benchmark_results/results.json at benchmark-results - concept-collection/ephys_compression

concept-collection / ephys_compression_tests

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7998 "algorithm_version": "3",

7999 "dataset_version": "2",

8000 "system_version": "v6",

8001 "reconstructed_url_raw": "https://tempory.net/f/memobin/ephys_compression_tests/reconstructed/wavpack-lossy-bps2.25/ibl-compression-np1-Probe00-ch101-110-filtered/v3-2-v6/reconstructed.dat"

8002 },

8003 {

8004 "compression_ratio": 6.369359136314901,

8005 "encode_time": 0.08219889900010457,

8006 "decode_time": 0.04948145999969711,

8007 "encode_mb_per_sec": 69.61219636810732,

8008 "decode_mb_per_sec": 115.64019934885766,

8009 "original_size": 6000000,

8010 "compressed_size": 942010,

8011 "array_shape": [

8012 300000,

8013 10

8014 ],

8015 "array_dtype": "int16",

8016 "timestamp": 1770049338.2843595,

8017 "cache_status": "cached",

8018 "rmse": 0.6790512989949041,

8019 "max_error": 5.0,

8020 "dataset": "ibl-compression-np1-Probe00-ch101-110-filtered",

8021 "algorithm": "wavpack-lossy-bps3",

8022 "algorithm_version": "3",

8023 "dataset_version": "2",

8024 "system_version": "v6",

8025 "reconstructed_url_raw": "https://tempory.net/f/memobin/ephys_compression_tests/reconstructed/wavpack-lossy-bps3/ibl-compression-np1-Probe00-ch101-110-filtered/v3-2-v6/reconstructed.dat"

8026 },

8027 {

8028 "compression_ratio": 5.430670253322665,

8029 "encode_time": 0.08574921449985595,

8030 "decode_time": 0.05006078050018914,

8031 "encode_mb_per_sec": 66.73000950285221,

8032 "decode_mb_per_sec": 114.30197134892616,

8033 "original_size": 6000000,

8034 "compressed_size": 1104836,

8035 "array_shape": [

8036 300000,

8037 10

8038 ],

8039 "array_dtype": "int16",

8040 "timestamp": 1770049342.517713,

8041 "cache_status": "cached",

8042 "rmse": 0.0,

8043 "max_error": 0.0,

8044 "dataset": "ibl-compression-np1-Probe00-ch101-110-filtered",

8045 "algorithm": "wavpack-lossy-bps4",

8046 "algorithm_version": "3",

8047 "dataset_version": "2",

8048 "system_version": "v6",

8049 "reconstructed_url_raw": "https://tempory.net/f/memobin/ephys_compression_tests/reconstructed/wavpack-lossy-bps4/ibl-compression-np1-Probe00-ch101-110-filtered/v3-2-v6/reconstructed.dat"

8050 },

8051 {

8052 "compression_ratio": 5.430670253322665,

8053 "encode_time": 0.08471801900009268,

8054 "decode_time": 0.04880981300038911,

8055 "encode_mb_per_sec": 67.54225330069676,

8056 "decode_mb_per_sec": 117.23146528735694,

8057 "original_size": 6000000,

8058 "compressed_size": 1104836,

8059 "array_shape": [

8060 300000,

8061 10

8062 ],

8063 "array_dtype": "int16",

8064 "timestamp": 1770049346.6125097,

8065 "cache_status": "cached",

8066 "rmse": 0.0,

8067 "max_error": 0.0,

8068 "dataset": "ibl-compression-np1-Probe00-ch101-110-filtered",

8069 "algorithm": "wavpack-lossy-bps5",

8070 "algorithm_version": "3",

8071 "dataset_version": "2",

8072 "system_version": "v6",

8073 "reconstructed_url_raw": "https://tempory.net/f/memobin/ephys_compression_tests/reconstructed/wavpack-lossy-bps5/ibl-compression-np1-Probe00-ch101-110-filtered/v3-2-v6/reconstructed.dat"

8074 },

8075 {

8076 "compression_ratio": 5.430670253322665,

8077 "encode_time": 0.08472793149985591,

8078 "decode_time": 0.04869090200008941,

8079 "encode_mb_per_sec": 67.53435138974484,

8080 "decode_mb_per_sec": 117.51776334780146,

8081 "original_size": 6000000,

8082 "compressed_size": 1104836,

8083 "array_shape": [

8084 300000,

8085 10

8086 ],

8087 "array_dtype": "int16",

8088 "timestamp": 1770049350.6831353,

8089 "cache_status": "cached",

8090 "rmse": 0.0,

8091 "max_error": 0.0,

8092 "dataset": "ibl-compression-np1-Probe00-ch101-110-filtered",

8093 "algorithm": "wavpack-lossy-bps6",

8094 "algorithm_version": "3",

8095 "dataset_version": "2",

8096 "system_version": "v6",

8097 "reconstructed_url_raw": "https://tempory.net/f/memobin/ephys_compression_tests/reconstructed/wavpack-lossy-bps6/ibl-compression-np1-Probe00-ch101-110-filtered/v3-2-v6/reconstructed.dat"

