ZHny7z33nsYMGAA/vWvfynVjXP//n0cPHgQV69exdGjRxW+XkzjQciLxEHJhQsXcObMGXbcyKtUqqVREPJ6oSDk9UFBSAsHIf0HKh+EXI1p0Tq0Nnv37kVERARqa2vRvn17DBw4ED4+PnB3d5c5Dbiurg7Z2dlISkrCpUuXkJeXB0NDQwQFBWHy5MlKl6PFpugWFhZKLFpWVFQEcTykzv4nQgghWqqFB6bqkilTpmDEiBE4cOAAoqKiEBYWBg6HAy6XC3t7e5iamsLExAR8Ph+VlZV49uwZRCIRGIaBiYkJxowZgwkTJrzyRq4ae9rLCjqAhpYQfX19eHl5wcfHB127doWnp6emikUIIURLtPQUXV1jZWWFjz76CB988AEuX76M69ev4969e3jy5IlUWmtra7zxxhvo06cPBgwY8MprkohpdGAqh8MBwzAwMDCAl5cXOyDV09NTZRUihBBCiPwMDQ0xdOhQDB06FEDD0hRlZWXsVGArK6tXbvFoisaCEG9vb4mgQ1afk67h+dcCkBwTIszVgzCXupsIIURh1B2jEZaWlmoLOl6ksafh2rVrNXWrVqPuyus3MJUQQtSFumN0T4t+JK+srAQAmUuoE0IIIRKoJUTnaDwIuXHjBo4dO4bU1FSJTXfeeOMNjBkzBr1799Z0kQghhGgDCkJ0jkaDkD/++AMnTpxgp+KamJiAw+GgqqoKt2/fxp07dzBmzBjMmjVLk8UihBCiBThQsjtG5SUhqqKxICQ2NhbHjx+HpaUlJk2ahLfeegumpqYAGhaiiY6Oxv79+3H8+HF4eHjgzTff1FTRCCGEENICNLYF7MmTJ2FgYIAffviBXYdfzMTEBKNGjcLatWuhr6+PU6dOaapYhBBCCGkhGgtCHjx4gG7dusHFxaXJNC4uLujWrRtycnI0VSxCCCHagnmFL9IqaSwIEQgEMDIyemk6IyMjCAQCDZSIEEKINhFP0VXmizTv8ePHcqe9du2ayu6rsSDEyckJycnJqKmpaTJNTU0NkpOT4eTkpKliEUII0RbUEqI2CxYsQGRkZLNpamtrsXnzZpWu+6WxIGTAgAEoLy/HmjVrUFBQIHX+8ePHWLt2LZ4/f06DUgkhhEijIERtGIZhA4yKigqp8xkZGViwYAHOnz+v0oYCjc2OGTduHOLj43H37l18+umncHd3h4ODAwCgqKgIWVlZEIlE6NSpE8aOHaupYhFCCCGvvY0bN2L9+vW4evUqG3D06NEDDMPgwIED2LdvH4RCId555x2VLqOhsSDE0NAQ33//PcLCwnD+/HlkZmYiMzOTPc/j8TBs2DBMnz4dhoaGmiqWWtHeMYQQokLKju+glpCXcnFxwfr167F7924cOnQI//nPfzBixAhkZ2cjLS0NlpaWmDdvHvr27avS+2r0aWhsbIyPP/4YH3zwAe7fv4+SkhIAgI2NDdzd3eUauKpNaO8YQghRIVoxVa309PQwffp09OrVC6tXr8bp06cBAD169MDChQthZWWl8nu2yEdyIyMjeHt7yzxXVlaGI0eO4MMPP9RsoQghhLRqHCWDEJodI7+qqiqcOnUKfD6fPZabm4ucnBz4+vqq/H4aG5j6MkVFRfj9998xa9YsREREtHRxCCGEtDY0MFWtkpKSMG/ePMTGxqJDhw745Zdf8K9//Qvl5eVYuXIl/vjjD5UvoaHWlhCRSISYmBjcvn0bZWVlsLKyQq9evTBgwABwuQ3xT1FREfbu3Yvo6GiIRCIAQL9+/dRZLEIIIdqIumPUZufOnThy5AgYhkFQUBCmTZsGfX19uLm5oVevXvj5559x4sQJJCYm4ssvv4Sbm5tK7qu2IEQoFGLlypVITExkN6wDgIsXL+Ly5ctYtmwZLly4gN9//x21tbVgGAb9+vXDlClT0KFDB3UVixBCCCEvOHz4MGxtbbFw4UJ07dpV4py3tzc2b96Mbdu24eLFi/jyyy9x6NAhldxXbUHIyZMncffuXRgYGGDo0KFo3749+Hw+bt68ifj4ePz66684d+4cGIaBr68vPvjgA3Ts2FFdxSGEEKLlaEyI+gwYMACffvopzMzMZJ43MTHBwoUL0bdvX2zdulVl91VbEBITEwMul4u1a9eiS5cu7PEJEyZg69atOHPmDDgcDkJCQjBu3Dh1FaNlcfUA7us1O4ajp9fSRdC44e8Ft3QRWkSdJa+li6BxRnp5LV0EzWtN72HUHaM2X3/9tVzpBgwYAC8vL5XdV20DUx89egRPT0+JAEQsKCgIQMO8ZJ0NQAghhKgWDUxtFWxtbVWWl9paQqqrq+Ho6CjznPg4jf0ghBAiL3V1x2zYsAFRUVFNnt+5cydsbW2RlJSEZcuWyUwTGhoKT09PiWMCgQC7d+9GdHQ0Kisr4ebmhmnTpklNdZU3nS5SWxDCMAw7A+ZFHE5D8x6P9/o15xJCCFGSmrpjRowYgR49ekhewjDYunUrHBwcpD75jxkzBp07d5Y4Jms/lY0bNyIuLg6BgYFwdnbGhQsXsGrVKqxZs0ZirSx506lTcnKyQul9fHxUcl9aP5wQQshrzdPTU6oVIyUlBbW1tRg8eLBUem9vbwQEBDSbZ0ZGBmJiYhASEsIOQRgyZAjmzp2LnTt3IjQ0VKF06rZs2TK2gUAeR48eVcl91RqEREVFNdnExeFwmj2vqgoSQgjRDZqcHXPp0iVwOBwMGjRI5nk+nw9DQ0PoNTEYPy4uDlwuF8OHD2ePifdICwsLQ1FREezt7eVOp25vvfWWzCCEYRg8e/YM9+/fB5/Ph5+fH0xNTVV2X7UGIY3XB3kd8frzIb2BnQGEDw1apkCEEKLNNDQ7pr6+HpcvX4anp6fMsY2bNm1CdXU1uFwuvL29ERISItU9k52dDRcXF5iYmEgcF0/WyMnJgb29vdzp1O2LL75o9nxlZSU2b96M3NxcrF+/XmX3VVsQcuzYMXVlrTXqrprQBnaEEKIqGgpCbt26hYqKCqmuGH19ffj7+6N3796wsLDAw4cPERERgSVLlmDdunVwd3dn05aUlMDa2loqb/Gx4uJihdK1NDMzM3zxxRf46KOP8Pfff+Ozzz5TSb6tZu8YQgghpDmcV/hSxKVLl6Cvr48BAwZIHPfy8sLSpUsxbNgw+Pn5YcKECWyrQFhYmETauro6GBhIt3qLJ2TU1dUplK41MDIyQpcuXZCQkKCyPCkIIYQQoj3UvEZIdXU14uPj4evrCwsLi5emd3Z2Rr9+/ZCYmAihUMge5/F4Mjd7EwcV4iBD3nStRXV1NSorK1WWHwUhhBBCyP+7du1ak7NimmJnZ4f6+nrU1tayx2xsbFBaWiqVVnxMPO1X3nStQUJCAlJSUuDs7KyyPGmKLiGEEK2gidkxFy9ehLGxMfr27Sv3NU+ePAGPx4ORkRF7rEOHDkhMTASfz5cYdJqens6eVySdum3atKnJc9XV1SgoKEBubi4YhlHpSucUhBBCCNEOah6YWl5ejrt372LgwIESAUXj85aWlhLHcnJykJCQgF69ekks0BkQEICIiAicOXOGXf9DIBAgMjISHh4e7IwXedOp24ULF16axt7eHlOmTMGQIUNUdl8KQgghhGgHNQchsbGxEAqFTXbFrFu3DjweD56enrCyssLDhw9x9uxZGBoa4oMPPpBI6+HhgYCAAISFhaG8vBxOTk6IiopCYWEh5s+fr3A6dVuzZk2T5wwMDGBtbd3kViyvgoIQQgghWkHd3TEXL16ElZUVunfvLvO8n58fLl26hKNHj4LP58PS0hL9+/fHlClTZI6TWLhwIcLDwyX2hFmxYoXUkufyplOnrl27auxejXGY131FMTXg8/mYNGkSas6/fuuEcI0MW7oIGsd4u788kQ6qs2xdo/Y1wSgpr6WLoHlcEQx6PsL+/fulFtTSFPF76qN2A8FwFf/szBHVw/VhTIvWgchGLSGEEEK0g4YWK3sdzJo1S+lrORwO/vjjD5WUQ21BSHPbIstDlQNfCCGEaD9N7h2j6woLC1u6CADUGIRs3LhRoR35xBiGAYfD0YkghPaOIYQQFaKWEJVpLVurqC0ImTx5slJBiC6hvWMIIUR1qCVE96gtCJk6daq6siaEEPI6opYQnUPLthNCCCGvmW+++QaHDh2Sea6wsBAVFRUaKUeLBCEVFRW4ffs2Ll26hNTU1JYoAiGEEG2jzOZ1yrae6LikpCQ8evRI5rnZs2fjr7/+0kg5NDpFt7y8HP/9739x5coViEQiAA2zYLy8vAAAZ8+exc6dO7F8+XJ4e3trsmiEEEJaORoTohkMw0BTS4hpLAipqKjAokWL8OTJE3To0AFvvPEGTp48KZHG398fv/32G65cuaJ0EJKVlYVdu3axLSweHh4ICQlBx44dJdIVFBQgPDwc9+7dQ0VFBezt7TFo0CCMGzdO5p4BYvv370d4eDjatWuHX3/9VakyEkIIUQKNCdE5GgtCDhw4gCdPnmDy5MnsoNUXgxBzc3O4ubkhOTlZqXtkZWVh8eLFsLOzw5QpU8AwDE6ePImlS5fip59+gqurKwCgqKgICxcuhKmpKUaNGgVzc3OkpaVhz549uH//PpYvXy4z/2fPnuHgwYPNBimEEELUg8MwgBKf0Dm0MHirpbEg5Nq1a3B2dn7prJk2bdooHYTs3r0bPB4PoaGhsLCwAAAMHjwYc+bMQVhYGJYtWwYAiI6ORlVVFX788Ue0b98eADB8+HAwDIOoqChUVlbCzMxMKv8dO3bAw8MDIpEIz58/V6qMhBBClEQtITpHYwNTi4uL0aFDh5em43A44PP5St0jJSUF3bt3ZwMQALCxsYG3tzeuX7+O6upqAGDzt7Kykrje2toaXC4X+vrSsVlycjLi4uIwe/ZspcpGCCGEEEkaawkxMTFBaWnpS9M9efIElpaWSt1DIBDA0FB6AzVDQ0PU19cjNzcXnp6e6Nq1Kw4dOoTNmzdj6tSpbHfM6dOnMXr0aKnuFqFQiN9//x3vvPMO3NzclCobIYSQV0MDU1UrKipK5hYrHA6nyXNiR48eVUkZNBaEdO7cGYmJiXjy5AnatGkjM01OTg6ys7MREBCg1D1cXV2Rnp4OoVAIPT09AA2BSUZGBoCG1hgA6NWrF6ZNm4YDBw4gPj6evX7ixIkIDg6WyvfMmTMoKirCd999p1iB9OT8yxcBENHKqoQQ0izqjlEpTc2AaY7GgpDRo0fj5s2b+P7777Fo0SK0bdtW4nxBQQF+/vlnAMCoUaOUusfIkSOxdetW/PLLLxg/fjwYhsH+/fvZFpi6ujo2rYODA3x8fODv7w9zc3PcuHEDBw8ehLW1NUaPHs2me/78OXbv3o1JkyYp3EJjNKRarnT1mQaoz3r9tkUnhBBFUEuI6uj83jEv6