From 47b0deba96a3863738cf16486ebdf5c1a0cd4836 Mon Sep 17 00:00:00 2001 From: aeturrell Date: Sun, 24 Mar 2024 10:40:28 +0000 Subject: [PATCH] Update documentation --- ...b111996e93a7289116d305e8c71ffe8895cbdc.svg | 3259 + ...3f0acab22fd606bf7c76f6d660f5655ac49dbf.svg | 2473 + ...a18c1ba4b8a4e3c975dd8a638fd56c05674d25.svg | 1628 + ...1c6faac25c0390eae0c52412aaf0498bb8d6b4.svg | 1311 + ...63c773159f43a36d01faa10382e65f9b331d3c.svg | 5494 + ...82485c94960a960fabc65725d2db7634dc957d.svg | 1600 + ...0fcdead8f2056328bd599c80675eac4cfef130.svg | 312 + ...74ab3ca08936b79b8cf68d882fea841a131c9d.svg | 43 + ...c4e742fc88aa752abc1e167bc9a95016be41bd.svg | 4543 + ...b33c66b7411915afe568faf49f848633a9ca17.svg | 3259 + ...db671257000515b39d2c91dcac1f72e7eb9066.svg | 1064 + ...797a45b695d81df632166fd8a417352df6298a.svg | 1394 + ...106c515067a00ef7c98b28c423651d7d4bcc9b.svg | 1513 + ...c0cef853f7547130c0063d58b98b027a651a63.svg | 792 + ...44d08ef0b5ca16041143a15b9049bb2906407b.svg | 854 + ...0a45281f64bc6e3f309edcaf089cf2fdba68ca.svg | 1211 + ...eb3e0cac1d02d31e872bfd2c410c79c73ffb4c.svg | 1292 + ...a06ecf698a556036cdb17a32f50091bf057af6.svg | 1271 + ...e21fa453f5d7194df818fabf0a173aa278967d.svg | 1537 + ...777f225ea4254128960109d174123fca83a7e7.svg | 1600 + ...4bbb632eb1d3362b1b5bb135c47bae145badaa.svg | 2830 + ...b558efa5e25dda524665df6c96ef0a93daed9c.svg | 1537 + ...9c4ec101a9715d0e331379788947af981d4ad5.svg | 1441 + ...e6f467bc5c7b9bc2c98235179793f2c30d6302.svg | 1512 + ...a0088d4914910060fae11424049cbffb933459.svg | 957 + ...ce0ae49d7bd5a70097e995fcc62c16c0882f39.svg | 1292 + ...5abbc83025c12d5f7e27bd65ab400a07604036.svg | 1470 + ...9a92f8279bccc2c1720854e6fbd1315c5dc562.svg | 1211 + ...63ac0c7057db4266ee7f60ce1dee9f1b26e542.svg | 957 + ...e693f4df8d2bf4c14e6854969fb391a51198a6.svg | 1225 + ...81852dccba44a2acb8824b043de2507b2c49fc.svg | 43 + ...942475253890a1c4e2fa29d50a2ed5c6ba0107.svg | 1419 + ...8d82dc993be31785b6ce150de28eaf3c8b4370.svg | 1435 + ...b281d2037fd7a8d654c959e63196725e98f188.svg | 1271 + ...b53cc4c2fbd5051c3ccd5735b685bb4c844042.svg | 1419 + ...22993e316d9b6074a576f4382a21924a560b93.svg | 1490 + ...150037fbf092ef2ffdf6313e968c684bacbeab.svg | 1628 + ...2dcaa88ea6b3d1e62cbbe2296be391593aabb2.svg | 312 + ...e2fb65a73bac6cc8690edca560730791716974.svg | 837 + ...0b9fe8a75064f33db7c43e3311d2a4391d26c0.png | Bin 0 -> 76177 bytes ...7fc87898dd248f94b2d57c9b41dff58cddb700.svg | 1535 + ...c447ae35917a080683db10ca1bc7d8ce08553a.svg | 660 + ...12ff8ec1f2ea62f6944606c24af42f8de3858e.svg | 1205 + ...64f7758a758818bda6388d1e7eb4584e67dee0.svg | 1205 + ...414daf7d2b77b8c53439eeefc54d2c253929a3.svg | 1171 + ...7c068952eb427c7df67dbde659547a8d9390e3.svg | 1526 + ...edcc666c61b018d8f4085a37ff53f652fd8a5b.svg | 1526 + ...27cc2a6a40bd8cc7533415df2681164a024b5c.svg | 1147 + ...2ccac2e7de25e57f229d660947631bb0bac106.svg | 43 + ...0a0a95b8ae16446a4e0fcb054b8d9b6be7a3d4.svg | 5494 + ...1ef79d41be9242a93b1958448296289f5f783b.svg | 2473 + ...78a3828edc14fdd875662dd117f46f2982d58e.svg | 854 + ...c8ac517a19885725dcd2fe117121d3a845985e.svg | 9567 ++ ...d80cb83eba36ad62ce0d74921eabb820a56dcc.svg | 1628 + ...65257bfbe86399d1ef425830cef9b77193f3b6.svg | 4543 + ...c97e30c6b7c5e646f8118ed2bf0de5b210c108.svg | 1147 + ...2f4d73b33feb45653bf8898c6e10b212d0fcf8.svg | 1394 + ...a6d070661b553abcb5ca134e6ae5f12dd1a2a9.svg | 837 + ...a28e50c61d4a10c727f01b13c2f768e90a2ed6.svg | 43 + ...63531e7847d93838c5ec994d0f6ee3f6d244f4.svg | 1728 + ...7cd29d2c4258910cb2c572141c1ffb013d2104.svg | 1171 + ...d9b21d3dbf0207ae3eaf0857e9f251977c3f1f.svg | 2830 + ...09da179471f77a800fe2ad5ba96fbf6b78a30d.svg | 1490 + ...c5088b91e19826937fc47851ff886020d48c41.svg | 466 + ...ae1068adef55eb1f96361e7295c1fa8d8561b6.svg | 9567 ++ ...ea342fe6781d7605bd84802370a6ff9e19fb87.svg | 466 + ...17ba9ea69c02459957f2bb1455961af77fb2c3.svg | 792 + ...d7ae0749fa2313ec5dcf8243cdb8c6406254e0.svg | 1435 + ...abbab135656d0f0395cdc609edccd9e6faf439.svg | 1512 + ...d56b3bab2bb809df95a3dcb6380cf094598350.svg | 1311 + ...a7386b2510df56428286bc3a5dd016e330c04d.svg | 957 + ...9f0effca6c62b503c092a12ac0aa29f26a558e.svg | 1728 + ...38a78a8446760d49049aa72813f0fa4e1be3ed.svg | 1470 + ...b64fdd9819a1442d9516dfc17ba1b0adf5a2f4.svg | 1225 + ...d21e718e8eb1cea83a6c6b0bc02d4d4615d05b.svg | 1441 + ...74e628ae69166229f3b9594fa34ba3763ea058.svg | 1535 + ...dfea70bd12f86e013771b04dd99fe655aae02e.svg | 1435 + ...9eefb3e336d334779b248f198f968921889bd2.svg | 1311 + ...515e9e5ea53aadd42448a4a916c1a110ed45b3.svg | 660 + ...f4c2ea02a691e37e2e81f15d478b918a4f3824.svg | 1513 + ...06aa807de3ccb5b72a006f2908a1477e62db1c.svg | 1490 + _sources/coming-from-stata.ipynb | 30 +- _sources/coming-from-stata.md | 28 +- _sources/data-sharing.ipynb | 4 +- _sources/data-sharing.md | 2 - _sources/econmt-regression.ipynb | 51 +- _sources/vis-common-plots.ipynb | 116772 ++++++++++++++- coming-from-stata.html | 28 +- data-sharing.html | 1 - econmt-regression.html | 379 +- reports/vis-common-plots.err.log | 221 +- searchindex.js | 2 +- vis-common-plots.html | 1206 +- 93 files changed, 253793 insertions(+), 1198 deletions(-) create mode 100644 _images/020ef9c76188300ac057bfb1f4b111996e93a7289116d305e8c71ffe8895cbdc.svg create mode 100644 _images/04ed7a67b2e1c2e38cf46b98593f0acab22fd606bf7c76f6d660f5655ac49dbf.svg create mode 100644 _images/050bab3a33318c8eb31b049574a18c1ba4b8a4e3c975dd8a638fd56c05674d25.svg create mode 100644 _images/0bdaec8b79a87f699e65e8b1dd1c6faac25c0390eae0c52412aaf0498bb8d6b4.svg create mode 100644 _images/0d06e80606d49ef1eb22f54fab63c773159f43a36d01faa10382e65f9b331d3c.svg create mode 100644 _images/0efe169729008f32d33d8b0e7482485c94960a960fabc65725d2db7634dc957d.svg create mode 100644 _images/10bfc933b12e2c3bbf5380517e0fcdead8f2056328bd599c80675eac4cfef130.svg create mode 100644 _images/17034f98b6e7197e4599f61dbe74ab3ca08936b79b8cf68d882fea841a131c9d.svg create mode 100644 _images/1b632f1b8e8305a024375644d0c4e742fc88aa752abc1e167bc9a95016be41bd.svg create mode 100644 _images/1d826f95d64ad9789063f3de94b33c66b7411915afe568faf49f848633a9ca17.svg create mode 100644 _images/242dba4ced800eb24022a700a8db671257000515b39d2c91dcac1f72e7eb9066.svg create mode 100644 _images/25c488517182fd3654eb715db2797a45b695d81df632166fd8a417352df6298a.svg create mode 100644 _images/29d9c78e0802e749d06e560854106c515067a00ef7c98b28c423651d7d4bcc9b.svg create mode 100644 _images/2b25378a52f75ca827177421dbc0cef853f7547130c0063d58b98b027a651a63.svg create mode 100644 _images/2b4766082791c20a18bfef942344d08ef0b5ca16041143a15b9049bb2906407b.svg create mode 100644 _images/2cd667248783cd795b4ba6db170a45281f64bc6e3f309edcaf089cf2fdba68ca.svg create mode 100644 _images/37f63136add0f3dff1dd8a92dfeb3e0cac1d02d31e872bfd2c410c79c73ffb4c.svg create mode 100644 _images/38a07f25aeae67745b241fc698a06ecf698a556036cdb17a32f50091bf057af6.svg create mode 100644 _images/394580fbd0e15a9df9a6c19645e21fa453f5d7194df818fabf0a173aa278967d.svg create mode 100644 _images/3c58fdd8ddac84f54a84be7491777f225ea4254128960109d174123fca83a7e7.svg create mode 100644 _images/40c2a413285dc74bcdc1cfa59f4bbb632eb1d3362b1b5bb135c47bae145badaa.svg create mode 100644 _images/416cce817aeb50820c68dacaafb558efa5e25dda524665df6c96ef0a93daed9c.svg create mode 100644 _images/43241be644d51d6e8c404edf039c4ec101a9715d0e331379788947af981d4ad5.svg create mode 100644 _images/434e52b516173ca339523825ebe6f467bc5c7b9bc2c98235179793f2c30d6302.svg create mode 100644 _images/4ef0eade339d200c13f616090ca0088d4914910060fae11424049cbffb933459.svg create mode 100644 _images/52ba7a0f12c0d6343890cb241fce0ae49d7bd5a70097e995fcc62c16c0882f39.svg create mode 100644 _images/579b278127947b78f3a9d3d78b5abbc83025c12d5f7e27bd65ab400a07604036.svg create mode 100644 _images/57b8337ce6e9bf02dd853bb0e59a92f8279bccc2c1720854e6fbd1315c5dc562.svg create mode 