{
  "src/tapestry/__init__.py": "eb4a6cc8e44ca438b81d3962912b0dfe9bd5a1d3ccb9c4bba519636c726e02f5",
  "src/tapestry/data/__init__.py": "0e5e8c1a344b834e9fbe5c8ebf53ce23ec94b4dfaf44f24d910053c79fc90ed1",
  "src/tapestry/data/__main__.py": "a76f51f7175262a4578a6a1b703ccddd7a9f5df5ce8c77c2fd770cdc7156c17d",
  "src/tapestry/data/catalog.py": "13499c78b7a5a78bb3cab92596584d0b162f54d6eaf70a900516ac70b1d77288",
  "src/tapestry/data/cli.py": "979b124bba8cfde7ab295b84720d883b09608a197cf46a95122ba59cd91b5ad1",
  "src/tapestry/data/columns.py": "e42e1e055d6ead8c1565dd301ae2debf19659dfa3bc2d64144593a89a587d0ff",
  "src/tapestry/data/geography.py": "fa60bfc8505408ac5b927920a3c640ed0285bc34086cf17b506e83644a545d11",
  "src/tapestry/data/http.py": "311555d79c88450310d1fa16a0cc6d0e22479595e5347436df2d16446fb71132",
  "src/tapestry/data/lineage.py": "d83aafd016502c910178dd95f8e51556784851418208446dbbec9ff9c46ed206",
  "src/tapestry/data/models.py": "619f4505bec3da56082898a8308e7b1e6a08751ee24336031eb51d7ccf7a90bd",
  "src/tapestry/data/repository.py": "aec9ad527ec2448e456273d57a9745146c3f9b66ab2c529811c50f4e5d10fa7e",
  "src/tapestry/data/selection.py": "25ae0e5829bd06134e705c29e72db894de20b5e35e24a3020ede21c32ce9f0c1",
  "src/tapestry/data/sources/__init__.py": "24f92fdd7483de7db31a41797eaeaa17cc98f4563c803c65f3c6ed0786d98750",
  "src/tapestry/data/sources/delphi.py": "2030d9f46787dc475c62017261a83d0290ae789918d6bd4cad6b4bba112a6880",
  "src/tapestry/data/sources/hub_history.py": "29c9378384f0477c047390ac57f65c0c0c325bb311592d4d38a32a344cdb0e95",
  "src/tapestry/data/sources/hubverse.py": "16eda4a25c052d1386bddb73bff3459b9698e6d4b0f916fa284ba474ea9017d2",
  "src/tapestry/data/sources/nwss_aux.py": "dee67f0d47439757884fa701acf391c4b481e2a8fb9001e796a23fbe76d273e1",
  "src/tapestry/data/sources/pophive.py": "5de4e010b80f590b92e5a477911f88ea3329d4d8955097dc44e7b44adcfd68c8",
  "src/tapestry/data/sources/socrata.py": "c35023c18940300828c1783f912213e8f81cd13bbd505d2b1513e1f32deea332",
  "src/tapestry/data/tables.py": "3bc170a59b89695c16a219c7160c7ce9d7b56030003a3751251672e131c430fb",
  "src/tapestry/dataset/__init__.py": "74be3033d6daf2095dd2de0a986c1dfecdfd2684975154df5e222cb3c94a2d4c",
  "src/tapestry/dataset/analyze_dataset.py": "a9aa3faae312d8ee28b4eed05ffe92b86fe40430198f2ac1261473b9022beb9c",
  "src/tapestry/dataset/build.py": "d12b1415bc6a6a78df9851433e04ec3a9cd9339b5d08d9475dd1a9f8c27b0e69",
  "src/tapestry/dataset/cv.py": "2360aaab3e38a9cc737947eca190cc6f8d33aefd09f4e428eb24045f48f68716",
  "src/tapestry/dataset/episodes.py": "23a16c39166960f3541381130755cc0ae3dfeb16c2dacbec51d67af9eb73bc5c",
  "src/tapestry/dataset/extract.py": "a509fe8cf3f1fc2cfa22b77faee936db5b370c60b3bd9a2f0ad242fb0d492c9c",
  "src/tapestry/dataset/finalization.py": "3158ac1433b781b8d24277993f61719ba544a96edc742a17a9c9c68d3caf4ebf",