tWrF4KCgnD48GHMnTsXTk5O4HA4uHXrFubNm4e8vDyIRCJMnDhR6em5I0aMQFFRESIiIthmpE6dOiEoKAgHDhxgu1liYmKwZcsW/P7777CzswPQMD1YJBJh586dGDhwIDuuJDw8HGZmZhKBibxqoowBoRwtHCKFsyaEkNcTBRQ6RaOLlX344Yfo1KkTDhw4gAcPHgAASktLUVpaCldXV0yaNAmDBg16pXtMnz4dQUFByM3NhampKdzc3BAWFgYAcHFxAQCcOnUK7u7ubAAi5ufnhwsXLiA7Oxs9evRAQUEBzp49i1mzZqGkpIRNJxAIIBQK8fTpU5iYmMDc3Fx2YYQc2sCOEEJURNkWDWoJab00GoQAwIABAzBgwACUl5fj6dOnYBgGdnZ2sLW1Vdk9zMzMJFpT7ty5Azs7O3adkLKyMplTcOvr6wE0DEQFGsaQiEQi/Pe//8V///tfqfSzZs1CYGAgzZghhBBClKCxIOTFtTcsLS2bHGORmZmJzp07q+S+sbGxyMzMxIwZM8DlNsxIdnZ2xu3bt5Gfn8+2jgAN3TRcLpedAdOuXTt2bZHGwsPDUV1djdmzZ8PJyUkl5SSEEPISyrZoUEtIq6WxIGTu3Ln44osv0L179ybTMAyDAwcOYN++fYiIiFD4HsnJydi3bx98fX1hbm6O9PR0REZGomfPnggMDGTTBQUF4ebNm1iyZAm7Yur169dx8+ZNvPPOO2yrjKWlJfr37y91H/GAHlnnCCGEqAd1x+gejQUhpaWlWLFiBQIDA/HBBx9ILQhWWFiIn3/+Gffu3ZNYbEwRtra24HK5OHz4MKqrq+Ho6Ihp06Zh7Nix7JRdAPDx8UFoaCj27NmDU6dOoaKiAo6OjggODsb48eNfqZ6EEELUhFFyekwrmIpKZNNYEPL999/j559/xrFjx3D37l18+eWX7JLp0dHR+P3338Hn8+Hr64vPP/9cqXs4OTnh22+/lSttly5dsHLlSqXus3btWqWuI4QQojxqCdE9GgtCvL29sXnzZmzduhUxMTFYuHAh3n//fdy/fx+xsbEwMDDA7NmzMWbMGE0ViRBCiDahMSE6R6OzY0xMTPDVV1+hd+/e2Lp1K/7++28AgLu7OxYuXCi1gBkhhBBCdJfGp+gKhUI8ePAAtbW17JKx9fX1EIloxS5CCCFN44gAKLH00su6Y5KSkmTOhASA0NBQeHp6st8LBALs3r0b0dHRqKyshJubG6ZNmwZfX1+pa+VNq0ieukajQUh+fj7Wr1+P7Oxs2NjY4JNPPkFMTAzbPTN9+nS89957miwSIYQQbaHm7pgxY8ZILQ/x4jIMGzduRFxcHAIDA+Hs7IwLFy5g1apVWLNmjdRq3/KmVSRPXaOxIOT06dPYsWMHamtrERAQgM8++wxmZmbo27cv+vTpg99++w07duzAzZs38fnnn8PGxkZTRSOEEKINGKUaQuQOQry9vZvdQDUjIwMxMTEICQlBUFAQAGDIkCGYO3cudu7cidDQUIXTKpKnLuJq6ka//fYbuFwuPv/8cyxevFhi4bJBgwZhy5Yt8Pb2xp07dzBv3jxNFUuteP354L0p+aXXTtDSxSKEEO3EMMp/yYnP57OrZr8oLi4OXC4Xw4cPZ4/xeDwMGzYMaWlpKCoqUjitInnqIo21hHh6emLhwoVo06aNzPN2dnZYs2YNDh8+jN27d2uqWGpVd9WE9o4hhBAVUXqqrZzXbdq0CdXV1eByufD29kZISIhE90x2djZcXFxgYmIicV2XLl0AADk5ObC3t1corSJ56iKNBSE//PADu2x6UzgcDsaPH4+ePXtqqFSEEEJed/r6+vD390fv3r1hYWGBhw8fIiIiAkuWLMG6devg7u4OACgpKYG1tbXU9eJjxcXF7DF50yqSpy7SWBDysgCksQ4dOqixJIQQQrSSmlpCvLy84OXlxX7v5+eHgIAAzJs3D2FhYVi1ahUAoK6uDgYGBlLX83g89ryYvGkVyVMXaWxMCCGEEPIqOIzyX4pydnZGv379kJiYyI4R4fF4EAikx/WJAwVx4KBIWkXy1EVqawmZNWsWOBwOVq9ejTZt2mDWrFlyX8vhcPDHH3+oq2iEEEK0kYb3jrGzs0N9fT1qa2thYmICGxsbmd0jpaWlAMBufgpA7rSK5KmL1BaEFBYWAgAbQYq/J4QQQpSh7oGpL3ry5Al4PB6MjIwANAwVSExMBJ/PlxhImp6ezp4XkzetInnqIrUFIeLt7pv6nhBCCFGImoKQ8vJyWFpaShzLyclBQkICevXqxY5pDAgIQEREBM6cOcOu6SEQCBAZGQkPDw+JWSzyplUkT12k8WXbCSGEkNZk3bp14PF48PT0hJWVFR4+fIizZ8/C0NAQH3zwAZvOw8MDAQEBCAsLQ3l5OZycnBAVFYXCwkLMnz9fIk950yqSpy5SexBy48YNXLt2DUVFRTAwMICbmxvefvvtJtcLIYQQQmThAGrZEdfPzw+XLl3C0aNHwefzYWlpif79+2PKlClwdnaWSLtw4UKEh4dL7POyYsUK+Pj4SOUrb1pF8tQ1HIZRcsSOHNavX4/Y2FgAYDer43A40NfXx9dffw0/Pz913bpF8fl8TJo0CTXnX7/FyrhGhi1dBI1jvN1buggtos5St0fty2KUlNfSRdA8rggGPR9h//79UgtqaYr4PfW5qD+U++xcDwvu1RatA5FNbS0h586dQ0xMDPT09PDWW2+hY8eOqK6uxvXr15GWloYNGzbgzz//hKmpqbqKQAghRJeo7SMzaSlqC0KioqLA4XCwcuVKdO/enT0+YcIEbNy4EdHR0bh69SrefvttdRWhxfH68/HidkvCXAMIH0ovTEMIIaR5Ss+OIa2W2oKQBw8ewMPDQyIAEZs4cSKioqLw4MEDdd2+VaC9YwghRIWUXSeEmlBaLbWtmFpdXQ0nJyeZ58SDUvl8vrpuTwghhJBWTm0tIQzDNLlfjPi4GsfEEkII0THUHaN7aJ0QQggh2oGCEJ2j1iAkKioKUVFRMs9xOJxmzx89elSdRSOEEKJlODQmROeoNQih7hZCCCEqI2rpAhBV09jeMa8lkRAQvV6zY0Q1tS1dBI3j3Elv6SK0CEPe6zfVXFRf39JF0Dy91vNhklpCdI/aZscQQgghhDSHBqYSQgjRDtSgoXMoCCGEEKIdqDtG51AQQgghRCvQOiG6h4IQNeL510J67xg9CHPpx04IIQqjlhCdQ09DNaq7Ykh7xxBCiIpwaIquzqHZMYQQQghpEdQSQgghRDtQd4zOoSCEEEKI9lAmnqBe8VaLghBCCCFagcPg/1tDiK6gIIQQQoh2YBglgxAKXForCkIIIYRoBxGoO0bH0OwYQgghhLQIagkhhBCiFTjUHaNzKAghhBCiHSgI0TkUhBBCCNEOFIToHApC1Ij2jiGEEBVS08DUjIwMREVFITExEYWFhTA3N4eHhweCg4Ph4uLCpktKSsKyZctk5hEaGgpPT0+JYwKBALt370Z0dDQqKyvh5uaGadOmwdfXV6l0uoiehmpEe8cQQojqqGtMyKFDh5CamoqAgAC4ubmhrKwMJ06cwOeff47169ejffv2EunHjBmDzp07SxxzcnKSynfjxo2Ii4tDYGAgnJ2dceHCBaxatQpr1qyBt7e3wul0EQUhhBBCXmtjx47FV199BQMDA/bYm2++iblz5+Kff/7Bl19+KZHe29sbAQEBzeaZkZGBmJgYhISEICgoCAAwZMgQzJ07Fzt37kRoaKhC6XQVTdElhBCiHcQtIcp8NcPLy0siAAEAZ2dntGvXDnl5eTKv4fP5EAqFTeYZFxcHLpeL4cOHs8d4PB6GDRuGtLQ0FBUVKZROV1FLCCGEEO2gwYGpDMOgrKwM7dq1kzq3adMmVFdXg8vlwtvbGyEhIVLdM9nZ2XBxcYGJiYnE8S5dugAAcnJyYG9vL3c6XUVBCCGEEO2gwSDk4sWLKC4uxvvvv88e09fXh7+/P3r37g0LCws8fPgQERERWLJkCdatWwd3d3c2bUlJCaytraXyFR8rLi5WKJ2uoiCEEEKIdtDQsu15eXnYtm0bPD09MWTIEPa4l5cXvLy82O/9/PwQEBCAefPmISwsDKtWrWLP1dXVSXXxAA1dLeLziqTTVToXhGRlZWHXrl1ITU0FAHh4eCAkJAQdO3aUSFdQUIDw8HDcu3cPFRUVsLe3x6BBgzBu3DgYGRmx6XJzc7F3715kZWWhtLQUhoaGaNeuHYKCgtC3b1+N1o0QQl5nmlgxtbS0FN9++y1MTEywZMkS6OnpNZve2dkZ/fr1w5UrVyAUCtn0PB4PAoFAKr04qBAHGfKm01U6NTA1KysLixcvxpMnTzBlyhRMnjwZBQUFWLp0KR49esSmKyoqwsKFC5Geno5Ro0Zh9uzZ8PT0xJ49e7B+/XqJPIuKilBdXY2hQ4fio48+wuTJkwEAq1evxpkzZzRaP0IIIepTVVWFlStXoqqqCqtWrYKtra1c19nZ2aG+vh61tbXsMRsbG5SWlkqlFR8T5y1vOl2lUy0hu3fvBo/HQ2hoKCwsLAAAgwcPxpw5cxAWFsYuMhMdHY2qqir8+OOP7Pzv4cOHg2EYREVFobKyEmZmZgCA3r17o3fv3hL3GTVqFL744gscOXJEYkQzIYQQNVJjS0hdXR1Wr16N/Px8fPfddzIHpDblyZMn4PF4Eq3oHTp0QGJiIvh8vsSg0/T0dPa8Iul0lU61hKSkpKB79+5sAAI0RJne3t64fv06qqurATRMrQIAKysrieutra3B5XKhr998bKanpwc7OztUVVWptgKEEEKaxjCASImvlwQuQqEQ69atQ1paGpYsWSK18qlYeXm51LGcnBwkJCTA19cXXO7/HqkBAQEQiUQSLeYCgQCRkZHw8PBgZ7zIm05X6VRLiEAggKGhodRxQ0ND1NfXIzc3F56enujatSsOHTqEzZs3Y+rUqTA3N0daWhpOnz6N0aNHS0SzYjU1NaitrQWfz0d8fDxu3ryJN998UxPVIoQQAijfEvKSa3bs2IH4+Hj07dsXFRUViI6Oljj/1ltvAQDWrVsHHo8HT09PWFlZ4eHDhzh79iwMDQ3xwQcfSFzj4eGBgIAAhIWFoby8HE5OToiKikJhYSHmz5+vcDpdpVNBiKurK9LT0yUGBwkEAmRkZAD431SnXr16Ydq0aThw4ADi4+PZ6ydOnIjg4GCZef/5559spMrlctG/f3/MmTOn+QLpyfliEQEQ0fLuhBDSLDUFIdnZ2QCAhIQEJCQkSJ0XByF+fn64dOkSjh49Cj6fD0tLS/Tv3x9TpkyBs7Oz1HULFy5EeHi4xJ4wK1asgI+Pj1LpdJFOBSEjR47E1q1b8csvv2D8+PFgGAb79+9nB/g0nurk4OAAHx8f+Pv7w9zcHDdu3MDBgwdhbW2N0aNHS+UdGBiIgIAAlJSUIDY2FiKRSOaI5saM3pZvalV9hh7qM6WnaBFCCFG/tWvXypUuMDAQgYGBcufL4/EwY8YMzJgxQyXpdJFOBSEjRoxAUVERIiIiEBUVBQDo1KkTgoKCcODAAbabJSYmBlu2bMHvv/8OOzs7AIC/vz9EIhF27tyJgQMHSowrAYC2bduibdu2ABrW9f/3v/+N1atX46effgKHI7sVoyaSBwjlaOEQKVtjQgh5jaipJYS0HJ0KQgBg+vTpCAoKQm5uLkxNTeHm5oawsDAAYLdkPnXqFNzd3dkARMzPzw8XLlxAdnY2evTo0ex9AgIC8OuvvyI/Px+urq6yEwk5tIsuIYSoinigqcIoCGmtdC4IAQAzMzOJ7Y/v3LkDOzs7NlgoKytjp+A2Vl9fDwDNbkokJu7aEc+0IYQQomaMCGCU+GDHUHNza6VTU3RliY2NRWZmJgIDA9npU87Ozrh//z7y8/Ml0sbExIDL5cLNzY09VlZWJpVnfX09oqKiwOPx2C4aQgghasZAyV10W7rgpCk61RKSnJyMffv2wdfXF+bm5khPT0dkZCR69uwpMZgoKCgIN2/exJIlSzBq1CiYm5vj+vXruHnzJt555x2JFep+/fVX8Pl8+Pj4wMbGBmVlZbh48SIePXqEmTNnwtjYuCWqSgghrx/qjtE5OhWE2Nragsvl4vDhw6iuroajoyOmTZuGsWPHSqz/7+Pjg9DQUOzZswenTp1CRUUFHB0dERwcjPHjx0vk+eabb+L8+fNsOmNjY3Tq1Akffvgh/Pz8NF1FQgghRGfoVBDi5OSEb7/9Vq60Xbp0wcqVK1+abuDAgRg4cOArlowQQsgro9kxOkenghBCCCE6jIIQnUNBCCGEEO1AQYjOoSCEEEKIdhCJlFzckabotlYUhBBCCNEO1BKicygIUSOefy0AyYV1hLl6EObSj50QQgihp6Ea1V0xpGXbCSFEVaglROdQEEIIIUQ7MEouVsahIKS1oiCEEEKIVmAYkXKLn9LeMa0WBSGEEEK0g7LLtlNLSKtFQQghhBDtQGNCdI7O76JLCCGEkNaJWkIIIYRoB2UXK+PQmJDWilpCdA2XgX5nAcB9zZofuQz0O9W9XvXmMNBzr3n9+rs5DPTc+K9nvTu+hr/vxsTdMcp8kVaJghBdwwX0uwhfv98sF/8ffLV0QTSIC+i7171edQYALgM9t+rXK+AEGn7fHWtfv993I4yIASMSKfH1mv2taBHqjiGEEKIdGEbJKboUhLRWFIQQQgjRDiJGyTEhFIS0VhSEqBHtHUMIIYQ07TXuXVS/uiuGqIuR/BLm6kOvfb1C+SiaXhnqLpNeO4FC6ZW9Rp35K1Mebts6taZXhrrLxHWuUSi9steoO39Fr+G61qo1vTJaY5leCSNS/ou0ShSEtAC99kK1pleGusuk116JIESJa9SZv1J1UPABrmh6Zai7THouij/wlblG3fkreo2eq4I/JwXTK6M1lulVNAxMVe6LtE7UL0AIIUQ7MCKAUWJnchqY2mpREEIIIUQrMIySA1Nft+ncWoSCEEIIIdqBWkJ0DgUhasCI/+D1mvrDZwB9RV4UCqTXe9m9VXCP5tI3+YlD0fwVvEapequ7zv9/jaJlkjf9q/yuVVEmvaY+kjLNnGvuHnJeI1Fvee+jojI1+zBT4+8aUP5v/FXT///3TGt4kOsDSi0UQk+6VovDtIq/LN3y7NkzhISEtHQxCCFEZf766y/Y2dm1yL3r6uowa9YslJaWKp2HtbU1tm/fDh6Pp8KSkVdFQYgaiEQilJSUwNjYGByOEk2HhBDSSjAMg+rqatjY2IDLbbkJlXV1daivV365An19fQpAWiEKQgghhBDSImidEEIIIYS0CApCCCGEENIiKAghhBBCSIugiUtaICsrC3v37sW9e/dQV1eHNm3a4N1330VgYCCbpqCgAOHh4bh37x4qKipgb2+PQYMGYdy4cTAyMpLKb9euXUhNTQUAeHh4ICQkBB07dtRovV5GnnorUheBQIDdu3cjOjoalZWVcHNzw7Rp0+Dr66uxOr2MKutcXV2Nw4cPIyMjAxkZGaisrMSCBQvw9ttva7RO8lBVvTMyMhAVFYXExEQUFhbC3NwcHh4eCA4OhouLi8br1RxV/q5zc3Oxd+9eZGVlobS0FIaGhmjXrh2CgoLQt29fjdaLEEXQwNRW7tatW1i9ejXc3d0xYMAAGBsb4/Hjx2AYhp0GXFRUhHnz5sHU1BTDhw+Hubk50tLScOHCBfj5+WH58uVsfllZWVi8eDHs7OwwfPhwMAyDkydPorKyEj/99BNcXV1bqqoS5Km3onUJDQ1FXFwcAgMD4ezsjAsXLiAzMxNr1qyBt7d3S1RTgqrr/PTpU8yaNQv29vZo06YNkpKSWmUQosp6r127FqmpqQgICICbmxvKyspw4sQJ1NTUYP369Wjfvn1LVpWl6t/1jRs3cPz4cXh6esLGxga1tbW4cuUKUlJS8Nlnn2H48OEtVVVCmseQVquqqoqZNm0as2bNGkYoFDaZbv/+/czo0aOZBw8eSBz/+eefmdGjRzMVFRXssZUrVzKTJ09mysvL2WPFxcXMhAkTmDVr1qi+EkqQt96K1CU9PZ0ZPXo0c+jQIfZYbW0tM3v2bOarr75SfSUUpI4619XVMSUlJQzDMExGRgYzevRo5vz58+qpgJJUXe979+4xdXV1Etfm5+cz48aNY9avX6/6CihBHb9rWerr65l58+YxH3/8sUrKTYg60JiQVuzSpUsoKytDcHAwuFwuampqIBJJr/rI5/MBAFZWVhLHra2tweVyoa//v163lJQUdO/eHRYWFuwxGxsbeHt74/r166iurlZPZRQgb70VqUtcXBy4XK7EJ0Iej4dhw4YhLS0NRUVF6q3US6ijzgYGBrC2ttZI+ZWl6np7eXnBwMBA4lpnZ2e0a9cOeXl56q2MnNTxu5ZFT08PdnZ2qKqqUnkdCFEVCkJasTt37sDExATFxcWYM2cOJkyYgEmTJmHr1q2oq/vflttdu3YFAGzevBnZ2dkoKipCbGwsTp8+jdGjR0uMCREIBDA0NJS6l6GhIerr65Gbm6v+ir2EvPVWpC7Z2dlwcXGBiYmJRNouXboAAHJyctRUG/moo87aQBP1ZhgGZWVlEg/zlqTOOtfU1KC8vByPHz/GkSNHcPPmTXTv3l2t9SHkVdDA1FasoKAAQqEQ3333HYYNG4bp06cjKSkJJ06cQFVVFRYtWgQA6NWrF6ZNm4YDBw4gPj6evX7ixIkIDg6WyNPV1RXp6ekQCoXQ09MD0PBml5GRAQAoLi7WUO2aJm+9FalLSUmJzFYB8bGWrrc66qwNNFHvixcvori4GO+//776KyQHddb5zz//xJkzZwAAXC4X/fv3x5w5czRUM0IUR0FIK1ZTU4Pa2lqMGDECH3/8MQDA398f9fX1OHPmDN5//304OzsDABwcHODj4wN/f3+Ym5vjxo0bOHjwIKytrTF69Gg2z5EjR2Lr1q345ZdfMH78eDAMg/3797N7MjT+JNZS5K23InWpq6uTaqYHwC7j3NL1VkedtYG6652Xl4dt27bB09MTQ4YM0Vi9mqPOOgcGBiIgIAAlJSWIjY2FSCSCQCDQaP0IUQQFIa2Y+AE5cOBAieODBg3CmTNnkJaWBmdnZ8TExGDLli34/fff2Q2m/P39IRKJsHPnTgwcOJBtih4xYgSKiooQERGBqKgoAECnTp0QFBSEAwcOSE3nbQny1luRuvB4PJlvxuI38pbeU0IdddYG6qx3aWkpvv32W5iYmGDJkiVsa0JLU2ed27Zti7Zt2wIAhgwZgn//+99YvXo1fvrpJ9rHirRKFIS0YjY2Nnj48KHUgFNLS0sAQGVlJQDg1KlTcHd3l9rh0s/PDxcuXEB2djZ69OjBHp8+fTqCgoKQm5sLU1NTuLm5ISwsDABaxVoK8tYbkL8uNjY2MpuvxZ8qbW1tVV0NhaijztpAXfWuqqrCypUrUVVVhR9++KHFf7+NafJ3HRAQgF9//RX5+fmtZvo9IY3RwNRWrFOnTgCk+35LSkoAgG3dKCsrkzm6XrzjpFAolDpnZmYGb29vuLm5AWgYLGdnZ9cq3qjkrbeYPHXp0KED8vPz2ZlEYunp6ez5lqSOOmsDddS7rq4Oq1evRn5+PlasWIF27dqpsQaK0+TvWtzS9+LfPSGtBQUhrdiAAQMAAOfPn5c4fu7cOejp6bGzYpydnXH//n3k5+dLpIuJiQGXy2XfvJoSGxuLzMxMBAYGtuhW3WLy1luWpuoSEBAAkUjEDtoDGgb5RUZGwsPDA/b29iquhWLUUWdtoOp6C4VCrFu3DmlpaViyZAk8PT3VV3glqeN3XVZWJpW2vr4eUVFR4PF4bBcNIa0Ndce0Yu7u7hg2bBjOnz8PoVAIHx8fJCUlIS4uDhMmTGCbmIOCgnDz5k0sWbIEo0aNgrm5Oa5fv46bN2/inXfekWiKTk5Oxr59++Dr6wtzc3Okp6cjMjISPXv2lFguuiXJW29F6uLh4YGAgACEhYWhvLwcTk5OiIqKQmFhIebPn98S1ZSgjjoDYGdciD91JyQksP8fPXo0TE1NNVdJGVRd7x07diA+Ph59+/ZFRUUFoqOjJe731ltvabR+sqjjd/3rr7+Cz+fDx8cHNjY2KCsrw8WLF/Ho0SPMnDkTxsbGLVFVQl6Klm1v5err63Hw4EFERkaipKQE9vb2GDVqFN577z2JdBkZGdizZw+ys7NRUVEBR0dHDBkyBOPHj5cYkPf48WP89ttvuH//Pqqrq9l0Y8eOlTl7pKXIU29F61JXV4fw8HBcvHhRYu+Ynj17arJqTVJHnWfOnInCwkKZ99u+fTscHR3VVh95qbLeS5cuRXJycpP3On78uFrrIi9V/65jYmJw/vx5PHjwABUVFTA2NkanTp0wevRo+Pn5abp6hMiNghBCCCGEtAjt6kAmhBBCiM6gIIQQQgghLYKCEEIIIYS0CApCCCGEENIiKAghhBBCSIugIIQQQgghLYKCEEIIIYS0CApCCCGEENIiaNl2AgAYM2aMxPccDgcmJiZo3749hgwZgnfeeUdiK/AxY8bAwcEBf/75p0bLuWfPHuzduxcLFizA22+/rdC1NTU1OHPmDBISEpCXl4fKykoYGhrC1dUVPXr0wDvvvAMHB4dXKp94xc7WshqpOjVenfT777+XuedJWloaFi1aBB8fH6xdu1bTRXyplvo7JoQ0oCCESBgyZAgAQCQS4cmTJ0hNTcW9e/eQmJiIRYsWtXDplJeamoq1a9eitLQUhoaG8PDwgJWVFfh8PjIzM7F//34cPnwYK1asQI8ePVq6uFpn9+7d+OGHH1q6GIQQLUNBCJHwxRdfSHx/+/ZtrFq1CjExMRg0aBD69u0LANi6dSv09bXjzyc7OxvLly9HXV0dxo8fj8mTJ8PIyIg9LxKJcO3aNezcuRPPnj1rwZJqJx6Ph5SUFNy9exfdu3dv6eIQQrQIjQkhzfL19WV3Hr127Rp7vG3btnBycmqpYsmNYRj8/PPPqKurw9SpU/Hhhx9KBCAAwOVy4e/vjw0bNqBz584tVFLtNXLkSAANrSGEEKII7fgoS1pUx44dAUCileDFvnShUIilS5ciNTUVc+bMwahRoyTySElJwbJly2BlZYXNmzfDwsKCPXfz5k2cOHECGRkZ4PP5sLW1Rb9+/TBx4kSJdMq4efMmcnNzYWdnh4kTJzab1tTUVGpr+5qaGhw5cgSxsbF48uQJ9PX10aFDB4wcORIDBw6UqwxPnz7FrFmzmhwX0dQ4F/EOuMePH8fJkydx6tQpPHnyBFZWVhg5ciSCgoLA4XCQlZWFPXv2IDU1FfX19ejevTs++ugjqfEtGzZsQFRUFL7//ntwOBzs3bsXmZmZAABvb2+EhISgXbt2ctWpsX79+iExMRGpqam4deuWXLsSv2xsT+O6iyUlJWHZsmUYMmQIQkJCEBYWhuvXr6O6uhodO3ZESEgIvLy8AACnT5/GqVOnUFBQAAsLCwwbNgyTJ08Glyv7c5dAIMCBAwdw8eJFFBcXw8bGBoMHD8bEiRPB4/Gk0guFQpw9exZRUVF4+PAhhEIhXFxcMHToUIwePVpi5+rG9Tl27BhOnDiBc+fOoaCgAC4uLvjll19e+vMiRFdRSwh5qerqagCQuVW8mJ6eHhYuXAhjY2Ps2LEDeXl57Lmqqir8/PPPYBgGn3/+uURgsXPnTqxcuRJ37tyBi4sL/Pz8oKenh6NHj+Krr75CaWnpK5X9xo0bAICAgACpB8PL8Pl8LF26FLt370Z5eTn69OkDLy8vZGRkIDQ0FP/9739fqWzy+uOPP7Bjxw44ODige/fuqKiowM6dO7Fnzx7cu3cPS5YsQUlJCXr06AFra2vEx8dj+fLlqK2tlZlfQkICvvnmG9TW1qJXr16wsbHBjRs3sGTJEqV/3lOnTgXQEFyoW1VVFRYtWoS7d++ia9eucHNzQ2pqKv79738jNzcX//3vf7F9+3bY2dmhe/fuqKqqwt69exEeHi4zP4ZhsHbtWhw+fBht27ZF7969UVlZif379+Pbb7+FUCiUSF9bW4sVK1bgt99+Q0FBATw8PNCjRw+UlpZi+/btWLt2LUQikcx7/frrr9ixYwesrKzg5+eHNm3aqPznQ4g2oZYQ0iyGYXD9+nUAgJubW7Np27Rpg48//hgbN27E+vXrsX79ehgYGOC3335DYWEhAgMD4evry6a/fPkyDh06hPbt22PZsmVwdnZm77lnzx7s27cPf/zxB77++muly5+dnQ0AcHd3V/jaXbt2ISsrC926dcM333wDExMTAEBeXh6WLVuG48ePo0ePHuw4GXW5fPkytmzZwnZ/5eXlYcGCBYiIiEBUVBRmzpyJESNGAGj4RL9y5UokJiYiNjZWZivDsWPHsGTJEvTv3x9Aw6f6devW4cqVKzh58iSmTZumcBn9/PzQqVMnpKen48aNG+jdu/cr1Lh58fHxGDx4MBYsWMCOSxK3rPz444+oqqqS+Hk9fPgQCxYswLFjxzBhwgQYGxtL5FdUVASGYfDrr7+yQUF5eTm++eYb3L17FydOnMB7773Hpt+xYwcSExPx5ptv4rPPPmNbz/h8PkJDQxEfH4+zZ8+yv5PGrl69io0bN6J9+/Zq+dkQom2oJYTIJBQKUVBQgE2bNiEtLQ0GBgZyTYkdOnQoAgICkJ2djfDwcFy8eBGXLl1C+/bt8eGHH0qkPXDgAABg0aJFbAACNEwPnjp1Kjp27Ii4uDiUl5crXY+KigoAgKWlpULX1dTU4Ny5c+ByuZgzZw4bgAAN42HEXTuNuwvU5f3335cYfyP+tF5bWws7OzuJh52BgQECAwMBNHRfyDJw4EA2AAEaWrEmTJgAoKHbTFlTpkwBoP6xISYmJvj4448lBka/99574HA4yMvLk/p5tWvXDn369EFtbS2ysrJk5jl58mSJVglLS0uEhIQAAE6ePMkeLysrw7lz52BnZ4cFCxZIdN+ZmJhg/vz50NfXx6lTp2TeZ/z48RSAENIItYQQCS+uFwIAxsbG+OKLL+QeiDp37lykp6fjyJEjMDQ0hIGBAb788kuJ7pyysjLk5OTA2dlZ5psyh8OBl5cXsrOzcf/+fbnGGahSVlYW6urq0KlTJ7Rt21bq/FtvvYX//ve/uHfvHkQiUZNjDVShceuRmHgNElnnxA/TprpWZF0jDgJfpfurb9++6Ny5MzIzM5GQkKC2FqJOnTrBzMxM4pipqSnMzMxQUVHR7M+rpKREZp5vvvmm1LFevXrBzMwMjx8/RklJCWxsbJCUlIT6+nr06tULhoaGUtdYW1vD2dkZubm5qK2tlUrj5+cndz0JeR1QEEIkiNcJ4XK57GJl/v7+Um/6zTEzM8OcOXPw3Xffobq6Gh9++CE6dOggkaawsBAAUFBQIDPwaez58+cK1uJ/zM3NAUDh1hTxw6qpBcfMzMxgamqKqqoqVFZWvvIA2ubY2tpKHRN3Kcg6J579IxAI5M5P3NLT1DXymjp1KlatWoU9e/aoLQiRVX6g4WdSUVHR7M9LVv3MzMwkWroac3BwQGVlJRuEiP9uz549i7NnzzZbTvFieI3Z29s3ew0hrxsKQoiEF9cJUVZsbCz7f1lN4OKBe9bW1jI/uTb2Km/cHTt2RGpqKu7fv89ONW5tmhrEKNZcK0vjVWzlpc5Wm969e8PDwwPp6em4evUqrK2tlcqnuZ/Jy+qszvqJy9WxY8eXjpGStY6OrJk2hLzOKAghKnfp0iVcunQJ7dq1g76+Pi5fvow+ffqwrSwAYGdnBwCwsLBQWeAjS+/evXHy5EnExcUhJCRE7hkyNjY2AP7XYvOiqqoqVFVVgcfjvbSVSPwwqqmpkXle1xZImzp1Kv7zn/9g7969+PTTT2Wmae5nIhQKUVZWps4iSqisrASfz5fZGlJUVATgf38P4r/bN954Ax9//LHGykiIrqKBqUSlioqK8Ntvv8HAwABfffUVvvzyS/B4PPz+++948uQJm87Ozg6urq7Iy8tDfn6+2srTq1cvtGvXDs+ePWMHwjaFz+cjNzcXQMO4Ax6Ph/v376OgoEAq7cWLFwE0PIxe9snbwsICenp6ePr0qdR0z/r6enb/FV3Rs2dPeHl5IScnB3FxcTLTiB/qsn734nEXmnT58mWpY7du3UJFRQXatGnDlrdbt27gcrlISEjQeBkJ0UUUhBCVEYlE2LBhA6qqqhAcHIwOHTqgXbt2+OCDD8Dn8/Hzzz9LPIQnTZoEkUiEtWvXslNpG3v+/PlL+91fhsPhsIHQnj178Pfff0t9+mYYBvHx8fjiiy/YxbuMjIwwbNgwiEQi/PbbbxLX5OfnY//+/QBkD+R9kYGBATw9PVFRUSEx00IoFOLPP//E06dPX6mOrZF43ZCmZol4e3sDaAjmGtf/yZMnGlt/pbG9e/dKlKO8vBx//fUXAEgsvGdra4thw4ahsLAQoaGhMgfyFhQUNBl8EUIkUXcMUZmIiAgkJSWhe/fuGDt2LHt8zJgxuHHjBm7fvo1//vkHkyZNAgAMHjwYDx8+xMGDB/HFF1+gQ4cO7MyOx48f48GDBzA2Nsa77777SuXq2LEjVq9ejbVr1+Kff/7B8ePH4enpCSsrK1RVVSErKwtlZWXg8XgS40+mT5+O9PR03LlzB7Nnz4a3tzdqa2uRmJiIuro6jBkzRu7Bl5MnT8Z//vMf/PHHH4iNjYW1tTWysrJQW1uLIUOGICoq6pXq2Nr06NED3t7eTU75dXJyYuu9YMEC9mebnp6OXr16oba2tsmuMFWzt7eHm5sbPvvsM3Tv3h16enpITExEVVUVunXrJhVozp49G0+fPsWVK1dw69YtdOjQAfb29qitrcXDhw/x+PFj+Pn5ISAgQCPlJ0SbURBCVEK8LoiZmRm++OILicGDHA4Hn3/+OebOnYu9e/fC1/f/2rlDVYWhOI7jv4HgLAZFk1EMFnd9AbEIzjoMJpPJFxANa76DL7BoMVgGDtuC1SCivoN9NwiWywWD3P8Fv596ws4p2xfOOftSo9GQ9PjQt9ttbTYbHY9H3W43FQoFlctl+b7/thd5s9nUarXSdrtVmqa6Xq+63+9yXVe1Wk39fl+9Xu+55y89bowsl0ut12vt93ulaapcLqd6vS7f99XpdF5+vud5WiwWiqJI5/NZruuq1WppPB4rjuO3rPG/GY1Gms/nv45Pp1OVSiXtdjsdDgdVKhUFQaAgCDSZTP5sno7jaDabKYoiJUnyvAkzGAw0HA5/nCPK5/MKw1BJkiiOY10uF51OJxWLRVWrVXW73Zd/6Q98OifLssx6EgAA4PNwJgQAAJggQgAAgAkiBAAAmCBCAACACSIEAACYIEIAAIAJIgQAAJggQgAAgAkiBAAAmCBCAACACSIEAACYIEIAAIAJIgQAAJj4Bqooky2Z22/vAAAAAElFTkSuQmCC", + "text/plain": [ + "
\n", + "\n" + ], + "text/plain": [ + "import lightkurve as lk
+import lightkurve as lk
import numpy as np
-%matplotlib inline
+%matplotlib inline
tpf_example = lk.search_targetpixelfile("KIC 7461601", mission="Kepler", cadence='long', quarter=10).download()
+tpf_example = lk.search_targetpixelfile("KIC 7461601", mission="Kepler", cadence='long', quarter=10).download()
tpf_example.plot();
+tpf_example.plot();
You can see how the light from this star is spread out across the TPF. The process of choosing which pixels to use for photometry is referred to as defining an aperture. By adding together the flux in multiple pixels, we get a more accurate measurement of the object’s brightness.