8098 },

8099 {

8100 "compression_ratio": 3.218212508226556,

8101 "encode_time": 3.7490723579999212,

8102 "decode_time": 0.1122860559999026,

8103 "encode_mb_per_sec": 1.5262564581415903,

8104 "decode_mb_per_sec": 50.95954121358099,

8105 "original_size": 6000000,

8106 "compressed_size": 1864389,

8107 "array_shape": [

8108 300000,

8109 10

8110 ],

8111 "array_dtype": "int16",

8112 "timestamp": 1770049361.2219024,

8113 "cache_status": "cached",

8114 "rmse": 0.0,

8115 "max_error": 0.0,

8116 "dataset": "ibl-compression-np1-Probe00-ch101-110-filtered",

8117 "algorithm": "lzma",

8118 "algorithm_version": "1",

8119 "dataset_version": "2",

8120 "system_version": "v6"

8121 },

8122 {

8123 "compression_ratio": 4.393116572080427,

8124 "encode_time": 4.152661830999932,

8125 "decode_time": 0.09119373499993344,

8126 "encode_mb_per_sec": 1.3779224341654785,

8127 "decode_mb_per_sec": 62.74604169290441,

8128 "original_size": 6000000,

8129 "compressed_size": 1365773,

8130 "array_shape": [

8131 300000,

8132 10

8133 ],

8134 "array_dtype": "int16",

8135 "timestamp": 1770049371.642012,

8136 "cache_status": "cached",

8137 "rmse": 0.0,

8138 "max_error": 0.0,

8139 "dataset": "ibl-compression-np1-Probe00-ch101-110-filtered",

8140 "algorithm": "lzma-delta",

8141 "algorithm_version": "1",

8142 "dataset_version": "2",

8143 "system_version": "v6"

8144 },

8145 {

8146 "compression_ratio": 2.441664564122181,

8147 "encode_time": 1.5573985320002066,

8148 "decode_time": 0.019014471999980742,

8149 "encode_mb_per_sec": 3.674105105960599,

8150 "decode_mb_per_sec": 300.93109598011955,

8151 "original_size": 6000000,

8152 "compressed_size": 2457340,

8153 "array_shape": [

8154 300000,

8155 10

8156 ],

8157 "array_dtype": "int16",

8158 "timestamp": 1770049376.8168786,

8159 "cache_status": "cached",

8160 "rmse": 0.0,

8161 "max_error": 0.0,

8162 "dataset": "ibl-compression-np1-Probe00-ch101-110-filtered",

8163 "algorithm": "zlib",

8164 "algorithm_version": "1",

8165 "dataset_version": "2",

8166 "system_version": "v6"

8167 },

8168 {

8169 "compression_ratio": 3.4900176769395337,

8170 "encode_time": 3.214542824000091,

8171 "decode_time": 0.038719640999943294,

8172 "encode_mb_per_sec": 1.7800496716721723,

8173 "decode_mb_per_sec": 147.78148119828592,

8174 "original_size": 6000000,

8175 "compressed_size": 1719189,

8176 "array_shape": [

8177 300000,

8178 10

8179 ],

8180 "array_dtype": "int16",

8181 "timestamp": 1770049385.1635854,

8182 "cache_status": "cached",

8183 "rmse": 0.0,

8184 "max_error": 0.0,

8185 "dataset": "ibl-compression-np1-Probe00-ch101-110-filtered",

8186 "algorithm": "zlib-delta",

8187 "algorithm_version": "1",

8188 "dataset_version": "2",

8189 "system_version": "v6"

8190 }

8191 ],

8192 "algorithms": [

8193 {

8194 "name": "blosc2-zstd-1",

8195 "description": "Blosc2 compression at level 1 (fastest compression).",

8196 "long_description": "# Blosc2 Algorithm\n\nBlosc2 is a modern, fast data compression library that builds upon the original Blosc library. It is designed for efficient compression of binary data, particularly optimized for in-memory compression of numerical arrays. Blosc2 uses block-oriented compression with support for multithreading and SIMD instructions.\n",

8197 "version": "1b",

8198 "tags": [

8199 "blosc2"

