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.
05.6k
1{2 "format": "oscr-script-manifest/1",3 "repository": "github.com/ucdrubinet/brainsec",4 "url": "https://github.com/ucdrubinet/BrainSec",5 "host": "github.com",6 "commit": "d641f5702d35be8c1ef234c704875808b9b6c0d1",7 "license": "GPL-3.0",8 "license_confirmed_by": "license file LICENSE",9 "redistribution": "yes",10 "files": [11 {12 "path": "LICENSE",13 "sha256": "3972dc9744f6499f0f9b2dbf76696f2ae7ad8af9b23dde66d6af86c9dfb36986",14 "language": "License",15 "lines": 674,16 "truncated": false,17 "block": 1,18 "row": 223519 },20 {21 "path": "Plaque_Quantification.ipynb",22 "sha256": "343cf369ce279b23555fc9421a447c245010565b5f38bad73db4024b4a3fb28f",23 "language": "Jupyter",24 "lines": 812,25 "truncated": false,26 "block": 6,27 "row": 95028 },29 {30 "path": "README.md",31 "sha256": "6e3d1254d0e39af7070042f80fea62df50d1076a20c9153aec280fcbf5dce018",32 "language": "Text",33 "lines": 116,34 "truncated": false,35 "block": 8,36 "row": 1049437 },38 {39 "path": "notebook/1) Preprocessing - Reinhard Normalization and WSI Tiling.ipynb",40 "sha256": "9b8e21d690d2f12aad4089fe776f4191aa370615576ea8ca31c4847da82be103",41 "language": "Jupyter",42 "lines": 114,43 "truncated": false,44 "block": 5,45 "row": 1665246 },47 {48 "path": "notebook/2) Visualization - Prediction Confidence Heatmaps.ipynb",49 "sha256": "702a8a239643508b0a07e92c4b5bf6b50ff3a64241b0572e722369d32d129269",50 "language": "Jupyter",51 "lines": 507,52 "truncated": false,53 "block": 6,54 "row": 59855 },56 {57 "path": "notebook/3) Analysis - Plaque Density Distribution.ipynb",58 "sha256": "00abffca85735d8d908011072560fda5c59441dd0b214b133df80208c58d3500",59 "language": "Jupyter",60 "lines": 1226,61 "truncated": false,62 "block": 6,63 "row": 115664 },65 {66 "path": "notebook/3) Analysis - Tissue Separation.ipynb",67 "sha256": "390491929409dff076d22f907e41c41716ea7f5f45c0fe8a3053173116d35855",68 "language": "Jupyter",69 "lines": 1223,70 "truncated": false,71 "block": 6,72 "row": 117273 },74 {75 "path": "notebook/ComputeMaskAccuracy.ipynb",76 "sha256": "55c2f73606eadd92e289b744a6c44b958beefd2427722ca9a37bf7611069a427",77 "language": "Jupyter",78 "lines": 526,79 "truncated": false,80 "block": 6,81 "row": 87382 },83 {84 "path": "notebook/Convert_binary_mask_to_xml.ipynb",85 "sha256": "142478c967a70fe95adbe3155f606bff1416caeef2874bc6000777aa7cb07ae6",86 "language": "Jupyter",87 "lines": 94,88 "truncated": false,89 "block": 5,90 "row": 1643991 },92 {93 "path": "notebook/Grad-CAM.ipynb",94 "sha256": "3cdae106ef553370de8e86a103204ad90fff9ecb70464b4c47c25b8bb139cc8e",95 "language": "Jupyter",96 "lines": 288,97 "truncated": false,98 "block": 5,99 "row": 17734100 },101 {102 "path": "notebook/Mask_Accuracy_Benchmark_Plotting.ipynb",103 "sha256": "683f30c276d0f869c0f5cdaed868e128889bc557f76920c7fcccffd436729c10",104 "language": "Jupyter",105 "lines": 153,106 "truncated": false,107 "block": 5,108 "row": 17221109 },110 {111 "path": "notebook/Numpy_Memmap_Experiment.ipynb",112 "sha256": "24a615ca2cb2ee06c3dbba8487e49cb8e748e7fdfe667677f672b424796888a9",113 "language": "Jupyter",114 "lines": 111,115 "truncated": false,116 "block": 5,117 "row": 16371118 },119 {120 "path": "notebook/Old1.2) Preprocessing - Plaque Detection and Image