100644 _images/593d6397cbad1881d9176862e963ac0c7057db4266ee7f60ce1dee9f1b26e542.svg create mode 100644 _images/5df4f02550524290ad042ef7b1e693f4df8d2bf4c14e6854969fb391a51198a6.svg create mode 100644 _images/5eaa8b6c13c8dc22f51c15ed3781852dccba44a2acb8824b043de2507b2c49fc.svg create mode 100644 _images/60d5562b693ef73f32086298ff942475253890a1c4e2fa29d50a2ed5c6ba0107.svg create mode 100644 _images/64a231811354b41e3589b065b38d82dc993be31785b6ce150de28eaf3c8b4370.svg create mode 100644 _images/651d85787eea030c258e86fdbcb281d2037fd7a8d654c959e63196725e98f188.svg create mode 100644 _images/652e7b67e55a683ea682dee0fab53cc4c2fbd5051c3ccd5735b685bb4c844042.svg create mode 100644 _images/683263c2761c44f88a3f01fb5f22993e316d9b6074a576f4382a21924a560b93.svg create mode 100644 _images/68ab900ee2101589d38b0bede2150037fbf092ef2ffdf6313e968c684bacbeab.svg create mode 100644 _images/6a2f19c79d472406da5ca0615b2dcaa88ea6b3d1e62cbbe2296be391593aabb2.svg create mode 100644 _images/6eeb3a5b69ff54837668dce770e2fb65a73bac6cc8690edca560730791716974.svg create mode 100644 _images/6f633f22d2c6fdbbf04f79282b0b9fe8a75064f33db7c43e3311d2a4391d26c0.png create mode 100644 _images/742e52f2f15e414e0800497e497fc87898dd248f94b2d57c9b41dff58cddb700.svg create mode 100644 _images/74d77756b41ba60d1d38df8eb5c447ae35917a080683db10ca1bc7d8ce08553a.svg create mode 100644 _images/76bf4d7e9e99ea46847f8586f512ff8ec1f2ea62f6944606c24af42f8de3858e.svg create mode 100644 _images/7b7e99bf4906a1384544b899b364f7758a758818bda6388d1e7eb4584e67dee0.svg create mode 100644 _images/7db0816439096a978a06e650f7414daf7d2b77b8c53439eeefc54d2c253929a3.svg create mode 100644 _images/80a682d83a913f6a7e37e956347c068952eb427c7df67dbde659547a8d9390e3.svg create mode 100644 _images/854500845595589798ae7e5b04edcc666c61b018d8f4085a37ff53f652fd8a5b.svg create mode 100644 _images/8933913098a6b843249b0a6db427cc2a6a40bd8cc7533415df2681164a024b5c.svg create mode 100644 _images/8cdd5dc269eea22a7fbd938e752ccac2e7de25e57f229d660947631bb0bac106.svg create mode 100644 _images/943798fa59e966bad758bd7f0c0a0a95b8ae16446a4e0fcb054b8d9b6be7a3d4.svg create mode 100644 _images/974a26595e62b5728787f74dee1ef79d41be9242a93b1958448296289f5f783b.svg create mode 100644 _images/a7419f0ef175fe401fea4e14ea78a3828edc14fdd875662dd117f46f2982d58e.svg create mode 100644 _images/aa19cdc6e30ec55d3405e116c8c8ac517a19885725dcd2fe117121d3a845985e.svg create mode 100644 _images/ad5dd6f1b26f2ff31fa49163cdd80cb83eba36ad62ce0d74921eabb820a56dcc.svg create mode 100644 _images/b1e180df3ad94883f1f180db4d65257bfbe86399d1ef425830cef9b77193f3b6.svg create mode 100644 _images/b5bb1970e8409c7c70449aeac4c97e30c6b7c5e646f8118ed2bf0de5b210c108.svg create mode 100644 _images/b80c32eaa2120597526b15cf892f4d73b33feb45653bf8898c6e10b212d0fcf8.svg create mode 100644 _images/b962af7b1040a82ca44a80e517a6d070661b553abcb5ca134e6ae5f12dd1a2a9.svg create mode 100644 _images/b9e6c1a362c28c67deb9a93552a28e50c61d4a10c727f01b13c2f768e90a2ed6.svg create mode 100644 _images/bff2452f5034a97282bffce9c763531e7847d93838c5ec994d0f6ee3f6d244f4.svg create mode 100644 _images/c01f81c5303c927a6f9a74a0067cd29d2c4258910cb2c572141c1ffb013d2104.svg create mode 100644 _images/c0285b269063bb81e9cdac0db7d9b21d3dbf0207ae3eaf0857e9f251977c3f1f.svg create mode 100644 _images/c259f8638588ac929f2ea3176409da179471f77a800fe2ad5ba96fbf6b78a30d.svg create mode 100644 _images/c2cbef828f554310f93699cff0c5088b91e19826937fc47851ff886020d48c41.svg create mode 100644 _images/c5a748b6347a4855086423ab8bae1068adef55eb1f96361e7295c1fa8d8561b6.svg create mode 100644 _images/c9521687efd6a517b74d320964ea342fe6781d7605bd84802370a6ff9e19fb87.svg create mode 100644 _images/c987bc1150ca9ca2141036161a17ba9ea69c02459957f2bb1455961af77fb2c3.svg create mode 100644 _images/cc82890f49ca18b7a5e466d37dd7ae0749fa2313ec5dcf8243cdb8c6406254e0.svg create mode 100644 _images/ccc09b63ab0808dc362c14666cabbab135656d0f0395cdc609edccd9e6faf439.svg create mode 100644 _images/ce18badf8e8bfe16c03e1ed8abd56b3bab2bb809df95a3dcb6380cf094598350.svg create mode 100644 _images/d44a180192945a345f841f66d7a7386b2510df56428286bc3a5dd016e330c04d.svg create mode 100644 _images/de62c3124743a8142fdd1eadac9f0effca6c62b503c092a12ac0aa29f26a558e.svg create mode 100644 _images/e6e4c928d7cefa6c4da9201bdb38a78a8446760d49049aa72813f0fa4e1be3ed.svg create mode 100644 _images/e83efcfaed61a765c2d2afaa8cb64fdd9819a1442d9516dfc17ba1b0adf5a2f4.svg create mode 100644 _images/e9b64610fee0ca2b9d2d1a7facd21e718e8eb1cea83a6c6b0bc02d4d4615d05b.svg create mode 100644 _images/ebbe19550dbe886de532f3120e74e628ae69166229f3b9594fa34ba3763ea058.svg create mode 100644 _images/f2d6a5dbc976c6e7f2da418be3dfea70bd12f86e013771b04dd99fe655aae02e.svg create mode 100644 _images/fa37f789ff050f0402122cb8619eefb3e336d334779b248f198f968921889bd2.svg create mode 100644 _images/fbfd4aa4278184cf9f6eb12851515e9e5ea53aadd42448a4a916c1a110ed45b3.svg create mode 100644 _images/fcd30e864a5f55290b942766a0f4c2ea02a691e37e2e81f15d478b918a4f3824.svg create mode 100644 _images/fcec49fb6f6a40d95a5a238e7906aa807de3ccb5b72a006f2908a1477e62db1c.svg diff --git a/_images/020ef9c76188300ac057bfb1f4b111996e93a7289116d305e8c71ffe8895cbdc.svg b/_images/020ef9c76188300ac057bfb1f4b111996e93a7289116d305e8c71ffe8895cbdc.svg new file mode 100644 index 0000000..85b44f2 --- /dev/null +++ b/_images/020ef9c76188300ac057bfb1f4b111996e93a7289116d305e8c71ffe8895cbdc.svg @@ -0,0 +1,3259 @@ + + + + + + + + 2024-03-24T10:38:54.620063 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/04ed7a67b2e1c2e38cf46b98593f0acab22fd606bf7c76f6d660f5655ac49dbf.svg b/_images/04ed7a67b2e1c2e38cf46b98593f0acab22fd606bf7c76f6d660f5655ac49dbf.svg new file mode 100644 index 0000000..c19ae90 --- /dev/null +++ b/_images/04ed7a67b2e1c2e38cf46b98593f0acab22fd606bf7c76f6d660f5655ac49dbf.svg @@ -0,0 +1,2473 @@ + + + + + + + + 2024-03-24T10:33:08.247094 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/050bab3a33318c8eb31b049574a18c1ba4b8a4e3c975dd8a638fd56c05674d25.svg b/_images/050bab3a33318c8eb31b049574a18c1ba4b8a4e3c975dd8a638fd56c05674d25.svg new file mode 100644 index 0000000..2cec9aa --- /dev/null +++ b/_images/050bab3a33318c8eb31b049574a18c1ba4b8a4e3c975dd8a638fd56c05674d25.svg @@ -0,0 +1,1628 @@ + + + + + + + + 2024-03-24T10:30:18.048055 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/0bdaec8b79a87f699e65e8b1dd1c6faac25c0390eae0c52412aaf0498bb8d6b4.svg b/_images/0bdaec8b79a87f699e65e8b1dd1c6faac25c0390eae0c52412aaf0498bb8d6b4.svg new file mode 100644 index 0000000..cd7ef3a --- /dev/null +++ b/_images/0bdaec8b79a87f699e65e8b1dd1c6faac25c0390eae0c52412aaf0498bb8d6b4.svg @@ -0,0 +1,1311 @@ + + + + + + + + 2024-03-24T10:38:51.986554 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/0d06e80606d49ef1eb22f54fab63c773159f43a36d01faa10382e65f9b331d3c.svg b/_images/0d06e80606d49ef1eb22f54fab63c773159f43a36d01faa10382e65f9b331d3c.svg new file mode 100644 index 0000000..2a41094 --- /dev/null +++ b/_images/0d06e80606d49ef1eb22f54fab63c773159f43a36d01faa10382e65f9b331d3c.svg @@ -0,0 +1,5494 @@ + + + + + + + + 2024-03-24T10:38:52.200162 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/0efe169729008f32d33d8b0e7482485c94960a960fabc65725d2db7634dc957d.svg b/_images/0efe169729008f32d33d8b0e7482485c94960a960fabc65725d2db7634dc957d.svg new file mode 100644 index 0000000..27155c3 --- /dev/null +++ b/_images/0efe169729008f32d33d8b0e7482485c94960a960fabc65725d2db7634dc957d.svg @@ -0,0 +1,1600 @@ + + + + + + + + 2024-03-24T10:38:56.631916 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/10bfc933b12e2c3bbf5380517e0fcdead8f2056328bd599c80675eac4cfef130.svg b/_images/10bfc933b12e2c3bbf5380517e0fcdead8f2056328bd599c80675eac4cfef130.svg new file mode 100644 index 0000000..2969d54 --- /dev/null +++ b/_images/10bfc933b12e2c3bbf5380517e0fcdead8f2056328bd599c80675eac4cfef130.svg @@ -0,0 +1,312 @@ + + + + + + + + 2024-03-24T10:39:01.791140 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/17034f98b6e7197e4599f61dbe74ab3ca08936b79b8cf68d882fea841a131c9d.svg b/_images/17034f98b6e7197e4599f61dbe74ab3ca08936b79b8cf68d882fea841a131c9d.svg new file mode 100644 index 0000000..e56e49a --- /dev/null +++ b/_images/17034f98b6e7197e4599f61dbe74ab3ca08936b79b8cf68d882fea841a131c9d.svg @@ -0,0 +1,43 @@ + + + + + + + + 2024-03-24T10:39:01.511952 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/1b632f1b8e8305a024375644d0c4e742fc88aa752abc1e167bc9a95016be41bd.svg b/_images/1b632f1b8e8305a024375644d0c4e742fc88aa752abc1e167bc9a95016be41bd.svg new file mode 100644 index 0000000..1953329 --- /dev/null +++ b/_images/1b632f1b8e8305a024375644d0c4e742fc88aa752abc1e167bc9a95016be41bd.svg @@ -0,0 +1,4543 @@ + + + + + + + + 2024-03-24T10:39:06.915935 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/1d826f95d64ad9789063f3de94b33c66b7411915afe568faf49f848633a9ca17.svg b/_images/1d826f95d64ad9789063f3de94b33c66b7411915afe568faf49f848633a9ca17.svg new file mode 100644 index 0000000..d434b88 --- /dev/null +++ b/_images/1d826f95d64ad9789063f3de94b33c66b7411915afe568faf49f848633a9ca17.svg @@ -0,0 +1,3259 @@ + + + + + + + + 2024-03-24T10:33:07.124360 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/242dba4ced800eb24022a700a8db671257000515b39d2c91dcac1f72e7eb9066.svg b/_images/242dba4ced800eb24022a700a8db671257000515b39d2c91dcac1f72e7eb9066.svg new file mode 100644 index 0000000..1bbb27f --- /dev/null +++ b/_images/242dba4ced800eb24022a700a8db671257000515b39d2c91dcac1f72e7eb9066.svg @@ -0,0 +1,1064 @@ + + + + + + + + 2024-03-24T10:30:07.754850 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/25c488517182fd3654eb715db2797a45b695d81df632166fd8a417352df6298a.svg b/_images/25c488517182fd3654eb715db2797a45b695d81df632166fd8a417352df6298a.svg new file mode 100644 index 0000000..66c4b7c --- /dev/null +++ b/_images/25c488517182fd3654eb715db2797a45b695d81df632166fd8a417352df6298a.svg @@ -0,0 +1,1394 @@ + + + + + + + + 2024-03-24T10:38:53.564957 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/29d9c78e0802e749d06e560854106c515067a00ef7c98b28c423651d7d4bcc9b.svg b/_images/29d9c78e0802e749d06e560854106c515067a00ef7c98b28c423651d7d4bcc9b.svg new file mode 100644 index 0000000..6e4f1ba --- /dev/null +++ b/_images/29d9c78e0802e749d06e560854106c515067a00ef7c98b28c423651d7d4bcc9b.svg @@ -0,0 +1,1513 @@ + + + + + + + + 2024-03-24T10:33:08.124562 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/2b25378a52f75ca827177421dbc0cef853f7547130c0063d58b98b027a651a63.svg b/_images/2b25378a52f75ca827177421dbc0cef853f7547130c0063d58b98b027a651a63.svg new file mode 100644 index 0000000..adc174e --- /dev/null +++ b/_images/2b25378a52f75ca827177421dbc0cef853f7547130c0063d58b98b027a651a63.svg @@ -0,0 +1,792 @@ + + + + + + + + 2024-03-24T10:38:55.913001 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/2b4766082791c20a18bfef942344d08ef0b5ca16041143a15b9049bb2906407b.svg b/_images/2b4766082791c20a18bfef942344d08ef0b5ca16041143a15b9049bb2906407b.svg new file mode 100644 index 0000000..dc004e4 --- /dev/null +++ b/_images/2b4766082791c20a18bfef942344d08ef0b5ca16041143a15b9049bb2906407b.svg @@ -0,0 +1,854 @@ + + + + + + + + 2024-03-24T10:33:08.472745 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/2cd667248783cd795b4ba6db170a45281f64bc6e3f309edcaf089cf2fdba68ca.svg b/_images/2cd667248783cd795b4ba6db170a45281f64bc6e3f309edcaf089cf2fdba68ca.svg new file mode 100644 index 0000000..8a028d5 --- /dev/null +++ b/_images/2cd667248783cd795b4ba6db170a45281f64bc6e3f309edcaf089cf2fdba68ca.svg @@ -0,0 +1,1211 @@ + + + + + + + + 2024-03-24T10:33:17.114699 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/37f63136add0f3dff1dd8a92dfeb3e0cac1d02d31e872bfd2c410c79c73ffb4c.svg b/_images/37f63136add0f3dff1dd8a92dfeb3e0cac1d02d31e872bfd2c410c79c73ffb4c.svg new file mode 100644 index 0000000..51dece3 --- /dev/null +++ b/_images/37f63136add0f3dff1dd8a92dfeb3e0cac1d02d31e872bfd2c410c79c73ffb4c.svg @@ -0,0 +1,1292 @@ + + + + + + + + 2024-03-24T10:33:07.748940 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/38a07f25aeae67745b241fc698a06ecf698a556036cdb17a32f50091bf057af6.svg b/_images/38a07f25aeae67745b241fc698a06ecf698a556036cdb17a32f50091bf057af6.svg new file mode 100644 index 0000000..6e7b8d7 --- /dev/null +++ b/_images/38a07f25aeae67745b241fc698a06ecf698a556036cdb17a32f50091bf057af6.svg @@ -0,0 +1,1271 @@ + + + + + + + + 2024-03-24T10:38:57.912675 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/394580fbd0e15a9df9a6c19645e21fa453f5d7194df818fabf0a173aa278967d.svg b/_images/394580fbd0e15a9df9a6c19645e21fa453f5d7194df818fabf0a173aa278967d.svg new file mode 100644 index 0000000..e65c21f --- /dev/null +++ b/_images/394580fbd0e15a9df9a6c19645e21fa453f5d7194df818fabf0a173aa278967d.svg @@ -0,0 +1,1537 @@ + + + + + + + + 2024-03-24T10:33:06.581315 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/3c58fdd8ddac84f54a84be7491777f225ea4254128960109d174123fca83a7e7.svg b/_images/3c58fdd8ddac84f54a84be7491777f225ea4254128960109d174123fca83a7e7.svg new file mode 100644 index 0000000..95a3a33 --- /dev/null +++ b/_images/3c58fdd8ddac84f54a84be7491777f225ea4254128960109d174123fca83a7e7.svg @@ -0,0 +1,1600 @@ + + + + + + + + 2024-03-24T10:33:09.113039 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/40c2a413285dc74bcdc1cfa59f4bbb632eb1d3362b1b5bb135c47bae145badaa.svg b/_images/40c2a413285dc74bcdc1cfa59f4bbb632eb1d3362b1b5bb135c47bae145badaa.svg new file mode 100644 index 0000000..841be4b --- /dev/null +++ b/_images/40c2a413285dc74bcdc1cfa59f4bbb632eb1d3362b1b5bb135c47bae145badaa.svg @@ -0,0 +1,2830 @@ + + + + + + + + 2024-03-24T10:33:17.461560 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/416cce817aeb50820c68dacaafb558efa5e25dda524665df6c96ef0a93daed9c.svg b/_images/416cce817aeb50820c68dacaafb558efa5e25dda524665df6c96ef0a93daed9c.svg new file mode 100644 index 0000000..3aac725 --- /dev/null +++ b/_images/416cce817aeb50820c68dacaafb558efa5e25dda524665df6c96ef0a93daed9c.svg @@ -0,0 +1,1537 @@ + + + + + + + + 2024-03-24T10:38:54.082399 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/43241be644d51d6e8c404edf039c4ec101a9715d0e331379788947af981d4ad5.svg b/_images/43241be644d51d6e8c404edf039c4ec101a9715d0e331379788947af981d4ad5.svg new file mode 100644 index 0000000..ca0bc8a --- /dev/null +++ b/_images/43241be644d51d6e8c404edf039c4ec101a9715d0e331379788947af981d4ad5.svg @@ -0,0 +1,1441 @@ + + + + + + + + 2024-03-24T10:33:09.765017 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/434e52b516173ca339523825ebe6f467bc5c7b9bc2c98235179793f2c30d6302.svg b/_images/434e52b516173ca339523825ebe6f467bc5c7b9bc2c98235179793f2c30d6302.svg new file mode 100644 index 0000000..4b714b7 --- /dev/null +++ b/_images/434e52b516173ca339523825ebe6f467bc5c7b9bc2c98235179793f2c30d6302.svg @@ -0,0 +1,1512 @@ + + + + + + + + 2024-03-24T10:38:54.865045 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/4ef0eade339d200c13f616090ca0088d4914910060fae11424049cbffb933459.svg b/_images/4ef0eade339d200c13f616090ca0088d4914910060fae11424049cbffb933459.svg new file mode 100644 index 0000000..f4f82c8 --- /dev/null +++ b/_images/4ef0eade339d200c13f616090ca0088d4914910060fae11424049cbffb933459.svg @@ -0,0 +1,957 @@ + + + + + + + + 2024-03-24T10:30:19.333330 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/52ba7a0f12c0d6343890cb241fce0ae49d7bd5a70097e995fcc62c16c0882f39.svg b/_images/52ba7a0f12c0d6343890cb241fce0ae49d7bd5a70097e995fcc62c16c0882f39.svg new file mode 100644 index 0000000..177a265 --- /dev/null +++ b/_images/52ba7a0f12c0d6343890cb241fce0ae49d7bd5a70097e995fcc62c16c0882f39.svg @@ -0,0 +1,1292 @@ + + + + + + + + 2024-03-24T10:38:55.243466 