  "src/tapestry/dataset/nwss.py": "31938b9da19c1870b318c9fc949444aeb6e22cb850ca1cd2ba1ba946b8dfed0a",
  "src/tapestry/dataset/reporting_error.py": "195424af083255cdfaf263c818d3b42553732d6238896c93b7c8ac1d5ce1b333",
  "src/tapestry/evaluation/__init__.py": "1af394c345a3f12e89b39794d3cd1e95ecce0f7f2abc01d6e1307d1c4a4bfcc3",
  "src/tapestry/evaluation/effects.py": "95cc3782417f9456082e4d2c1b72ed505582c9d3c180ff6b765607e492f1419f",
  "src/tapestry/evaluation/hubs.py": "710079cefd98b7d1cf82ed50bc403cf66e83bcf5bc41e38432cecb955547cefb",
  "src/tapestry/evaluation/plots.py": "34ae99d742a646569342b04b72955148a7af015b9162f72b293c0b4e16780350",
  "src/tapestry/evaluation/quantiles.py": "8124880da499f0002704bcbed96f865a4ab610b57b602ae18c85dc58195d791e",
  "src/tapestry/evaluation/report_layout.py": "ae62edb5d447756989c841870b759dcad421301ead006c77dd606078234a77a3",
  "src/tapestry/evaluation/reports.py": "4ab2b59a751e7d1342eb4b40859f060e5e2da734e36231a4fb07d7d331de3999",
  "src/tapestry/evaluation/standard.py": "c407fecd87101d90530706f41a2e9f4907911641b11dc934067f611218d47af3",
  "src/tapestry/evaluation/totals.py": "c518ae0e37fba61a7dedc1e222f5f8520510b8d3b6afc45337fb33b11f7fea8c",
  "src/tapestry/evaluation/trajectory.py": "569ec8a38149931d7bac2369fad819fb1fc80aed0899ea738f583c59ea604445",
  "src/tapestry/experiment/__init__.py": "8702be85be8bb3671f37522eaf46a28ce80fc4d956341f35794d549b26978500",
  "src/tapestry/experiment/augmentation.py": "b41109a24f2e44c60e66ebdb1ec81b134c49cf8c67c06d3fac4a3db47b6e2f37",
  "src/tapestry/experiment/baseline.py": "58256d12fb221b5b47e9d8ee48cadc4d42bbe28317ba53c5e87a2eccad43586a",
  "src/tapestry/experiment/dispatch.py": "48284ae093711b0c5bd3b5b8ac78a112912920733d0da6cc732563099ddff600",
  "src/tapestry/experiment/finalization.py": "98a8a0cfc4ff1a3c7046b3f228d09aafed8949c82cb3cc90b54471860053e464",
  "src/tapestry/experiment/fitting.py": "ae6d386263aed4eefed81e240319663f2f786cedfbe6cd096fb0c8fe14388f9f",
  "src/tapestry/experiment/nowcast_baseline.py": "c3960d8e82e5b8832520596301fb6470c2e22db49dfb9affe8b6df809c443bfd",
  "src/tapestry/experiment/pilot.py": "47190cc045efc478d07dda9093d98e15bc5860867f1efdf275975da14b3b8ed1",
  "src/tapestry/experiment/planner.py": "06ff030af9282029e454cd1705de78b17a0381387aed28ba0a5f9b764d185ce5",
  "src/tapestry/experiment/provenance.py": "d52be18aa424b7d8150f8f89e9669c98bbe2b9e2536927d0b3dd018ff4b48483",
  "src/tapestry/experiment/replay.py": "329a4c41cb575f960fd4d3849efee5e76b6dfedc1354775456835baff9333826",
  "src/tapestry/experiment/training.py": "9a1e95317a17302567a86c5cafe9d72ea5c9770310e03c920cd9214ea2274618",
  "src/tapestry/experiment/two_stage.py": "3fb7d605aae36194e14a490fedeae4765eb884a5e82de073c054c271c00dadcc",
