| { |
| "format": "oscr-script-manifest/1", |
| "repository": "github.com/yenlin-chen/geometric_tm-archive", |
| "url": "https://github.com/yenlin-chen/Geometric_Tm-archive/tree/1.0.0", |
| "host": "github.com", |
| "commit": "8826fe69f472138a30af78b1fd93f81b86fef251", |
| "license": "MIT", |
| "license_confirmed_by": "license file LICENSE", |
| "redistribution": "yes", |
| "files": [ |
| { |
| "path": "LICENSE", |
| "sha256": "737c5714eb069e70d594aaa8a99782677156e549f6ea3bf2b9c3e6311ea4bd9b", |
| "language": "License", |
| "lines": 21, |
| "truncated": false, |
| "block": 6, |
| "row": 1556 |
| }, |
| { |
| "path": "README.md", |
| "sha256": "a3d816a3655743346cd48a3b01780fa8b21a1594a2ac6df63b71c9201f52ebdf", |
| "language": "Text", |
| "lines": 43, |
| "truncated": false, |
| "block": 8, |
| "row": 10057 |
| }, |
| { |
| "path": "experiments/M1/backbone_O-contact_12-codir_X-coord_X-deform_X/test_distr-DeepSTABp.py", |
| "sha256": "5e489471e5ef47fc89b1427f2436a55140b275002196b3967ed2dd787caa9ed1", |
| "language": "Python", |
| "lines": 447, |
| "truncated": false, |
| "block": 7, |
| "row": 6219 |
| }, |
| { |
| "path": "experiments/M1/backbone_O-contact_12-codir_X-coord_X-deform_X/train-10fold.py", |
| "sha256": "2f04d83eb02b65497076fdd5c04cde4e6eaf892dca61ee8982355abdb1e18259", |
| "language": "Python", |
| "lines": 576, |
| "truncated": false, |
| "block": 7, |
| "row": 8783 |
| }, |
| { |
| "path": "experiments/M2/backbone_O-contact_12-codir_X-coord_X-deform_X/test_distr-DeepSTABp.py", |
| "sha256": "ca0de75c5c2890b3ef97f5326476c1003bb88f69a73c877c744a679a0721d6f6", |
| "language": "Python", |
| "lines": 447, |
| "truncated": false, |
| "block": 7, |
| "row": 6218 |
| }, |
| { |
| "path": "experiments/M2/backbone_O-contact_12-codir_X-coord_X-deform_X/train-10fold.py", |
| "sha256": "2f3b58aa43d5809701c466374179d5f39c17aa39d6295dfbea5d8c745eb61391", |
| "language": "Python", |
| "lines": 576, |
| "truncated": false, |
| "block": 7, |
| "row": 8780 |
| }, |
| { |
| "path": "experiments/M2/backbone_X-contact_12-codir_1CONT-coord_1CONT-deform_1CONT/test_distr-DeepSTABp.py", |
| "sha256": "9a440ddb461ba9591772e7215c345e8c74fafb66b2c717fab88933b72b4d22be", |
| "language": "Python", |
| "lines": 447, |
| "truncated": false, |
| "block": 7, |
| "row": 6227 |
| }, |
| { |
| "path": "experiments/M2/backbone_X-contact_12-codir_1CONT-coord_1CONT-deform_1CONT/train-10fold.py", |
| "sha256": "da9323809cca6c2777c6a7461a2ad3cd755f557200992305ce00c1c5e7de0110", |
| "language": "Python", |
| "lines": 576, |
| "truncated": false, |
| "block": 7, |
| "row": 8788 |
| }, |
| { |
| "path": "experiments/S1/backbone_X-contact_12-codir_X-coord_X-deform_X/test_distr-DeepSTABp.py", |
| "sha256": "e1f51833569dabd1257da7705adf4d6fc25c50f65fd6d76a7fa2b6f97bb91f19", |
| "language": "Python", |
| "lines": 447, |
| "truncated": false, |
| "block": 7, |
| "row": 6210 |
| }, |
| { |
| "path": "experiments/S1/backbone_X-contact_12-codir_X-coord_X-deform_X/train-10fold.py", |
| "sha256": "6cc77306c43d37e4159ae7c80c403712c025c3d9b1125263b5608973e1ba24f9", |
| "language": "Python", |
| "lines": 576, |
| "truncated": false, |
| "block": 7, |
| "row": 8775 |
| }, |
| { |
| "path": "experiments/S1/backbone_X-contact_X-codir_20N-coord_X-deform_X/test_distr-DeepSTABp.py", |
| "sha256": "fc38d342077bac521d69d7d9a63ab2df1762257571e31a8b8252187b38602eb2", |
| "language": "Python", |
| "lines": 447, |
| "truncated": false, |
| "block": 7, |
| "row": 6206 |
| }, |
| { |
| "path": "experiments/S1/backbone_X-contact_X-codir_20N-coord_X-deform_X/train-10fold.py", |