TPFs are designed to be big enough such that the vast majority of a target’s flux is captured within the available pixels. Because light from a star doesn’t spread out like a rectangle (we’ll talk about this more below), each TPF has pixels which we won’t need to use for our photometry. In the example above, the star has a Kepler magnitude of 13.4; this is analogous to a visual magnitude, and defined based on higher resolution photometry taken prior to the Kepler mission. For a star this bright, we can pick out six pixels by eye that almost certainly contain signal. Fainter targets are spread out across fewer pixels, and brighter targets may cover well over 20 pixels. Extremely bright targets will saturate the detector, making it very hard to perform photometry on them. We won’t be covering saturated stars in this tutorial; if you’re interested in working with them, the Halophot Python package is a good place to start.
@@ -733,10 +736,13 @@tpf_example.plot(aperture_mask='pipeline');
+tpf_example.plot(aperture_mask='pipeline');
You might not expect the star’s aperture to be this shape — stars are point sources, after all. In many cases, stars in Kepler will look circular, as we’d expect. However, at the edge of the focal plane, some targets may appear smeared in one direction. And even for a star with circular spread, the aperture may be a different shape. The way the spread of light interacts with the detector is defined by something called the point spread function (PSF), or pixel response function (PRF). Kepler’s PSF/PRF is described in detail in Section 3.5 of the Kepler Instrument Handbook.
We’ve looked at a basic example in this section, but as we’ll see later in this tutorial, it can be hard to determine the best aperture for a given target by eye. Choosing the best aperture for your target can be something of a balancing act: using a larger aperture will minimize the effects of PSF changes and spacecraft motion, but conversely it increases the presence of shot noise. This is why Kepler and K2 data were processed with an “optimal aperture.” Brightness, PSF, shot noise, crowding, and many other factors went into predicting the optimal aperture for producing a SAP or PDCSAP light curve with NASA’s Kepler and K2 pipelines. You can read more about the process of creating these apertures in Smith et al., 2016, which is equivalent to Chapter 7 of the Kepler Data Processing Handbook.
@@ -763,7 +769,7 @@tpf = lk.search_targetpixelfile("KIC 2437317", mission="Kepler", cadence="long", quarter=10).download(quality_bitmask="hard")
+tpf = lk.search_targetpixelfile("KIC 2437317", mission="Kepler", cadence="long", quarter=10).download(quality_bitmask="hard")
tpf.plot();
+tpf.plot();
Notice that this TPF is more crowded than the one we looked at in Section 1. We can still point out which pixels probably contain the star’s signal, and which ones are dominated by noise. However, there’s also a background star in this TPF: the bright pixel to the left. We want to avoid picking up signal from that star.
We can use Lightkurve to plot the pipeline aperture:
tpf.plot(aperture_mask='pipeline');
+tpf.plot(aperture_mask='pipeline');
The pipeline aperture is small in this case, but it avoids capturing any light from the background star. Now we can generate the light curve that results from using that aperture as follows:
lc = tpf.to_lightcurve(aperture_mask="pipeline")
+lc = tpf.to_lightcurve(aperture_mask="pipeline")
lc.plot();
The signal you can see here is caused by rotational modulation.
Notice the spikes around 935 and 970 days. These coincide with breaks in the data collection during which Kepler was pointed at Earth to downlink data. These breaks are associated with small focus changes of the Kepler telescope, which are caused by the thermal effects associated with pointing Kepler’s antenna towards Earth. These artifical peaks are a form of systematic noise. We’ll see more evidence of systematic noise in the light curves we look at throughout this tutorial. As we’re only focused on performing photometry, we won’t do any light curve corrections to remove such noise, but you can read more about how to do so in other tutorials.
@@ -808,18 +823,29 @@ax = tpf.to_lightcurve(aperture_mask = 'pipeline').normalize().plot(label='pipeline', linewidth=2);
+ax = tpf.to_lightcurve(aperture_mask = 'pipeline').normalize().plot(label='pipeline', linewidth=2);
tpf.to_lightcurve(aperture_mask = 'threshold').normalize().plot(ax=ax, label='threshold', linewidth=2);
tpf.to_lightcurve(aperture_mask = 'all').normalize().plot(ax=ax, label='all', linewidth=2);
Our star is fairly bright, with a Kepler magnitude of 14.6, so we don’t see much variation in the noise levels between these three light curves. But as you can see, using all of the pixels means including light from the background star which dilutes our star’s signal, making the rotation harder to detect by eye. On the other hand, using all of the pixels reduces the systematic noise near days 935 and 970, because telescope focus changes have less impact when a large aperture mask is used.
The threshold mask light curve looks less diluted than the one using all of the pixels, but it still looks markedly different from the light curve from the pipeline aperture. We can use another of Lightkurve’s functions to see why this is the case:
tpf.create_threshold_mask()
+tpf.create_threshold_mask()
+
+
+
array([[False, False, False, False, False],
+ [False, False, False, False, False],
+ [False, False, False, True, False],
+ [False, False, False, False, False]])
custom_threshold_mask = tpf.create_threshold_mask(threshold=1)
+custom_threshold_mask = tpf.create_threshold_mask(threshold=1)
custom_threshold_mask
array([[False, False, False, False, False],
+ [False, False, True, True, False],
+ [False, False, True, True, False],
+ [False, False, False, False, False]])
+
Now we’ve recreated the pipeline mask, which we can use to perform photometry again. As we’d expect, it produces the same results as the pipeline mask:
ax = tpf.to_lightcurve(aperture_mask='pipeline').normalize().plot(label='pipeline', alpha=.3)
+ax = tpf.to_lightcurve(aperture_mask='pipeline').normalize().plot(label='pipeline', alpha=.3)
tpf.to_lightcurve(aperture_mask='threshold').normalize().plot(ax=ax, label='threshold', alpha=.3)
tpf.to_lightcurve(aperture_mask='all').normalize().plot(ax=ax, label='all', alpha=.3)
tpf.to_lightcurve(aperture_mask=custom_threshold_mask).normalize().plot(ax=ax, label='custom threshold', linewidth=2);
tpf_crowded = lk.search_targetpixelfile("KIC 2437901", mission="Kepler", cadence="long", quarter=10).download(quality_bitmask="hard")
+tpf_crowded = lk.search_targetpixelfile("KIC 2437901", mission="Kepler", cadence="long", quarter=10).download(quality_bitmask="hard")
tpf_crowded.plot(aperture_mask='pipeline');
+tpf_crowded.plot(aperture_mask='pipeline');
We’re going to focus on that star in the top right. It’s not captured in the pipeline aperture, but it’s a serendipitous find in the TPF, so we can perform our own photometry on it with a custom aperture. Let’s see what the threshold mask looks like for this TPF:
tpf_crowded.create_threshold_mask(threshold=1)
+tpf_crowded.create_threshold_mask(threshold=1)
+
+
+
array([[False, False, False, False, False, False, False, False],
+ [False, False, False, False, False, False, False, False],
+ [False, False, False, True, True, True, False, False],
+ [False, False, False, True, True, False, False, False],
+ [False, False, False, True, True, False, False, False],
+ [False, False, False, False, False, False, False, False],
+ [False, False, False, False, False, False, False, False]])
tpf_crowded.plot(aperture_mask=tpf_crowded.create_threshold_mask(threshold=1));
+tpf_crowded.plot(aperture_mask=tpf_crowded.create_threshold_mask(threshold=1));
No luck. Our custom aperture still selects the bright star in the center. When the thresholding method yields multiple contiguous regions, the default behavior of the create_threshold_mask
method is to return the region closest to the center. We can modify this behavior by passing a reference_pixel
argument to specify the region we are most interested in. The argument is defined such that reference_pixel=(0,0)
refers to the lower left corner. Hence, we can select the region in the upper right corner as follows:
custom_mask = tpf_crowded.create_threshold_mask(threshold=1, reference_pixel=(7,7))
+custom_mask = tpf_crowded.create_threshold_mask(threshold=1, reference_pixel=(7,7))
tpf_crowded.plot(aperture_mask=custom_mask);
Now we can perform aperture photometry using this mask:
lc_background_star = tpf_crowded.to_lightcurve(aperture_mask=custom_mask)
+lc_background_star = tpf_crowded.to_lightcurve(aperture_mask=custom_mask)
lc_background_star.plot();
Looks like there’s some signal in this star — a two-day period! In fact, this star is KIC 2437948, a likely binary (Colman et al., in prep).