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/579b278127947b78f3a9d3d78b5abbc83025c12d5f7e27bd65ab400a07604036.svg b/_images/579b278127947b78f3a9d3d78b5abbc83025c12d5f7e27bd65ab400a07604036.svg new file mode 100644 index 0000000..72a9839 --- /dev/null +++ b/_images/579b278127947b78f3a9d3d78b5abbc83025c12d5f7e27bd65ab400a07604036.svg @@ -0,0 +1,1470 @@ + + + + + + + + 2024-03-24T10:33:05.719230 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/57b8337ce6e9bf02dd853bb0e59a92f8279bccc2c1720854e6fbd1315c5dc562.svg b/_images/57b8337ce6e9bf02dd853bb0e59a92f8279bccc2c1720854e6fbd1315c5dc562.svg new file mode 100644 index 0000000..4085ef1 --- /dev/null +++ b/_images/57b8337ce6e9bf02dd853bb0e59a92f8279bccc2c1720854e6fbd1315c5dc562.svg @@ -0,0 +1,1211 @@ + + + + + + + + 2024-03-24T10:39:06.006888 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/593d6397cbad1881d9176862e963ac0c7057db4266ee7f60ce1dee9f1b26e542.svg b/_images/593d6397cbad1881d9176862e963ac0c7057db4266ee7f60ce1dee9f1b26e542.svg new file mode 100644 index 0000000..3d3a5c4 --- /dev/null +++ b/_images/593d6397cbad1881d9176862e963ac0c7057db4266ee7f60ce1dee9f1b26e542.svg @@ -0,0 +1,957 @@ + + + + + + + + 2024-03-24T10:33:02.839141 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/5df4f02550524290ad042ef7b1e693f4df8d2bf4c14e6854969fb391a51198a6.svg b/_images/5df4f02550524290ad042ef7b1e693f4df8d2bf4c14e6854969fb391a51198a6.svg new file mode 100644 index 0000000..c3d8a56 --- /dev/null +++ b/_images/5df4f02550524290ad042ef7b1e693f4df8d2bf4c14e6854969fb391a51198a6.svg @@ -0,0 +1,1225 @@ + + + + + + + + 2024-03-24T10:33:08.795737 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/5eaa8b6c13c8dc22f51c15ed3781852dccba44a2acb8824b043de2507b2c49fc.svg b/_images/5eaa8b6c13c8dc22f51c15ed3781852dccba44a2acb8824b043de2507b2c49fc.svg new file mode 100644 index 0000000..b0da457 --- /dev/null +++ b/_images/5eaa8b6c13c8dc22f51c15ed3781852dccba44a2acb8824b043de2507b2c49fc.svg @@ -0,0 +1,43 @@ + + + + + + + + 2024-03-24T10:33:11.382734 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/60d5562b693ef73f32086298ff942475253890a1c4e2fa29d50a2ed5c6ba0107.svg b/_images/60d5562b693ef73f32086298ff942475253890a1c4e2fa29d50a2ed5c6ba0107.svg new file mode 100644 index 0000000..6eb9da7 --- /dev/null +++ b/_images/60d5562b693ef73f32086298ff942475253890a1c4e2fa29d50a2ed5c6ba0107.svg @@ -0,0 +1,1419 @@ + + + + + + + + 2024-03-24T10:33:05.624962 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/64a231811354b41e3589b065b38d82dc993be31785b6ce150de28eaf3c8b4370.svg b/_images/64a231811354b41e3589b065b38d82dc993be31785b6ce150de28eaf3c8b4370.svg new file mode 100644 index 0000000..f9125a8 --- /dev/null +++ b/_images/64a231811354b41e3589b065b38d82dc993be31785b6ce150de28eaf3c8b4370.svg @@ -0,0 +1,1435 @@ + + + + + + + + 2024-03-24T10:33:02.603341 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/651d85787eea030c258e86fdbcb281d2037fd7a8d654c959e63196725e98f188.svg b/_images/651d85787eea030c258e86fdbcb281d2037fd7a8d654c959e63196725e98f188.svg new file mode 100644 index 0000000..5b104a4 --- /dev/null +++ b/_images/651d85787eea030c258e86fdbcb281d2037fd7a8d654c959e63196725e98f188.svg @@ -0,0 +1,1271 @@ + + + + + + + + 2024-03-24T10:33:10.413171 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/652e7b67e55a683ea682dee0fab53cc4c2fbd5051c3ccd5735b685bb4c844042.svg b/_images/652e7b67e55a683ea682dee0fab53cc4c2fbd5051c3ccd5735b685bb4c844042.svg new file mode 100644 index 0000000..86f0046 --- /dev/null +++ b/_images/652e7b67e55a683ea682dee0fab53cc4c2fbd5051c3ccd5735b685bb4c844042.svg @@ -0,0 +1,1419 @@ + + + + + + + + 2024-03-24T10:38:53.146898 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/683263c2761c44f88a3f01fb5f22993e316d9b6074a576f4382a21924a560b93.svg b/_images/683263c2761c44f88a3f01fb5f22993e316d9b6074a576f4382a21924a560b93.svg new file mode 100644 index 0000000..ed7b9ee --- /dev/null +++ b/_images/683263c2761c44f88a3f01fb5f22993e316d9b6074a576f4382a21924a560b93.svg @@ -0,0 +1,1490 @@ + + + + + + + + 2024-03-24T10:38:49.775670 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/68ab900ee2101589d38b0bede2150037fbf092ef2ffdf6313e968c684bacbeab.svg b/_images/68ab900ee2101589d38b0bede2150037fbf092ef2ffdf6313e968c684bacbeab.svg new file mode 100644 index 0000000..f2f9402 --- /dev/null +++ b/_images/68ab900ee2101589d38b0bede2150037fbf092ef2ffdf6313e968c684bacbeab.svg @@ -0,0 +1,1628 @@ + + + + + + + + 2024-03-24T10:33:01.796939 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/6a2f19c79d472406da5ca0615b2dcaa88ea6b3d1e62cbbe2296be391593aabb2.svg b/_images/6a2f19c79d472406da5ca0615b2dcaa88ea6b3d1e62cbbe2296be391593aabb2.svg new file mode 100644 index 0000000..d68abf5 --- /dev/null +++ b/_images/6a2f19c79d472406da5ca0615b2dcaa88ea6b3d1e62cbbe2296be391593aabb2.svg @@ -0,0 +1,312 @@ + + + + + + + + 2024-03-24T10:33:14.030494 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/6eeb3a5b69ff54837668dce770e2fb65a73bac6cc8690edca560730791716974.svg b/_images/6eeb3a5b69ff54837668dce770e2fb65a73bac6cc8690edca560730791716974.svg new file mode 100644 index 0000000..b48bffd --- /dev/null +++ b/_images/6eeb3a5b69ff54837668dce770e2fb65a73bac6cc8690edca560730791716974.svg @@ -0,0 +1,837 @@ + + + + + + + + 2024-03-24T10:38:58.169218 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/6f633f22d2c6fdbbf04f79282b0b9fe8a75064f33db7c43e3311d2a4391d26c0.png b/_images/6f633f22d2c6fdbbf04f79282b0b9fe8a75064f33db7c43e3311d2a4391d26c0.png new file mode 100644 index 0000000000000000000000000000000000000000..4e40522d5ac149f4b14ff07314fab6dc19209424 GIT binary patch literal 76177 zcmc$`byQUC+crGjCQ2E!fLI6!7<4Ntp@4J@DI(n^L#hZWDIrQX!Z0%k44on%ARR-4 zA~}S_fDD4~wfAs8zvp@0_5JsK>vOGp-6%V*eeE-j^Ei)lzkaByKy#e+I0Av7QBu64 zfj}I>A`k~r$Bux1$%#7f75q=!?XI4irsESgPcs(_go>G)lbxfRo%N%09u_XH){YK0 z1;hkyTs>#y=H}!oAt-47Uk3;{x>yRH+u8d9E<)v`sPBqEum?l`9f+7-wLly|Ae8Rh z(t4V>IOgetoF4kSy7Kn1*UFa<9UL7lB8UGmJNo%DmBpE3Msh6A-l-apH@0GW-eH5U zDI>20N*5jrtmZO%Ea|;6_Ld zpAD7({Yh}v8>9S$a1uJCOZlcOA7cgMhfL4Qo||9^kEsJ4Y8PIlAN!fQJ!=#hZTm>98gL)C2`KW(i({W03Q z_(1+o0%oH5N`o}Er#u?s@05ur1!-uW{!C6hq$@JBm9VyRP^a`+!>?MZ#UNoS$wx+? z9rU6L*aj|wzrOx;>~EOwDDN@K^Zft*u>UW=3TW@ZlMPGu3gOLkKh%6}BRnf)?f3fg z##J&l_Fd{t*Gg+AoDfTpnR@~_ZiyDMggUAlDw-*n;he2^i#mSJ?I$NDDoSsgVEG@G zemkVXnu2+Kq4KmTFooiOD2M;rK8TuGqA{q-P93sc53v;@CWQ@Gpb-ZnBDF!PD zcsFTu8iA1G`;a~Kqbo1B(e}6yaf#-J%FWwJ;6iR!VKc&P4CVP^Xfd?<#o2OT0ZcRD zU{Mm6Awvz&xu&AI!awAz(YZPvHMUp*jMC}={&a zLbjaB0G^qyb3LE7a~)jE?J?y>PN|DOJ&=b}a*9Q+b{<3^y57RJU3@#KnKOHK_2sk| zbmJS8%fE7!Y}k;(7D&;7Cs4QtIY!rUeQT=kUdbE3|L4D2{`5G}xgL7#f7~pxmrrHv zhMEIG8|)v=ax7zV5Onox`;SE}AKhe`(&zesHHE+7+ULHL;4aN~;kziGR`4LqH&iBf znV)`SBw?Yb^Ij4>_uPBf{|s8LQIAQ9&jx-*$D>k`4a#mE=na*#1lMLiL3uGw;_T$T zOY%ay>Gka$Rp6b=?^XF|rZM__l8#z}_WhUunhhLpg`;lh($7Fy{rI{(T!wyNlHWmi z)??7`<@3h1GA5P-2kazQH}2x@>Ndl$rXGXO;`6(beHpG%O|woT=YYk0qK865;>Ct* zF};#T;?=zoUhSdQ7dCeE?`!K%(F`P`T$2iWe)Lz`x*&_G!2T^ep}~1_Y;!TJI7uuk zS5C&N(5(o;RE3$D+^S)7Gw3NcDVuQbh(*qPkkX6WHkTMkD7$4FFW&i*rI3N1V9W*gS`|E(d$}Lbx|3`F*X<;Y-4d z9>AteIpIq%&Uf>umm11ExEF1pQmuB=ayB=+!&2V_oa}K6dW)uM>IWsP7lwA_ZNRBy z!gKubL(BuT2t+y?w7`JI%l_y%RrY~PnI$OXrzie}{ppa|FFu3px9(Ap+1yNsOJ6C{ zMR7OD(ZjlX!H2Ry4)|=EGV{a=S7oe5^e7ief5onApn96o9l9+)`~V4xYT^Axnl95W zA~ko{w^gJiJn_oB3s)DZ!NpnOClKkudB3TV6z9&=F<$Dv;Huj;MMCyLOA}6)DI+hS zac(QsTMLUtoddtVH@LU*5_PXo8 zGcqA$)pbs(2VCr9FbC>utH=R_T#Snt^%!dVntR)WW4WEXZV5Ve0>vMpNK(QOTi;JG z@yFj!|K5K>uqt(TXA3zbfIz&`Sp6i> z{{9{MOIef3T6U&??I%_56KKlP!tPi&k>)ZYl%r0aI<@hufsRi=K;Y*N6%|$db@QOi zAmnk`KffA8Rv_1os>tMqOfIhB+H*WA!sO=c&)mELTiD{?GP&3HKRbA|*lk`zsYgY0 zEc4s9ZyCm(O)QpkZ}_lX8}q%fNl9bx_<{8YG7odh6Ou*GW$NhZ-gR;oz|Z$J zq~iNx2$P`BxvpPFR?