8200 ],

8201 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/blosc2/__init__.py"

8202 },

8203 {

8204 "name": "blosc2-zstd-5",

8205 "description": "Blosc2 compression at level 5 (balanced speed/compression).",

8206 "long_description": "# Blosc2 Algorithm\n\nBlosc2 is a modern, fast data compression library that builds upon the original Blosc library. It is designed for efficient compression of binary data, particularly optimized for in-memory compression of numerical arrays. Blosc2 uses block-oriented compression with support for multithreading and SIMD instructions.\n",

8207 "version": "1",

8208 "tags": [

8209 "blosc2"

8210 ],

8211 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/blosc2/__init__.py"

8212 },

8213 {

8214 "name": "blosc2-zstd-9",

8215 "description": "Blosc2 compression at level 9 (maximum compression).",

8216 "long_description": "# Blosc2 Algorithm\n\nBlosc2 is a modern, fast data compression library that builds upon the original Blosc library. It is designed for efficient compression of binary data, particularly optimized for in-memory compression of numerical arrays. Blosc2 uses block-oriented compression with support for multithreading and SIMD instructions.\n",

8217 "version": "1",

8218 "tags": [

8219 "blosc2"

8220 ],

8221 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/blosc2/__init__.py"

8222 },

8223 {

8224 "name": "ans",

8225 "description": "ANS",

8226 "long_description": "# ANS\n",

8227 "version": "1",

8228 "tags": [

8229 "ans"

8230 ],

8231 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/ans/__init__.py"

8232 },

8233 {

8234 "name": "ans-delta",

8235 "description": "ANS with delta encoding",

8236 "long_description": "# ANS\n",

8237 "version": "1",

8238 "tags": [

8239 "ans",

8240 "delta"

8241 ],

8242 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/ans/__init__.py"

8243 },

8244 {

8245 "name": "ans-delta2",

8246 "description": "ANS with delta2 encoding",

8247 "long_description": "# ANS\n",

8248 "version": "1",

8249 "tags": [

8250 "ans",

8251 "delta2"

8252 ],

8253 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/ans/__init__.py"

8254 },

8255 {

8256 "name": "ans-lpc2",

8257 "description": "ANS with auto-regressive prediction encoding of order 2",

8258 "long_description": "# ANS\n",

8259 "version": "1.5",

8260 "tags": [

8261 "ans",

8262 "lpc2"

8263 ],

8264 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/ans/__init__.py"

8265 },

8266 {

8267 "name": "ans-lpc8",

8268 "description": "ANS with auto-regressive prediction encoding of order 8",

8269 "long_description": "# ANS\n",

8270 "version": "1.5",

8271 "tags": [

8272 "ans",

8273 "lpc8"

8274 ],

8275 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/ans/__init__.py"

8276 },

8277 {

8278 "name": "ans-lpc2-lossy-tol1",

8279 "description": "ANS with lossy linear predictive coding of order 2 and tolerance 1",

8280 "long_description": "# ANS\n",

8281 "version": "14",

8282 "tags": [

8283 "ans",

8284 "lossy",

8285 "lpc2"

8286 ],

8287 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/ans/__init__.py"

8288 },

8289 {

8290 "name": "ans-lpc2-lossy-tol2",

8291 "description": "ANS with lossy linear predictive coding of order 2 and tolerance 2",

8292 "long_description": "# ANS\n",

8293 "version": "14",

8294 "tags": [

8295 "ans",

8296 "lossy",

8297 "lpc2"

8298 ],

8299 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/ans/__init__.py"

8300 },

8301 {

8302 "name": "ans-lpc2-lossy-tol3",

8303 "description": "ANS with lossy linear predictive coding of order 2 and tolerance 3",

8304 "long_description": "# ANS\n",

8305 "version": "14",

8306 "tags": [

8307 "ans",

8308 "lossy",

8309 "lpc2"

8310 ],

8311 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/ans/__init__.py"

8312 },

8313 {

8314 "name": "ans-lpc2-lossy-tol4",

8315 "description": "ANS with lossy linear predictive coding of order 2 and tolerance 4",

8316 "long_description": "# ANS\n",

8317 "version": "14",

8318 "tags": [

8319 "ans",

8320 "lossy",

8321 "lpc2"

8322 ],

8323 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/ans/__init__.py"

8324 },

8325 {

8326 "name": "ans-lpc2-lossy-tol6",

8327 "description": "ANS with lossy linear predictive coding of order 2 and tolerance 6",

8328 "long_description": "# ANS\n",

8329 "version": "14",

8330 "tags": [

8331 "ans",

8332 "lossy",

8333 "lpc2"

