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135 lines
10 KiB
TOML
135 lines
10 KiB
TOML
[[package]]
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category = "main"
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description = "Composable style cycles"
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name = "cycler"
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optional = false
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python-versions = "*"
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version = "0.10.0"
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[package.dependencies]
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six = "*"
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[[package]]
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category = "main"
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description = "A fast implementation of the Cassowary constraint solver"
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name = "kiwisolver"
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optional = false
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python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
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version = "1.1.0"
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[package.dependencies]
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setuptools = "*"
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[[package]]
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category = "main"
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description = "Python plotting package"
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name = "matplotlib"
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optional = false
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python-versions = ">=3.6"
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version = "3.1.2"
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[package.dependencies]
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cycler = ">=0.10"
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kiwisolver = ">=1.0.1"
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numpy = ">=1.11"
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pyparsing = ">=2.0.1,<2.0.4 || >2.0.4,<2.1.2 || >2.1.2,<2.1.6 || >2.1.6"
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python-dateutil = ">=2.1"
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[[package]]
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category = "main"
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description = "NumPy is the fundamental package for array computing with Python."
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name = "numpy"
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optional = false
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python-versions = ">=3.5"
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version = "1.17.4"
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[[package]]
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category = "main"
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description = "Powerful data structures for data analysis, time series, and statistics"
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name = "pandas"
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optional = false
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python-versions = ">=3.5.3"
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version = "0.25.3"
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[package.dependencies]
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numpy = ">=1.13.3"
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python-dateutil = ">=2.6.1"
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pytz = ">=2017.2"
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[[package]]
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category = "main"
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description = "Python parsing module"
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name = "pyparsing"