Cropping.ipynb",121 "sha256": "776e88877de03d2f3f970a0a183bc67cedb4b13fa52ccca110a5f4442d8b5f39",122 "language": "Jupyter",123 "lines": 502,124 "truncated": false,125 "block": 6,126 "row": 608127 },128 {129 "path": "notebook/Old1.3) Preprocessing - Dataset Splitting and Size Filtering.ipynb",130 "sha256": "53d2dac142fa64263a6086064ddfe157634e2ec7fe46c96b44c67a7eb415a7c0",131 "language": "Jupyter",132 "lines": 92,133 "truncated": false,134 "block": 5,135 "row": 16293136 },137 {138 "path": "notebook/Old2.1) CNN Models - Model Training and Development.ipynb",139 "sha256": "75da29532f04d41e544bd8061e6671b6818f62ad3d64213a1f3b008ae0900b90",140 "language": "Jupyter",141 "lines": 408,142 "truncated": false,143 "block": 6,144 "row": 429145 },146 {147 "path": "notebook/Old2.2) CNN Models - Test Cases (box).ipynb",148 "sha256": "b60ee8717dff213db1cad852ee725655a7ed6e5d6f4919b48c8a12e2029a1b59",149 "language": "Jupyter",150 "lines": 329,151 "truncated": false,152 "block": 6,153 "row": 64154 },155 {156 "path": "notebook/Old2.2) CNN Models - Test Cases.ipynb",157 "sha256": "fa0a4a52fefb9f34b4a46d83afcc0f31593a7b4030ff17d902abf35667b39d6c",158 "language": "Jupyter",159 "lines": 332,160 "truncated": false,161 "block": 6,162 "row": 54163 },164 {165 "path": "notebook/Old4.1) Saliency Mapping - Feature Occlusion.ipynb",166 "sha256": "fb6a65e3601edc5c974c695b49ab48cbd464e09ff07b025127e8f7ac06734cf2",167 "language": "Jupyter",168 "lines": 205,169 "truncated": false,170 "block": 5,171 "row": 17352172 },173 {174 "path": "notebook/Old4.2) Saliency Mapping - Guided Grad-CAM.ipynb",175 "sha256": "8224bc925845f568ecebaefc83ad2c817ad36e42f3ef40ed0eafc2945df5adce",176 "language": "Jupyter",177 "lines": 387,178 "truncated": false,179 "block": 6,180 "row": 396181 },182 {183 "path": "notebook/Old5.1) Whole Slide Scoring - Tissue Area WSI Segmentation.ipynb",184 "sha256": "c8d1ffdd9890e5397232605af84db9333f9d353dc783223ff838d05edab3de84",185 "language": "Jupyter",186 "lines": 766,187 "truncated": false,188 "block": 6,189 "row": 790190 },191 {192 "path": "notebook/Old5.2) Whole Slide Scoring - Prediction Confidence Segmentation.ipynb",193 "sha256": "f8f521b247f53f0ec1ffa6594a201827380ed8ba906779ece89f4ff38f400c09",194 "language": "Jupyter",195 "lines": 103,196 "truncated": false,197 "block": 5,198 "row": 16610199 },200 {201 "path": "notebook/Old5.3) Whole Slide Scoring - CNN Score vs. CERAD-like Scores.ipynb",202 "sha256": "d1fb90039ac0d00f968e0fe754f9d809c65430c8186f75352ea84bceec06b4cb",203 "language": "Jupyter",204 "lines": 154,205 "truncated": false,206 "block": 5,207 "row": 17111208 },209 {210 "path": "notebook/Post-processing.ipynb",211 "sha256": "7f831474a72caed6baaf06312528331a70bb0a5bd90acf3391a49cde2a1ac9c9",212 "language": "Jupyter",213 "lines": 125,214 "truncated": false,215 "block": 5,216 "row": 16333217 },218 {219 "path": "notebook/PowerAnalysis.ipynb",220 "sha256": "4f15cab1871e1b5aa2aa62620892a90be1f9681adb64c2707cf29ccfabe5558f",221 "language": "Jupyter",222 "lines": 490,223 "truncated": false,224 "block": 6,225 "row": 754226 },227 {228 "path": "notebook/TensorBoard_Plotting.ipynb",229 "sha256": "0f9f8cbd0847135303382fbe710434827419ed1fd0dbf1864e9ac9c57b834b9b",230 "language": "Jupyter",231 "lines": 420,232 "truncated": false,233 "block": 6,234 "row": 677235 },236 {237 "path": "notebook/baseline_code/eval.ipynb",238 "sha256": "8966c1685afdd17fbdb51d340591f23416d67089a9c7e92fbd3ce233da13e1b2",239 "language": "Jupyter",240 "lines": 237,241 "truncated": false,242 "block": 5,243 "row": 17664244 },245 {246 "path": "notebook/baseline_code/fcn.ipynb",247 "sha256": "a361aba3b89db253e649c2b1de45e5c8e16d24391a952fea47d30f2e0fb4d6fe",248 "language": "Jupyter",249 "lines": 109,250 "truncated": false,251 "block": 5,252 "row": 16829253 },254 {255 "path": "notebook/baseline_code/fcn.py",256 "sha256": "003ddb8c11ba34b4b6df9d74a1878226ca06854d87e05db608614d5d4696ff9c",257 "language": "Python",258 "lines": 261,259 "truncated": false,260 "block": 7,261 "row": 7005262 },263 {264 "path": "notebook/baseline_code/test_visual_model.ipynb",265 "sha256": "d93fbf75ed9040d50dddbe0e3f7ce7abf8c701d034d8f5e7690ce38d8020c0ae",266 "language": "Jupyter",267 "lines": 288,268 "truncated": false,269 "block": 5,270 "row": 17688271 },272 {273 "path": "notebook/baseline_code/training.ipynb",274 "sha256": "aefe214054c07d8d6e97dee91dfe9143cc937c309c427f898917a7c45d1405a9",275 "language": "Jupyter",276 "lines": 205,277 "truncated": false,278 "block": 5,279 "row": 17471280 },281 {282 "path": "pyscripts/1_preprocessing.py",283 "sha256": "2d177891349b730906f5205dae686d26fbed99013e5908494e032ea25727fe53",284 "language": "Python",285 "lines": 110,286 "truncated": false,287 "block": 6,288 "row": 38317289 },290 {291 "path": "pyscripts/1_preprocessing_czi.py",292 "sha256": "3fba32afb654ef9c59007c324100bc0ccd806bca1f7aeaeac5ddf9e6382d93ed",293 "language": "Python",294 "lines": 212,295 "truncated": false,296 "block": 6,297 "row": 43174298 },299 {300 "path": "pyscripts/2_inference.py",301 "sha256": "33698934ff6ffcac95ac9c5790bd0a93761fe3b6ce40566ce26d6d5bc804e371",302 "language": "Python",303 "lines": 339,304 "truncated": false,305 "block": 7,306 "row": 5608307 },308 {309 "path": "pyscripts/2_inference_czi.py",310 "sha256": "6143924de7533f32a34e7e143182a22327998c170dd065b65153b45af37fdb74",311 "language": "Python",312 "lines": 399,313 "truncated": false,314 "block": 7,315 "row": 7844316 },317 {318 "path": "pyscripts/3_postprocessing.py",319 "sha256": "555e3d9475329c63cc7babb549c11bb9cf8914e0bac9ac96bc1b8184803accd4",320 "language": "Python",321 "lines": 419,322 "truncated": false,323 "block": 7,324 "row": 6725325 },326 {327 "path": "pyscripts/3_postprocessing_nobraingsegpostprop.py",328 "sha256": "1c24908f52801bc616a6b3c40018add009170e64701f3e532a997efbeccd08e6",329 "language": "Python",330 "lines": 435,331 "truncated": false,332 "block": 7,333 "row": 7293334 },335 {336 "path": "qupath/scripts/CombineAnnotationTiles.py",337 "sha256": "2a655580cf46f70207fd22d998b14ef8c9711d3c2689b57ab75b28ed631e13f2",338 "language": "Python",339 "lines": 118,340 "truncated": false,341 "block": 6,342 "row": 39624343 },344 {345 "path": "setup.sh",346 "sha256": "4a03f384e3b435be1d5f4fc0adb98354858c8b98c5d95e54e3324436c9bd5d54",347 "language": "Shell",348 "lines": 245,349 "truncated": false,350 "block": 8,351 "row": 9223352 },353 {354 "path": "src/gSLICr/NVTimer.h",355 "sha256": "7412a31bc440e7fae1074a45fa26b8080b7bde91df8662154be34c2ada929d04",356 "language": "C/C++",357 "lines": 496,358 "truncated": false,359 "block": 5,360 "row": 9120361 },362 {363 "path": "src/gSLICr/ORUtils/CUDADefines.h",364 "sha256": "3db7db7c9e78699b1530cbef40c529b026fec091354af7bad5f2fedcd43deab2",365 "language": "C/C++",366 "lines": 54,367 "truncated": false,368 "block": 5,369 "row": 6213370 },371 {372 "path": "src/gSLICr/ORUtils/Cholesky.h",373 "sha256": "5d6244ead8dd99bc020682fa296a00e5d676030d591bc8c08e46115991f95c4b",374 "language": "C/C++",375 "lines": 73,376 "truncated": false,377 "block": 5,378 "row": 6326379 },380 {381 "path": "src/gSLICr/ORUtils/Dummy.cpp",382 "sha256": "f3e8f625ea669c45de7cf3e9a260854559de01426daf1e2cee9d51fb67711c38",383 "language": "C++",384 "lines": 4,385 "truncated": false,386 "block": 5,387 "row": 1006388 },389 {390 "path": "src/gSLICr/ORUtils/Image.h",391 "sha256": "991c20b4f6b3aaca9152a496a62b53df74d4b75b6de4474a254e2e334d095802",392 "language": "C/C++",393 "lines": 69,394 "truncated": false,395 "block": 5,396 "row": 6634397 },398 {399 "path": "src/gSLICr/ORUtils/LexicalCast.h",400 "sha256": "643ed5024aa5d40d8c728a341ec5e9a1f61f7dbb0ac50a78d6f9d274812faa77",401 "language": "C/C++",402 "lines": 29,403 "truncated": false,404 "block": 5,405 "row": 5961406 },407 {408 "path": "src/gSLICr/ORUtils/MathUtils.h",409 "sha256": "cfa4792b38d3c0274254c11c9abdd1f6a43e9102e745dfc6340e3fe4dd2849f8",410 "language": "C/C++",411 "lines": 57,412 "truncated": false,413 "block": 5,414 "row": 6090415 },416 {417 "path": "src/gSLICr/ORUtils/Matrix.h",418 "sha256": "9985b95558622a03d55657fd47450902d28758675d8a36fb97d21a047b4dd456",419 "language": "C/C++",420 "lines": 444,421 "truncated": false,422 "block": 5,423 "row": 9494424 },425 {426 "path": "src/gSLICr/ORUtils/MemoryBlock.h",427 "sha256": "9b3a1ae701d0f7a0cd468d45a4810d4a48cff4440418c8246840df2dcf2a4a4a",428 "language": "C/C++",429 "lines": 293,430 "truncated": false,431 "block": 5,432 "row": 8443433 },434 {435 "path": "src/gSLICr/ORUtils/MemoryBlockPersister.h",436 "sha256": "99f206ad5cce85691a02131af82d1083c2d291645000d9f339fd807b27bd8deb",437 "language": "C/C++",438 "lines": 201,439 "truncated": false,440 "block": 5,441 "row": 8410442 },443 {444 "path": "src/gSLICr/ORUtils/MetalContext.h",445 "sha256": "d0f9388928b713b42f14e4e779a481344405317c2344646724c3611b3e003e44",446 "language": "C/C++",447 "lines": 40,448 "truncated": false,449 "block": 5,450 "row": 6214451 },452 {453 "path": "src/gSLICr/ORUtils/PlatformIndependence.h",454 "sha256": "613f26e57b2a61c44da042e44e029df068115bc4dd7f896213b50d210c405d00",455 "language": "C/C++",456 "lines": 35,457 "truncated": false,458 "block": 5,459 "row": 6024460 },461 {462 "path": "src/gSLICr/ORUtils/Vector.h",463 "sha256": "a8790b46b81701c7074fb983aa703a86a526414806977417d06898ed2b140859",464 "language": "C/C++",465 "lines": 851,466 "truncated": false,467 "block": 5,468 "row": 9750469 },470 {471 "path": "src/gSLICr/demo.cpp",472 "sha256": "85e350195a337166f2d1b529cf68fe924a4245684942473d604ac6fc02ae6b6e",473 "language": "C++",474 "lines": 119,475 "truncated": false,476 "block": 5,477 "row": 2957478 },479 {480 "path": "src/gSLICr/gSLICr_Lib/engines/gSLICr_core_engine.cpp",481 "sha256": "83a50d6ec0051d04021209f472ce32563b2ef183d4219505e03514db05d974d1",482 "language": "C++",483 "lines": 57,484 "truncated": false,485 "block": 5,486 "row": 1804487 },488 {489 "path": "src/gSLICr/gSLICr_Lib/engines/gSLICr_core_engine.h",490 "sha256": "3b408e457c5ba8da78ace38bff20bc5e03e029c21cca7126a0423823807811df",491 "language": "C/C++",492 "lines": 38,493 "truncated": false,494 "block": 5,495 "row": 5970496 },497 {498 "path": "src/gSLICr/gSLICr_Lib/engines/gSLICr_seg_engine.cpp",499 "sha256": "20e93846f6d3b0d120649454c8aec78f09552559fab819002e32ab9190989553",500 "language": "C++",501 "lines": 61,502 "truncated": false,503 "block": 5,504 "row": 1936505 },506 {507 "path": "src/gSLICr/gSLICr_Lib/engines/gSLICr_seg_engine.h",508 "sha256": "ba131451f965a316589af81ca711ad526a9b165b881b169878a996fe42839cc3",509 "language": "C/C++",510 "lines": 63,511 "truncated": false,512 "block": 5,513 "row": 6357514 },515 {516 "path": "src/gSLICr/gSLICr_Lib/engines/gSLICr_seg_engine_GPU.cu",517 "sha256": "16691484386a410ae87ea2699c309aef9053e2144cad23e11cffb7eac8b1550c",518 "language": "CUDA",519 "lines": 393,520 "truncated": false,521 "block": 5,522 "row": 10090523 },524 {525 "path": "src/gSLICr/gSLICr_Lib/engines/gSLICr_seg_engine_GPU.h",526 "sha256": "cfdfeb5c08861e05f09a47935fd1a778d7b12d78f34bcd1492994e7e501d633a",527 "language": "C/C++",528 "lines": 34,529 "truncated": false,530 "block": 5,531 "row": 5910532 },533 {534 "path": "src/gSLICr/gSLICr_Lib/engines/gSLICr_seg_engine_shared.h",535 "sha256": "f7406d1164b6e0a2670468014d56d967df18a1f03d8c552bc4d8e6e6b92b4a68",536 "language": "C/C++",537 "lines": 290,538 "truncated": false,539 "block": 5,540 "row": 8734541 },542 {543 "path": "src/gSLICr/gSLICr_Lib/gSLICr.h",544 "sha256": "0571b177cbe50109b43966b1d4181eb60939fe586fde67d54d94fbe16d562be6",545 "language": "C/C++",546 "lines": 83,547 "truncated": false,548 "block": 5,549 "row": 7103550 },551 {552 "path": "src/gSLICr/gSLICr_Lib/gSLICr_defines.h",553 "sha256": "c3ef99aa51413217d785c62b48bab5bd36e73329fa876fda3e2c67db223b3620",554 "language": "C/C++",555 "lines": 100,556 "truncated": false,557 "block": 5,558 "row": 6916559 },560 {561 "path": "src/gSLICr/gSLICr_Lib/objects/gSLICr_settings.h",562 "sha256": "41ee280903f6039a269461a0078f730420cb79ebf1fe39725675ddd7b5460ce4",563 "language": "C/C++",564 "lines": 24,565 "truncated": false,566 "block": 5,567 "row": 5689568 },569 {570 "path": "src/gSLICr/gSLICr_Lib/objects/gSLICr_spixel_info.h",571 "sha256": "32f3fe54257c9341185f4213d925aaa15ad1efcd87c4c1d5966c720f8111370b",572 "language": "C/C++",573 "lines": 20,574 "truncated": false,575 "block": 5,576 "row": 5649577 },578 {579 "path": "src/image_helper.py",580 "sha256": "ad174a0c0b4a165aa1863aeb9806baf32f3df00f321327190d88ff26b3cdf688",581 "language": "Python",582 "lines": 292,583 "truncated": false,584 "block": 7,585 "row": 3989586 },587 {588 "path": "src/networks/dataset.py",589 "sha256": "5644d85d68014771c6b9cde0ee2892d0e298fdff1a78abac5b99a2c21a31728f",590 "language": "Python",591 "lines": 758,592 "truncated": false,593 "block": 7,594 "row": 10875595 },596 {597 "path": "src/networks/losses.py",598 "sha256": "054c93a0a6eac9439f7b562dfc5a4dbb527c4c2d4badf90d198f2ad8a66d6552",599 "language": "Python",600 "lines": 118,601 "truncated": false,602 "block": 6,603 "row": 41587604 },605 {606 "path": "src/networks/metrics.py",607 "sha256": "3ccf7c33b11cf27782c25b9aa1d379e6512f09ec631a6d9cac581c3616392899",608 "language": "Python",609 "lines": 138,610 "truncated": false,611 "block": 6,612 "row": 42928613 },614 {615 "path": "src/networks/models/FCN.py",616 "sha256": "a3a46207af9b862dcb4ee20eb22c4d6c98c27e982a24f7e5311e4ac175987955",617 "language": "Python",618 "lines": 112,619 "truncated": false,620 "block": 6,621 "row": 39583622 },623 {624 "path": "src/networks/models/UNet.py",625 "sha256": "3d945974a3ccabaf94fd3a9d8e930c74539652fa1d6145e7dd220a6deb59bbae",626 "language": "Python",627 "lines": 171,628 "truncated": false,629 "block": 7,630 "row": 1105631 },632 {633 "path": "src/networks/models/models.py",634 "sha256": "7349902bb35bc0df249db7902ff3f5eba53b8baf7d0c6b14d431df112272e252",635 "language": "Python",636 "lines": 58,637 "truncated": false,638 "block": 6,639 "row": 34450640 },641 {642 "path": "src/postproc.py",643 "sha256": "12b20f7651a8655c4d6437398bc6805abc79a08b88ff002ec0c869d21392c296",644 "language": "Python",645 "lines": 450,646 "truncated": false,647 "block": 7,648 "row": 7594649 },650 {651 "path": "src/predict.py",652 "sha256": "6ab4eb8fc850dd1832b3b8aa5564ca9b04307e48fab148597e34c8e9aacc9459",653 "language": "Python",654 "lines": 187,655 "truncated": false,656 "block": 6,657 "row": 45226658 },659 {660 "path": "src/tissue_seg.py",661 "sha256": "6a015fd66b5510bc4724cbe95aa485de54932c3596330d4b74d15cb9187b13c9",662 "language": "Python",663 "lines": 179,664 "truncated": false,665 "block": 6,666 "row": 44103667 },668 {669 "path": "src/train.py",670 "sha256": "2c7bda6e1b3f92047b45d84c82aa321323ef998c69e3e0c2cfeba25ade55b54e",671 "language": "Python",672 "lines": 342,673 "truncated": false,674 "block": 7,675 "row": 6036676 },677 {678 "path": "src/utils/color_deconv.py",679 "sha256": "98a280b9ae898c28cce9d9db52792db7b901abf223d6c3589a7234a9c2291b1c",680 "language": "Python",681 "lines": 73,682 "truncated": false,683 "block": 6,684 "row": 35937685 },686 {687 "path": "src/utils/compute_mask_accuracy.py",688 "sha256": "19fd5d6c7feefcec2831841c56e4dc22a562f805fb08c5145927e79e575335a1",689 "language": "Python",690 "lines": 438,691 "truncated": false,692 "block": 7,693 "row": 8612694 },695 {696 "path": "src/utils/numpy_pil_helper.py",697 "sha256": "7c38d03c1e1496456af2b2deab186e97fcb0275833fefa3872232e955e429f2a",698 "language": "Python",699 "lines": 112,700 "truncated": false,701 "block": 6,702 "row": 40516703 },704 {705 "path": "src/utils/separate_tissue.py",706 "sha256": "4e5ee34961124b8a482b603b1464854cbeaa595d2262ef213a5c582ac42eb376",707 "language": "Python",708 "lines": 521,709 "truncated": false,710 "block": 7,711 "row": 9214712 },713 {714 "path": "src/utils/svs_to_png.py",715 "sha256": "eebe3f2d7d58a930229f02ab050a5acdabe489deadf7fe2c05830e9a036d2b47",716 "language": "Python",717 "lines": 140,718 "truncated": false,719 "block": 6,720 "row": 41046721 },722 {723 "path": "tests/test_model_losses.py",724 "sha256": "0e2853c25b66e68c0ecab970d8a8c19cdad08295463162f55684ffb94f897201",725 "language": "Python",726 "lines": 676,727 "truncated": false,728 "block": 7,729 "row": 10297730 },731 {732 "path": "tests/test_train_predict.py",733 "sha256": "0ec38e89cdf5d0e14e3620386792c7114cbcb0d2668f050ed173d50a2e3185a2",734 "language": "Python",735 "lines": 236,736 "truncated": false,737 "block": 7,738 "row": 2803739 }740 ]741}