&Y zH>u@KHEeVA`sz%E_9G=KQ8BTxm*U#8!q`L?!X=lx20xh#TlZW{@?I{Fzh?G)k4&h| z%X^cS18h*nP&IhD%qH-L<|Fw9!Kzv%5Wx)~>y!J*oNXPiBij8ATRy{zF)Y`R52EhI zl#b`6{QWb#_Ebut+5^}DY9TiV+^xUKNaWY{;I;*mYL0x6*;gTz?GKm!VKY_v9Ih8< zZ@E+Bvb(U_n71WTqpPU;s@r5)LeuERsjG|MjQ@iL6rIWw&3tP>b; zVadZ{-}TP-g|ggqTWw_Gla@A05V7ych>*#PkloXZ67pEolm7c>-9;?j!Ic#RrB8Rg zb~bYK@87oZt;m||^h{0*EMtZ}_81OaxkL7^&(e4`QllXetzY!SdU5a*7vX(&F4!4` zm8$jdi}Rd+e+LWH{Qe=b63ec;S6h7c)jjh}?d+x=yt3}iWX!w+O`jBy}V$6(_JJ3n_UJ`<*@ zG*f{f)G^VV-n7urF9kA|ePe&$y&wE-GU+v&H19oT#D7e4U?RkSnAoeiYz;6m-8e>*QVR>mYp0*J>|2> zBTsgr{Pz(cBjTS=((*RQEuFa`Pthx_v{4{cu-^f9btpxOjyk=#Xx$vn4h1i_FKp#& zYc9K+rM0ma8yg$vTlsKyL=d_Vs$Yd?X}Y>(=DF~9(I+T& z@ur8ZtgNhx-OUla&-R+Rv1>EC=Nz_jIm(Q=x$1J1vU1+^ z*3Ib8v^~A2f4YcIuWxG`7?`_DB5Y5Ue5ojo^S#@TnjFf?TmSt6|64C^IYma*=qB*2 z{H6cc<@XskiI}%`wOeQ|+bkZA<9b!||7EhkEE_v`tN+RQoJU$rv zlC<03UvDVv0ts;PvD-?_#}O=ZcJ|6+8=`N#xTKn}yF$t;NEoJ~>XlP@(x%O^zQ2?X z?SuQ_-Vu|PK3h&lnp~QdxHRwVu$YGKPqL}zu+2ig^f`(afN9qdT-}x0_?DdQ#C@q0|_M>BIOeDE-VsRk7e;YQwt{ZUHWH$_L%azOteP|FR znJGg|q8TnM9ZkoA3&g`0pg-IbZSGF!Gb~RGbNuV$ry0>g zuJyr(LA47zmO*_r`u=h!YDcZ%2T-8g-{LH-JcLWeBrgT^&H9z7@QEFmT;^gX(772iG+%{i#m{H$e_P z0`GykomC38k>_4rq=C9u(w?|~n9p7SY*#|(psMke%WU|3TG2gsx?KA~?d&+2Q9F2r zG?WD-f|$^8_=V6%D345mJhHl$_NS&wwD35?wC8 zfl=yK++xYc3jlz3!~2tZH<32jq0*(SuxCns0q0Y7*S3TK#(r~O;V0;N^xFA){B>d7 z)lVI!?Lt}pH8nchjr}kHmN*Y1%wpv&)z=lIHM1M>eXxUC;%yeH-jTp7l;EHoVc_sr zwSt^@fD6BSNf`BmgkL)<20`?|qwtGV{rWQJF|pXpNZ!V_ByJ8LQEC8HL337=O}q_? zllJJNXp=4AiQe};h#^%J2+U4JIbH9+dE(w;C0ntDS~+Be0pZW#E#$t$&!gk1(ykqf zUd3c}Ixcj3p|v&CEg{U6T73bs)H)^N2>;!Qq{P!2=#QIoZ^hBtD-;lHwF0k#o-v1( zn0H^5$gVL`UJJWowl(ZdR-KdsM08aaa{$zT345cXUwJQVD&DXs2NXCn2l+i6dPtg! zVb)058WO<_YFRfySlggp4FZ=%PHc2drKeT`kkft0kXpzM|xB0I- z68^h8b|u5_`rP@Tjg~I=Svu-mhFHSM72Cv~wnlFZGwxd41J-x@ldjdJ>xxZbo894l5e*O`mNBs-W- zlDy9kU;ilOfU=lFrI5W3GFukR(7i|bjN7nZLF1v2VQL8#+?_8K zu~NA8l5%-2$PfZ9-O!wJn~gCPXTMi4Lpr?iHL;%;omUD!0u%fQ1>XXab1UXQ*K^@} zT2W9zRV72gGL>-D3&`p?J7mNZ+D4meKcWjfa_9=|k*{nhr;nC)F5%34Z{D^E88|aH z=IDWhti9kMygt1SN~+Gij{+4bUajPcqb(&>qhXJ&V>~tWNXXm(`@`I*KHggSuR+1R ze;qRaHXJuc=H7kyh!o_!oUxjEtAAie*wrIBk61Mz43NlGxFkg&Tb5FC(VZOh8Y3nK zXzm%@TUmOzcrZN-uVSzR^2m+vkVksPEd1+{1u)@*uxq|Xtx{`MK=xFl%B?MZTq`Vdtjb(`y=B>xFxSNCeQhYr#L{@l;vUxz*$%fyODbiZ$Q-y2mvBomii4yX` zY>tFVgT`rhIIIls-vr}Wdd7F<2MFD7SaBR55^_)x6L9E8Qg(UC*Vc;3$i-kKau~a- zjatBvZm9Ia2V_!Wn^Bp~BiS(4E}qO1w}u1NIhf znKCQuO9myMy5Q3#DC)g!lafE06QR3^&*lQjMTc_nQcyWL)Y_B*xcjot#K*sAt>lb8Wa`J5 zD5u-DP&u@}r+6ddHI?1zmlmypc_ol3I~7vgWtsF%xra`Wja37kx4N+Pch^AJ-57R9 z?hrV3vVVSmE6Kh8iys$$Y=@8JFNo6$Fk1p4fx`UGT@#STl&Z2s0dFkytDW&JX@{a- zT?_n< z+Ru~&_OB5A_Ee6^sF13F<X(2w3>ery1|pq2AF#3Pqjudk;yRqf ztZ3kkaMPPj>A4@5!h-6VEb(OkT;)DRG(s_jV2|FPXK7XvPHOD9*gqTJeNuXt9bEn0 z{t^LpbsJ%5=(}`*D|2tS85vWG{|Xz*eM*LHxc@4oJ2dyg8BXHyfd@*!BcT)tyIVMA z6V=m@PtyI|8n3b3Gsl#S!{u*}%u*lAT@u9fpI#??r&ZpP)SNpgVD)B{OeQs&Rz;e zITR2S3HG)pG`C#&O$1f$-`RHeVSH^5^bTH89IftFQZMku*oJA~i}B5DT^C$+(CJMu zk9db0-oVt?iR1GXd(Uri;eWi5ke2bp?_*W^BzOe@4T(1^f;(8oE@YNitCc7FJD%pI z`3KPzMEPw-+47%ft@E*r8asCVGQPz=I4GFVzuf;V#I$zDHuUod3uNx~98JJGD7=QU z3cV5K0CPoEC)=iC!^VoFdV@M4Qs=g3voUV2kKNd)38CGHp3UCJR1@W>b5wn3LL z+Adp;TwOQ@Ku`p1B31_)u5FBk1b_4-{EQ?cO_us}ZBIbpARs)9azNT3GXSeMZu7ie zjwJUwI(BXTfx&9(6y^OHa9y~h>Z!uQ6<6eJSV>?4o4lrFpzjF5C^sqiA>{Z-D{u4W znWh8#TJxk(ZV*}P2ZfW=DcD93s&#=U$K1a23~}L?wjoahJpfhKLx^){Df@KVjWl#n zsLJG?;!+!ahtwfjNqGi}nTy?`4Ne;o;LOIdP;rrlI32ae2hLM24@Uxo>h0Txp-&#P zY5U~!>D|1vYg=!y#FtkBIhq6lHiOBF?(K?Q5!kc;$&RYYc{bIMyUIzzl@lVc{#VV1 zikZ=8T=;`ocz{=h*N*-JS-RW~j8PDQEr+L23!x{+Zrcz&ViscQO7)-=zC;P?^p^oY z)^y^itz1N3B>8CD=3J5X4!qNwlyGn?gE>y#^8riR#?Qe>vg?XNU-p>AfDpL8BwTqR z0`+aukvFqvT9<5mDwhC`vKzMnh|XY;1>6MY_7U_4{^N}UkT>?{ZT*I@SimM_uU4R^wy_^W7ztVKlr$vBj$(s${Tex-t+wj@TaP?US9wb?^1CLd6$gC3Qqi zW8;ibO0_#$5VxEx?R~EVBw)myB#xwLH=;!YK!;N7@ebNXv%TMjyvXe;Q)f8Y z@Q#E7h{!XIlGp*9iKF`oa6-U6n#D{WOVWwlX(qy)$-ka^uHx>Jf*6~qL(Ih_Kk__z zyVKuq)V8Rwa5()Eber4|wIz)7%YHIyY|oS*0Kplb5^dcS={hKG6zFIj(S0T-2k6MC zi(xD`?*V%BQyHl35O;1uuHGWfdnLEK<9Vy;?h?);*K+n^l1M)KokFPO}|J zdAZiiPnE2oVq4OwSjx7 zNt*Y*PTTn;EUzEk_&O>Tt22OvjC0^s>*PY04>~61jOq!%#RXtC;CMhon;Wa^wJ|p; zGFii5tCYgr(96teWg@-wy#mU(sVO zht6dKwuV7tB-td_a(w7q?@xe}C;3k`I# zeGIyWX-7Gp$~lzoSg8Tp1##zFanTOcV$gBqqaCO*s5BW$iWb`L8;6jm+{pEBS9ZQ; z7Z+>gB%^G8bxNZ)x*!rBEQ*i*nALnwR=lw7yVlm$i%Oa~s{JqMWUQ2MGMvczrO)$j zU$0Sr$mXe)HT{@;0)7h<6u%lWZC;o2P9*(ba zU5WpY8oVqe-$E6Nw-S`-iNDia7k}qs!BieilK{9uUVeTI=!J0Z{W=O5WBm~!a8;)>aVg!p=11IVb3wb{<6#rK zQix#s+-Bi^Q0I4tXH5opZMzeHcT;%zK>hG{%U{7TIwmF)TysleYt(h51{6Gq)Wd*E z<201GLnot)mVT*SPlh_oMWDkx{aAmH9f9B$T<|btcWL7Zah3Z35Ak&9V5 z++U|%T4&eM#l=kv9*BX9cCX#3Dqoifmpkj`?vdA&w-1InuH4joiK@XjZk%c$CjvGakR^yY#UoqF1kcMf$8#BU`-p&Snx1NS_+QE0jovb1 zA#J^B0}ysYn%LR#u`3S6prBHqOY2oWz6Ev>P%+~4gMyD}zf8OR zw}v&HsO{3`DF^{geu05Lg8hMpPv~;I#BFB>%YrBIJ*ROXshv79j)H>^E_914 zl^Tjb4Qux#HK_l%->@z|Ldm+f!dM5DG&>fUG};8Q-L(^XXgRp%+kwj^ghT<9mNbJs zsB)+6`3Dl!D0Cf#H%GXcLLKWC@r7Pt@yxwVLS^w&l^O^a?DOVwM`y#G7q9@!tGIwn z-EvdjmQrWD>Bap&2_PrWg5i$KGUCX)jIq;n)y8f3+ipb%a639ii%>goPlT4PdP zfE2B21;qK!mi?ABg*QjsdD|Q8*?V_l0b~sIhah8&Q!++CHD#C7pA75o#*WC|%-rm+ z*hTFSji73(jzWqe)SJFvb8E9`=;Oc1l_@hDjWH-MhbulYiadw@<*6TrP_gvd8Ee`e zvw;&GuyHhtbq@m%35yO8^jQ^;FM^KC|KQHGi7*jHp=}ZBc6xw2cO4IiG%(Zu@7#G8 zrQ`wJdBjpK5MI3O8(6v8&&TQT3G%Cd&+Y&P-_!4=G>actHU2tnbNCjz;GkWmQ0H<{ zZS!61x?z;C^%8`wDXa!$CU+!ZBS3(5*_~2=-eM)QR|$vUm^AS5V?G>{y5GR{fZRiz zKnv8flef{v8@DGG8Mt$#K(|O|fXyU;e#)iLZ97ng3lAI?wbo7oM4hk-_&sHp0xlT( z-7^Vehq!Z!=mh4@_lb3wJLfdKp#|tbF4Ex;5381-gAm4kB?9{aBAj<%31$Ywxh`k! z^(7FCi&Ei|ubTp2>#n;ihY`Rt{)ann*Yv_SLs4~~IM3x6sk@MlRXYyooXA1yHMTOEJRw0@JL*r0$MPcW@4WM3b z==skQjQ}O4e)^tUQGq1TJDKV`;jOyQ?{4JCYU}F{ot#LJ^u+Fg_&dMi|F=?Ad^)DY zkrrG?E@WScvgbK)3V^-mkQ(J7A@mTa2yM(fL0!@jg*+_kF!~XsTO*J@T*S^UjFjS< zLRm6SUbuG!q^fM|ypJChOUF^@5(kR}P_qHjc)sORg{?9ciUorEO*BBdLKwZ+^W7oc z2=gx|s+?AUSVKB`5sjmQ$eh2XrKgperr&V<#ucBi`?K181v{ z3cqpH;B#7lND;*Y)(sUlYq{08Z6uHLk~T)zfEGsR7{aQ$;IOX$F3|+qt3NNJz>TNf zBp}@N&GyF4o0o*CosNRjyJQ|D4Vski-juh2bS|Ye!%*ukVDqY7eLr}N-1SablfSzw z{?n^Z!@ccs#??Q!c1S(<1=&eE#yDN5(R(r1aiZqC4X8Rnr$_v3kVCc4`wEN8fuPe@ zp~5H11ObH9>f=Z!ck+A=x~j_1f4j}Zt@|#M_1>TD=BiB~CyBIC_scBqG2FP)K==t=>G_O!|V;zQ|&WA zb~0e{m#m9HmZ}DsSxweGtJoU%b|28Ud}%nlz72yVfSV)Dm=&Zt)7}Y`|2(z3+D;hH zx|DAK-Od7pmhOeIUdrj^=@qhw&wlji5iIQ3=#p1wH-KLF(pZ-aOT}i@0WSRKLO>rm z;(dJNP9UtF6#(%{E0MI&$Yj!YT1_ggr$+;r&k2DtIoZ9{h=}*dX%Y%F%z1$trms&+ zJgGymxNrG?qwRbpZy;|h8Xx-Dsk3wYZLn+7HanU3p+&J+s_<^)j-BT z-rK6h^{Ps$Gz8NT=5tc80I&v=!4t`2*(5#kjs3PQq{(v`3%xlJ9xLORl-)(k_!|~) z7zK^bE|)+H9jq%$ylUipZ_Z4``#74EB09iB|G_l_Jg}ag8pEG;@-}R>2>d_u6|Ck=LMlEP=Re-)lNW~i|ibC0`h?M&rDhr-;CiC|S^_RO^ zsw7EhLk57Y-ZXGu88^zdhRqKI`v|+w-@Akfh8d8J+g)`Kh=Kcp)o!D+y!`zA zAgpGC!d2g|)`UpF(J6#1(Mn<$U#Oiog(TT0LAlouUtF8MX0YowprJkR^o4L~Tbjw( zH66fTAqVvzQ$!+x2^dd9`_VFW03PFQu~bx;i!DnYM4%ary=XsJkTx`{tfoeA^jl|x zUND65DIp2LHvg}J6k?fb0-zrMJ__(Sx%qt&l!0X=`E{(pd7v(Vq=KJ&n4nj@!}8yi zK7j3RBTg+JM7;7aJApky&3vv`hy(+O)>-unOB~iTP^`y**+U@g`4)4ILkq8K`MNZr)Y~!7slf-h ztJDATWOq(eP-}^6H&3MJFFc~WG0Y7Y{rf#jM2R_oGRabUzLi;leG{OI!-(mC z(>kRq35FG}3*;;(-neb81PJbYPZ2BPxC;b|i}yz%N@A1GqARm!c$@lvqeFmB;BEQ8 zI)Q)^3b3vlAbZ^J_T-)o?BmUoMm=7(>G$4+V5`*$3M#Z)=`%BOU(KzSw~;&nfB}+d z0Rsc}ebDS-*80VdbqK0F+Q|nEKuZk5ly~BRP(Q%wrivyc$prca&^1?2YQ~9`Y679T z#LdzKP*O&lG3%59+Y8Wvq^&O7*LJ^`TMX-JvFCU0+9jael>6pxCGjuPBIv0O=^i^F z8F&}O1wcJ-0KI&xAXfax4x~zJ!yjIF_aPPPCOJR${O2mTp2uaGfh-nHg}JSwcX<^Z zO!dF!O#;fJ21g=k{ymT%#qh66|Qc!&F)Pi4fY?ZhAmou<>n#4ODoyLO5N? zzpxcvF5!k9Gb`^^{=@vtfBKs&hXj&AN)CVAT;iwjdT+Cp_i+3liC!LI)h#v7oS%nE zUna4${nr4;KKJv%`Q_;uaFf2zqo%xI}A669*oui?U{uSx8>?!_0>P8>q$HJ6kgmjQvl9(VfGsXh!+R3%YN z{q^hbD$7ofR4-q>anW}ripy-oiv*1!EKfz=1yc&CmU$hl{THrQgX^X)C4$7fc}54w zaCe^LX)0ScdV&+&r!woI+VmI`@~Cd`^DueIM?a>fNRuz_$`EkOeSVH!ye~zHeEMJZ%N-y~Y*bIBVOZ6C*UKi4&Yn#>z>3$SAA)?bHV<#s< zbGG*yfSd1sNV&Kh)+RwrZCzmO zQg&Vr7d5{s+|7@}O&xXtm+x-6M(z9e2iHO@q*`_Tk7`v_E5YIBDylkO*1tyP&oBAu z>FM13{0C9R4r3BZJt2ZMA3?%4C>?bP38G;ec!x=2DC{{^?C#j8B&%i@Dt|z8jr@<< z+``YRBU&Gkf62K&%#)N=ouD248OWS1&sKJ&Ciy54=aKxm28O!lUQL6 z(CXFJUs7-UZldcA)PLG{!xXfIjqkch+_JAW!ISX(tOE|r63i}*LVs?qnhv`EA_56e z)M>gs@p{Z4ZJ8z@QR}dfq^3FMBxPDBpx^2d;vr zkly<}RWX+y(uXstRV;n2a}A#Tu^o_PQ~ND`Ijoa*$ey4<8{AF5aj_z+g9fh|^q8k< zzUzlDcg+c4mzY*o$2)2Xt4|N|^5Qhry^KPzYM>#$JgKbe#)ogA#Rjn(hnX}tOQ_A& zs(~j6U;g%X44+eAup&6dp1-dSOkFXO3fy{#Fm*jihc)(^W}v`SX;A7wueC~$n}nKlUd)&}Yd)l7TIwAEG&T(@rZj#Sxjp+yKt50J1tuQ|zU z<^vh@#PFbioe>PWWN?