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optional = false
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python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
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version = "2.4.5"
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[[package]]
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category = "main"
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description = "Extensions to the standard Python datetime module"
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name = "python-dateutil"
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optional = false
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python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
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version = "2.8.1"
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[package.dependencies]
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six = ">=1.5"
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[[package]]
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category = "main"
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description = "World timezone definitions, modern and historical"
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name = "pytz"
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optional = false
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python-versions = "*"
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version = "2019.3"
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[[package]]
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category = "main"
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description = "SciPy: Scientific Library for Python"
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name = "scipy"
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optional = false
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python-versions = ">=3.5"
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version = "1.3.3"
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[package.dependencies]
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numpy = ">=1.13.3"
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[[package]]
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category = "main"
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description = "seaborn: statistical data visualization"
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name = "seaborn"
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optional = false
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python-versions = "*"
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version = "0.9.0"
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[package.dependencies]
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matplotlib = ">=1.4.3"
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numpy = ">=1.9.3"
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pandas = ">=0.15.2"
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scipy = ">=0.14.0"
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[[package]]
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category = "main"
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description = "Python 2 and 3 compatibility utilities"
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name = "six"
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optional = false
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python-versions = ">=2.6, !=3.0.*, !=3.1.*"
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version = "1.13.0"
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[metadata]
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content-hash = "4e8082311e9378f77d7a1accb8cd080faf04d14d5f7beba06a8e2f950698f9f3"
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python-versions = "^3.7"
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[metadata.hashes]
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cycler = ["1d8a5ae1ff6c5cf9b93e8811e581232ad8920aeec647c37316ceac982b08cb2d", "cd7b2d1018258d7247a71425e9f26463dfb444d411c39569972f4ce586b0c9d8"]
