Team Ai
Datasetpublic

OpenScientificCodeRegistry/Database

Open Scientific Code Registry (OSCR): the authors' scripts The code published by the authors of open-access neuroscience papers, as found and verified by Open Scientific Code Registry (OSCR). Each file is here exactly as it is at the source, at the verified commit, under the license of its repository. 421,275 unique files (3,782 MB of text) from 8,849 repositories, in 24 Parquet block(s). Only files whose repository's license allows redistribution, confirmed by the repository's… See the full description on the dataset page: https://huggingface.co/datasets/OpenScientificCodeRegistry/Database.

sourceHugging Faceotherupdated 2d agoView on Hugging Face
0likes5.6kdownloads
github.com__jaydu1__causarray.json804 linesDownload Raw Back to f0
1{2 "format": "oscr-script-manifest/1",3 "repository": "github.com/jaydu1/causarray",4 "url": "https://github.com/jaydu1/causarray",5 "host": "github.com",6 "commit": "14d482803af83879330625e27ed64c49a9b0b9e0",7 "license": "MIT",8 "license_confirmed_by": "license file LICENSE",9 "redistribution": "yes",10 "files": [11  {12   "path": "LICENSE",13   "sha256": "e3314494081832d2f874ab2ed00d69ef4704a3649746c1845a62457456f684d8",14   "language": "License",15   "lines": 21,16   "truncated": false,17   "block": 10,18   "row": 605919  },20  {21   "path": "README.md",22   "sha256": "9de36ac880729a6777f556cec339a9492d42fdcb3e8c1c574fce16f4f6d0e5d6",23   "language": "Text",24   "lines": 150,25   "truncated": false,26   "block": 10,27   "row": 1994728  },29  {30   "path": "causarray/DR_estimation.py",31   "sha256": "236c221853bfd712b06c4256a0c39e4d797e6ad9b9d38ec39682e1f08d2c974b",32   "language": "Python",33   "lines": 1068,34   "truncated": false,35   "block": 10,36   "row": 1749037  },38  {39   "path": "causarray/DR_inference.py",40   "sha256": "6b8f4b76545180872571e4263678d7acb586c2d9638d48c0017569f0d8c27079",41   "language": "Python",42   "lines": 201,43   "truncated": false,44   "block": 10,45   "row": 1396746  },47  {48   "path": "causarray/DR_learner.py",49   "sha256": "806accc82a7d61a699ea6fe14ed9b514d2ba60af2607412ba51f23f9602a0809",50   "language": "Python",51   "lines": 1257,52   "truncated": false,53   "block": 10,54   "row": 1759155  },56  {57   "path": "causarray/__about__.py",58   "sha256": "91447944015cec709e8aa7655f7e9d64e1e4508e7023a57fe3746911c0fc6fed",59   "language": "Python",60   "lines": 1,61   "truncated": false,62   "block": 3,63   "row": 1735264  },65  {66   "path": "causarray/__init__.py",67   "sha256": "0698867252c758322cd773da9fffe9213987aab7a5ed5ac9842d2312f752849e",68   "language": "Python",69   "lines": 43,70   "truncated": false,71   "block": 10,72   "row": 1159973  },74  {75   "path": "causarray/diagnostics.py",76   "sha256": "87b753660ff4e7e838c1910e6b1f28e0be51f3d4523dca448baa6960f9e2a050",77   "language": "Python",78   "lines": 628,79   "truncated": false,80   "block": 10,81   "row": 1698682  },83  {84   "path": "causarray/gcate.py",85   "sha256": "012ce0263e92605699d5e30c0f1c0c92a1eea0bec127cfa1fcbda70a479e9de1",86   "language": "Python",87   "lines": 646,88   "truncated": false,89   "block": 10,90   "row": 1709291  },92  {93   "path": "causarray/gcate_glm.py",94   "sha256": "d2d1671c867e3d8db46b6f835be15abc097f3d5e2678b634e15c118951d7b76f",95   "language": "Python",96   "lines": 453,97   "truncated": false,98   "block": 10,99   "row": 16419100  },101  {102   "path": "causarray/gcate_likelihood.py",103   "sha256": "91c631e31952746e953fbdbc76c2ab5fcdb339e3f61305711e9f9847f6d4d508",104   "language": "Python",105   "lines": 279,106   "truncated": false,107   "block": 10,108   "row": 15409109  },110  {111   "path": "causarray/gcate_opt.py",112   "sha256": "7ed49bea741cfc407ece570f7de5cb388124750ac6baf18708596789349cf062",113   "language": "Python",114   "lines": 461,115   "truncated": false,116   "block": 10,117   "row": 16587118  },119  {120   "path": "causarray/nb_glm_fast.py",121   "sha256": "4e449043de702c73ef06fd73dc23b12308f1dc4814ac37aa05e168f30559c482",122   "language": "Python",123   "lines": 506,124   "truncated": false,125   "block": 10,126   "row": 16588127  },128  {129   "path": "causarray/utils.py",130   "sha256": "a669eba1c4e2c8ad204bed66a37bf10db4d4a6d8801940bd2d887a6863426aa7",131   "language": "Python",132   "lines": 339,133   "truncated": false,134   "block": 10,135   "row": 15503136  },137  {138   "path": "docs/source/conf.py",139   "sha256": "c24e86f42e725d84a662bbf09bb248f0cf453488218d2bb311d154766b13f8c2",140   "language": "Python",141   "lines": 180,142   "truncated": false,143   "block": 10,144   "row": 14147145  },146  {147   "path": "docs/source/tutorial/SCARF/SCARF-py.ipynb",148   "sha256": "bc6ef5233eb0781d63310ee7bfb13b07cf39a23a2fdc49149413986e3cbd3d05",149   "language": "Jupyter",150   "lines": 1154,151   "truncated": false,152   "block": 10,153   "row": 5943154  },155  {156   "path": "docs/source/tutorial/SCARF/prep_scarf_data.py",157   "sha256": "db3617241800d068d473331ae1c91a8ec1e1d61825471aa9f31000c9faf749e4",158   "language": "Python",159   "lines": 125,160   "truncated": false,161   "block": 10,162   "row": 14152163  },164  {165   "path": "docs/source/tutorial/case_control/1_preprocess_sea_ad.py",166   "sha256": "170d62b0554788c463d4856c01f735912eea69fb37e27ea45a3464a65e9d054c",167   "language": "Python",168   "lines": 176,169   "truncated": false,170   "block": 10,171   "row": 14654172  },173  {174   "path": "docs/source/tutorial/case_control/sea_ad_case_control.ipynb",175   "sha256": "0055964083135ee103404a5268c184cfa54e65ae990bf2998e8e05ca3c6acf29",176   "language": "Jupyter",177   "lines": 263,178   "truncated": false,179   "block": 10,180   "row": 5630181  },182  {183   "path": "docs/source/tutorial/perturbseq/perturbseq-py.ipynb",184   "sha256": "8c597aaaba70917a11f5f2ee3bf37cff5917412420e3f3f823e001442b68311e",185   "language": "Jupyter",186   "lines": 334,187   "truncated": false,188   "block": 10,189   "row": 5759190  },191  {192   "path": "docs/source/tutorial/perturbseq/perturbseq-r.Rmd",193   "sha256": "eb0a09802bd24fda0113e99ed66d951bee470ea4d49733670160a2866e591240",194   "language": "R",195   "lines": 393,196   "truncated": false,197   "block": 10,198   "row": 18817199  },200  {201   "path": "docs/source/tutorial/replogle/1_prep_tutorial_data.py",202   "sha256": "004b81fcd21580b9ec504ba0832c62352a1a79927c189ce3ec987664970629f6",203   "language": "Python",204   "lines": 184,205   "truncated": false,206   "block": 10,207   "row": 14886208  },209  {210   "path": "docs/source/tutorial/replogle/2_estimate_r.py",211   "sha256": "b1d642db1b570a6dba98892896572dc1d5f8d4201fdfbb55a63e38defdfad0ac",212   "language": "Python",213   "lines": 21,214   "truncated": false,215   "block": 10,216   "row": 11112217  },218  {219   "path": "docs/source/tutorial/replogle/3_run_batch.py",220   "sha256": "61fcc81dc87cb53676ca2276de4e70ff3392c1bf9292a49b98e402f598d34133",221   "language": "Python",222   "lines": 35,223   "truncated": false,224   "block": 10,225   "row": 11745226  },227  {228   "path": "docs/source/tutorial/replogle/4_cache_propensity_batch.py",229   "sha256": "a5f078f12088f8da0d909d1b4e7b1a07a0e2ea8dc76f19c954b4aa65e5ed0298",230   "language": "Python",231   "lines": 422,232   "truncated": false,233   "block": 10,234   "row": 16413235  },236  {237   "path": "docs/source/tutorial/replogle/5_refit_propensity.py",238   "sha256": "f5ccf0c57f903464dba71aa9772c38a1e238720c90bd3e5d8138e4cddfc2f0bc",239   "language": "Python",240   "lines": 140,241   "truncated": false,242   "block": 10,243   "row": 14721244  },245  {246   "path": "docs/source/tutorial/replogle/replogle-py.ipynb",247   "sha256": "5cf26be95bab82f9b1dc18b586667b2209fbd47c5133b19bc97d552d31d3e086",248   "language": "Jupyter",249   "lines": 639,250   "truncated": false,251   "block": 10,252   "row": 5874253  },254  {255   "path": "paper/AD/GO.R",256   "sha256": "965dcb2b0b5169a49b93e66cee82382384a59ae1bdcb32086874acd847ffe882",257   "language": "R",258   "lines": 81,259   "truncated": false,260   "block": 10,261   "row": 18277262  },263  {264   "path": "paper/AD/Plot.ipynb",265   "sha256": "0f9945201c9bbd7d344a6901e49693b6742aee5cb468fd1d46e26208efebb146",266   "language": "Jupyter",267   "lines": 528,268   "truncated": false,269   "block": 10,270   "row": 5798271  },272  {273   "path": "paper/ROSMAP-AD/1-preprocess.R",274   "sha256": "0ea0a447d5725c5f7eac60d902f610ac100175870de09f618bd7bfe58c5f1212",275   "language": "R",276   "lines": 157,277   "truncated": false,278   "block": 10,279   "row": 18405280  },281  {282   "path": "paper/ROSMAP-AD/1-preprocess.ipynb",283   "sha256": "6a7f5f04f76554063dca5a4a4281548a03d43cbc50807c4ed6ec9ee302bca21e",284   "language": "Jupyter",285   "lines": 160,286   "truncated": false,287   "block": 10,288   "row": 5222289  },290  {291   "path": "paper/ROSMAP-AD/2-DE.R",292   "sha256": "b0158aad8cab1829e60c9aab26112931706b30d5ddb4e70e82505d44147284e6",293   "language": "R",294   "lines": 180,295   "truncated": false,296   "block": 10,297   "row": 18508298  },299  {300   "path": "paper/ROSMAP-AD/3-GO.R",301   "sha256": "ad568801d7e31457ce4791bdc4ae8fc0f810b7e0fdaa62a87251576d7a720f78",302   "language": "R",303   "lines": 168,304   "truncated": false,305   "block": 10,306   "row": 18516307  },308  {309   "path": "paper/ROSMAP-AD/4-CATE.py",310   "sha256": "c70b9e0fba016d114c34038154ca5ef6db990ed430ccf1d7bb31011799326df3",311   "language": "Python",312   "lines": 99,313   "truncated": false,314   "block": 10,315   "row": 12556316  },317  {318   "path": "paper/ROSMAP-AD/run.sh",319   "sha256": "6b4954f2fed755851c6caaa8cf59227cc59bd64c561b7b5e1c14bad1d4130c8e",320   "language": "Shell",321   "lines": 14,322   "truncated": false,323   "block": 10,324   "row": 19118325  },326  {327   "path": "paper/SEA-AD/1-preprocess.R",328   "sha256": "1e337cb6c445c46924ef706e0ff48489c32b2d28ab7ff14c19ec72168d6ec23c",329   "language": "R",330   "lines": 67,331   "truncated": false,332   "block": 10,333   "row": 18125334  },335  {336   "path": "paper/SEA-AD/1-preprocess.py",337   "sha256": "12b992f24c67a122809331bd23855c54c6dede796670b3c79f4156e0263fbde8",338   "language": "Python",339   "lines": 144,340   "truncated": false,341   "block": 10,342   "row": 13226343  },344  {345   "path": "paper/SEA-AD/2-DE.R",346   "sha256": "f1eeffacae76efb2e0d1ee73574dcc2f6d1ddf75dab7faafc8f31beae0863e57",347   "language": "R",348   "lines": 199,349   "truncated": false,350   "block": 10,351   "row": 18558352  },353  {354   "path": "paper/SEA-AD/3-GO.R",355   "sha256": "72184dc1d196fb292730fe31186fef3e3c028ac0038058dd097cda448dd1d075",356   "language": "R",357   "lines": 180,358   "truncated": false,359   "block": 10,360   "row": 18535361  },362  {363   "path": "paper/SEA-AD/run.sh",364   "sha256": "8ae065bf0a6c7dd4c6ca3e86064f35abd5ed592c697dc7dfc488d60748b3659b",365   "language": "Shell",366   "lines": 17,367   "truncated": false,368   "block": 10,369   "row": 19225370  },371  {372   "path": "paper/methods/R_functions.R",373   "sha256": "122e7906ed53bf2327b16fa1cd1ad983d0a85bf9beab5de480c9b4d41b1f3ec0",374   "language": "R",375   "lines": 576,376   "truncated": false,377   "block": 10,378   "row": 18832379  },380  {381   "path": "paper/methods/causarray/DR_estimation.py",382   "sha256": "b4f01281293a210c16c738c23568a42e8ef4d9250805475fa45409d6a8cd4c49",383   "language": "Python",384   "lines": 330,385   "truncated": false,386   "block": 10,387   "row": 15707388  },389  {390   "path": "paper/methods/causarray/DR_inference.py",391   "sha256": "abf14828d646ac3bf5524bffbc7dc1a7a67422ce26b5ab3011efbf18a9d9e82b",392   "language": "Python",393   "lines": 195,394   "truncated": false,395   "block": 10,396   "row": 13818397  },398  {399   "path": "paper/methods/causarray/DR_learner.py",400   "sha256": "701fce20949a0d51975b6f5b5f5f2da3387c7d2d3f5ca1ae3fa77faa1c23ea14",401   "language": "Python",402   "lines": 324,403   "truncated": false,404   "block": 10,405   "row": 15596406  },407  {408   "path": "paper/methods/causarray/__about__.py",409   "sha256": "d66a6d13342285b772aaaccc81d9ecfe3e5918af60cfb851d9bb2003f4e78cee",410   "language": "Python",411   "lines": 1,412   "truncated": false,413   "block": 10,414   "row": 9603415  },416  {417   "path": "paper/methods/causarray/__init__.py",418   "sha256": "6d3c82cfd118dcd55c7056f8ef6c3b82a331ae689d06cc4766396c7cc6b03ec9",419   "language": "Python",420   "lines": 21,421   "truncated": false,422   "block": 10,423   "row": 10406424  },425  {426   "path": "paper/methods/causarray/gcate.py",427   "sha256": "a0e46ef650ef53a0067f75b961bbfd58cfa5381925cb06797008b428a6dc710c",428   "language": "Python",429   "lines": 246,430   "truncated": false,431   "block": 10,432   "row": 15175433  },434  {435   "path": "paper/methods/causarray/gcate_glm.py",436   "sha256": "862f5ffbb1f72d9ed4eccbdf1943e97bf7d7134f584a813e69401b91bf648dbe",437   "language": "Python",438   "lines": 266,439   "truncated": false,440   "block": 10,441   "row": 15237442  },443  {444   "path": "paper/methods/causarray/gcate_likelihood.py",445   "sha256": "b2bb36aa5dae121827dfceb0d65112e3513f19bd042874f9603216fb6e580de7",446   "language": "Python",447   "lines": 143,448   "truncated": false,449   "block": 10,450   "row": 13644451  },452  {453   "path": "paper/methods/causarray/gcate_opt.py",454   "sha256": "de5c45bee503a417408406a98c12f6413cef755c1fc384fd0f6eca624c26f2fa",455   "language": "Python",456   "lines": 294,457   "truncated": false,458   "block": 10,459   "row": 15494460  },461  {462   "path": "paper/methods/causarray/utils.py",463   "sha256": "11ed01ebae84b381b46bc21e378d29097ab4e338a963435bf9acdce106efd74f",464   "language": "Python",465   "lines": 246,466   "truncated": false,467   "block": 10,468   "row": 14546469  },470  {471   "path": "paper/methods/cinemaot.py",472   "sha256": "906379c420f698186473d7f9407b3153cb0b3392a5166c190aa32dc2724df0fa",473   "language": "Python",474   "lines": 136,475   "truncated": false,476   "block": 10,477   "row": 13505478  },479  {480   "path": "paper/methods/cinemaot/__init__.py",481   "sha256": "c883a2f2effc5c8966d8ced740ea925dbbf2c2f8cb3568e475a0930010061e92",482   "language": "Python",483   "lines": 3,484   "truncated": false,485   "block": 10,486   "row": 9855487  },488  {489   "path": "paper/methods/cinemaot/benchmark.py",490   "sha256": "3e4be01de4ff7b999891af2f92fa4159c8c8bc34292bcb796bf8605677269ab4",491   "language": "Python",492   "lines": 293,493   "truncated": false,494   "block": 10,495   "row": 15684496  },497  {498   "path": "paper/methods/cinemaot/cinemaot.py",499   "sha256": "5b6e67f79b033bf3f3d4d88a9692b20dac434c898b7655555bd94dc46cf9e4a1",500   "language": "Python",501   "lines": 545,502   "truncated": false,503   "block": 10,504   "row": 16967505  },506  {507   "path": "paper/methods/cinemaot/sinkhorn_knopp.py",508   "sha256": "5ff271280dda8da950c701f3b242edddfd8ea68ede59800c49c7cce951124595",509   "language": "Python",510   "lines": 171,511   "truncated": false,512   "block": 10,513   "row": 13877514  },515  {516   "path": "paper/methods/cinemaot/utils.py",517   "sha256": "4fe9dc36c110e5acec3cee795031d3b44b3588906957d37af099a3f19498464c",518   "language": "Python",519   "lines": 286,520   "truncated": false,521   "block": 10,522   "row": 16084523  },524  {525   "path": "paper/methods/metrics.py",526   "sha256": "a1451883b27102815cb0dfcf5c4403e1f395eac21bc0b96c270ce0a0aaf320ab",527   "language": "Python",528   "lines": 307,529   "truncated": false,530   "block": 10,531   "row": 15463532  },533  {534   "path": "paper/perturbseq/1-preprocess.R",535   "sha256": "05366257701506222bc5ab86fd0478f39d86bb703453dac56d09f0f59bb31b7b",536   "language": "R",537   "lines": 14,538   "truncated": false,539   "block": 10,540   "row": 17877541  },542  {543   "path": "paper/perturbseq/2-DE.R",544   "sha256": "1fb555348f5b23c0bd188fe9c03d93977920fa99e999293e63b73987f89d9f6d",545   "language": "R",546   "lines": 197,547   "truncated": false,548   "block": 10,549   "row": 18522550  },551  {552   "path": "paper/perturbseq/3-GO.R",553   "sha256": "d3b711f87f77529b7abc993f3989ce29237406692ec831568926bf2ec2dbc8bb",554   "language": "R",555   "lines": 225,556   "truncated": false,557   "block": 10,558   "row": 18578559  },560  {561   "path": "paper/perturbseq/Plot.ipynb",562   "sha256": "ab79b011696d258f675519747858c233323d9fdd121cab442dd43753d7af9283",563   "language": "Jupyter",564   "lines": 432,565   "truncated": false,566   "block": 10,567   "row": 5742568  },569  {570   "path": "paper/perturbseq/run.sh",571   "sha256": "a07516f3cd09d3e5806c97222c276881f12e9fd8f26c97751653d81d1189bdef",572   "language": "Shell",573   "lines": 11,574   "truncated": false,575   "block": 10,576   "row": 19053577  },578  {579   "path": "paper/simu_nb/Plot.ipynb",580   "sha256": "58deb72b1b8a9fed806c9cdf532f7ccbb312c95334f4906f3567f5fdd6c61560",581   "language": "Jupyter",582   "lines": 246,583   "truncated": false,584   "block": 10,585   "row": 5565586  },587  {588   "path": "paper/simu_nb/simu_nb.sh",589   "sha256": "48abbd8bb4333b2334583aadac7c4e9a3fcc602523dcda04620e2f216b5801f5",590   "language": "Shell",591   "lines": 13,592   "truncated": false,593   "block": 10,594   "row": 19160595  },596  {597   "path": "paper/simu_nb/simu_nb_data.R",598   "sha256": "b6a0f195b3756ab03bfeccde94a2b514e157d46e7b24601ab81b7141f2b81194",599   "language": "R",600   "lines": 100,601   "truncated": false,602   "block": 10,603   "row": 18275604  },605  {606   "path": "paper/simu_nb/simu_nb_fit.R",607   "sha256": "41ffc908a44add586d48c824fb1dd6a1a21683949564d9da0a377f01fd711787",608   "language": "R",609   "lines": 180,610   "truncated": false,611   "block": 10,612   "row": 18526613  },614  {615   "path": "paper/simu_nb/simu_nb_plot.py",616   "sha256": "5e1a60549620acae852a5aed9f4cc81db5e961eaf5f63f7d512e1ee25bf4b889",617   "language": "Python",618   "lines": 233,619   "truncated": false,620   "block": 10,621   "row": 15021622  },623  {624   "path": "paper/simu_poi/Plot.ipynb",625   "sha256": "5e1334abfa8bae5e2f553c356c4ced422c76ce7e6cef10eca37be24cb77300a1",626   "language": "Jupyter",627   "lines": 300,628   "truncated": false,629   "block": 10,630   "row": 5624631  },632  {633   "path": "paper/simu_poi/simu_poi.sh",634   "sha256": "a752f8fd809aec5434158287bbeb6a18fb8365922ab262b6c718bcfb37e7f8cd",635   "language": "Shell",636   "lines": 10,637   "truncated": false,638   "block": 10,639   "row": 19084640  },641  {642   "path": "paper/simu_poi/simu_poi_data.py",643   "sha256": "bc2d6740d24237c57f4b5742546942594e7f5b64555f76f06634dd5c10927f1d",644   "language": "Python",645   "lines": 176,646   "truncated": false,647   "block": 10,648   "row": 13864649  },650  {651   "path": "paper/simu_poi/simu_poi_fit.R",652   "sha256": "b909cc47da6c806a4ca9c7d18a6c6cd6807b063c0eebd593fafb5d094f8a41a1",653   "language": "R",654   "lines": 181,655   "truncated": false,656   "block": 10,657   "row": 18546658  },659  {660   "path": "paper/simu_poi/simu_poi_plot.py",661   "sha256": "e6502926dcda4cc4f82fba7dce170a0ff2bd5cbe07fbeb1831cf53775edacb4c",662   "language": "Python",663   "lines": 204,664   "truncated": false,665   "block": 10,666   "row": 14570667  },668  {669   "path": "tests/test_DR_learner.py",670   "sha256": "d817e103b3151541099ac92c920c4979d47a2d747c888435ae46c51fdae8c453",671   "language": "Python",672   "lines": 199,673   "truncated": false,674   "block": 10,675   "row": 14949676  },677  {678   "path": "tests/test_batch_fitting.py",679   "sha256": "7bf7fc0af5ee20077d711dd4c9b3412efb16a403a7c460f36daa562788e8f535",680   "language": "Python",681   "lines": 525,682   "truncated": false,683   "block": 10,684   "row": 16758685  },686  {687   "path": "tests/test_deconfounding.py",688   "sha256": "79b24b1a8160aa9d62ecd28a889370ebcc61b6b343dff3df6bf3214463add996",689   "language": "Python",690   "lines": 123,691   "truncated": false,692   "block": 10,693   "row": 13500694  },695  {696   "path": "tests/test_diagnostics.py",697   "sha256": "df6eecae8f401ab2b7fd439975242c46df122470caffcd704809039e874be11d",698   "language": "Python",699   "lines": 155,700   "truncated": false,701   "block": 10,702   "row": 13705703  },704  {705   "path": "tests/test_estimate_r.py",706   "sha256": "76e7416e6412ae00d0fe75e72b52dc43d5699410653c71ace6225726f86a1a0a",707   "language": "Python",708   "lines": 25,709   "truncated": false,710   "block": 10,711   "row": 11029712  },713  {714   "path": "tests/test_gcate.py",715   "sha256": "e88b5089a7bad69c8e7897d3e8b6c3e0f96b9c874819dd23b4ef1a64a37f9903",716   "language": "Python",717   "lines": 128,718   "truncated": false,719   "block": 10,720   "row": 13412721  },722  {723   "path": "tests/test_gcate_convergence.py",724   "sha256": "56946ebabc608eb6833650f64c8f5a5193e01dac298b6c53c80f3099eb8732b1",725   "language": "Python",726   "lines": 362,727   "truncated": false,728   "block": 10,729   "row": 16109730  },731  {732   "path": "tests/test_inference_comprehensive.py",733   "sha256": "37cdfdbbf307647ffcd5dd179790e5720f9976a41933ca4da5efcfcd5b606a27",734   "language": "Python",735   "lines": 791,736   "truncated": false,737   "block": 10,738   "row": 17337739  },740  {741   "path": "tests/test_likelihood_kernels.py",742   "sha256": "b287042413182d8bcc7a5a9bc19d1c5187d7c0a820a6cc8ed44487d15771276c",743   "language": "Python",744   "lines": 62,745   "truncated": false,746   "block": 10,747   "row": 12337748  },749  {750   "path": "tests/test_nb_glm_fast.py",751   "sha256": "21811f4c6894784011b9c5f5ef49707d9f6c41e372e589f351b9067c55649594",752   "language": "Python",753   "lines": 521,754   "truncated": false,755   "block": 10,756   "row": 16710757  },758  {759   "path": "tests/test_nb_glm_integration.py",760   "sha256": "448e8acd81408dacb1d0e35590c85304fe450e2dd9c7e3cf20ec1b8ddc7b0032",761   "language": "Python",762   "lines": 356,763   "truncated": false,764   "block": 10,765   "row": 16095766  },767  {768   "path": "tests/test_propensity.py",769   "sha256": "ca6c38c1e0622e4e33e0215339a29d61b89875b4e90a919a1dd87b7da8e407a9",770   "language": "Python",771   "lines": 687,772   "truncated": false,773   "block": 10,774   "row": 17028775  },776  {777   "path": "tests/test_review_regressions.py",778   "sha256": "e901a109a8dad92d9f1c4d5ed42402948bde0d283febd1fcbef0b6addd3ba0c6",779   "language": "Python",780   "lines": 147,781   "truncated": false,782   "block": 10,783   "row": 14232784  },785  {786   "path": "tests/test_small_arm_inference.py",787   "sha256": "9c0122b118041799766bf769758eef851243ca13957c9b30dda490d8bf4549d4",788   "language": "Python",789   "lines": 320,790   "truncated": false,791   "block": 10,792   "row": 16122793  },794  {795   "path": "tests/test_structured_glm.py",796   "sha256": "e3ab52a51540719598086016c4a476e3131892f4478a0865dea38cae7d931d4e",797   "language": "Python",798   "lines": 104,799   "truncated": false,800   "block": 10,801   "row": 13802802  }803 ]804}