8334 ],

8335 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/ans/__init__.py"

8336 },

8337 {

8338 "name": "ans-lpc2-lossy-tol8",

8339 "description": "ANS with lossy linear predictive coding of order 2 and tolerance 8",

8340 "long_description": "# ANS\n",

8341 "version": "14",

8342 "tags": [

8343 "ans",

8344 "lossy",

8345 "lpc2"

8346 ],

8347 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/ans/__init__.py"

8348 },

8349 {

8350 "name": "ans-lpc2-lossy-tol12",

8351 "description": "ANS with lossy linear predictive coding of order 2 and tolerance 12",

8352 "long_description": "# ANS\n",

8353 "version": "14",

8354 "tags": [

8355 "ans",

8356 "lossy",

8357 "lpc2"

8358 ],

8359 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/ans/__init__.py"

8360 },

8361 {

8362 "name": "ans-lpc2-lossy-tol16",

8363 "description": "ANS with lossy linear predictive coding of order 2 and tolerance 16",

8364 "long_description": "# ANS\n",

8365 "version": "14",

8366 "tags": [

8367 "ans",

8368 "lossy",

8369 "lpc2"

8370 ],

8371 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/ans/__init__.py"

8372 },

8373 {

8374 "name": "ans-lpc8-lossy-tol1",

8375 "description": "ANS with lossy linear predictive coding of order 8 and tolerance 1",

8376 "long_description": "# ANS\n",

8377 "version": "14",

8378 "tags": [

8379 "ans",

8380 "lossy",

8381 "lpc8"

8382 ],

8383 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/ans/__init__.py"

8384 },

8385 {

8386 "name": "ans-lpc8-lossy-tol2",

8387 "description": "ANS with lossy linear predictive coding of order 8 and tolerance 2",

8388 "long_description": "# ANS\n",

8389 "version": "14",

8390 "tags": [

8391 "ans",

8392 "lossy",

8393 "lpc8"

8394 ],

8395 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/ans/__init__.py"

8396 },

8397 {

8398 "name": "ans-lpc8-lossy-tol3",

8399 "description": "ANS with lossy linear predictive coding of order 8 and tolerance 3",

8400 "long_description": "# ANS\n",

8401 "version": "14",

8402 "tags": [

8403 "ans",

8404 "lossy",

8405 "lpc8"

8406 ],

8407 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/ans/__init__.py"

8408 },

8409 {

8410 "name": "ans-lpc8-lossy-tol4",

8411 "description": "ANS with lossy linear predictive coding of order 8 and tolerance 4",

8412 "long_description": "# ANS\n",

8413 "version": "14",

8414 "tags": [

8415 "ans",

8416 "lossy",

8417 "lpc8"

8418 ],

8419 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/ans/__init__.py"

8420 },

8421 {

8422 "name": "ans-lpc8-lossy-tol6",

8423 "description": "ANS with lossy linear predictive coding of order 8 and tolerance 6",

8424 "long_description": "# ANS\n",

8425 "version": "14",

8426 "tags": [

8427 "ans",

8428 "lossy",

8429 "lpc8"

8430 ],

8431 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/ans/__init__.py"

8432 },

8433 {

8434 "name": "ans-lpc8-lossy-tol8",

8435 "description": "ANS with lossy linear predictive coding of order 8 and tolerance 8",

8436 "long_description": "# ANS\n",

8437 "version": "14",

8438 "tags": [

8439 "ans",

8440 "lossy",

8441 "lpc8"

8442 ],

8443 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/ans/__init__.py"

8444 },

8445 {

8446 "name": "ans-lpc8-lossy-tol12",

8447 "description": "ANS with lossy linear predictive coding of order 8 and tolerance 12",

8448 "long_description": "# ANS\n",

8449 "version": "14",

8450 "tags": [

8451 "ans",

8452 "lossy",

8453 "lpc8"

8454 ],

8455 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/ans/__init__.py"

8456 },

8457 {

8458 "name": "ans-lpc8-lossy-tol16",

8459 "description": "ANS with lossy linear predictive coding of order 8 and tolerance 16",

8460 "long_description": "# ANS\n",

8461 "version": "14",

8462 "tags": [

8463 "ans",

8464 "lossy",

8465 "lpc8"

8466 ],

8467 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/ans/__init__.py"

8468 },

8469 {

8470 "name": "wavpack",

8471 "description": "WavPack",

8472 "long_description": "# wavpack\n",

8473 "version": "1",

8474 "tags": [

8475 "wavpack"

8476 ],

8477 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/wavpack/__init__.py"