| "sha256": "19368859e99818a30f3ab901738b537d829fd61f2aba319d29acaf419cd2ca26", |
| "language": "Python", |
| "lines": 576, |
| "truncated": false, |
| "block": 7, |
| "row": 8772 |
| }, |
| { |
| "path": "experiments/S1/backbone_X-contact_X-codir_X-coord_1DCONT-deform_X/test_distr-DeepSTABp.py", |
| "sha256": "ae0253dae848ef9fefc95e1c71184906fbb431ddb55e83401bd3d1ccdcd29c1a", |
| "language": "Python", |
| "lines": 447, |
| "truncated": false, |
| "block": 7, |
| "row": 6208 |
| }, |
| { |
| "path": "experiments/S1/backbone_X-contact_X-codir_X-coord_1DCONT-deform_X/train-10fold.py", |
| "sha256": "acb039c4d24be011938b643383fbc0a8fa3af299ff39f859d2b744a55365397d", |
| "language": "Python", |
| "lines": 576, |
| "truncated": false, |
| "block": 7, |
| "row": 8774 |
| }, |
| { |
| "path": "experiments/S1/backbone_X-contact_X-codir_X-coord_X-deform_2DSIGMA/test_distr-DeepSTABp.py", |
| "sha256": "8ddd75637c17f1ad12658447e3560ac8cb9406ddcfc8d06c26beee6b33a5e085", |
| "language": "Python", |
| "lines": 447, |
| "truncated": false, |
| "block": 7, |
| "row": 6211 |
| }, |
| { |
| "path": "experiments/S1/backbone_X-contact_X-codir_X-coord_X-deform_2DSIGMA/train-10fold.py", |
| "sha256": "6769788292ea8e8a5cf2ebf6533d12b0c4e338926d0b9af504ecab030af6c987", |
| "language": "Python", |
| "lines": 576, |
| "truncated": false, |
| "block": 7, |
| "row": 8776 |
| }, |
| { |
| "path": "experiments/build folds/build folds.ipynb", |
| "sha256": "08b62cd6db90522595175c935bfd08661eb45af6758267c18513fd15c380f78e", |
| "language": "Jupyter", |
| "lines": 85, |
| "truncated": false, |
| "block": 5, |
| "row": 16232 |
| }, |
| { |
| "path": "notebooks (analysis and plots)/20250721-2 critical residue analysis for best performing protein/SCR-1.retreive uniprotkb info.ipynb", |
| "sha256": "9c5d04edbd4e74ffb2e49c2f2e4f7317cab34113f556a2c476182fa999677919", |
| "language": "Jupyter", |
| "lines": 140, |
| "truncated": false, |
| "block": 5, |
| "row": 16602 |
| }, |
| { |
| "path": "notebooks (analysis and plots)/20250721-2 critical residue analysis for best performing protein/SCR-2.critical residue analysis.ipynb", |
| "sha256": "047e4f15a986bee9c68de5d3b61e52684fddf15cf8c635e45d451062aba59e76", |
| "language": "Jupyter", |
| "lines": 268, |
| "truncated": false, |
| "block": 5, |
| "row": 17344 |
| }, |
| { |
| "path": "notebooks (analysis and plots)/20250721-2 critical residue analysis for best performing protein/SCR-3.plot against sequence.ipynb", |
| "sha256": "b19f1d3b008143ed9ecabf921d23b21bcb4c64741cb93218a473f7bd9e5f03a3", |
| "language": "Jupyter", |
| "lines": 551, |
| "truncated": false, |
| "block": 6, |
| "row": 332 |
| }, |
| { |
| "path": "notebooks (analysis and plots)/20250721-2 critical residue analysis for best performing protein/SCR-4.plot on structures.ipynb", |
| "sha256": "d561654033454357f4259053ae14e799979a4b0ff7f628c38e4785ba08e540cb", |
| "language": "Jupyter", |
| "lines": 631, |
| "truncated": false, |
| "block": 6, |
| "row": 622 |
| }, |
| { |
| "path": "notebooks (analysis and plots)/20250916-1 analysis of best performing proteins/analysis.ipynb", |
| "sha256": "8a60099f6be7c7e9c44c1a7ac157b84368b8703e19498e2cbc4910b68f56ff67", |
| "language": "Jupyter", |
| "lines": 676, |
| "truncated": false, |
| "block": 6, |
| "row": 450 |
| }, |
| { |
| "path": "notebooks (analysis and plots)/20250916-1 analysis of best performing proteins/edge count distribution.ipynb", |
| "sha256": "30eafc355d9603398b90bb27cdfb5f325bc7a4da0b0ec9049e85b904c282264c", |
| "language": "Jupyter", |
| "lines": 312, |