}h%(Qf6t`0%l{-wm6EeJ?#NnKySyLJXMetJnb(6t6~>@%|- zJbco|mNOo`sf$8T<1wOBP}4p7*8(5}d4PYeitF=}y{-!c-_ZCG^Y#=0$cwD=O7^X&g^Ia&3saQLIni?X8pWpfhS7@q6+V!jukJ9o*kYl5n@_pSp3AO@{RA zsg&6Y)%V``f{&P)qs&2RK&jC>@4INd0<_Bu$!LWIK|xGpGgIWn4^eyTO$L`5%Y`!4 z?hSe?zjEc_>c|pcp=vi?p*y6dJ!LyZN z5ARi5zhl*0hdo_ie)}QOWFPA65Y%-L5fB|cBCDyJa#IfHS9M8m#6%;5+t>u{jpb+w zwn3QQ1lvj`5p#b0c;pKRA2R5>^rkn1xst;t7(r(enw9v>em;JBb0u@dI58uK5lc6) zAmz6^h1afF3A)~@?C9qi!~UfrX<;#3ceeQV6CBO^>^v|amxn z#_77UZenH^-n&ZIG&#}7{8(Xo`$U&pu;?z^71yk=Ngk{_6iRO@PpE{F|pv?(M>axfbpeEnoo4H9HP~GLY?!;n567Ee&9h zB)}~Ycef_uE@owB&W%j$d1N9FxpkMKa;qwgSNwXPGRr|6|LTJB?ho>H8(C4gAQPZj z{-%|4LlY?ySd%?YD6>Vk@W0{8;pfN~We?}10^(S0!gjc~QyrIT@W~sG_qu$rk+=lv z>GULxF&yTUc ze?OJ}Rd=xBXVm@Sbm5WnWj5Y2{&E05TDO-(MUv+)RLswchQ+ZQ`}N%6WUmnDo{G|b zaC{=8s~x6mzxHF`AKC^nvB158(~NXUWlsn5v3j2CmE}*P5A2VMTR|G)IUu)s*@yB} z8W413&KP|EYpL)Fg>fwPU^i6KxO?}d@zd{zp$;Szy=nh;k5zgSGL@p;z`PaOmVmYI ze`kNvP(NdQiXbnD0otlJk>pFK$qPGiG=M9{+)YXo{^x+EHa@KM$~)1aZuc+wSXSJE z^jo&u69&u73cnZ|Cuy+_Puxk1@#)|Co?iMQp{?HihbmkSIoU1;s4+-+k5?-|I(}znkWj1+*g&Kzs9|lcaEgzpMsN>s_B#G>P11&43uqi`Urq=NA zr8L2Df*Nc7dh-U5f85ciCQOLE;1i2Lw@`&}t(R9F*IV!LI@Az(YKpdfLc-;!Zm+%( z&ufRq@+9gsg)hHmn4k%K+d~h)xQjj}7WwA(0wjxXQ(ZazF6Cb?lbyHBM)e$h*XGiQ z`()h#h#}J{HK%&cXA4A53-z<_UQ!cdGUu6S2J|zI;nMZ%gtRqhPq52O{3JH98`Jn> zF%gUOunNQ*|^ll+`u0@=J{-;*0z_{y>8#m z=M#HeGheqI^X0@V&7NyGBbWV>dk^Fw-o@_o>&WoBk}Na7!ZY#`!+W>9Nu3pJ8oaJRn)O(mkO`-<=d z;WFKdM)q{+SI**4CQpxk4bHN!u65qiai7D41bet?6Lz$W`{~{8YJe$akja+>t@jLa zrn>0KT}g&KDyjx{WV~c(gTn#DvAFz=Lt}o^P9WH7*LNdG9JPQ|>)q{b2B5*-IT(l< zKQkHv4@B40?7g25C`sU4XQ17`$WtNrw;<$C9cmN=ev7Myrmjcl1 zz17tX>eGxKg-8@px~b9?3|zBO!)HH(jWFa7&1qM|`gUEY;CS zYEh(VkU6Le`D}y+Vm}k90gE_V$wq2v?V_9LWEqneH5U6Fm>j+Tp6z_mlR))>D|el> zSsKPG?C!n(Y3}UoT(PrUsa7I zGUzWqwZNaVqdD|IyWcBf5YIq%2SHaNLiUkwLN@L4`E?syUPATwlFQjoFNj@AAzUWh zB8xLWqFoVn-}i?-YW|CVcm(keed6XQ1HxI*K`)@SoG37R@uWSTK_fxbF&P}slarXf zJXR?>5eZ%!*Da$;kN%fT#vd0K7oG3~xT!(wd+Ixhyz$!>y-{B%%{8$y!mlq|=}`P* z)c%GDZNYSoIR{Qw%&lf^(g)k5Zxps(PP??ccGRvg>AuFJMD(ghV>ZYo)G2D>Wf^=j z1C0(?^Ma|0J8nQ{ahBIc3S~N=0b;~qTh-vS6vLa!@~BdWah`qF2tT5En5Ll%jOGsK zX&%1iWWlXs(iL()kF;{wmITN)E&??%A+0iJy3qBYeR_3m3uw6rlWLkk+#gTZ8~^#G zF)=VYPkr;*94G2P8ooFO|R}F;b8|#fsQG<54G;a zV1Jet^YT_(1^z0`U8BBqSj~WXtR6~GFXZQz0)b8jY{=-iUa`@-%mjx9n{aCMa-u}~ zT~4WeZXa1W>g--9Vn3XD>%|fMu_I1_U{2J}homU|`_B#bD;S!A36lzBu9Ab1kB#Gr z%7f`~X3TFQ+<5>iDZoFtNQ96*@kGaj4eL|(_1jVw$4~onb^zjP3c6($n zaMXasWCQplg61PlhGR3I#wF4^SR4t6cAlU2Ev_suxGyRc9;ye849QMp^PJv+*mXBU zdqzVqDGq*vV#SdXdrfAap;m7 zXYQu_Qc+$|>4C8{prQ(J3UQZYAcpJaFj7wed~dVhQ@@R52>y)8_IGe$Gq?Bs8Pt_v z^S)UFwAE@D4Od(wZab(`OPhU~w-2R}tQLF!k6iJqFlU>lKEvIWoA<%A3(dgaodJ}4 z%PZX!EDxi{Q3Si%7fZv~*jVeHOeN6Gx6+pnIyw3p^uUFoLHV7ue<$sIP<~LF4B(0B!vW zoW7Z$p9wm;phnREQUC*h>_4B+@vLwtw|fsJUR9jBiolF{Ly_)$+yqMo&;?&zq6^q< zEISLxJI#B_hDC(kF-)HiWVXcaax`h3sKD3MXv>5Mlm`vi z#t^3aQ5qT=8vwoV@$ub{I&TsJpc6 z7Q1CXAr_w)s=jk5b1DE0Eb{^#pktmeG0HNT5G%P`8~TsjeNc>pV<`3NXcmya3 zx;PAonQQ?|3m6wky^#vs?arDa^@+;I$13k1I7fY+!_a=B#t8KPfHdJ4MD!;l@F{^> zCAxX)?a0`*GvFy|;>BIFvmbm&2i5l+c!<8fT+It(m+3`jLzmM4wPgG^ef8r{pbvq{ zL~=ikIjjxvM5M>I%|A5I$nObv-ci;Bk!_qP`-q@3^=rH97rJl&tw_u9as9XUY;OOv z!r>=4VUW^gW&k(XWP}X%@DQd=bNuaUM=d{hATbf$IghKp+iy@=eNhhyK2s!J6a~f^ zs-f?xNCdqC4S**|8g?Mn9Iy5F7e7n8?IQzyM*?u|bbgNDYZVf}14#8pIp;vN$X#~3 z(iP~ZcyXI^(G1=%@PNga{xi(GyD{AT#=Xe>Y=AGYJD_0%Li<948-X&Wd3hat4f9&s zNxGM&-G@C>3%_qRzTmzplGF(mHNc=+h1{K(KInS)|FI8oh?{!FZrJi##nId@u`~8e zC!n7cTqMp`EWI%U1N#=kCOu*o=K3Q_KZ4;TmmNz3AhRs?2RuTeYEG3g?$l}dVAsZ` zraa&Vtw4bcqM!V8J|ziKpCx4g zPgRzBL0N!@+2aTnT{hgY18qFjtXjA&3uL807oOJ**&y5W3yx5Pn-6Wu< zyPU(vG1}-J18NGY>u(|~j!Dz=!;I3xG3j56g_BgDvU$Kz2kY)kw)4U}mhoGS2znJ@ zIY3U7o0=+TVPT=*gR|^Rh8A36KN9}i_};ysi@C+cLOuNdoc!*@TrpjkMpjtj1>@+9 z`LjU12m<7_EreX3OS4$)hXt)CHf=#&x`b`oHY4afn(ErlNGf?BHK%hs@jk80AA}uused+B#yh}x_v!tD zlG@CBYE?BlTZT!{6n&Q;MASep9Ed2OpwR~?;9N&vBC>vkhC@m(su*acKpnt$vuJg1 z1?YU#pf5Xc%ricF1(><{<_V76qbbPj^tC3W`uH9y4y>Mf=}^Emz`lYxG;Nd%*lU8< zAFlP%=aOZ#d)r-@5J2&06HpBGEE{>4tz3XlcC*^)(mcVVzNjBiS4G;f`dzAEU~90u zn~E_69MdMFj&*nZb5naDgRS0N)!DTgRs^JQz-G&}cE#j_7eNdpCc1X0gT#K8Hdvry z?);c*r*vya#{d}SaH~Jeq!<-M$BBi$D5I>X*ac(_nL|aEZ9ujGRPCsrZxzlDrNe{> zXT93>SmD`wHZv!SzllvD;kV@F=5uA!&UpW;nOcjV!Fp1lf15vVO@((6* z%ut~`?9(_~IoAe;fo+N!@D37AdtXPG$CTfAw#?5lm-2gT02%Qb`47|_8Lq&c&-9xu zep~UYSO#RQBe;J*vhu5g6!H}CAFeasx^X~X7Q|`Ai~@N|$YE4Wsi%5t9R2bH6UTL8 zvyjSut~!1B!7H~A@Et!3&hMyO6Y3AsWNXWAlT=juTqLNWBJ$*}nDY_-<^*aW(g2?@ zXyz<5=PP@GZUNO|4K{z3mwvnSG5;e)<w~5lz^8#YR^J2JQYE=j(65_uY3NjatZ(4@Vc(_jQ@ayr^ljA^;FKFh#CvG}t-fe` z2KDEnECm+^reB|cp$pA=X}ELGqp5SK2pi&3Q9|3a+aX6?JBbJ)*k869!n>S>!pmlO z_QMS1sr9yLl5kCq+Wj~E@}9|Dx_2C9ju-?Hlmd~JwAjobBP~9i{I2)c&2KMoL@0Dh zt%rg(@dbclctG0~d?gBhStY(t2SZum&f(nz(hdnnAOuwzDZPp4#yK!IIJa1x9DiH~ zeQT(hxGq{Q{Hf*F_T{3A=cR%=-nV70elmEJNCe?np*rk(HzrVzptLmeNaZtajd7_7 z7#J1F901?o^79z8n(3@}sZxdM3B1RW!T+J_Dxk7lv+idAf=Y;_0wN&N2AwLP0s=~R zBi-E~A&7v0ptMMLcS&P_bT`s1-T!&<-kCeI=69``b=O_q_j}`c&U4N_d+)O~*CA+N zU=VzGmkfD5LX&7mo=Fv3lNeEgh7ojm2o%ahO8<7Q>GU&p#-3LFEcV|gflsi4Q!2hErDgh|bm6DTC?r zIaqlmPtVKa3CONVy6uO`Who_rXO|Mtirf<2uD6>;3ot7au#s1lm9C3F1Bmtb7{JZn zJlgMxiL?>6XJ8#v3Js07DVZOG8%$)?=qyfZdaqN%mpv$Huv-_>Q`fzQ&k~qIS)UK+ zd^#^vIe**orHGmyI=+YVbQYo_zQNz0w@Bn9mJ)MaKaZ||4Z)FI;N;CKyRD#{KjDrP ztGf96h$yaK9nv5s<>HpbEqXdSx=sNseYslxciG3 z<^I$PLXg-SStY5B3ODgC89=#8JsIpLKXX7*Q@f`URs=2195-NKa7$)kzwcpp%F=xL zbzL02jU(bG!M%*dafzIM;sfmX*GGQ|SC2O*v6e^GNiu&PI9HV0iFWqao^mmkm?mpw zZET9D4dC<-K7OH@r%`(rL61YJU3Mp4FANlsiB{yO_f>eA9?e z28ig_u&t=MKtBoHHOK=dhnaPg8abR?Za^}yGH#l^a7%DD=w3!fWfQ(7mRV^;GWM}^ z&USkFz~Tt=lQs6nc6`F1=$^2vW5t4V9sCX8F)_AjvA>t#FQeF1>c94tqmPckB{RQZ z_Y(yw@D|)_9Ee*THeQ;HR+jDgGs#Iwea4`0ZeL)>)%`;%`UunwQBV()XXXq`C4zb( z737|4m@KGIg65oWJvD=r4QtUu9dp#f#3dAX7YzI{7G>E+eX-+rcF)2&FKr?Gr)|ll z)z{nc(k6+zlz`&9X|{d`+x-_ck^E{qCq@?LRlQC}h||x`7;pB(*k#lMUr4P=Mr@;8 ze}~4+4d})7cwyp$bKCCIlNS7KARnd{7IwoudJo@~Kw_IfRj!KD#cKjvN5-<%aO_0h z%d0t6z6F5BRZDuRhpq^%qU@P8^e4yP?R_K7aSow3s-(|KmDUsyhd9T!6-@R>U3O6x zT9a_%rNaHq`I}L<7a}}1JU?zb{Rd% zQ^rAIgs z{F49#oSO}*4CtVW6R@l(1G@AnV328cti?8LY1^>;=Jna^dRuFnhO%E893Pfdtd%HC zh85F+6neS$)PI1&xL2GA8*vE8je|E16wLS3(6^3APNNYE*Z&$q!{l12Nvu?2s+u?$ zN)5H~yj{&f3^g4eUyQ1J?lVWrOjpa++h7wpou0mhzR2Q|s|et5%)LZ^oTnXcg6BJXk2p0apd_PbC*9CXbd*mBSW0|yy6`3>9*HU#I$pman z*Ww~hZdGwRen4IYRz-1A!{T*unWKI3k~%1a>mlMTg@uLn z9t4J6k^-s-n>x!B^F<~kyDQb)@@@_W-n^qeR88U{Cl1;%K(1iTl=LY=#HG5I>Je)s z@cXk7F*|m2yYVUA({n6pW!%v5nzpd`6y4j`$0z((&L*e*hHiESpOofCUvm>@STt@z zw~*bPCGLHbx281B%`Foe9DROBs~MExTZk2=%qS<%a^nsMpwzE?)?#r`)I|@?ZF*$o zh4-ICzXyivV}!R3`8QycXQ}`Nlq)%(iYQ$ydM>m^#}Hi~o(C~7sH%^C@84Y*VH=nP zGWwT1PBLsLa_QO9_C{1fWZcX-`tTs1iY-(a)ggA#4R{iKc)fIVQQoCYfchyHp`cV} ze2g-Nq|7r3&cp7;rY{U_t+@O3G_<8`+J@Q3Ylr#cbdey3o!aP|JC15DnJbh)&CNmX zdbA$o_I5LfQ`5%Aru^ji0C>`-mFtfKi0^_NxA^X7_2g>1b=;%b)+S}$MLy^)_CO65 zpxr~mS6jCN1f0^p+lz!=SI>0G*ko}4Mn0JWY@?GWQn}zSIJ6&65O=8p6zFru5#bASSu0ot8C=#ZfGmvf>9L_wZQ_oQrP-E{$fN z9HY#6UtCkkQY?6kWFXY@h->EicN#S#40LoBawYDb(HH{jR@`KwW>YZ|eQF;Q9Nivl z2$(oiNz16Eyq?YMJy#N&+_3wReHmk0V>>$KGN%VFNX9a$8D%x?`ApPe$c8 z{6jrOo4L5}8H=(#bun_Z!1_yB;Eux*jjZXNW_=$rXId(J|E?TBa!E97hxd!-1#nz{ znsngqPi1|*2JSjQfp-ZmVNu{w((jRRwFr1wRq%obM`kh5pzK9~*yV}(Me0G%)JxgU z{Bf=zhWHNb5l}B63SV6tu_x1(7U}*Y#6Fr|PngFlj7x!Ru&+PAqG9sH)%*Bx_}lCH!nTx#zhT>rYA2%FJv{N!`D~UF;V&Fi1VKH2P(8^1mhY@m9B7j^Ei62Z2vU zqIX3ZVvD1}z*MG5uBapx>UOKbQ=OKkbo$Z;?!I=`SCYQK&TmZKS=eX-iq%iROu#9d zthJ3H8dr^0Qx6o$N1%G_0Ls?;@bF~Vem@s%1HaDt4?jCw7}zxrfD!QcT-|<+T9f?@ z0!^rcY*n}kCvIles8V7Le~rwJtDbTH*z?-arQBN{^{1xPUt7e)tr>v21Aat-PBFmW z-PpzPQH<62B&)W}#dv7AYnAxAy|4E`MtN1*)xxHrj~l>;4zf$dVCY&Kqx|H+%6Tcz zta*&}g5Sg)uXyw?MLB4pvfNweDM>vK9v=N-lLhi1QF&RM+Sw&`_r8Q3VF zf0q-g#zEC={^oQ21qU~~o4@^y3V}Fd>6jANy@7I%5t21Wv^EBNzo!9|?^Csl0Uf_v z0~@fnYjaAVZ4MII#)by{FYaed2I$S70ZFKQC-TL9JrE(41~{8FkBNrD03!xl9|h#N zelX-8U_sB$rWPYL6;;JFFkEXxNrxW1L=nK9COG1 zJ$pbW=6;`wmHqqoZ)&!sN~9+gRdpP$Y#L0B6a^F@Fc5M&r$hyw90GgWU}fb!ot|yv ze1B`$=^Anr<#{SQx`G?R18Z;|`vVjtm`1dYwM}lJk=j|2nbG#uuI?S;U2p3-iy>;n zbhL-UHHDXdhD;==O{5%1fJa-?1hvwR&!iF0;{C5B+PjIs=DTr8O9fjuv4M^%X|IbA za_2u?