  "src/tapestry/experiment/weekend.py": "f0fbb51568b6697f60eb69b887d6645a5c092a345984e3984afcc211433822d3",
  "src/tapestry/explorer/__init__.py": "ad4d45cf9e5eb10d2d4f93b7005fabd8ebcd74f915d4886efd0659b4940a7620",
  "src/tapestry/explorer/__main__.py": "fcf7570a2ee1b9bb83f970c61cd54a7449c9ee9b345964674e55e49df2ba028e",
  "src/tapestry/explorer/cli.py": "46748312e8299c7e7b15bb054a0a37dea9b961ad19a66e3bc496328b785fa6d2",
  "src/tapestry/explorer/export.py": "91cb1ce2feb8a4c246f05c54f1e7749a8bda196fe103be3e33f02e91645a55c8",
  "src/tapestry/explorer/index.py": "27a43b23bd1c53c5fab856c4cb4833b28b4b0a45f1fc55e8b9e2725120ed9a23",
  "src/tapestry/explorer/nowcast_ridges.py": "403986c49b4e4ab7e60017e03f20e2c413b2e4588733a5e6c445b12a4b155a42",
  "src/tapestry/explorer/revisions.py": "61a1b310f41c6b2248f1785d92d63e92e8b00b61bc63cb42556d05679fd37437",
  "src/tapestry/explorer/server.py": "623bb485aba36957b86455ba2fee41ad185c06b5319b8b6f1bcefb406a9cd7f3",
  "src/tapestry/model/__init__.py": "2ed0e768c8d259a5925e4946a885643675c30426521b53b8473a2077a3622dbf",
  "src/tapestry/model/baselinenowcast.py": "f1facb2ec2948b600303936027aa03fec1d33f832c1b6b3d9e532476d933068f",
  "src/tapestry/model/context_nowcast.py": "3b0be563cee7d4629d36b93e70fdce33ac46986378d15fedd07181f1dc179f50",
  "src/tapestry/model/covariates.py": "0fc19d8c65a1f98a37d2a8937bd61f501150ffdf07671ad2d08a8e1d31f8d8b8",
  "src/tapestry/model/finalization.py": "35fc0dbf952edce3f3335e4875c31c0143d589443a34e65272012b05fe10fc77",
  "src/tapestry/model/geographic.py": "20da5c8f4dcaa7764ba8c596d2d37ace3eb5e7ae96cd705b9f41a0f5622b4b17",
  "src/tapestry/model/network.py": "523dc54dd9471bf7ee524311dbe08b5ef48d7ca32061967e505ea8badbcc1ef2",
  "src/tapestry/model/objective.py": "b0efc7acf521ec9fcbc28784d4c32c59d1cb33ddd6942ba99d57af215c7e9383",
  "src/tapestry/model/pipeline.py": "442e222d059da1371a5572f2f874da335f8fd9b6f402685965689d06504444f1",
  "src/tapestry/model/revision_regression.py": "9686b2cb9fc51968af653bad7149af8561587821ac806f1c16931aed40af78db",
  "src/tapestry/model/revision_tree.py": "e556f0cc7a56361423c04e45699be949972011ff5a1964d4cb6baf9bb9479ee6",
  "src/tapestry/model/revision_uncertainty.py": "3fc4e15a166c0690cf7d8952e364cf43f4ce5f65e664e256f04767f69a6b708e",
  "src/tapestry/model/scenario.py": "15cd8c14e8ffb945e4c735a3e6c47e63d329ae52ec6d3a840b88b417d908070e",
  "src/tapestry/model/series.py": "3c0f77915aaa0443445dd9747a01381f8de84c8ee86030d166b50404f5b80035",
  "scripts/plan_b4_flu.py": "88d734dfabfdb9b24f5b64c1039be80b7d0f2eefe1cab6e7e53d0ab1efb970fd",
  "scripts/launch_b4_flu.sh": "36a8c2a28533fe4b156c7c5591f12880ee0707a872852dd370929868a6932a8d",
  "scripts/jlessler.sbatch": "6d75dec88b2d30f51e7e370ec20ddc9a34bb35bbd9890e7cc648d840ea94bcc3",
  "pyproject.toml": "6e7672afbd093a527d71ba6e7457de6e260a17c7fe15d33fe2bfb755f86d8cd4"
}