We could make that signal even clearer by being more selective, and manually assigning True
to the individual pixels we want to use. The downside to this method is that it takes time and manual work to create a mask by hand. However, when working with small volumes of data, such as one quarter or campaign at a time, or one star, this method can produce good results. In what follows, we will demonstrate three alternative methods to create a mask by hand.
custom_mask = np.zeros((tpf_crowded[0].shape[1:]), dtype='bool')
+custom_mask = np.zeros((tpf_crowded[0].shape[1:]), dtype='bool')
custom_mask[-3:,5:] = True
custom_mask
array([[False, False, False, False, False, False, False, False],
+ [False, False, False, False, False, False, False, False],
+ [False, False, False, False, False, False, False, False],
+ [False, False, False, False, False, False, False, False],
+ [False, False, False, False, False, True, True, True],
+ [False, False, False, False, False, True, True, True],
+ [False, False, False, False, False, True, True, True]])
+
Let’s plot the custom mask on top of the TPF. Notice that the y-axis in our array appears reversed, as described above. When handling images and NumPy arrays, it’s good practice to plot them every time, and make sure you’re seeing what you expect.
tpf_crowded.plot(aperture_mask=custom_mask);
+tpf_crowded.plot(aperture_mask=custom_mask);
tpf_crowded.plot();
+tpf_crowded.plot();
tpf_cut = tpf_crowded.cutout(center=(8,7))
+tpf_cut = tpf_crowded.cutout(center=(8,7))
tpf_cut.plot();
+tpf_cut.plot();
This is still a TargetPixelFile
object, so we can use all of the same functions:
tpf_cut.create_threshold_mask(threshold=1)
+tpf_cut.create_threshold_mask(threshold=1)
+
+
+
array([[False, False, False],
+ [False, True, False],
+ [False, False, False]])
cut_mask = np.zeros(tpf_cut[0].shape[1:], dtype='bool')
+cut_mask = np.zeros(tpf_cut[0].shape[1:], dtype='bool')
cut_mask[:,1] = True
cut_mask[1,2] = True
@@ -970,21 +1057,34 @@ 3.3 “Cutting out” a background star
+array([[False, True, False],
+ [False, True, True],
+ [False, True, False]])
+
+
+
-tpf_cut.plot(aperture_mask=cut_mask);
+tpf_cut.plot(aperture_mask=cut_mask);
+
+
+
-lc_background_star_cut = tpf_cut.to_lightcurve(aperture_mask=cut_mask)
+lc_background_star_cut = tpf_cut.to_lightcurve(aperture_mask=cut_mask)
lc_background_star_cut.plot();
+
+
+
And there’s our signal again, nice and clear!
@@ -1004,10 +1104,194 @@ Citing Lightkurve and Astropylightkurve or astropy
for published research, please cite the authors. Click the buttons below to copy BibTeX entries to your clipboard.
-lk.show_citation_instructions()
+lk.show_citation_instructions()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ When using Lightkurve, we kindly request that you cite the following packages:
+
+ -
+ lightkurve
+
+
+ -
+ astropy
+
+
+ -
+ astroquery
+
+ — if you are using search_lightcurve() or search_targetpixelfile().
+
+ -
+ tesscut
+
+ — if you are using search_tesscut().
+
+
+
+
+
diff --git a/searchindex.js b/searchindex.js
index 6d75eceba..609df152c 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["README", "contributing/CODE_OF_CONDUCT", "contributing/CONTRIBUTING", "contributing/notebook_template/notebook_template", "intro", "notebooks/GALEX/mis-mosaic/mis_mosaic", "notebooks/GALEX/mis_mosaic/mis_mosaic", "notebooks/HSC/HCV", "notebooks/HSC/HCV_API/HCV_API_demo", "notebooks/HSC/HCV_CASJOBS/HCV_casjobs_demo", "notebooks/HSC/HSCV3_API/hscv3_api", "notebooks/HSC/HSCV3_SMC_API/hscv3_smc_api", "notebooks/HSC/HSC_TAP/HSC_TAP", "notebooks/HSC/README", "notebooks/HSC/SWEEPS", "notebooks/HSC/SWEEPS_HSCV3P1/sweeps_hscv3p1", "notebooks/HSC/SWEEPS_HSCV3P1_API/sweeps_hscv3p1_api", "notebooks/HSC/queries", "notebooks/IUE/exploring_UV_extinction_curves/exploring_UV_extinction_curves", "notebooks/JWST/Engineering_Database_Retreival/EDB_Retrieval", "notebooks/JWST/SI_keyword_exoplanet_search/SI_keyword_exoplanet_search", "notebooks/JWST/download_by_program_id/download_by_program_id", "notebooks/JWST/duplication_checking/duplication_astroquery_api", 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15, 16, 22, 23, 55, 57, 79, 82], "cell": [2, 3, 5, 6, 18, 20, 21, 22, 23, 35, 48, 50, 55, 58, 60, 76, 79, 81, 82, 83, 84, 85, 87, 89, 91, 92, 93, 94, 95, 96], "larg": [2, 3, 5, 6, 8, 9, 11, 12, 16, 18, 20, 22, 23, 26, 27, 30, 31, 32, 33, 35, 36, 40, 43, 44, 55, 56, 57, 59, 60, 62, 65, 72, 77, 79, 81, 82, 88, 90], "clear": [2, 32, 38, 42, 43, 45, 53, 54, 55, 60, 61, 74, 85, 87, 94, 95], "ouput": 2, "befor": [2, 3, 8, 21, 22, 23, 24, 25, 26, 27, 31, 35, 37, 38, 40, 41, 42, 43, 53, 54, 55, 57, 58, 60, 61, 72, 74, 76, 79, 82, 83, 84, 85, 91, 92, 93, 94], "requir": [2, 5, 6, 8, 9, 10, 11, 15, 16, 18, 19, 20, 21, 30, 31, 32, 33, 35, 38, 40, 41, 42, 44, 45, 46, 49, 54, 56, 57, 58, 61, 64, 68, 73, 76, 79, 81, 82, 85, 87, 93, 94, 95], "txt": [2, 74, 79, 82], "next": [2, 3, 12, 19, 20, 21, 22, 23, 30, 31, 32, 35, 43, 46, 48, 50, 51, 53, 55, 57, 60, 63, 69, 73, 74, 83, 84, 85, 87, 91, 92, 94, 95], "line": [2, 3, 12, 18, 19, 21, 25, 31, 32, 37, 38, 40, 41, 43, 48, 49, 50, 51, 53, 54, 55, 58, 60, 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87, 89, 91, 92, 93, 94, 95, 96], "fix": [2, 12, 20, 22, 23, 36, 38, 40, 54, 55, 56, 58, 60, 69, 76, 85, 93, 94], "find": [2, 5, 6, 18, 20, 21, 23, 24, 25, 30, 31, 35, 36, 40, 41, 43, 44, 45, 46, 47, 48, 50, 51, 52, 53, 55, 56, 57, 62, 66, 67, 69, 70, 75, 77, 78, 79, 81, 82, 83, 84, 85, 87, 89, 91, 92, 94, 95, 96], "rather": [2, 12, 16, 20, 21, 23, 37, 43, 46, 55, 60, 62, 69, 74, 75, 79, 81, 82, 83, 87, 91, 95], "thean": 2, "correctli": [2, 5, 6, 19, 57, 75, 85, 94], "hit": [2, 35, 38], "sai": [2, 18, 30, 48, 50, 51, 60], "successfulli": [2, 27, 33, 35], "finish": [2, 3, 8, 9, 75, 84, 92], "off": [2, 35, 37, 48, 55, 57, 58, 60, 61, 72, 83, 84, 85, 87, 91, 92, 93, 94, 95], "7": [2, 7, 8, 9, 12, 15, 16, 18, 22, 23, 26, 27, 31, 35, 36, 37, 38, 42, 43, 45, 46, 48, 53, 55, 60, 63, 64, 66, 67, 70, 71, 72, 75, 76, 77, 79, 82, 85, 94], "abil": [2, 12, 27, 35, 36, 39, 44, 84, 92], "cannot": [2, 27, 36, 43, 76, 79, 82], "simpler": [2, 12, 16, 56, 57, 69], "avail": [2, 4, 8, 9, 11, 12, 15, 20, 21, 22, 23, 27, 32, 33, 35, 36, 37, 40, 41, 42, 43, 49, 53, 54, 55, 57, 58, 60, 62, 63, 64, 68, 71, 72, 75, 77, 78, 79, 82, 83, 87, 89, 91, 93, 95, 96], "still": [2, 8, 9, 18, 19, 21, 22, 23, 30, 32, 35, 36, 40, 43, 53, 55, 60, 61, 75, 81, 83, 91], "describ": [2, 3, 8, 9, 10, 11, 12, 15, 16, 19, 20, 22, 26, 27, 32, 37, 40, 43, 44, 45, 46, 55, 56, 60, 65, 69, 72, 73, 75], "instead": [2, 3, 8, 9, 18, 21, 24, 30, 31, 32, 40, 41, 42, 46, 55, 60, 62, 69, 70, 71, 72, 73, 74, 81, 84, 85, 87, 92, 94, 95], "merg": [2, 84, 92], "singl": [2, 3, 5, 6, 8, 9, 10, 12, 15, 16, 19, 27, 36, 38, 40, 41, 43, 44, 49, 50, 56, 57, 60, 63, 64, 69, 73, 74, 75, 78, 79, 81, 82, 84, 89, 92, 96], "disadvantag": 2, "eras": 2, "whole": [2, 10, 11, 36, 40, 53, 60, 69, 74], "implement": [2, 27, 30, 31, 33, 53, 55, 58, 93], "readi": [2, 18, 51, 55, 60, 79, 82, 83, 91], "click": [2, 27, 30, 31, 32, 33, 35, 36, 37, 38, 40, 41, 42, 43, 44, 45, 46, 51, 53, 54, 60, 81], "downward": [2, 35], "point": [2, 8, 9, 11, 12, 15, 16, 18, 19, 20, 21, 24, 25, 26, 27, 31, 32, 33, 36, 37, 38, 40, 42, 43, 44, 49, 51, 53, 54, 55, 56, 58, 60, 61, 64, 65, 66, 69, 72, 75, 76, 79, 81, 82, 83, 84, 85, 91, 92, 93, 94], "arrow": [2, 31, 36, 44, 85], "choos": [2, 