#!y9-upYIZy~Fg>?exNY3Ei+Q%@G5er!lnoJPoK%StP(m*x(+F1C?^lKFDpT zL=ZtnPl2A~cSkz9w3nO};@3%)<~H_BI10_D!*B9&>)Etq`s^yCxLO<$Z!LrvYwGz@ z^W=yKvGmd)R*w~2&f@#$&dB!I%RkiF^p_rzhzv6&A+C<-)AvHdiJH{U(+I zbNPo}M7wMcI6}f*1aQ{VliN4w!5B&e=xUg0;E=TyBrl(q`!;J5IG?uD)|~GnuVyS| zQg6*iUJ0N50o<52BCt>cXbcjH%?5nQxw?xir$wy8#!OmqxO%dOIYJgmju{a+^-vyD zQ$(0464G{-W*YIfK>okvV&Y6*n=*JB`zFC{7kCltepbn zzNDcwJz}T+VbC1u5l~Z38oUta1JV~D0iAk@<4Y`Xf!IsmJg0Dt^*o9l4-!q_&vYTu zF)`*cA+Q=n0)F!2jhWUikmaO8ym{XLtJIQDy4?B)pKllu%Xmsn5ZA|GO9iqGwEWJ?@W1sNj|H^Qgh0 zNFi&DKlcAo;h7?F0-rz`+@8K>h;SJBxt=iVHJGJ7#DLSQE=v$iUfcz^8;(08c3Tit zDe$y_MfCt+&n^T711%TBkg?Ty`RxvJ^>zkB-u)@7?}at2n|Hj@^tQUdi3gxcFL5W8 z&^c&!ohm7klZ-l9#a)UG;{X9y-fL{G&ISrA&^~*Cy0@osh1Lss;@6k6vC}~0foRF} zVjUiXt|r+4UEO`ev?RN95^Kb9F>9eC;dNt7F_>E&=;yG^3F{W^*8?7iEt_iW_r1{Ela3xr%G|V%i5L?1%Mw9V*)6_Z8s$Zf0*06A9p92_|QOrOjrD~w&s6~w=g&QP=Xa})y7r( zX`x*JA0OWaOstWv5Gj|XPDbo%``1@_d3h5iSAEYSt^^RYz~hupAtP%iqxiT#K8zY@ zqx!&4E^?48Go4A*J$;C4e)i@B7g$$_NkL7QEFP8Y&s_c#9L8xkor{W!Kt%K&Onu*) z7hMnv1L98t7@E3NYN=;XRDek1k_fWBd2)~oq40>-$J4E5+sZ+8$Y!%3vo+r}n;5qW z5sPy#+5PrQVxRni5Xs$j@JZBv@KYv+E&T~GBAx)-vZoM@`S*U|gP@X7)hO~-1*iK3 zkk0_q`L78Prz#6D@K9;M=wZE_1w3G*qXqezPH;C5D$Rx!KGhu52d>_LVTU{|K@-XX zImdf-XG>2am|y||+tJFRVO+(D4OO)#i=5ow)o7{Z2c@bpl_#1T)tAi7(8`_9bJli zBpj@_Yjzu67pB}QXDgH}2b#M)aP15Fh~9UneoA~Cmu1N5cZZLwc%rw?lPAAi?-s5O zq7X&};=*bMk~X4>1kf14OUoOB4rZdvs6`;`_{$hQkb#5}gnfT8{z12bFy#e9WPlGZ zUh%yUeCH4truy^3OA2)*#T=lD^vN%7i-kb`#tLrxYrS<)NBpub{EArT!=G!=6kxNM z6hewd#63Q{=GZZSjHh^@Ia9qx?IIx~)GSC2O*L{8*9OYW>{h35MfT#0W+c&fDl_0; zkRMt-7L7(+rc09Cacy{I+QnQRW+Cd5Cr8%Q3%lM~UTSRP(5D-th9@GY{k!;BDgw4I# zxQz7l1>e;Q6=v~|cODAt*5V-8YO&$>VmR16i8hP1Ba^X24GZ_ib9Z2!Dp^}jOmOdi z^@jGrAA#>bu8fn74Z^6wa+?ew%FD>fC6|_R+AMU7fIHa>V`F3G5^ZHjnrbhW7fJm3 zRab9Q750yAe)n*90 zasQ{@w>=Oq7JIY&HS|#2kzg8l&WU{~y8KrmK2KRYYm(|g2AJ-J@hg-+k=mdoiu~7X zC7q}Hqcnp34v4WEO1x0S+-A&W$6&P5A*4GUO!ez(C+>7t80CX9p%-l`Ur>U17>52t z?G!Hks(TeeO7OlL91HhC65qb>A2-eP7%&*DtjO$%@1gwnIoyoNcN$2!KLbo0=dv9! z(-y-}e@7=WS%vw-{`N8wTe)H;$hP=e_&-!P~6J3hk z{#U-!H)o9t*#PWXN+2W5MCdI*;u^3Sbk04ObMULT@=lmh*UE3gI2HSwAdZnd^;JP( zKx-QesY0n$&1=!1FN4Cp3xF?}z@|;O+@~RPu$&lNZp8A^^cwT%91L}X;Sh+f`ljo) zqmoi2H64scfi4qj@JD>;MHa5CAlJj?U?3#K_LYL_q@aWvIkQ|6S>#P{8Bab?%yQlDzsUzatXZRSj5#caB~+*Xg%eWA{jugil%J4Y+&r2>eYpZ6 zU`?lNXq53Q=E=9>w})~HDItpUK{^`F}Oj@!|avk zz#UF>qec~$-M6d|{^hX9>&i4@{r~~JS0eVjkAHV!x#U`xU`2Q%tK|lkkTa)~np$l2 zPSvWx&dSI>kcw=;CrS2ee(fwmr~;(fPTM`y-NdMO(XlUOO4INU&Z*42lY~IaEsalc?-7uP?zT4h01drjf@r!bdVhNnA#zKF6BNV}H3JQI?Q@-^I& zdRK0DylM4TDoRp7gnoQaoGK$8 zAu;$IHyur_ixeLKnTSYSSPd%fp47G}AWx)a7M?$?xSe8l+rWq$Qc-1)zIuCm?}JC4 zy365uGt5~sS*JR4V*!{Pz$ZlJtTFRd1p$$#$Wp8x)BBoj__tYs{g_E0Pcf8SH2}1X z#0LSs9^))!!n#Wnd;G}X&DE-)x_Bk4{e#g(3Bz8*^TIulY4UzHP`eNY9dygD+q{;w zf`L`Q75sg{`lZ{C(tMniBcGAzrN~TZ!EF_5Weuw&$}0PVkiw{A3YyxNVxRZJ#FFdH z7b3xzE!M$FFhfF~*Tr7`^dL(~XbkeGT2uOaJ`e(W)Mcr*m7W&d&{HPyfdCD(z6{{r zAQcKgIdMMNWa-C82O01^z&@l8{t@tnt6;Zu{0XYc+d$iT(Cr6C?C&X6t-rlv{=Ii@ zu&`ojoXOjpJ}p&XW(q!sk@|}wLf0~B=vD-fw~Hj8+xEU2oquD&c3vXcpqRTbBoW39 z{_{wDAa(7$JOHS{)L-YV7r+3sokTC1Qe=Op<|L~I8ifl`u8=#<#gA`62Hk#g;C3R( z#>R#WJY&`U_Wo(f1=NeyY2%LuP#ySW3%GrWo&r-sC)~*Q=Y9u48=f6V&yl*N zf!rBbcng3J_98_EOZD!}Ulu6$7T^PbgGz>7pAHO55FiNaF>DOKiF~ktif>mro0!Hx zwh?a1*bQFWv?KK}Hsk&(VovBzO*iQ&bn0OW1u#w;4#MMtZIA_HyS$xO5nvOHLTT8B z4KJW_AEy-Bs{$_^VepezADsH-lX8?6XpOY_9{u=ZIzX5=izG?yu0qbuVL$usta0%1 z@g>HFgnR}GF_c$^fI%+w<*2vp{N!2XMdlPC35~Ts%AN2j^tr)CYXK&IfH7O4t6mz! z@9sBD!ayYT+R}5$<5q`Jf>AXHeDmNn;rjSr9xCZ+T)(|uf&C*8`(&Vc5SVo0b7cL& z!NrvscG}j_;_36Mz(BA>CqX+a5 zH@jHbppZtamz>R@BM_2^DF4YN=S*&ieP9lwrnz#>6zClXNEfJ)OJUL!HNw2O}3qhbFCY93XV5x=B zNKElixVMm;z#$eU%lzYxWXGyqY({MQz{zQOtVSL2E-tj378Q~DW6AP|KbDpfn~qfa zL^ZqKhpsi$h=0`S2f4#VRidmAs|V-WU(VUs-#3uE)sKUuOr#TtXGSiwJY&K29cv)} zf|*+ahZ8uW&z`-9z7~{D01!XxdQS~+QvzU8BY(m(D9v}LfOUPr;=-cMfz3&%`l4T7 zpT3! z-gS}0o~c<@Jarih6_6vRW#Qv{$oxT?KTbUsXjG*YB2+=gGa8kLl8#A@=)gq2Gnv4bY?je+wMPPNUpkBE{EBMSFST@RJ~Gx;4{M zTY&_b>a8p@Jf$-acmU4&uOt7LIk8f8-t&uu%k8lV!#7KfrQ^d*=)31UV!&6idSN6C zbrBwAE_HB;zw*_qvqxZK){SUzfGIHJySfTr7_?SFAe2o-6hlCQHR&Nm-S`1>A*$WJ z;=+gy^MXGG>b|h&R+X4f%d~ZiCNa!TNg8u8t3;;mIqK4oZCNm|6V0FS6$rubN`og4 zg{hBWkR8kl>Al5+w4XL17(XWRXEv?nt&!XopuLy9VZNpuvj=2^3-}4Op8BU}N)m zeKyO3vUZ~mDaZ)65$i7TkVF{1W&{p<$QEtK2KGGs3}RAe{j;a6c~~*5q(}jWw?f7Z z;t9Y5W>d___VSVjFbVFMMd3-&GL%wJV?$$gs|n$k%$H&6-_PS@>E-xcPE%Joy7xo`zS}s2c=^&3P zq&f>*!aRZH^+FZu72G4xtSM?Ym~|?lc?t_H`)NjgW1B&u#{X~kBC9e-9g%UFjsgO(cZP#t1dK>!CcXRu+Zrk;N92BNZiT&g+e-*jZrGeIJaLPPt?IO?jjpEE}nP#xOhS z#ED4~*)NdhT3n<=zP1PZfQ8LdXvlmP_TNNw2tDbt;3pxas2BmROJNZaX~V-Rr*lSG z4MMI5vVZM1hH-y?jnUMxM+P#X`4=;o8Li*VnIB8G(43xMzz zxV0Sq8BCIjV0s_&rQWnYL03S~X;ATi`5tnG4lwPS{&QyCtlQq10=dZ71w!y7NQTtz z)Z^!1#U5hhUTWVG;Csz8Sskm#2La)xoS#tFK0^3#&owF_lZQjcpOC2&L=Q56+&{Qs z-tb8ozU9WA3=D8hPp8iUJQx{VT5hvAm@bY&y>he>=>`7){T&bbsyzJ)NELY}BVpEn zqw_H-3@Je2p|HAt1;zF^R+0mA17lp7|MTCmTVt0jy)B%7IjlMg;5pSw!Cf(j_+dnB!ZJq4ov2Zc;77=~)9^kS*v4 z(6h6%cbdR>8AHV4B>em9dB`p{XC}$^4M&2{^d5(h^6ky+d!CV_h~a*Fng!^m`PNvC zR=ZUEIfU>oWl_^{aKJ3h46&V%PhER>cvAiSbBD1cq8=n4UsJ5zxxa*S=iJp~Z%y}bc9Sl4 zu_!+ANX~<{ zCsOSeUn%L537g%@FlEr}^z_3dsoG`RDQIU2#zyfuv4e8-ykht|pnBrlxPHFASA)Yp z)fepiY_t<7xw1GDbrwT`tZFyMPHb&U-OV?Fh(rSq`EyyX)03e{=`zaK&yVD{y^aob zXJ;o01&Kq6%SgIx25P+cxxcUPQ|q+HWU*yt*ws6GvI;d{&fB=pEHQ2KR~Dw6S3l@^ z&FNhG4F!MC*Y~!&2u?OcnKQc*M2D%Ho7SiYFn-qLCk-_SL(eRVT zRolV!%4GX<+ppjGVra8DvEwARUZft=5WgX}S@HKBMsAK9ZZ8d>+@T3NvaKQ~N2sIN z1ku^lDZ8kM05xt~L2KuZT$7QDpH$L}rQzi0kltA7D&rT+Nt^>wxL{mEt(DECT;%qA z@1WH}G|ag1{obhSfk`wN?6g>c*a+ngE|zZ3+4N72k5H(D4$c8VYIy5cM11sl%d&9C zP7V!?)r>tJyEwV`%$QHs?5HzzT z+0f`r^~qjOsgVdb>%x%R^%6Vo_2!-Isf-hcE2}YS-WhRc2$ApS9S5)b68gF7^#HR6 z;kx#`KhxMA78XUOsO7)>#CGlqDk`aQ3I>f1EBNPlPwsZ+)V4_3%5)e{3)o8 zw&X`s4Sxx#9n0nG8aCEtYbiTsxCMeiuB(K*2Z?*4}JY9s=a;VUcL7Q1e^-G(H>}%WmTPeh)Xk?5Me* zYRFrFVhv5fNwVk5yw;zq-;bllNfyFKNcFs6K4=f3A`SMab81xI@3qAV1Oc)7lEd({ zHAKWUxV5}P9$3v7tC6y67iHF`)PTi4vON2F3T+D{%DW%92YLU8yo{V+qEKh-H>8r;HFDAD&{c6KFIpah16+$O!N0;lW zrP(;`tFg0>zX;%HB~ay>=-ygU(R%ovy->hP@dGt!FSVZ!o5*KP@sLh0oa{r6Fy|EN z#^&ZJFww4qWc=NS4=D}by$I0dC&#@c;IH`*Jt?_(AkjBI&Slh2E}z$d*I$0~oE@{s z6Kfs)vy8noe_sx6AdxlRGXt81OM^uhp~H!-q@-k8{pEo#5I7$3f}L7_wF}SZgoH5r zuOvbLzU#DtW^b1iBa3krRrrCsFG!6QgZ(N#NVvE+{^tw1Wo^#ZGPOPq1Iqi0jEJzw zxX*w1KJ66mff7sUny5ZlsfP#1h3cC|-LXm2oCaHB4xU%XesN*B`S>$wrCTz#%fqvb zs)^{z;du6Kjo(el4PM4mzilhFcf4wMbw9c-+pq-!Hnr<(c9ABMRc%cM85gy3&{XPLsuEKw=otMwd*-}$d zFn}J@415Ih{Y`^Q*uo*ysXd07$|cxf^-c_Yg6!v)?x%kv0<5>$H~3fU22tvrYx`XNT3$w|6dnIBcu{t7!5Y;xWUuj}y^C?Pnu5_bfX4wrCWvttr( zj<=Fq;#2i(i;X`Mo-yWM1p#4Ag^Zo0SYzlLBXQGTrwtuVVr>tw_wt-}OqT`c#8P+6z}Mc z3>i-^DBNm8RVfoVeVgB zNi*!P@IhBNGG!SRP<~xcWZYlc{?Vy5|GS&5) zTr%?CeG02*HGtH{d%m*sJ5?lZ%eGEf?A(Ok&OL4+Q2TaGm9 zvGDhAJ-8O%H1Zw&T&82?fhOA^PmEx;g_F*8ZoS5ii|lUhVfq?>j+9VAaBo*0+b7&! z8Cev-TkFjg&FP!+z##vU5XAom>}2S-Lg0n4V%}sm94=xdyG9skaTs!W)mb5cy}2Fr zeJacX4vc2z!CX@h%$=-nuR2mM{<&rSWGTi{r0zW{5_0GfMlGUHW=Vq_uFx-dkTD%R z2$$^=@p|mtD%P22>On%GlGe;ao8GZ7cg-@OnPI90?OLFEdh<-ue7}R_nUK=6r-{?m z&vSAQ6~i^}MXUkocM8&ohmMZrAtki%T5)~bQ*#Z1(IAgApA5&?}2a; zr+qPYRCuEHcd3g~e_iR;)4Xv*0FaS)2a;(Fk&yT0Ot1(sA0^0X)<3%6`$Me$E5l{` z108}$9{BcZId47n3@vtz$>hd@-yHP4qze0!Tm*ztj}5+kL@P8=M^W$q{L`RI_<<%K*l6v^nhK^+uN! zxHt%KGQaxJy%t%L zMQYC(h^ENP46LQFH4=V(JuN^s9|st$W~HHs_9y?cw%H*{D4d^kzek>@$)ILf5|x8tWuC?NLktH~KxE}j0oZoH`-NiiV%-%e@ zv5F4KHurWLWuy|lNL}YE9ma?luZtW?YrPn3?h12w$z;}4SgK(u-xt!L ziQ^R{BzJ7qBG-_W>bh%uf2JeVZ26&qI0jb1TS0OPv9ZkkUh>MylB*mGOG`0=teB7j zemYxnp3sO7{DwbO(pOZ&OZMg559w$5wH+x+{XnQi{hfuI)iaTO5Li zp?mYxxNoD+zwebmjinBsuj1~;ldZC0!4;kemjYk? z$$gdBU=~vEHgX~z#hEc#xWCT^jEp5%)ljSM_PHI&LsiiLsVal|9u^FV5EuvdHyY^u zQKG(VE%vR$_=@-xJuNB9NFl#qDP+*|W;i9UifC-N zm9EmY@T`rv>hO#h+&t@L?~ykd$H%TnR)B$mFkcB~w7CkVrh8?J*%go$zeWaA+-6~E z2CCp2V5QN^)YD)oH?z?7MI|?mzRo%PeV(1E_*0r{#GU=!X-9UKTR=To=YFqD_GUOE zJ`2MvF12omHeExsThl=kE1W3#OUU(%mLp^#>RTc|4x<`A!`X>iD{(t05qy;KFmCUM zp2i(wXrSz@#&xda1xrf5&d(roiZ(?