8478 },

8479 {

8480 "name": "wavpack-delta",

8481 "description": "WavPack with delta encoding",

8482 "long_description": "# wavpack\n",

8483 "version": "1",

8484 "tags": [

8485 "wavpack",

8486 "delta"

8487 ],

8488 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/wavpack/__init__.py"

8489 },

8490 {

8491 "name": "wavpack-lossy-bps2.25",

8492 "description": "WavPack lossy with 2.25 bits per sample",

8493 "long_description": "# wavpack\n",

8494 "version": "3",

8495 "tags": [

8496 "wavpack",

8497 "lossy"

8498 ],

8499 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/wavpack/__init__.py"

8500 },

8501 {

8502 "name": "wavpack-lossy-bps3",

8503 "description": "WavPack lossy with 3 bits per sample",

8504 "long_description": "# wavpack\n",

8505 "version": "3",

8506 "tags": [

8507 "wavpack",

8508 "lossy"

8509 ],

8510 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/wavpack/__init__.py"

8511 },

8512 {

8513 "name": "wavpack-lossy-bps4",

8514 "description": "WavPack lossy with 4 bits per sample",

8515 "long_description": "# wavpack\n",

8516 "version": "3",

8517 "tags": [

8518 "wavpack",

8519 "lossy"

8520 ],

8521 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/wavpack/__init__.py"

8522 },

8523 {

8524 "name": "wavpack-lossy-bps5",

8525 "description": "WavPack lossy with 5 bits per sample",

8526 "long_description": "# wavpack\n",

8527 "version": "3",

8528 "tags": [

8529 "wavpack",

8530 "lossy"

8531 ],

8532 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/wavpack/__init__.py"

8533 },

8534 {

8535 "name": "wavpack-lossy-bps6",

8536 "description": "WavPack lossy with 6 bits per sample",

8537 "long_description": "# wavpack\n",

8538 "version": "3",

8539 "tags": [

8540 "wavpack",

8541 "lossy"

8542 ],

8543 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/wavpack/__init__.py"

8544 },

8545 {

8546 "name": "lzma",

8547 "description": "LZMA compression at level 9 (maximum compression)",

8548 "long_description": "# LZMA Algorithm\n\nLZMA (Lempel-Ziv-Markov chain Algorithm) is a lossless data compression algorithm that provides a high compression ratio. It is the default and general compression method of 7z format in the 7-Zip program. LZMA uses a dictionary compression scheme and features a high compression ratio with variable dictionary size, while still maintaining fast decompression speed.\n\nThe LZMA algorithm is particularly effective for compressing large files and is widely used in various applications including file archivers, software distribution, and embedded systems. Python's built-in `lzma` module provides access to this compression algorithm.\n",

8549 "version": "1",

8550 "tags": [

8551 "lzma"

8552 ],

8553 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/lzma/__init__.py"

8554 },

8555 {

8556 "name": "lzma-delta",

8557 "description": "LZMA compression at level 9 (maximum compression) with delta encoding",

8558 "long_description": "# LZMA Algorithm\n\nLZMA (Lempel-Ziv-Markov chain Algorithm) is a lossless data compression algorithm that provides a high compression ratio. It is the default and general compression method of 7z format in the 7-Zip program. LZMA uses a dictionary compression scheme and features a high compression ratio with variable dictionary size, while still maintaining fast decompression speed.\n\nThe LZMA algorithm is particularly effective for compressing large files and is widely used in various applications including file archivers, software distribution, and embedded systems. Python's built-in `lzma` module provides access to this compression algorithm.\n",

8559 "version": "1",

8560 "tags": [

8561 "lzma",

8562 "delta"

8563 ],

8564 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/lzma/__init__.py"

8565 },

8566 {

8567 "name": "zlib",

8568 "description": "zlib compression at level 9 (maximum compression)",

8569 "long_description": "# zlib Algorithm\n\nzlib is a widely-used, general-purpose data compression library that implements the DEFLATE compression algorithm. DEFLATE is a combination of LZ77 (Lempel-Ziv 1977) and Huffman coding. The zlib library is extremely portable, efficient, and free for use in any application.\n\nzlib compression is commonly used in many applications including PNG image compression, HTTP compression, and git repositories. It provides a good balance between compression ratio and speed, making it suitable for a wide range of compression tasks. Python includes zlib as a built-in module, making it readily available without external dependencies.\n",

8570 "version": "1",

8571 "tags": [

8572 "zlib"

8573 ],

8574 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/zlib/__init__.py"

8575 },

8576 {

8577 "name": "zlib-delta",

8578 "description": "zlib compression at level 9 (maximum compression) with delta encoding",