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kiwisolver = ["05b5b061e09f60f56244adc885c4a7867da25ca387376b02c1efc29cc16bcd0f", "26f4fbd6f5e1dabff70a9ba0d2c4bd30761086454aa30dddc5b52764ee4852b7", "3b2378ad387f49cbb328205bda569b9f87288d6bc1bf4cd683c34523a2341efe", "400599c0fe58d21522cae0e8b22318e09d9729451b17ee61ba8e1e7c0346565c", "47b8cb81a7d18dbaf4fed6a61c3cecdb5adec7b4ac292bddb0d016d57e8507d5", "53eaed412477c836e1b9522c19858a8557d6e595077830146182225613b11a75", "58e626e1f7dfbb620d08d457325a4cdac65d1809680009f46bf41eaf74ad0187", "5a52e1b006bfa5be04fe4debbcdd2688432a9af4b207a3f429c74ad625022641", "5c7ca4e449ac9f99b3b9d4693debb1d6d237d1542dd6a56b3305fe8a9620f883", "682e54f0ce8f45981878756d7203fd01e188cc6c8b2c5e2cf03675390b4534d5", "79bfb2f0bd7cbf9ea256612c9523367e5ec51d7cd616ae20ca2c90f575d839a2", "7f4dd50874177d2bb060d74769210f3bce1af87a8c7cf5b37d032ebf94f0aca3", "8944a16020c07b682df861207b7e0efcd2f46c7488619cb55f65882279119389", "8aa7009437640beb2768bfd06da049bad0df85f47ff18426261acecd1cf00897", "939f36f21a8c571686eb491acfffa9c7f1ac345087281b412d63ea39ca14ec4a", "9733b7f64bd9f807832d673355f79703f81f0b3e52bfce420fc00d8cb28c6a6c", "a02f6c3e229d0b7220bd74600e9351e18bc0c361b05f29adae0d10599ae0e326", "a0c0a9f06872330d0dd31b45607197caab3c22777600e88031bfe66799e70bb0", "acc4df99308111585121db217681f1ce0eecb48d3a828a2f9bbf9773f4937e9e", "b64916959e4ae0ac78af7c3e8cef4becee0c0e9694ad477b4c6b3a536de6a544", "d3fcf0819dc3fea58be1fd1ca390851bdb719a549850e708ed858503ff25d995", "d52e3b1868a4e8fd18b5cb15055c76820df514e26aa84cc02f593d99fef6707f", "db1a5d3cc4ae943d674718d6c47d2d82488ddd94b93b9e12d24aabdbfe48caee", "e3a21a720791712ed721c7b95d433e036134de6f18c77dbe96119eaf7aa08004", "e8bf074363ce2babeb4764d94f8e65efd22e6a7c74860a4f05a6947afc020ff2", "f16814a4a96dc04bf1da7d53ee8d5b1d6decfc1a92a63349bb15d37b6a263dd9", "f2b22153870ca5cf2ab9c940d7bc38e8e9089fa0f7e5856ea195e1cf4ff43d5a", "f790f8b3dff3d53453de6a7b7ddd173d2e020fb160baff578d578065b108a05f"]
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matplotlib = ["08ccc8922eb4792b91c652d3e6d46b1c99073f1284d1b6705155643e8046463a", "161dcd807c0c3232f4dcd4a12a382d52004a498174cbfafd40646106c5bcdcc8", "1f9e885bfa1b148d16f82a6672d043ecf11197f6c71ae222d0546db706e52eb2", "2d6ab54015a7c0d727c33e36f85f5c5e4172059efdd067f7527f6e5d16ad01aa", "5d2e408a2813abf664bd79431107543ecb449136912eb55bb312317edecf597e", "61c8b740a008218eb604de518eb411c4953db0cb725dd0b32adf8a81771cab9e", "80f10af8378fccc136da40ea6aa4a920767476cdfb3241acb93ef4f0465dbf57", "819d4860315468b482f38f1afe45a5437f60f03eaede495d5ff89f2eeac89500", "8cc0e44905c2c8fda5637cad6f311eb9517017515a034247ab93d0cf99f8bb7a", "8e8e2c2fe3d873108735c6ee9884e6f36f467df4a143136209cff303b183bada", "98c2ffeab8b79a4e3a0af5dd9939f92980eb6e3fec10f7f313df5f35a84dacab", "d59bb0e82002ac49f4152963f8a1079e66794a4f454457fd2f0dcc7bf0797d30", "ee59b7bb9eb75932fe3787e54e61c99b628155b0cedc907864f24723ba55b309"]
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numpy = ["0a7a1dd123aecc9f0076934288ceed7fd9a81ba3919f11a855a7887cbe82a02f", "0c0763787133dfeec19904c22c7e358b231c87ba3206b211652f8cbe1241deb6", "3d52298d0be333583739f1aec9026f3b09fdfe3ddf7c7028cb16d9d2af1cca7e", "43bb4b70585f1c2d153e45323a886839f98af8bfa810f7014b20be714c37c447", "475963c5b9e116c38ad7347e154e5651d05a2286d86455671f5b1eebba5feb76", "64874913367f18eb3013b16123c9fed113962e75d809fca5b78ebfbb73ed93ba", "683828e50c339fc9e68720396f2de14253992c495fdddef77a1e17de55f1decc", "6ca4000c4a6f95a78c33c7dadbb9495c10880be9c89316aa536eac359ab820ae", "75fd817b7061f6378e4659dd792c84c0b60533e867f83e0d1e52d5d8e53df88c", "7d81d784bdbed30137aca242ab307f3e65c8d93f4c7b7d8f322110b2e90177f9", "8d0af8d3664f142414fd5b15cabfd3b6cc3ef242a3c7a7493257025be5a6955f", "9679831005fb16c6df3dd35d17aa31dc0d4d7573d84f0b44cc481490a65c7725", "a8f67ebfae9f575d85fa859b54d3bdecaeece74e3274b0b5c5f804d7ca789fe1", "acbf5c52db4adb366c064d0b7c7899e3e778d89db585feadd23b06b587d64761", "ada4805ed51f5bcaa3a06d3dd94939351869c095e30a2b54264f5a5004b52170", "c7354e8f0eca5c110b7e978034cd86ed98a7a5ffcf69ca97535445a595e07b8e", "e2e9d8c87120ba2c591f60e32736b82b67f72c37ba88a4c23c81b5b8fa49c018", "e467c57121fe1b78a8f68dd9255fbb3bb3f4f7547c6b9e109f31d14569f490c3", "ede47b98de79565fcd7f2decb475e2dcc85ee4097743e551fe26cfc7eb3ff143", "f58913e9227400f1395c7b800503ebfdb0772f1c33ff8cb4d6451c06cabdf316", "fe39f5fd4103ec4ca3cb8600b19216cd1ff316b4990f4c0b6057ad982c0a34d5"]
