{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":50160,"databundleVersionId":7921029,"sourceType":"competition"},{"sourceId":8128137,"sourceType":"datasetVersion","datasetId":4803907}],"dockerImageVersionId":30698,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Quick Initial Analysis on Tax Registry Data\n#### I had not dug into the tax registry data until now.  I wanted to show how I usually start exploring.\n\n#### I use SQL a lot at work, so it makes sense for me to use duckdb in this competition.\n\n#### I noticed that 34% of the train records don't have any tax registry data... I guessed that the default rate would be higher for those without tax records, but it is actually opposite with them being 27 basis pts (bps) better than avg (which is 3.14%).\n\n#### I also noticed the folks with tax registry A have a worse default rate (higher)... 4.0 vs 3.14, which is 86 bps higher.\n\n#### Even more interesting, is when a person has A combined with B or C, the default rate gets worse (higher)... for A&B, it is 5.98%, which is 284 bps higher than avg (low sample though)... and for A&C, it is 3.3%, which is 20 bps higher than avg.  \n\n","metadata":{}},{"cell_type":"code","source":"#since I am better at SQL, will try to use duckdb to wrangle this data\n!pip install /kaggle/input/duckdb-0-10-1/duckdb-0.10.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-05-07T18:44:38.943965Z","iopub.execute_input":"2024-05-07T18:44:38.944380Z","iopub.status.idle":"2024-05-07T18:44:55.740490Z","shell.execute_reply.started":"2024-05-07T18:44:38.944348Z","shell.execute_reply":"2024-05-07T18:44:55.739172Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import duckdb \nconn = duckdb.connect(database=':memory:', read_only=False)","metadata":{"execution":{"iopub.status.busy":"2024-05-07T18:44:55.743658Z","iopub.execute_input":"2024-05-07T18:44:55.744162Z","iopub.status.idle":"2024-05-07T18:44:55.906641Z","shell.execute_reply.started":"2024-05-07T18:44:55.744110Z","shell.execute_reply":"2024-05-07T18:44:55.905444Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"my_sql = f\"\"\"\n\nselect \n  --checking which tax tables have data\n  case when tr_a1.case_id is not null then 1 else 0 end as a1_not_null\n  ,case when tr_b1.case_id is not null then 1 else 0 end as b1_not_null\n  ,case when tr_c1.case_id is not null then 1 else 0 end as c1_not_null  \n  , count(base.case_id) as case_count --case count for this grouping\n  \n  --what percent of the train records for this grouping\n  , round(count(*) / sum(count(*)) over(),4) as pct_of_train\n  \n  --what is the avg target for this grouping\n  , round(avg(base.target),4) as target\n  \n  --avg default rate is 3.14%... so I compare this group to the 3.14% and get a bps from avg\n  , round(10000*(.0314372 - avg(base.target)),0) as bps_from_avg  \n  \nfrom parquet_scan('/kaggle/input/home-credit-credit-risk-model-stability/parquet_files/train/train_base.parquet') base \n  left outer join (\n      select distinct case_id \n      from parquet_scan('/kaggle/input/home-credit-credit-risk-model-stability/parquet_files/train/train_tax_registry_a*.parquet')\n  ) tr_a1 on base.case_id = tr_a1.case_id\n  left outer join (\n      select distinct case_id \n      from parquet_scan('/kaggle/input/home-credit-credit-risk-model-stability/parquet_files/train/train_tax_registry_b*.parquet')\n  ) tr_b1 on base.case_id = tr_b1.case_id\n  left outer join (\n      select distinct case_id \n      from parquet_scan('/kaggle/input/home-credit-credit-risk-model-stability/parquet_files/train/train_tax_registry_c*.parquet')\n  ) tr_c1 on base.case_id = tr_c1.case_id    \ngroup by 1,2,3\norder by 1,2,3\n\n\"\"\"\n\nconn.sql(my_sql)","metadata":{"execution":{"iopub.status.busy":"2024-05-07T18:44:55.912316Z","iopub.execute_input":"2024-05-07T18:44:55.912653Z","iopub.status.idle":"2024-05-07T18:44:56.411945Z","shell.execute_reply.started":"2024-05-07T18:44:55.912615Z","shell.execute_reply":"2024-05-07T18:44:56.410703Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![image.png](attachment:4418b2e8-d712-47