8579 "long_description": "# zlib Algorithm\n\nzlib is a widely-used, general-purpose data compression library that implements the DEFLATE compression algorithm. DEFLATE is a combination of LZ77 (Lempel-Ziv 1977) and Huffman coding. The zlib library is extremely portable, efficient, and free for use in any application.\n\nzlib compression is commonly used in many applications including PNG image compression, HTTP compression, and git repositories. It provides a good balance between compression ratio and speed, making it suitable for a wide range of compression tasks. Python includes zlib as a built-in module, making it readily available without external dependencies.\n",

8580 "version": "1",

8581 "tags": [

8582 "zlib",

8583 "delta"

8584 ],

8585 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/algorithms/zlib/__init__.py"

8586 }

8587 ],

8588 "datasets": [

8589 {

8590 "name": "retina512-ch0-seg2-6",

8591 "description": "Retina512 example dataset",

8592 "long_description": "Examples from Vyom Raval",

8593 "version": "1",

8594 "tags": [

8595 "real",

8596 "ecephys",

8597 "timeseries",

8598 "single-channel",

8599 "integer",

8600 "correlated"

8601 ],

8602 "data_url_raw": "https://tempory.net/f/memobin/ephys_compression_tests/datasets/retina512-ch0-seg2-6/1/retina512-ch0-seg2-6-1.dat",

8603 "data_url_npy": "https://tempory.net/f/memobin/ephys_compression_tests/datasets/retina512-ch0-seg2-6/1/retina512-ch0-seg2-6-1.npy",

8604 "data_url_json": "https://tempory.net/f/memobin/ephys_compression_tests/datasets/retina512-ch0-seg2-6/1/retina512-ch0-seg2-6-1.json",

8605 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/datasets/retina512/__init__.py"

8606 },

8607 {

8608 "name": "retina512-ch0-seg2-6-filtered",

8609 "description": "Retina512 example dataset (bandpass filtered 300-4000 Hz)",

8610 "long_description": "Examples from Vyom Raval",

8611 "version": "1",

8612 "tags": [

8613 "real",

8614 "ecephys",

8615 "timeseries",

8616 "single-channel",

8617 "integer",

8618 "correlated",

8619 "filtered",

8620 "bandpass"

8621 ],

8622 "data_url_raw": "https://tempory.net/f/memobin/ephys_compression_tests/datasets/retina512-ch0-seg2-6-filtered/1/retina512-ch0-seg2-6-filtered-1.dat",

8623 "data_url_npy": "https://tempory.net/f/memobin/ephys_compression_tests/datasets/retina512-ch0-seg2-6-filtered/1/retina512-ch0-seg2-6-filtered-1.npy",

8624 "data_url_json": "https://tempory.net/f/memobin/ephys_compression_tests/datasets/retina512-ch0-seg2-6-filtered/1/retina512-ch0-seg2-6-filtered-1.json",

8625 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/datasets/retina512/__init__.py"

8626 },

8627 {

8628 "name": "aind-compression-np2-ProbeB-ch101",

8629 "description": "AIND CH101 dataset",

8630 "long_description": "Examples from AIND Compression Benchmark\n\nSee: https://allenneuraldynamics.github.io/data.html#aind-benchmark-dataephys-compression",

8631 "version": "2",

8632 "tags": [

8633 "real",

8634 "ecephys",

8635 "timeseries",

8636 "integer",

8637 "correlated",

8638 "single-channel"

8639 ],

8640 "data_url_raw": "https://tempory.net/f/memobin/ephys_compression_tests/datasets/aind-compression-np2-ProbeB-ch101/2/aind-compression-np2-ProbeB-ch101-2.dat",

8641 "data_url_npy": "https://tempory.net/f/memobin/ephys_compression_tests/datasets/aind-compression-np2-ProbeB-ch101/2/aind-compression-np2-ProbeB-ch101-2.npy",

8642 "data_url_json": "https://tempory.net/f/memobin/ephys_compression_tests/datasets/aind-compression-np2-ProbeB-ch101/2/aind-compression-np2-ProbeB-ch101-2.json",

8643 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/datasets/aind-compression/__init__.py"

8644 },

8645 {

8646 "name": "aind-compression-np2-ProbeB-ch101-110",

8647 "description": "AIND CH101-110 dataset",

8648 "long_description": "Examples from AIND Compression Benchmark\n\nSee: https://allenneuraldynamics.github.io/data.html#aind-benchmark-dataephys-compression",