5, 6, 20, 21, 22, 23, 27, 48, 53, 57, 60, 68, 72, 74, 75, 79, 81, 82, 85, 89, 94, 96], "doubl": [2, 27, 30, 53, 60], "pass": [2, 18, 19, 27, 33, 35, 38, 40, 41, 43, 45, 46, 58, 60, 61, 76, 79, 81, 82, 83, 91, 93], "That": [2, 8, 9, 19, 22, 23, 41, 42, 53, 55, 57, 60, 72, 74], "techniqu": [2, 53], "must": [2, 8, 9, 10, 11, 12, 15, 16, 20, 21, 22, 23, 30, 32, 44, 53, 54, 55, 57, 58, 60, 68, 69, 74, 75, 79, 81, 82, 89, 93, 96], "build": [2, 35, 42, 58, 93], "three": [3, 10, 19, 20, 27, 31, 35, 36, 37, 38, 41, 42, 43, 44, 46, 55, 57, 58, 63, 74, 77, 81, 84, 88, 90, 92, 93], "five": [3, 5, 6, 8, 9, 20, 21, 27, 32, 36, 40, 57, 73, 77, 81, 84, 87, 92, 95], "bloom": 3, "taxonomi": 3, "By": [3, 5, 6, 18, 21, 27, 30, 32, 33, 35, 36, 37, 38, 40, 41, 42, 43, 44, 45, 46, 53, 54, 55, 57, 60, 69, 75, 79, 81, 82, 85, 89, 94, 96], "end": [3, 5, 6, 18, 19, 21, 24, 25, 26, 27, 30, 32, 33, 35, 36, 37, 38, 40, 41, 42, 43, 44, 45, 46, 48, 53, 54, 55, 60, 65, 66, 67, 69, 74, 75, 79, 81, 82, 83, 85, 87, 89, 91, 94, 95, 96], "apertur": [3, 5, 6, 22, 23, 27, 28, 35, 36, 37, 38, 39, 40, 42, 44, 53, 60, 61, 70, 73, 74, 76, 77, 79, 81, 82], "photometri": [3, 8, 9, 10, 11, 16, 24, 27, 28, 35, 36, 37, 38, 39, 40, 42, 44, 45, 50, 51, 53, 56, 60, 61, 66, 72], "turn": [3, 27, 30, 32, 40, 41, 42, 43, 45, 62, 83, 87, 91, 95], "seri": [3, 19, 20, 22, 23, 24, 27, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 43, 44, 45, 46, 50, 53, 54, 60, 66, 68, 69, 72, 73, 75, 76, 78], "dimension": [3, 41, 43, 48, 55], "imag": [3, 4, 12, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 46, 51, 54, 55, 56, 58, 59, 60, 61, 62, 64, 66, 67, 71, 72, 74, 79, 80, 81, 82, 83, 85, 87, 89, 91, 93, 94, 95, 96, 97], "abl": [3, 5, 6, 12, 18, 20, 21, 27, 30, 32, 33, 35, 36, 37, 38, 40, 41, 43, 44, 45, 46, 51, 53, 54, 55, 56, 58, 69, 74, 75, 79, 81, 82, 85, 93, 94], "kepler": [3, 4, 24, 25, 26, 27, 29, 31, 32, 34, 35, 37, 38, 39, 44, 52, 54, 56, 62, 64, 71, 72, 76, 81, 83, 89, 91, 97], "k2": [3, 4, 37, 38, 39, 41, 42, 43, 44, 53, 71, 81, 83, 91, 97], "target": [3, 5, 6, 8, 9, 10, 11, 21, 24, 27, 28, 29, 30, 33, 35, 36, 37, 38, 39, 40, 42, 43, 49, 51, 53, 54, 57, 62, 63, 64, 65, 66, 68, 69, 71, 73, 74, 75, 79, 82, 84, 85, 88, 90, 92, 94], "light": [3, 11, 18, 25, 28, 29, 30, 32, 35, 36, 38, 39, 41, 48, 49, 50, 52, 53, 54, 55, 59, 62, 64, 65, 75, 76, 80, 84, 85, 92, 94], "curv": [3, 11, 25, 28, 29, 30, 31, 32, 35, 36, 37, 38, 39, 41, 48, 49, 50, 52, 53, 54, 59, 62, 64, 65, 76, 81], "quarter": [3, 28, 30, 31, 32, 33, 35, 36, 38, 39, 40, 41, 43, 44, 45, 46, 49, 53, 89], "campaign": [3, 23, 24, 25, 26, 27, 32, 35, 36, 38, 40, 42, 43, 44, 45, 53], "short": [3, 5, 6, 18, 25, 27, 30, 31, 32, 33, 35, 38, 40, 41, 49, 50, 51, 55, 61, 74], "explain": [3, 20, 27, 31, 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68, 70, 72, 73, 74, 75, 76, 77, 79, 82, 89, 93, 96], "g": [2, 8, 9, 10, 11, 15, 16, 20, 22, 23, 24, 25, 26, 27, 30, 31, 32, 33, 35, 36, 37, 42, 43, 44, 45, 46, 49, 54, 55, 56, 57, 58, 60, 64, 65, 66, 67, 74, 75, 89, 93, 96], "wrote": [2, 72], "numpi": [2, 3, 5, 6, 8, 9, 10, 11, 12, 15, 16, 18, 19, 22, 24, 25, 26, 31, 33, 35, 36, 37, 38, 40, 41, 45, 46, 48, 49, 50, 53, 55, 56, 57, 58, 60, 61, 63, 64, 65, 66, 67, 68, 70, 71, 72, 74, 75, 76, 77, 79, 81, 82, 85, 93, 94], "v1": [2, 58, 93], "14": [2, 8, 9, 11, 15, 16, 26, 35, 37, 42, 43, 45, 48, 53, 55, 56, 60, 61, 63, 68, 70, 72, 75, 77, 79, 82, 85, 89, 94], "0": [2, 3, 5, 6, 8, 9, 10, 11, 13, 15, 16, 18, 19, 20, 22, 23, 24, 25, 26, 27, 30, 31, 32, 33, 35, 36, 37, 38, 41, 42, 43, 44, 45, 46, 48, 49, 50, 51, 53, 54, 55, 57, 58, 60, 61, 63, 64, 65, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 77, 79, 81, 82, 83, 84, 85, 87, 89, 91, 92, 93, 94, 95, 96], "1": [2, 3, 5, 6, 8, 9, 10, 11, 12, 13, 16, 18, 19, 20, 21, 24, 25, 26, 28, 42, 48, 49, 50, 51, 57, 58, 60, 63, 64, 65, 66, 67, 68, 70, 73, 74, 75, 76, 77, 79, 82, 84, 85, 87, 92, 93, 94, 95], "A": [2, 3, 5, 6, 9, 10, 11, 12, 15, 16, 18, 19, 20, 21, 22, 23, 27, 28, 30, 31, 32, 33, 35, 36, 37, 40, 41, 42, 43, 44, 45, 46, 47, 48, 51, 52, 53, 54, 55, 60, 62, 73, 74, 75, 76, 79, 82, 83, 85, 91, 94], "discourag": [2, 85, 94], "occur": [2, 27, 30, 35, 37, 38, 44, 45, 46, 54, 66, 74], "access": [2, 3, 4, 5, 6, 8, 9, 10, 11, 15, 16, 17, 20, 24, 25, 27, 30, 31, 33, 35, 42, 43, 45, 46, 48, 50, 51, 60, 62, 66, 67, 70, 72, 74, 76, 77, 78, 79, 81, 82, 83, 84, 85, 86, 87, 90, 91, 92, 94, 95, 97], "via": [2, 8, 9, 12, 16, 20, 40, 56, 68, 79, 82, 87, 95], "mast": [2, 3, 5, 6, 9, 11, 13, 15, 17, 19, 22, 23, 24, 25, 26, 27, 35, 38, 40, 41, 42, 43, 45, 48, 49, 50, 51, 54, 55, 58, 60, 61, 62, 63, 64, 65, 66, 67, 68, 70, 71, 72, 75, 76, 77, 78, 79, 81, 82, 84, 86, 88, 90, 92, 93, 97], "more": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 30, 31, 32, 33, 35, 36, 37, 38, 40, 41, 42, 43, 44, 45, 46, 48, 49, 50, 51, 53, 54, 55, 56, 57, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 71, 72, 73, 74, 75, 76, 77, 79, 81, 82, 83, 84, 85, 87, 89, 91, 92, 94, 95, 96], "detail": [2, 3, 4, 5, 6, 8, 9, 11, 12, 16, 18, 19, 21, 22, 23, 24, 25, 30, 31, 32, 40, 41, 42, 43, 46, 48, 49, 50, 51, 53, 55, 56, 57, 64, 66, 67, 72, 73, 75, 76, 81, 84, 89, 92, 96, 97], "guidanc": [2, 35, 38], "handl": [2, 3, 5, 6, 12, 15, 16, 19, 31, 35, 36, 37, 38, 39, 43, 48, 50, 51, 53, 55, 56, 57, 58, 60, 69, 70, 72, 76, 77, 79, 80, 82, 84, 85, 92, 93, 94], "If": [2, 3, 5, 6, 8, 9, 10, 11, 15, 16, 18, 20, 21, 22, 23, 24, 25, 27, 30, 31, 32, 33, 35, 36, 37, 38, 40, 41, 42, 43, 44, 45, 46, 53, 54, 55, 57, 58, 60, 63, 67, 68, 69, 72, 75, 77, 79, 81, 82, 83, 84, 85, 87, 89, 91, 92, 93, 94, 95, 96], "need": [2, 3, 5, 6, 8, 9, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 30, 31, 32, 33, 39, 40, 41, 43, 46, 48, 50, 51, 53, 55, 57, 58, 60, 61, 65, 66, 68, 70, 71, 72, 73, 75, 76, 79, 81, 82, 83, 84, 85, 87, 91, 92, 93, 94, 95], "addit": [2, 11, 15, 16, 26, 30, 31, 32, 35, 36, 40, 41, 44, 45, 46, 48, 49, 50, 55, 57, 59, 63, 64, 65, 68, 72, 78, 79, 81, 82, 83, 87, 89, 91, 95, 96, 97], "sure": [2, 3, 5, 6, 18, 19, 21, 27, 35, 38, 42, 43, 46, 53, 60, 81, 83, 84, 87, 89, 91, 92, 95, 96], "name": [2, 5, 6, 12, 15, 16, 18, 20, 21, 26, 30, 32, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 56, 58, 60, 61, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 76, 77, 79, 81, 82, 84, 87, 89, 92, 93, 95, 96], "except": [2, 8, 10, 11, 15, 16, 19, 22, 23, 49, 55, 63, 72, 75, 76], "repo": 2, "gitignor": 2, "exampl": [2, 3, 8, 9, 10, 11, 12, 18, 19, 20, 21, 22, 23, 24, 25, 27, 30, 31, 32, 33, 35, 36, 37, 38, 40, 41, 42, 43, 44, 45, 46, 48, 50, 51, 53, 54, 55, 56, 57, 60, 66, 67, 69, 70, 71, 75, 76, 77, 79, 81, 82, 83, 84, 85, 87, 89, 91, 92, 94, 95, 96, 97], "diagram": [2, 16, 60], "jpg": [2, 5, 6, 57, 83, 84, 89, 91, 92], "drizzlepac": 2, "sky_match": 2, "don": [2, 15, 18, 21, 35, 40, 43, 54, 55, 58, 60, 66, 85, 93, 94], "t": [2, 3, 5, 6, 12, 15, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 30, 31, 32, 33, 35, 36, 37, 40, 41, 42, 43, 44, 45, 46, 48, 49, 53, 54, 55, 56, 58, 60, 61, 64, 65, 66, 67, 71, 74, 79, 81, 82, 84, 85, 87, 92, 93, 94, 95], "forget": [2, 21], "won": [2, 5, 6, 24, 25, 26, 35, 43, 49, 54, 55, 58, 64, 65, 66, 67, 93], "push": 2, "github": [2, 9, 15, 79, 82, 85], "s": [2, 3, 5, 6, 8, 9, 10, 11, 12, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 32, 33, 35, 36, 37, 38, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 54, 