a6-ab0a-4fb99f4dc972.png)","metadata":{},"attachments":{"4418b2e8-d712-47a6-ab0a-4fb99f4dc972.png":{"image/png":"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0jtAggpVvwDGQF2yy/y3muxW2g1wDuNfWa7uYutGIILEb2jcOrICpqx1wfKA29xaWt/R+j68CJIsnmrMoL+DFUXBRcGi7ZbcBGpXruY8xF5ShHIe5OtK5PydH8S0iYp3fuWudDCXD3nwbgI1GO8xeZns738NKBVJqwNIOqzhBR6tfKiXoZbr0A3aMUDJg7slltUI79HtLRtDtwXZlbovV3un7fm4y0quSKFG6LS5jw1zfn2wTlVHZEh6Va9aFA77npZSdGcu7xsU+OiGtEYskja26L57ZrZD1I5aDv3uCcr/Q7LkHXR9acdtVKqTnO9KnDAbkqY6pWXI+2iBMwZFQ8q5kAIOb94Skvm911OcEXZcJ5ubtcDeTqJzhuzCqy6Ppw2LPzUaqH63r3pzucDz61Xp90JlImad73asLjWRFh45aT5lwadr7rCWzpyVTq3dRGfp81SegfDFLQBRH2GUEuvb697lVzh+ToX8zqdwUOVaWC3nMIV7s63ddV4zppD95XiQ1eEcUN2VGdRveYtZ67y9MkpnanGTi+tXnvhi46FnxtU2T+LaawKNPvYfHQQAbnF0mbtmTkAhmbYuqjw/D29f+5Zlcrc2W1965p9ff95FvTRHSwWrkdF7uhtUNkcCSL5SgTOBIW8R2+eVnnWy9MJcF1xuF+hxs/e1bTGHZdW68x0ChZow1n+v/B0iSp0Tlf23j+ueG+eZr3nji4TYMrMzLKVHL5+oK2TGq3bMjGzQutcJuqf08nyaWgDiPrMcE1XF9xXf2arBd1DrPOn86u0vgQweWC33CLeaiynzlzTWYtFupeHNfOzrp+wSMXlFnW/m12WLMUH5qOgGkNHxATo0BkLoqWnzv2kQ7VJ1HgNb+ToGHddpO1YvHLDCVyvIwvbft7lH2d0esKS6U8nBGZiISZd6p64eVA85yy4+J+fp/vwvUut5SUqW1GuvP4tohO+rznUl5kyLS07IwN8fTVcJsD0+XpGdcfO11fnRNtlr7OmRij3+cPFVYp8Ox1tAFGfQXSMV0kNg7bFG+B4SkB2gd1ArlChEjrPKo+5530tULlSofqRG26zQ1snZoebuPJ2i7Z+d4IL3ujGysWPX+ZzjrdKEA8WHez29di2Nkvmet7iYvJBJCOpi4zw9LzTytvMdv9W9sIVBgphEWHMueSUds31OgQlELY1dkRY6TwpIUTN7QEFlgo/4utVqE2TapEdWf6OvaCHVsrUyttLaldOqeRdHx6x4LS0+R3zsiaIzRNil3pgBNLD1JWl1hK1v8gIT8rOnGGS2gCiPmPUV+fo8KGO35Jh0O63FlUeFs1ZkFVgN5A73Lj3F10WG77XtfB8VwsuIzJ2aJ0ay753fuGVjinWAmSOzp6JwHHDC+Qe7737tx8e+l5ZEYsXDdr1wjx6ca+1MfkQ9ukYSV2kYoFZ4ByHhWeNms7olg5hGUDgLLt2Z6H7opbS6zkKuEOyWKLuC52vNp5oT2pwRKoPJ1weFru0rvLkBi30hNRoVOw9l552YBRQC8eShPvx9WqOCpcbPzZbwndsfufteJ5/K4T9ZNAdLp32bSoehSbDSj2nwsr4vIxEqVGbIqUtUZPWBhD1mcHE2G03nZjGASsdMAVgN3AHeFIOeR3dhk4aI6LuiStggiJk4wELnD4NnYoxNuKv87muBZLpEIh3zO5HT0DTIwcgiRHVRSLoF2UkpR2Mne8bYpWCB8WBPZyjQ8LH9ORr/5n4t5yEwsjikN/O/xpf/Lh5HZITzPPika3uc8cnHIZkYrNth0g6reKxdUfDAqjyCCaPrmusJ70wKxH2QXuqkJzHs0H7RjIdbQBRnyEWnnEx5l6h17CZiqAcOSkJZAXYDeSd60+HgVjSIPJKNwlXcASNixKBp7TUJz5eTxjTsamBt03IdszlZ1m/SSdyAqaKv++N+Qe9pKuLbLhBzGsXjaD3hYglIuzKsWsiJq780POC8u+QyauVcgqBNCxGrG3ueM8cnedj0kb9dn72PSvCe3+7J+idCbNB3A6RnscSJw5VOFtseQTjw7wEwNY1qnNVp6o3Shg6b7GhjKHRxGloA4j6LCHD4dzAyXCN9mBJ3F1cBQEyA+yWU0SsapupyU8yzBpRMd9JvHh6valYUde7GDh/SMUvQcEdeN+3CkkIhmkEzvNWpSZdpp6UbIWVvb9+FWD0G1dAgCHrIiXS+f+B+QyOwF14ZUKhzLmgXX276UmkZjTGi7mX0Z8mzXv3LqkY497Ow3jQ3nF5paD5jeE83xf57WYOQNRv587QodQhgbh43mxdwnZxv1vNY5HXY9q0C5VHla6pfxu4PaG6xsy18OsaGZF0Qw3D5xOYgjb46YYxn3uRjPa5PEBlDDIB7JZPYLd8It7NPaJdNMLZAna5m8Cu8aANuZsMYFd46gEAAAAAAMg5EPUAAAAAAADkHIh6AAAAAAAAcg5EPQAAAAAAADkHoh4AAAAAAICcA1EPAAAAAABAzoGoBwAAAAAAIOdA1GcNeR+ptxBCxIp/ILPYBXeil7oH2cRfgEq2l95ql/eDNHlWVsqM+xt7Tm2Ri3bFrNAZSve5uXd8JEzwb1CuRkNwYbCodO9HaLGeHpunKE/ynnnvbybbxo312RPa7kBZ6fmbcHngzVu0C4yT8EJ5sgXqmkB+vUU9NGFNB1GfJSTzrJ5T44tePl2vQDdoxQMmj67sd2iJauYIyANityqdH7RVebv80iDaLN0T8Zgyz3KDVL2oUW3Z7Hvo69XKl5J2skUsjHL9aYe2HvP1Zl+j011WZrTXNrfdpdgZ1ZAG/wYryo4Atmdpc56abroPIB47b8xKqOp6vdKmL9xD5YnPy+qxgfIkAsesEKz/JrgS8VgZ67OzKFerhep7x7XdNSc/9z57zbtebVhca2JUvDyrN7+uYbsubtG8tRu3EeerAwjzKWg6iPoMoZbo3l73Cnrh+ToX8zqdwUOVaUS4dF9c0vvns+YIyAVfP9DWSY3W7VLvMyu0vk1U/3z3u9Hp8qwIlTrVXqxR0RzxkLRzl7mPghu0nU2ixs9lc8DSpe5JhYoPzC5TfFgxnzRSF0pDCiE/Slh0H9VZwKx5y9Qv/NygyskpnaURKWzPw/0K29O7mtYOKtRqnWlhHC5P5rxfnuT7ReBMUMh7jPnZeX/DWf6/8HSJKnROV2P2yoLxcv3pkOrLDVqz9ZBqI1p0+me6ruA0NB1EfWa4pqsL7qs/s9WC9nrU+dP5VVpfApgGhefvIT5yyPXVOdF22WuIZRi2us8fLq5MQ313SZNnO2+4/tluRv5d5zMLpIdXfcJjuP7a2yI62I0QcAtU5obR81gp8d9y6r4OnbGAKl65w9YYsRwe3ZlaempFt/ZCtvhf97s51I/vXWotL1HZ2lN5vltEJ3xfc4iWi4EOYGF23i9PP87o9IT3/3TCHSYWYjKBZwd3k8ezgRETcUC0vqWx+nQ0HUR9BtGxdyU6rbSpLd6AVBkIAHArJCSABUaptUTtL+K9Q0OtPZM1akZ64nVj1drsUtkOWR/XqO4OS4vXlhq063ltgyy8kmuIqiLsTDiG13n4cUXnLLa2vpW94fBBQyVAP0RcSBujw5ua2wOKDM4bKsZYhZs0qWY90k/KVDvZog9e506P9HiIMGZJc0q7xq5tziFbVIqcizEuxvTsAXSH1vXQWuqr8t16651vwHncnJv0XIP7Tmuz5NnFnSuhRlz2D31bGAfEoExS00HUZwwp9IcPdXzXe24Qu99aVHnYM/gNABgF+1WaOypSW0SGxLAqj1zQ23j/0KJk/tgPJ4ii5p5/skaNZev1FDF3To3X8THBqpGz6X48T1uLYZET7FDoUAl0toZHRkhKKvRK2piNJ7qDNj8bZ6kQLNpLi11aV6Kc7a86YPM0q8TrAm2ozp0RR3NnVOb9gKdz2e3oFWjlBZ+f2MjYOJ/dR8Xec3elHeoQq46sulY2Pd/Az/MSvuOcV2UCwn4SyKilbxfuaF5wm2CF/cwK7ao4eJOn94jWWZQPoskmrekg6jNDgWYf8/+2m8rwmgErHQBAalRogIhHZ0KaCskJDbfeO1SYhOtVZJFi95W3XNdVsR7Or2dUVwLKXi9DzmZfGks1ClDxRf+TDeXpb21+0CE2M7MslRCPPHq4s7qsJwX6IVU6LMWd3xDLgyKxnKHGF6czF+4Eiy09gbRBRS5PnoCR66fWMZvAszPSWa3KCFfiJFcJQTMfo5BRD/MRTJIClSvB+T0B0c92JRbl6TTZdDQdRH2GWHjGxXi/6semmolH5YTYVwDALVANp/vWlQ59CMR231NmVui9J8xka1ODBZF6c4cRK1JXeSJccOuqgLCTTcIURBDxZ897GYxllhh9Px7bxNz/7t3dTDjz5z+A26AFS2tzx/MA64mATqy4woaohDzFM2VaWma77NkwKP47mXxaKUcKWJmjIiFtnmdeXV+nQ/dtOX2uHy3jf3ZP0DsTZmP5+o7/1o3xD6Lms/T8NjB2THhNXBugbEwR84xMCGf4NafT0HQQ9VnCeKw8D5mK20tRQYDpYgu08kj6Hs778WrEPCND3vrVdNZ+EmcbNTH0zjFsnuW6qn1w7scAD1JXSachEKbBDeVFg9qOd3PhlRkGt+fl1Zn93rQDUiFeR/1aPZ2uah5JolfZUqCVtyYOXtmlpGKEXS+kFsTm3t/WQ69llOt12In+m/D142Wsz65Gn+SDGxfPm+MwCLyHXpUX5y1AXnnUm8rvqX8buD3BPNszvydkNxVGM0g9NAVN99MNYz73IhntcxmVad6A3fIJ7JZPZPLcHtEuGuFsAbvcTWDXeNCG3E0GsCs89QAAAAAAAOQciHoAAAAAAAByDkQ9AAAAAAAAOQeiHgAAAAAAgJwDUQ8AAAAAAEDOgagHAAAAAAAg50DUAwAAAAAAkHMg6jOIrMQnCxVg8aIcIe+R9RapCK1GCDJMcHGRl95ql3ccede389xz/7IrZWpk5UTvnLO5dZKtp/T2zl9dNrygi7eFy0Uw7YP1XWixnsD9QV+GqotCtgutkOlh808o34TzVU8blnR+zMTn2TSkTBub/j3nk+satP3TIEVdE8i3A5apCWsDiPpMoSuNHVqimjkCcoAU+NVzvQz+5aVZtRAiJPtIeavS+UFb2e3yS4Nos3Q/GlRZ1VWe2WzNx1tUckTIwiv/nE2bCv8rPjB/wA1VaXOemvb67TpVPYEnq2+Grj+WGm2eZu0KmqqR1Cv42r/xV3HUdnHPqfvHiSjgM2RddP1pR62UqtNdr7bcKz7ZPntbNL8dbqXkuF6RU12vVtJ0RQyLp0W+ztqV89R54PyY6Ztnk+m8KdHW42ZC2vAzcvrXtitm35JU18h5tP2TJ0VdI6LcrDSr/8ZZCTiJKWgDiPoMIRVq98UlvX8+a46APND5fYta2+teQS88X+eKuU5n8LZkm68faOukRut2qXcWuuvbRPXP9088Fh+GRUiQ6z9PnTzODeFRnSoHa95y5ws/s+g/OaWzGIHW+Rz8eykzIu58Ie/Spe6J04Fgkn4f0AxbFxWev+f2x67TukBlLg+tb12zr5F2aosatPbMHPCQzpwjeJ6U+btb1P2ud68/HVJ9ma+zNlflrUWnf6aV1cMweJ4NwOLscL9CjZ+9q2ntoEKt1hnf2afzpkp1Tv+1h+aAJaGuQds/LZLqGsk3IsoHEPIO09AGEPUZQirU6EYOZJdrurogqj2zlb3u+df50/nVJBorcFuur86JtsteIy9D39V9/nBxFWio7z7XdNZqOXk4TIc+bJIjaHRDuPTUij/tgW05Ai6AEkSOoOG/P2OBVLxyh6Vd75WIyZbv0eLrdzb7/T6gmUBdpGzBeeH1Ckv4W/B4NnCdCKhwp2E8DJhnw3zvUmt5icpW2Cmvf4vohO9rDsmxKufzZsRS/kl1Ddr+aZFQ1/w4o9OTeaI/nbCt1KM709EGEPUAjAgdh1xSw9dt8eJMpLECQyPDq1xZl1pL1JYwE7ehvsuY55Y8qzyvMaJCeVgdb5OPNFJyvR6+bm5HN1birSLHQ0o/ruicxdTWt3JwyNtpLFX4zzFRVX6fGfqG6EnPSOoiJVJd77S15W4qr6XyWjue+cLTJarsH/rhNkZATZZ0eTYW/s0qtnqVqCkhOJyTr9Tz8H2P6lQ73vDzeRT3ta7JMH3rGunMsQw/pV1TV7W5pgyGKqZhktoAoh6AEVBfnaPDhzpeUoavu99aVHlYNGdBZtmv0txRkdpSYb9doYLyyBXpXljuyYYnqi9fdKkUOXFQvPRRXnLxbpVUyIBcv/FEe6XmZ0P+WxZBErbge0gtQY+mDoXwBY5qBK1djuf5u+7RJOYhGUldJOJ1VUSqE3YgIv+iQbveiEs82hPNNpYyZY5JyMmuiikWgcPbHtE6C5zJ1ZMp82wcJyzmFru0LnnyksW76pzqeSIqJOlxs3/H8z7XNdPEdsS8LVjPJdY13DH183yBVl7UBhrNnbQ2gKgHYCgKNPuY/7fddGJRB2wswFQozM7zf4PCQw2Th0IE7gUq/tl6HX164qAVLESWiSoHbUfE6PAGNzZVCMeUKmZmWQr1fpeH6Qh4IR7S+TiuUWvzQ0SnA/iMqC4SEbQoHnnXvmzLz3UtbK04YtFv993J5SLoSxKi86XXay1hJqoTaYQtscCZTD2ZPs9G8qCoJooHnskT5Tp8TYl2kzYqNEft64nAqGumSOilAKpDZk4l1jVi91uPpkxHG0DUAzAkC8+4584VuNewmUlR5X5eGzB9lJB133QQ55W++2jx7sQMK0x6vPCFiKZA5UqFG74dL5Qi8nrbYDrhGxoTx/q7L9G1+PdjjsWr6sY6K0EJr2Yi6eoiG4IS8eYZR9D7QkTT80YkeaMRd/jEw2mFsi/okycWioe0Sgne7ZGRMs/Gpc1MmZaWOc/u2RAxHW5TqZT5zr1ve5IQCxFz3ptSUNdkmD51jbJ7nQ49z71rdwcbyhgKy5mKNrjpR+ftzaPf/jI7YOxIej961LO97ZjzaYHdJk/Adm9vbpX6sNsU+Ovm7TBlTfj+x80vG3/c/G12c0G4ron4/X/9Fn3c8vfHX/pc//fNHxt8PDY/m/P2+vDfpfh9ieTRLqMgsS6yaf/LzR/fzSGDsrmb7mrr/TuFfE8gfYNlydu8vwme/+XjLS0zhF3751khPm3Cebbf71ff05P3+9Q14fwe9TdpkPugDRmMxLomRb6194hK+8D9x68NfpL/GH3fi/Q+PpfpMmI2N8gwsFs+gd3yiXg394h23fhhMH1gl7sJ7BoP2pC7yQB2RfgNAAAAAAAAOQeiHgAAAAAAgJwDUQ8AAAAAAEDOgagHAAAAAAAg50DUAwAAAAAAkHMg6gEAAAAAAMg5EPUAAAAAAADkHIj6DCKr8snqZO7S2yDjyHtkzRLhkSs1guwi772OWA3wvmPrIbu99FZVFOzKm2aLSDtZMdQ7/y+7EqfBprnZeuu65PuDGIasi4J2f6eXyzeE84Rske2UtW/A7iGbTqGe7PdsyfTLk0nPlv7ZbblB+z8hEuuiDr1zzgfrwRRMWBtA1GcKXfB3aIlq5gjIAVIprJ5T44tdIpxoa3HQBgNMA9XI7xEtbZsDQMMNUam1RG279P2XBlFgif0dOq20zbL4TartVwONnaRr9aLhXd98vEUlTwRxPbd3SkumvFwe16i+GmzsOm9KtPVYltmPvj+IYdi6SOy+OU9Na7ftOlVDHbLKgbW73jZ6lrwX+27R/Ha4FSvQylv/usvj+cnWkymerR/982TSs6V8dv6N1Ysa1ZbNPhgzSXWRaLIqnds8r+rBUvoO1xS0AUR9hpCGsvvikt4/nzVHQB7o/L5Fre11WpnR+4Xn69wpq9MZPC3ZhivcnW/rdPl2hVDiglxfnRM9nvVX7JyZpXnzUSg8f8/1lD27QGXuFLW+dc1+hz5stqj2wl/xc+HnBlX2z0xjJgLnvVde6EmZy0uLut/NPtvlcL9CjZ/t6okLtHZQoVbrLLUAu68MVxexgDmqs2hf4xTXKLudnNLZAN5Face2qEFrz8yBOB4UqWI+jp8hn23QPJn0bJHnO/Rutc7lZo2K5ggYNwl10dcPtHVSo3Vb182s0DrXdfXP6WT5NLQBRH2GkIay1+sBss01XV0Q1Z7Zyl737Ov86fwKEiTTcAX9HsupR1J4usQi3PdEdt5wnl5eorJt/BKpUPGB+SioTsE5XaURUN+71HK/S3lYW0QnXbLdBhDFsHURp+9JhZae2q4Yi8xFFiWuyElCOsqbRI3Xfocujus/T1nwlD2RPV6GfLYB82TSs0WdV2VsuwkNkCGUc8OxkxqB3OcPF1fRnbkA09EGEPUAjAgdC1lSYQlt8eJ4nksAcoZ0eC7btNQqqVjQKjXViEakUJOQgZAXs7zdoq3ffW+WEizmcxjdYWjQWljMyNC1xKGuEjUl3CFtpwAMWReJ+JDrq0THEqYSFCGtTZ0n1Baa6yCeSTrY9T2fPfjxySUR/16emRT9ny2Rvnky6dn6nFcjATVqwskwVWLrIq7jlN0kJPGLjPAM5mCYpDaAqAdgBNRX5+jwoY67k7CE7rcWVR5iEBXkFCVeSioc8JLFfeOiyo1SRCyo/J2EDBw7Q9jMwisdc6yEH29nz0QAzdNsSOxpzxeLmXCH4WSLSotdWpc41ssNWvhxxfKp93rQy3B1EXfGFq3dJV5eexvnZ7V1ZDRZxRarzeQLK+xVPHiDdr2wrCgWaMO7fp26i5OcENr/2RJJzJNJzxZ3njsaMgfhmO+p/g6MFNsR87bomPbYukjqsaOinh8k59SoTTF1iNSktQFEPQBDUaDZx/y/7aYTYzxgYwFAxtCxoDYUQOJOWcAt1+nQnawqjeWieGbbESEDroBhAfWABVCoIZRGVHksv4TEjIo3rgSPD9iQ3k+GrYs4fZf1RFjfnjpsJRBK5VGgcsWPDO98rmvha8UTd/bsfrRw13MxJhOmOOizhRg4TyY9m3P+xxmdnmjxp0Vnibbs/gATeUEMatTRr4tUh8ycssTVRYVZmUkUFPo9841imY42gKgHYEgWntVUb95ruMzkmnKP0AEgR7hxo0Z4eI2RI+j9BisG9bentOTEWfuNaNDDr5gp09Jyi7b2rKC51pMcK+UUDen9Jl1dZENQwq/X0yK9FXjL0WH8XAq2645MiDYxwwuvXOHE2zH/lmX9BqTIOHG+Xiaf+nHu4yTts8WkzaB5MunZ3PM9olM60Cwlj/lzXMgbGBl96yI1cbZOVS/MzLwEwIuTN5jwnHA42jS0AUR9lrAZY07Hn9qee7SXA2SGJxvmVVjaXjrestcbALKGH+OqJj/ZcJFQxXwfWXjFwoIcrysL+HkWGVacKU8+/z8QX+0IIWkoveMqZMFtMHXDqMMh7LW8eV5JMzLgfX9JxaImdh7A0HWRhNfo1+7p61UMsScsreA1m3TUvsQI9kj88uZfHyGkxkT/Z0siKU8mPdt0nx3EkVQXyYijG0qo52KkzvNT0AY/3TDmcy8iMj+X6RKTN/IF7JZPYLd8Ip7oPaJdeNWyBexyN4Fd40EbcjcZwK7w1AMAAAAAAJBzIOoBAAAAAADIORD1AAAAAAAA5ByIegAAAAAAAHIORD0AAAAAAAA5B6IeAAAAAACAnANRDwAAAAAAQM6BqM8gduEWLDqVL2C3PBJcFOblJ28NVZCKuNVJQwsVxS3oJe8cl/ORy+EHbYNylRJ5p7WXbmG7JBNYOGzuHVvBJcGu1p5m67FZ6Hy03cdH/2dLov+zd94459QWSvuEtAn+NtRFIyMpT4bqmdh8YcvVoIsTDlkeBwWiPlPoSmOHlqhmjoA8ALvlE7Fblc4P2np59i8Nos0SxOMAXH/aoa3HtZ58L8dlxU297L1ekbFXpHD6723R/HZEqVENsV690S6fn37l0nuMpNvqOTW+6DTTK6gOIF5ZgJQ256lp0ry5XaeqI7w7b0ps76axSdiuYk+9yqw6r1bSdEUMiyezCq2+Xq/Qu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