8649 "version": "2",

8650 "tags": [

8651 "real",

8652 "ecephys",

8653 "timeseries",

8654 "integer",

8655 "correlated",

8656 "multi-channel"

8657 ],

8658 "data_url_raw": "https://tempory.net/f/memobin/ephys_compression_tests/datasets/aind-compression-np2-ProbeB-ch101-110/2/aind-compression-np2-ProbeB-ch101-110-2.dat",

8659 "data_url_npy": "https://tempory.net/f/memobin/ephys_compression_tests/datasets/aind-compression-np2-ProbeB-ch101-110/2/aind-compression-np2-ProbeB-ch101-110-2.npy",

8660 "data_url_json": "https://tempory.net/f/memobin/ephys_compression_tests/datasets/aind-compression-np2-ProbeB-ch101-110/2/aind-compression-np2-ProbeB-ch101-110-2.json",

8661 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/datasets/aind-compression/__init__.py"

8662 },

8663 {

8664 "name": "aind-compression-np1-ProbeA-ch101-110",

8665 "description": "AIND NP1 ProbeA CH101-110 dataset",

8666 "long_description": "Examples from AIND Compression Benchmark\n\nSee: https://allenneuraldynamics.github.io/data.html#aind-benchmark-dataephys-compression",

8667 "version": "2",

8668 "tags": [

8669 "real",

8670 "ecephys",

8671 "timeseries",

8672 "integer",

8673 "correlated",

8674 "multi-channel"

8675 ],

8676 "data_url_raw": "https://tempory.net/f/memobin/ephys_compression_tests/datasets/aind-compression-np1-ProbeA-ch101-110/2/aind-compression-np1-ProbeA-ch101-110-2.dat",

8677 "data_url_npy": "https://tempory.net/f/memobin/ephys_compression_tests/datasets/aind-compression-np1-ProbeA-ch101-110/2/aind-compression-np1-ProbeA-ch101-110-2.npy",

8678 "data_url_json": "https://tempory.net/f/memobin/ephys_compression_tests/datasets/aind-compression-np1-ProbeA-ch101-110/2/aind-compression-np1-ProbeA-ch101-110-2.json",

8679 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/datasets/aind-compression/__init__.py"

8680 },

8681 {

8682 "name": "ibl-compression-np1-Probe00-ch101-110",

8683 "description": "IBL NP1 Probe00 CH101-110 dataset",

8684 "long_description": "Examples from AIND Compression Benchmark\n\nSee: https://allenneuraldynamics.github.io/data.html#aind-benchmark-dataephys-compression",

8685 "version": "2",

8686 "tags": [

8687 "real",

8688 "ecephys",

8689 "timeseries",

8690 "integer",

8691 "correlated",

8692 "multi-channel"

8693 ],

8694 "data_url_raw": "https://tempory.net/f/memobin/ephys_compression_tests/datasets/ibl-compression-np1-Probe00-ch101-110/2/ibl-compression-np1-Probe00-ch101-110-2.dat",

8695 "data_url_npy": "https://tempory.net/f/memobin/ephys_compression_tests/datasets/ibl-compression-np1-Probe00-ch101-110/2/ibl-compression-np1-Probe00-ch101-110-2.npy",

8696 "data_url_json": "https://tempory.net/f/memobin/ephys_compression_tests/datasets/ibl-compression-np1-Probe00-ch101-110/2/ibl-compression-np1-Probe00-ch101-110-2.json",

8697 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/datasets/aind-compression/__init__.py"

8698 },

8699 {

8700 "name": "aind-compression-np2-ProbeB-ch101-filtered",

8701 "description": "AIND CH101 dataset (bandpass filtered 300-4000 Hz)",

8702 "long_description": "Examples from AIND Compression Benchmark\n\nSee: https://allenneuraldynamics.github.io/data.html#aind-benchmark-dataephys-compression",

8703 "version": "2",

8704 "tags": [

8705 "real",

8706 "ecephys",

8707 "timeseries",

8708 "integer",

8709 "correlated",

8710 "single-channel",

8711 "filtered",

8712 "bandpass"

8713 ],

8714 "data_url_raw": "https://tempory.net/f/memobin/ephys_compression_tests/datasets/aind-compression-np2-ProbeB-ch101-filtered/2/aind-compression-np2-ProbeB-ch101-filtered-2.dat",

8715 "data_url_npy": "https://tempory.net/f/memobin/ephys_compression_tests/datasets/aind-compression-np2-ProbeB-ch101-filtered/2/aind-compression-np2-ProbeB-ch101-filtered-2.npy",

8716 "data_url_json": "https://tempory.net/f/memobin/ephys_compression_tests/datasets/aind-compression-np2-ProbeB-ch101-filtered/2/aind-compression-np2-ProbeB-ch101-filtered-2.json",