55, 56, 57, 58, 60, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 87, 89, 91, 92, 93, 94, 95, 96], "us": [2, 3, 5, 6, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 26, 28, 29, 32, 35, 36, 37, 38, 39, 42, 44, 47, 48, 49, 50, 52, 53, 55, 57, 58, 59, 62, 64, 65, 66, 71, 72, 73, 74, 78, 79, 80, 82, 84, 85, 86, 87, 90, 92, 93, 94, 95], "web": [2, 8, 9, 10, 12, 15, 16, 19, 22, 23, 27, 30, 31, 32, 33, 35, 36, 37, 42, 43, 44, 45, 46, 54, 56, 57, 58, 60, 69, 75, 79, 82, 93], "site": [2, 12, 21, 32, 35, 36, 45, 56, 58, 61, 71, 93], "Be": [2, 3, 21, 27, 30, 32, 33, 35, 36, 37, 40, 41, 43, 44, 45, 46, 53, 54, 81, 85, 87, 94, 95], "descript": [2, 3, 4, 10, 11, 12, 16, 18, 20, 21, 22, 23, 27, 29, 30, 34, 39, 45, 47, 48, 49, 50, 51, 52, 55, 59, 62, 64, 69, 72, 73, 74, 78, 80, 83, 85, 88, 90, 91, 94], "suffici": [2, 12, 43, 55, 75], "someon": 2, "understand": [2, 3, 5, 6, 18, 21, 27, 30, 31, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46, 50, 53, 54, 55, 58, 60, 72, 74, 75, 81, 83, 85, 91, 93, 94], "context": [2, 3, 9, 15, 19, 35, 43], "onc": [2, 18, 19, 20, 21, 26, 35, 45, 49, 53, 58, 63, 69, 72, 74, 75, 76, 83, 91, 93], "ve": [2, 3, 5, 6, 25, 27, 30, 35, 36, 43, 46, 48, 50, 51, 53, 55, 58, 67, 74, 76, 79, 81, 82, 85, 89, 93, 94, 96], "ci": [2, 8, 9, 11, 12, 13, 16], "test": [2, 10, 12, 15, 19, 31, 35, 55, 56, 57, 75, 76, 81, 89, 96], "run": [2, 3, 5, 6, 8, 9, 10, 11, 12, 15, 16, 18, 19, 20, 21, 27, 35, 37, 43, 44, 48, 50, 53, 56, 57, 58, 61, 63, 69, 70, 72, 74, 76, 77, 83, 84, 85, 87, 89, 91, 92, 93, 94, 95, 96], "chang": [2, 5, 6, 15, 16, 18, 19, 20, 25, 26, 27, 30, 32, 33, 35, 37, 38, 40, 41, 42, 43, 44, 46, 48, 49, 50, 52, 55, 58, 60, 61, 65, 67, 70, 72, 73, 74, 75, 76, 79, 82, 85, 87, 93, 94, 95], "actual": [2, 3, 5, 6, 20, 22, 23, 36, 42, 43, 53, 55, 57, 75, 79, 82, 85, 87, 94, 95], "import": [2, 8, 9, 10, 11, 15, 16, 19, 22, 23, 24, 25, 49, 64, 66, 67, 71, 75], "element": [2, 3, 56, 60, 68, 69, 74, 75, 76, 79, 82, 85, 94], "hygin": 2, "referenc": [2, 15, 16, 24, 25, 66, 67, 79, 82, 89, 96], "outlin": [2, 3, 36, 37, 45], "check": [2, 3, 8, 9, 11, 15, 16, 18, 21, 25, 27, 33, 35, 36, 38, 53, 56, 57, 58, 60, 75, 76, 83, 87, 88, 90, 91, 93, 95], "desir": [2, 18, 19, 20, 21, 22, 23, 27, 30, 45, 55, 57, 70, 71, 76, 77, 83, 84, 85, 87, 91, 92, 94, 95], "becaus": [2, 5, 6, 8, 9, 11, 12, 15, 22, 23, 27, 30, 31, 32, 33, 35, 36, 37, 40, 42, 43, 46, 51, 55, 58, 60, 61, 63, 68, 73, 74, 75, 77, 79, 81, 82, 85, 93, 94], "sometim": [2, 18, 21, 22, 23, 35, 40, 46, 53, 54, 55, 58, 69, 89, 93, 96], "caus": [2, 12, 27, 30, 32, 34, 35, 36, 37, 38, 42, 43, 44, 46, 53, 54, 58, 60, 61, 65, 72, 83, 85, 87, 91, 93, 95], "quickli": [2, 12, 21, 45, 53, 76], "unmanag": 2, "track": [2, 25, 43, 53, 67], "simpli": [2, 69, 72, 73, 74, 89, 96], "delet": [2, 16], "hidden": 2, "bloat": 2, "To": [2, 5, 6, 8, 9, 19, 21, 22, 23, 25, 27, 30, 31, 32, 33, 35, 36, 37, 38, 40, 42, 46, 48, 50, 51, 53, 55, 57, 58, 60, 61, 67, 69, 70, 72, 73, 74, 75, 76, 79, 81, 82, 83, 84, 85, 87, 91, 92, 93, 94, 95, 97], "truli": 2, "remov": [2, 8, 9, 10, 11, 13, 15, 16, 18, 20, 21, 28, 31, 32, 35, 36, 37, 40, 41, 42, 43, 52, 54, 55, 57, 58, 61, 63, 70, 72, 73, 74, 75, 79, 80, 82, 85, 93, 94], "rewrit": 2, "There": [2, 5, 6, 15, 16, 18, 21, 22, 23, 24, 25, 26, 27, 32, 35, 37, 38, 43, 44, 45, 46, 55, 56, 58, 60, 63, 65, 68, 69, 74, 75, 79, 80, 81, 82, 83, 84, 86, 87, 90, 91, 92, 93, 95], "two": [2, 3, 5, 6, 8, 9, 10, 11, 15, 16, 20, 21, 22, 23, 25, 27, 30, 31, 32, 33, 35, 36, 37, 38, 40, 43, 45, 46, 48, 53, 55, 56, 57, 58, 60, 61, 64, 65, 67, 68, 70, 72, 74, 75, 76, 79, 82, 84, 85, 89, 92, 93, 94, 96], "destroi": 2, "harder": [2, 36, 43, 57], "correct": [2, 5, 6, 22, 23, 24, 25, 26, 35, 36, 37, 38, 40, 43, 48, 49, 51, 58, 64, 65, 66, 67, 72, 74, 75, 76, 79, 82, 93], "allow": [2, 5, 6, 8, 9, 10, 11, 12, 15, 16, 18, 20, 21, 22, 23, 26, 32, 33, 35, 40, 41, 43, 44, 45, 51, 54, 56, 58, 65, 68, 69, 72, 75, 76, 79, 81, 82, 83, 84, 85, 87, 89, 91, 92, 93, 95, 96], "re": [2, 3, 5, 6, 15, 18, 21, 29, 30, 31, 32, 35, 36, 37, 38, 43, 44, 46, 50, 51, 53, 55, 57, 58, 60, 63, 76, 83, 84, 87, 91, 92, 93, 95], "write": [2, 3, 19, 48, 50, 51, 57, 60, 63, 69, 73, 76, 77, 80, 83, 89, 91, 96], "rebas": 2, "command": [2, 9, 15, 19, 26, 48, 50, 51, 58, 65, 70, 75, 77, 93], "best": [2, 8, 9, 10, 12, 18, 22, 23, 24, 27, 43, 44, 49, 55, 58, 64, 66, 74, 75, 79, 81, 82, 87, 93, 95], "abov": [2, 5, 6, 12, 15, 18, 19, 20, 21, 24, 25, 27, 31, 32, 33, 35, 36, 37, 38, 40, 41, 42, 43, 44, 45, 46, 47, 48, 50, 51, 53, 54, 55, 60, 66, 67, 69, 70, 72, 73, 74, 75, 77, 79, 81, 82, 83, 85, 87, 89, 91, 94, 95, 96], "problem": [2, 3, 8, 9, 10, 11, 16, 30, 35, 37, 55, 69, 73, 81, 85], "ha": [2, 5, 6, 8, 9, 10, 11, 12, 15, 16, 20, 22, 23, 24, 25, 26, 27, 30, 31, 32, 33, 35, 36, 37, 38, 40, 41, 42, 43, 44, 45, 46, 49, 50, 51, 53, 54, 55, 56, 57, 58, 59, 60, 63, 64, 65, 66, 67, 69, 70, 72, 74, 75, 76, 77, 79, 81, 82, 84, 85, 87, 92, 93, 94, 95, 97], "advantag": [2, 36, 70, 77, 87, 95], "oppos": 2, "itself": [2, 12, 20, 30, 31, 40, 52, 53, 55, 57, 69, 79, 82], "step": [2, 5, 6, 8, 9, 10, 11, 15, 16, 18, 20, 21, 24, 25, 27, 31, 57, 58, 66, 67, 72, 75, 76, 87, 93, 95], "rel": [2, 15, 16, 18, 19, 22, 27, 30, 37, 42, 46, 49, 54, 58, 60, 61, 64, 68, 70, 72, 75, 79, 82, 93], "i": [2, 3, 5, 6, 8, 9, 10, 11, 12, 15, 16, 18, 19, 22, 23, 24, 25, 26, 27, 30, 31, 32, 33, 35, 36, 37, 40, 42, 43, 44, 45, 46, 49, 54, 55, 56, 57, 60, 61, 63, 64, 65, 66, 67, 68, 72, 73, 74, 75, 76, 77, 79, 82, 84, 85, 87, 92, 94, 95], "rm": [2, 30, 32, 36], "filetobedelet": 2, "record": [2, 19, 24, 25, 40, 41, 42, 62, 66, 67, 81], "sha": 2, "determin": [2, 3, 5, 6, 8, 9, 11, 19, 22, 23, 26, 27, 30, 32, 37, 43, 46, 55, 60, 65, 68, 70, 71, 72, 73, 76, 77, 85, 94], "introduc": [2, 27, 36, 40, 41, 42, 44, 46], "first": [2, 3, 5, 6, 7, 8, 9, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 30, 31, 32, 33, 35, 36, 37, 38, 40, 41, 42, 43, 44, 45, 48, 49, 50, 51, 53, 55, 58, 60, 63, 64, 65, 66, 67, 68, 69, 71, 72, 73, 74, 75, 76, 79, 81, 82, 83, 84, 85, 86, 87, 89, 90, 91, 92, 93, 94, 95, 96], "place": [2, 22, 23, 29, 31, 40, 42, 43, 55, 60, 61, 65, 70, 72, 77, 85, 94], "rememb": [2, 25, 31, 35, 60, 67, 70, 73, 77, 79, 81, 82], "usual": [2, 3, 8, 9, 15, 32, 35, 38, 40, 42, 43, 48, 53, 55, 57, 75], "most": [2, 3, 5, 6, 10, 15, 16, 21, 22, 23, 27, 31, 33, 35, 36, 37, 39, 40, 41, 43, 44, 46, 49, 53, 55, 56, 60, 63, 69, 72, 75, 77, 79, 82, 83, 85, 91, 94], "easili": [2, 22, 23, 30, 60, 68, 69, 73, 76], "found": [2, 5, 6, 7, 8, 10, 11, 12, 15, 16, 18, 20, 22, 23, 24, 27, 35, 36, 40, 44, 45, 48, 49, 50, 51, 55, 56, 60, 63, 64, 68, 70, 71, 72, 73, 75, 76, 77, 81, 83, 84, 85, 89, 91, 92, 94], "log": [2, 8, 9, 18, 30, 31, 46, 49, 64, 75, 76, 83, 91], "just": [2, 3, 5, 6, 12, 15, 18, 19, 20, 22, 23, 24, 25, 26, 30, 31, 40, 41, 42, 46, 49, 53, 55, 56, 60, 63, 64, 65, 66, 67, 68, 69, 72, 73, 74, 75, 77, 79, 81, 82, 83, 85, 91, 94], "trick": [2, 53], "which": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 41, 42, 43, 44, 45, 46, 48, 49, 50, 51, 53, 54, 55, 56, 57, 58, 60, 63, 64, 65, 66, 67, 68, 69, 71, 72, 73, 74, 75, 76, 77, 79, 81, 82, 84, 85, 86, 87, 89, 90, 92, 93, 94, 95, 96, 97], "basic": [2, 21, 23, 32, 33, 36, 37, 43, 46, 53, 55, 56, 60, 72, 76, 79, 81, 82, 89, 96], "mean": [2, 5, 6, 8, 9, 10, 11, 12, 16, 19, 24, 25, 27, 30, 32, 34, 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