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pandas = ["00dff3a8e337f5ed7ad295d98a31821d3d0fe7792da82d78d7fd79b89c03ea9d", "22361b1597c8c2ffd697aa9bf85423afa9e1fcfa6b1ea821054a244d5f24d75e", "255920e63850dc512ce356233081098554d641ba99c3767dde9e9f35630f994b", "26382aab9c119735908d94d2c5c08020a4a0a82969b7e5eefb92f902b3b30ad7", "33970f4cacdd9a0ddb8f21e151bfb9f178afb7c36eb7c25b9094c02876f385c2", "4545467a637e0e1393f7d05d61dace89689ad6d6f66f267f86fff737b702cce9", "52da74df8a9c9a103af0a72c9d5fdc8e0183a90884278db7f386b5692a2220a4", "61741f5aeb252f39c3031d11405305b6d10ce663c53bc3112705d7ad66c013d0", "6a3ac2c87e4e32a969921d1428525f09462770c349147aa8e9ab95f88c71ec71", "7458c48e3d15b8aaa7d575be60e1e4dd70348efcd9376656b72fecd55c59a4c3", "78bf638993219311377ce9836b3dc05f627a666d0dbc8cec37c0ff3c9ada673b", "8153705d6545fd9eb6dd2bc79301bff08825d2e2f716d5dced48daafc2d0b81f", "975c461accd14e89d71772e89108a050fa824c0b87a67d34cedf245f6681fc17", "9962957a27bfb70ab64103d0a7b42fa59c642fb4ed4cb75d0227b7bb9228535d", "adc3d3a3f9e59a38d923e90e20c4922fc62d1e5a03d083440468c6d8f3f1ae0a", "bbe3eb765a0b1e578833d243e2814b60c825b7fdbf4cdfe8e8aae8a08ed56ecf", "df8864824b1fe488cf778c3650ee59c3a0d8f42e53707de167ba6b4f7d35f133", "e45055c30a608076e31a9fcd780a956ed3b1fa20db61561b8d88b79259f526f7", "ee50c2142cdcf41995655d499a157d0a812fce55c97d9aad13bc1eef837ed36c"]
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pyparsing = ["20f995ecd72f2a1f4bf6b072b63b22e2eb457836601e76d6e5dfcd75436acc1f", "4ca62001be367f01bd3e92ecbb79070272a9d4964dce6a48a82ff0b8bc7e683a"]
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python-dateutil = ["73ebfe9dbf22e832286dafa60473e4cd239f8592f699aa5adaf10050e6e1823c", "75bb3f31ea686f1197762692a9ee6a7550b59fc6ca3a1f4b5d7e32fb98e2da2a"]
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pytz = ["1c557d7d0e871de1f5ccd5833f60fb2550652da6be2693c1e02300743d21500d", "b02c06db6cf09c12dd25137e563b31700d3b80fcc4ad23abb7a315f2789819be"]
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scipy = ["0b8c9dc042b9a47912b18b036b4844029384a5b8d89b64a4901ac3e06876e5f6", "18ad034be955df046b5a27924cdb3db0e8e1d76aaa22c635403fe7aee17f1482", "225d0b5e140bb66df23d438c7b535303ce8e533f94454f4e5bde5f8d109103ea", "2f690ba68ed7caa7c30b6dc48c1deed22c78f3840fa4736083ef4f2bd8baa19e", "4b8746f4a755bdb2eeb39d6e253a60481e165cfd74fdfb54d27394bd2c9ec8ac", "4ba2ce1a58fe117e993cf316a149cf9926c7c5000c0cdc4bc7c56ae8325612f6", "546f0dc020b155b8711159d53c87b36591d31f3327c47974a4fb6b50d91589c2", "583f2ccd6a112656c9feb2345761d2b19e9213a094cfced4e7d2c1cae4173272", "64bf4e8ae0db2d42b58477817f648d81e77f0b381d0ea4427385bba3f959380a", "7be424ee09bed7ced36c9457f99c826ce199fd0c0f5b272cf3d098ff7b29e3ae", "869465c7ff89fc0a1e2ea1642b0c65f1b3c05030f3a4c0d53d6a57b2dba7c242", "884e619821f47eccd42979488d10fa1e15dbe9f3b7660b1c8c928d203bd3c1a3", "a42b0d02150ef4747e225c31c976a304de5dc8202ec35a27111b7bb8176e5f13", "a70308bb065562afb936c963780deab359966d71ab4f230368b154dde3136ea4", "b01ea5e4cf95a93dc335089f8fbe97852f56fdb74afff238cbdf09793103b6b7", "b7b8cf45f9a48f23084f19deb9384a1cccb5e92fbc879b12f97dc4d56fb2eb92", "bb0899d3f8b9fe8ef95b79210cf0deb6709542889fadaa438eeb3a28001e09e7", "c008f1b58f99f1d1cc546957b3effe448365e0a217df1f1894e358906e91edad", "cfee99d085d562a7e3c4afe51ac1fe9b434363489e565a130459307f30077973", "dfcb0f0a2d8e958611e0b56536285bb435f03746b6feac0e29f045f7c6caf164", "f5d47351aeb1cb6bda14a8908e56648926a6b2d714f89717c71f7ada41282141"]
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seaborn = ["42e627b24e849c2d3bbfd059e00005f6afbc4a76e4895baf44ae23fe8a4b09a5", "76c83f794ca320fb6b23a7c6192d5e185a5fcf4758966a0c0a54baee46d41e2f"]
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six = ["1f1b7d42e254082a9db6279deae68afb421ceba6158efa6131de7b3003ee93fd", "30f610279e8b2578cab6db20741130331735c781b56053c59c4076da27f06b66"]
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