8717 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/datasets/aind-compression/__init__.py"

8718 },

8719 {

8720 "name": "aind-compression-np2-ProbeB-ch101-110-filtered",

8721 "description": "AIND CH101-110 dataset (bandpass filtered 300-4000 Hz)",

8722 "long_description": "Examples from AIND Compression Benchmark\n\nSee: https://allenneuraldynamics.github.io/data.html#aind-benchmark-dataephys-compression",

8723 "version": "2",

8724 "tags": [

8725 "real",

8726 "ecephys",

8727 "timeseries",

8728 "integer",

8729 "correlated",

8730 "multi-channel",

8731 "filtered",

8732 "bandpass"

8733 ],

8734 "data_url_raw": "https://tempory.net/f/memobin/ephys_compression_tests/datasets/aind-compression-np2-ProbeB-ch101-110-filtered/2/aind-compression-np2-ProbeB-ch101-110-filtered-2.dat",

8735 "data_url_npy": "https://tempory.net/f/memobin/ephys_compression_tests/datasets/aind-compression-np2-ProbeB-ch101-110-filtered/2/aind-compression-np2-ProbeB-ch101-110-filtered-2.npy",

8736 "data_url_json": "https://tempory.net/f/memobin/ephys_compression_tests/datasets/aind-compression-np2-ProbeB-ch101-110-filtered/2/aind-compression-np2-ProbeB-ch101-110-filtered-2.json",

8737 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/datasets/aind-compression/__init__.py"

8738 },

8739 {

8740 "name": "aind-compression-np1-ProbeA-ch101-110-filtered",

8741 "description": "AIND NP1 ProbeA CH101-110 dataset (bandpass filtered 300-4000 Hz)",

8742 "long_description": "Examples from AIND Compression Benchmark\n\nSee: https://allenneuraldynamics.github.io/data.html#aind-benchmark-dataephys-compression",

8743 "version": "2",

8744 "tags": [

8745 "real",

8746 "ecephys",

8747 "timeseries",

8748 "integer",

8749 "correlated",

8750 "multi-channel",

8751 "filtered",

8752 "bandpass"

8753 ],

8754 "data_url_raw": "https://tempory.net/f/memobin/ephys_compression_tests/datasets/aind-compression-np1-ProbeA-ch101-110-filtered/2/aind-compression-np1-ProbeA-ch101-110-filtered-2.dat",

8755 "data_url_npy": "https://tempory.net/f/memobin/ephys_compression_tests/datasets/aind-compression-np1-ProbeA-ch101-110-filtered/2/aind-compression-np1-ProbeA-ch101-110-filtered-2.npy",

8756 "data_url_json": "https://tempory.net/f/memobin/ephys_compression_tests/datasets/aind-compression-np1-ProbeA-ch101-110-filtered/2/aind-compression-np1-ProbeA-ch101-110-filtered-2.json",

8757 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/datasets/aind-compression/__init__.py"

8758 },

8759 {

8760 "name": "ibl-compression-np1-Probe00-ch101-110-filtered",

8761 "description": "IBL NP1 Probe00 CH101-110 dataset (bandpass filtered 300-4000 Hz)",

8762 "long_description": "Examples from AIND Compression Benchmark\n\nSee: https://allenneuraldynamics.github.io/data.html#aind-benchmark-dataephys-compression",

8763 "version": "2",

8764 "tags": [

8765 "real",

8766 "ecephys",

8767 "timeseries",

8768 "integer",

8769 "correlated",

8770 "multi-channel",

8771 "filtered",

8772 "bandpass"

8773 ],

8774 "data_url_raw": "https://tempory.net/f/memobin/ephys_compression_tests/datasets/ibl-compression-np1-Probe00-ch101-110-filtered/2/ibl-compression-np1-Probe00-ch101-110-filtered-2.dat",

8775 "data_url_npy": "https://tempory.net/f/memobin/ephys_compression_tests/datasets/ibl-compression-np1-Probe00-ch101-110-filtered/2/ibl-compression-np1-Probe00-ch101-110-filtered-2.npy",

8776 "data_url_json": "https://tempory.net/f/memobin/ephys_compression_tests/datasets/ibl-compression-np1-Probe00-ch101-110-filtered/2/ibl-compression-np1-Probe00-ch101-110-filtered-2.json",

8777 "source_file": "https://github.com/concept-collection/ephys_compression_tests/blob/main/python/ephys_compression_tests/datasets/aind-compression/__init__.py"

8778 }

8779 ]

8780}