| "truncated": false, |
| "block": 5, |
| "row": 17475 |
| }, |
| { |
| "path": "src/data_collation/collate DeepSTABp - 20241121 - list of files inferred from deepstabp code.ipynb", |
| "sha256": "dfcfe9484c648e0a8ba6d88e4553d8bc68fd2834568e39c3db707f9bebe22ce3", |
| "language": "Jupyter", |
| "lines": 484, |
| "truncated": false, |
| "block": 6, |
| "row": 328 |
| }, |
| { |
| "path": "src/data_collation/project_directories.py", |
| "sha256": "d840619ba3e0bc356abebcc2fa6a32dda7f52ff2d7d0ba9ee32dee68d58d7fc6", |
| "language": "Python", |
| "lines": 15, |
| "truncated": false, |
| "block": 6, |
| "row": 22240 |
| }, |
| { |
| "path": "src/ml_modules/__init__.py", |
| "sha256": "dd88bf12e86704c31147813f308daa54138e53589c9b1764bc0888b8854a04b0", |
| "language": "Python", |
| "lines": 6, |
| "truncated": false, |
| "block": 6, |
| "row": 20600 |
| }, |
| { |
| "path": "src/ml_modules/data/__init__.py", |
| "sha256": "1376793ecca28c98c3f363a03e7646db680282c32682561adc61bf3ddf0b7a37", |
| "language": "Python", |
| "lines": 41, |
| "truncated": false, |
| "block": 6, |
| "row": 24267 |
| }, |
| { |
| "path": "src/ml_modules/data/datasets.py", |
| "sha256": "e77c9f0a5f8b96d03a646b97cff1c6b33a90ad2fc9f83385d12eaaff1ab217bf", |
| "language": "Python", |
| "lines": 689, |
| "truncated": false, |
| "block": 7, |
| "row": 9899 |
| }, |
| { |
| "path": "src/ml_modules/data/encoders.py", |
| "sha256": "5b7a2011c400403290368697fdc7263c1e58dbfcd95ba65797a6d086638cb00e", |
| "language": "Python", |
| "lines": 56, |
| "truncated": false, |
| "block": 6, |
| "row": 29776 |
| }, |
| { |
| "path": "src/ml_modules/data/enm.py", |
| "sha256": "62b8120a6c9fe40b54eb2c9158b36acfb16523e33bedd90c8d05c820446f5550", |
| "language": "Python", |
| "lines": 421, |
| "truncated": false, |
| "block": 7, |
| "row": 5805 |
| }, |
| { |
| "path": "src/ml_modules/data/retrievers.py", |
| "sha256": "67f096125935d1052344bcdcdd24eec222ec694bfc726d98e5b0e7b2bb775a91", |
| "language": "Python", |
| "lines": 402, |
| "truncated": false, |
| "block": 7, |
| "row": 6106 |
| }, |
| { |
| "path": "src/ml_modules/data/summarize_couplings.py", |
| "sha256": "f9c47a5decedc50083106f4acfc845ffae74e65c4de77ff1bfb6b3dcbfb8b6af", |
| "language": "Python", |
| "lines": 249, |
| "truncated": false, |
| "block": 7, |
| "row": 823 |
| }, |
| { |
| "path": "src/ml_modules/data/transforms.py", |
| "sha256": "51810a0194253900b54017623d0b8d5e414818f04a00f2324d434560303b5183", |
| "language": "Python", |
| "lines": 39, |
| "truncated": false, |
| "block": 6, |
| "row": 25027 |
| }, |
| { |
| "path": "src/ml_modules/training/mGCNConv.py", |
| "sha256": "30caa7c28eb814c03a119619ef3fec77371b850ae70ffa693653192289c84eea", |
| "language": "Python", |
| "lines": 143, |
| "truncated": false, |
| "block": 6, |
| "row": 40921 |
| }, |
| { |
| "path": "src/ml_modules/training/metrics.py", |
| "sha256": "3bd176523c71b5d3d6d7550478f00e5410816485a62d8bf4635fa0f11a8a52f9", |
| "language": "Python", |
| "lines": 102, |
| "truncated": false, |
| "block": 6, |
| "row": 35324 |
| }, |
| { |
| "path": "src/ml_modules/training/model_arch.py", |
| "sha256": "21ef053b49f59070f7d894e74a37cb57efb4a85923ac8034bb81a65d7589e7dc", |
| "language": "Python", |
| "lines": 1051, |
| "truncated": false, |
| "block": 7, |
| "row": 11179 |
| }, |
| { |
| "path": "src/ml_modules/training/trainer.py", |
| "sha256": "91972bec4f5c0dbca25e8b56e7a5e45be261e2027205d58558ab023dc1e6b026", |
| "language": "Python", |
| "lines": 249, |
| "truncated": false, |
| "block": 7, |
| "row": 1129 |
| } |
| ] |
| } |