~oVNGu43+}OLgif~{CH~`S{Howi=Om{8)lMI z*J}1~&8}y-7bQ7Kshx)P_jtAp*{r0Ofve;WV^54nE4jwm=)HzL*5UO zyHl>>$V!q@>qnD^;g}6T*?nF0I9#{8@1U3tU{lG)T;DmW_X`#Y{@o9fLfU(Va$1hi zg$?RW_o*ZAg-~mL|9uG-xb^VP?yl;dg51Yf8-2>pWjei#WKGu2i{xc8jpUN&G(#g8LU_9h<};rEN7dCn5_KoG}23P<+6{&ixN+LF3&rX^GGz`MW{@dcY36bi<7 zyrqq`zuAyLg(V4BHvhfQP_hu-Ydu~UKxnL{RXfv2xODvKika(kwuysu*()kXpBVFWdG;Er*ei;` z4$As$*P(Ju$z~t-6k}ImHuL6Ckj_;8^>Is~;>%mM4e>j9wBhalK9j}K10E0H?2xFO zv7hiJ$_3>QWe^lAyg-Px2jb%+R{EQRJDLQp#|y&3t6UtkXS@|L?T^eOw03?-a*Rh{ z%xb!-g=p-sr2I`@N>;qb9Cw2J2gK(sI_t%l!~< z4Bct-pmF#uZHLAk#ikPEul|8GP@JqUvD%sj5wR@Ok;n%{ z$vIJsa;o`3VnJ)+C733A1!B~AZP9>lmjUgv4U}4+Ba}XDn|{clm9wA{HR5@NK3Vcj z!oU7E7oCFIyH|@sne5KW<~i*a-n~H1M63%hevK9zWfanI72C#O)pBxa>*ZAfjtNH-jK>>dGJzsN)82FtAl_1+6U?9H_3S6oPAvN>ra}g6 zPwzg~K01s4Qm+ALXle$2LQOy_aM5Pl{ioO!4d0HN+)|z&Q!gYEC*+yBlJcsNKxsKC zsypPS-LG9zciDRA;WFORJoqkK@1)i9-rrUyO-IT#b5u)A$s(hoff>#%Ys<7u&&FStcB-7!+gI_QyJ1&(F=jUtMKnc=qsBEM>3Gh1p2zyLqRG zlN6~Z0~#+FUU9k}asozU0R_E&k%v>ewVj9*L`us@*~6_AEXE_2Q*U>Vn2*PE_C?=BHt zlOLPuOL%JvANZGJB%8-_CRITOO^tW?FI$l?(QlY1eP!iJu9h)@+QHVPtyR zgz@3p+RVCWp&d4#Zf-^ig&UWG&yjG^Zh!gp%$PXzB2n-2E?3EG?o+@TY=XKFK%$l6 zoXa!MMDW|x&6-rjx{w67K}h&nQ^^w{N}ad<4FDjT|IEKs$hChsg!w(QRxtBk2AQrL z&$0D5`tPpG$ID)q^5BTLJq(9BLsY8|D1GauEo*h|O|ZD_T?EwSwTcyBuDpA{LgyA1 z9vK@mB1#kDfF?Q$hClJ-hx3ss4t8w8WK3w!Df}RwewP%-tfeu(?rN&(y!`aGy~TnO z)pl&QM^TwmcMb`DQmve3$>!G9 z!w_mPF%+mpPTPADr|=6ozhjIkz&VYu79Wr!aW{??#si9q0~2fr{{_W58y2_Jiex-+klhVAnM~m*_-+g5EROX zhXOG04pPU%vubAJ8czZ0s~9_CUI8B^I`RSWOW6&zn0$xU+ofx5Uk$JG>Us0DjMc-V z@|NGlDY!pr=9FC?kMAVe_bIg$bM%EoK|$B$euqBBUmrKA)V{+$$zyI#NlGBnLoddc z>kVRVpl{jqRuIFI((uI`&IyH$Oa3Pgi)x!E<$}`S5XM9_r%9)@!e=MV6&@o*_# z+slfd5FU> z7jHcN0OO~}9J;)w&nX@#eIuW7%2Jp?jYrC3@P-+ALZP9tYOo)mqGwI_&beDgksV0% zj6XBj@XbjJ>X>vBsK&i`5xljiueXIs=fs&iL3P|=ANSb zP2==G?jTFXnlw;8+#)#@0aV9wlJgi%UD1EW1~jpVLacX2lSNlH%f;(%J&kAJXiR&qdZw_~zvt3NAos>Zta z0X{L=5#aFgLnw;wsr&TSHzd=q>9^5s==*WEal$`{*`1Qz@{u+zDNbA1WC6R~p1fAT zjE;1XXkBIWdB(WVSHgQ=yG(6p|L0clGv6#-TlrV)nIAx64;@ZH=alE@lW^Vlko=Dw zTf4$<70j*wsIt-!mDzrc-eJxuFF}#dcp$G8!)*FUaH!>^iq#Nv!}1Eoqs&Buyt>f; zwHQOM3|@D!->|1al9}8HyN7Bw$p_x~|L88&TCeA;kuCBTR3Xa$W5g_VkDTE-7m&xzWV>X!Nf#JU@I*$uRuBUFh>!=zOsHk zOn*VNwpSa;m~BiZ>ki}B{9(gue_hf^mARCwZ)u(#eGG|~i8N8$dx%|ZXk2t#a>uwv z5?F*Yegyw}$wyv7!pO2dSGG(c+G!Z6X@@W}^M#nBP_O5SQGQ`E*~z{Dl^Nx;z@WNf zf(QkkvGXQgO2Mn1`na>-B6ROG(8G#zP5KE0LzX^q`3;h;k9L+5r*gD-}2uUB(?5NU!&r%2nq=N0y%)i?3Ig? z)=JaWBv?k#40_Cp;V-&4`}?c#TFG3fTm$41>JBS~U~gAa3mK;HGwPkD%KyD82cR%X zg2Kc$@hlW?XL9^H)ALM(Z>=cCp0SGL*B3A6Q@hS@)_79srrprjV<-d9>IDe-D^oFQ zQl_@B2`JZOIBfUjwTk+m5NR>BNY=fnyZYm&n9#4QS-U#_^Cx8?L+fe`u%FV`Z8zbv zD8ImKJIgp+!c0Vu`OqjoC5Hk)iKfvmHmd6E^BN)#?-ghL5Q-hR6Kv*DM?Cv&4Z5D` zq|TJR5g{*p6!7Q)b*lSKcOmb8o*!@sx@`e{WQpD`gZ`J9u4?9}5J zGKmj*GC3vyyez(XmRU_jfJe%1nG$S~o%j&`+UHS^&y%;1JakC^5n~pq|8ERAt?660 z@2&nv8uE9P#wv>CKMgL&5L^zbS=d`^Gq1FWxKoR~`x`+e+{&8DQFeJtXvZSn zJVQeW8RXjoe%1~6(FgERM5DAnKP^%c!xSh+d7(Yyv7mspk(I zG?I0DeHZ3H)%*zvmyK#X&_)6Lw?CivoXC2|n~@JMvD?B}ujDW$)Gb_ukQvC_@CMeI zz1)>>f@BRfiJLcBYfB!xUHxCLl(wUka-zqR!~9b!`Y2s&qil$muJ_me>U$Rz4J@P& zCaGEB(u8V?kIQ;i#w3U{g zp1OH|MKRptyi2!NvtZY`S<%R6&?)I0#EvHEm+0qugUcXC1igf33VbY==l!^|FC@L2 z;{DU%|99r}8B;EF?5Lgu;7I&xx9Uds95+Kc)R>5m+4Y=;;EQr^w`hHoXppMOSUJKE z#DthZ{tZoK|3}_ecvYEp@g7A%$pNH8rCS)yapb(`eQNJtJyEG4ElW+C@nH`VHM`~)sRSECg8K~a zUgZY-GKrIw+JZ=uE#4!65n)4*yR@;Et5UqakDX0b*Y?T|e1=-g*FO!NM0l-SYL6*$*Y$gpP%etv)FoQz@K`!Y3RH2-n)!3zdO%e?i62xcJp^ zcD~7jvPv@?u&^VS`g(-`O@O*+jDdjR^#x29b9`>=JYHN1sn?Hu|1#~t01e!@*Cpwe z3fq<19|JODty>j>n}Em!@SxsWSc=!sA{NuJkdRYA_=HcPDgU($?~6m_OVG&WOx&Za zGH>X8#NQ|bm#H6rO#^;OHozOF?J{50mZ{yO)RnlN^lX`0BkmKa*MV&ujS%EQCls6qRWSdxBS8oTVk^n10P}+iJsdGt5H>ahoV>5X~iS+HG+CJ#T zFDYETDkGxGl7mZcYM*%A@!nmw|6SDwGiWLjYn0AT9y#ep4)-k4to7{b0(Xg7ak6gV zo5AV=`LduIj>gf>7gmrpJSWz%+b(cwY>`niX+39@z$(w{%lC0^xup$DJ%QtH{g0)t z`LB-QJH@-C*#P)l#Wdq-r17`=^{Ias_l&)#GUY+Tb>mW^gm7=QO?^2XenH zd9&n_qhU}ldgU)TMguptZm5rHjU2Z2wg4vHl5N~kSB92zf!3^rc_uCd6bv`ekoRt! z-gd8)L?d&=4l4RNTi)bh!Eemj>+o2tYyO(<%84`jqc9!vUkN?IpaN5dXsFzMvqn2CGi* zSTBi_m3#mb(x^CpR}4~}|0H#b8iuCJ&9Y!}rD?F8|6DBZ%zh~GMmf|?0JvysM?5C- zPj4-rCo@tO|FxjWeKR>5j`=%z-sE(c680NOIKXiQuDF}RX$r_ayrF7q9erm>#zTiS zbYNXh-m`uvMi6e(+R!e1%ePv7$+=?vYv3S)10G(;EE{?+aFLY{mobO;Rlx+lQx>Rw z#>(laKo=1x)*5mNlL{da=`G+H>tc_eaT=cSvMiYq`z3`sKtDdk!wgdVACG5@Jf5<4 z|AW~lOT7aVkHnr(C~M=~dfW>lL0+Tt%<$~fGFbY8ssYVgzzv(cZJM5S?W$jXyz^@N zF5o1^v_zNP`F2=xJT=T{T2fjb6grn_PLOZt@S5*<|@c zLZGVg8fSV&-GdrRCds?jvbA%e_K|KTk{kv7Gr+s`bFCOEY8y%SSpdmA;v*O0he$5>s*u2iXQhq zaNCn20(g#O%|sl!oEO*oj$tkt*;AE)K19RQ4!k9&seQ~W*jy$Au~Ma|a1*i{K)AAj z?Kq zi4g$8QSrD4Et48Xy-_X_)Rz%SklV0(n&F(uHxu>V|FI1P-HZm1U}tIC0m?Jn9WR9# zbiAytx);~kdeYEdo|y+245SgjauaPh)k*)5ce8S&o-wAiU%n+e!J2V3vw_S>H@(>$ zSp|IG`yTR#y=}WFp+_*za*j`s6xy!4^=}H|?lTtMQ;oWa*@5LfdbaSEEN*_xyrJCd zx_^%%?JY(9+?CqfxD(>0-gj!}R37)bS*eJ{bt+-*HmCopruXhxqS;`n$^+2OKU?Q4 z*duLW2UI+W3}2<@v(A^zIN_IB-WB?)8%6E(nS}XzN(J3XiQCaiPcWXaXe+CZ1pzLe zjF}%bn~W4FFp?3^$UR3Z1rt?_kqV(7?QQcdbN>p4V6l*Y;6K4KMe)GSE8Vtiw3Kc4 z%?XHqfe(oA7uuE-Jdc-O!t|ly-MbsGs#i(z86d?G;40;FDm9DA8P|nKSj_5BzR35Sd zUoKuf4|M-$08qWw(A%@a?sqdTCg&NeP^)*!KcJIO`y1)zGMHpXMbJU{fmwo=pexr- zSj@rf6|5cY%^|=MSDciVauyzPn!0J#6Gk1dd;4-4x0_d@-j)y<%+hp})g&?~4aBFc zHGyL5kHtyrp*ZqR&+WVB=`h`*i#<1=ex=9i6 zeEy8TEGrd)g}ml?Sj!h#{8A+qZ@D32%s<}LGY-DH)?e#c1KaJ(llZ2-?~<}p>%a8f z#Ol16ezGQV(bdy_%8CkfE<_uub#G3$!6c-<^;ifce&Zzt=r@oGvd9(P_LGfLNZrt9 zGEHY$avI*{18(L1H}Jj`N{^rDbtj3DUb+>|hwrvYi9SYLAUFt_d?ItFVvw%O3yQfv z_x}EDhFsN>nM)WQ2G(D3cPi-x(S=t#TPhP8Uu^?E-2yl#pi-8#EB7s8{oci;&K~l8 zJTzMd7V2#dpV8z3`M_y+YZParh5dG)6Cz*c2nii)v8))R@AB#|XYkw|E7Kf$m_%SI znFZ@~?b+TOi^C=IcMH1FygUGv-Tk9dP-uzY@Gzx(XVL)7C*YYU^?5D{oDde6Ylxzx z*bob~7IoXS6MU~{`;YD7kgy101>5y+_TYP)zZ{#uYkkL=hltzSY@=r4{$rr2td#JB^BL!_S53_R25IL+nkAWPyZ>6_lix)6pm1eQGauDz5Nrs=5fx$ z_*j1iwa}Ii9RnKcmz3So3w<|%M!jqHRG#n0^YP(_=m*j4w=!is`g%B7Md^noZ$!x0 zGQf1k{;%oGlg$9U^$!Ymx6e1p;z!W7?b^5&*4N*C<6;#RoA;(1=cshABmh<7NgK3* zG&Rr>3Vqdy#nFo~Yjp&DBGR}VL)$oqs{e>oTJUn zERMk-kP^bmN_y7~a^Js~dBv;k{9>osSV_||)d}*oAJ7tcS?S?|&~dWFqn)**OmzrE zpq3AFPNLlhjW9oZ0itw<*%P+k<^KT4US996l=v7^vZIZebVJ|GNRW8!yuYJfzY^Ko zGvr(Gg=2uvwQgiAwBk$7E1jGciKzRk$zS6pwDRR{1YdRV;w_~xGW%aIwTj`L$KLu@ zFbm~n9TGdW``Yx=Id2x+46ON$0tCsHwACmM9rCb?74=TMym7r}dcEt1=F^v?c|L08 zU18oJg5C1qd3aEynn{-x|2!m{l_yfH^KQ_zLFzEQ;uRRNaNWF_&sA?`4|jiV=hu7Y zV5KS#kKtH4k}RUvXH|RwvLDSSf|GdU3=!MmN`QdIT^!dlEegE-7E!5JKvDc>p>3Dm z25i9o=1sx(`oH&zoDPs@#J%!{8+FK_LY>-7{yaaAIo6QD$wMu?n&6*RRLgjVzj)i= zwlcd|nJS*f{pi0ix;hkVw}_rGb#e!o(r(+OUym0v(?&4F$!e9ypfj0RviNx}CUA8l zs8m(|0$%=N{JWdO1EB!2N|#V9I^#<=WW{JcjW+s=?9ty=2| z%$VIg0PioeB{gh)de+U}G#ie#D**5wpiLweI@XZQA@2p->^Su${Sg)J)V1U1Ev~xR z>q`%MS@Ka21M6}!vq3)B*G&@w)1KE+QK7w(>SkI|#t{|+SKq`>oX6~Jj89nz{pa4u zKxx^>+JO+%xm3(}BY|%&&{ChTVcltGhJbX+=_j6^?B~Q zUD2JXklaYmJhN}82{u@B7?(9Kl%g zegoce;6L9o7$D(qy3IoIqF;2{Dj{buEzen+p(M8*yJ7@Df2ivkSdnvk0oN@QIkyGo zj6nsHH(Rbe`~cCOPs?1djnzfHQogRqa|s9Pp8A-yWjrVRykmuXzIzon`KagqTXNj3 z9t)dQ9HH8OkCHC=Xg{^}MFm3rBujp0=u^T>Z}_jwGq8K6#rPquqgz&`ao07@8` zkkE@>)lhOr`c_rwTh*a&brbFYjzy$e@caUE!%Ird$&paR3WT zpB^@=ZB#GEoXX|1s)qw~HqXL^9S9yyBo8k%C#np>0^*kv;%n8hvgLNm~SD zbrxXn*Kn{%Jcyj+9YMC*|Ep;CoG}HHq7SsQe;$>23}_Tx?dhl8gQ1@hW0Qa2^9r_; zq$q~{O*{9V{(KtRfe+OzgfLWLZYJ;gKiB17uV!d(cu^7yoFG!~@l5hshd;QZhqQh? zFE!!-4)DCb%62<%@WTY59mNk#`gz(ba10TrHm=(WP^L3T+f?4*A)NbX(U;XWy(u*A zw%uT{dls1pUvpXJ$$zU05*b>{Af*699!Xe*7pInu;(tgbi803-1Pz>E1 z?;+c$fcZOC-uO`XNh&y2a6NDIibei>rs-~AfGIEMXJv(@FHy~a6P z*cWXaVID+o|DG!6*N*g%P=j1XG!7{YH@4~{wpNlBLcjlGGbbPVG+#8bw>Hx2;N@1# zHTAtW5~+76Tsin>YbM`ur3zq`wkxb#OD059c~Q)~s#B>2=|mlXk*|iO%~dr8j(HZ? z%*I{t@UQ%O$PHys1Rkp3U`VoCItuKbOt>YiuB^RjZ5T%{NSxm*P)dP*43dqyhO}%t z{-^gzvY!_Dla~jYUXVX_6LS2x5VkUCq-`&f1#gQ7yd5mZS<r zQw^D+Yi?*qm*Y@u%yP{oM9*3aN&OC`5}gq+x6d3zdg`8&)`semIyZXC-PvZ0iw`+1 zU0hB&u%p8vKL6EWH&@W1F67B}T-Y98;)weF&_`^9)U5vJ-~Y3`(2O=_1V5XeEz;BU z4)hA&%}tJ?v(gpG!?E|vzUK>G2Dc+Cb`TA`9ww`d^sJV~3)S|IiVx!ow`BP}h`jhv z3XzX^1o&wIn*}mA*~1z9G`b5-Y2UNkuZ7h0KH4;2B|q{8;n&YzC#3oDzlLq695^o# zoxoDDG>LA`i@j1fHj^28craziB68Gdbk2-SQV2c%9v~inmcoXLO1dC`fRpX9!5~t7HTob}WC;OsQqpNUxZ#x?{*{?ij^(U4 z4hn5`lXuhRS7 z9#pTc63A^!3Fi%h2m#ZExQtvdJ_KlLO$M(Xty9LmS85LHaxZ;xjx>l)zKoXfgzG1| zDaKrV%B^$ty+ZfO6YGfZd(ZWzseuH)K8o^DtlVa<&BC(FOnayq)QHo?4WH+~KHuSQ zXfC1FO*e0XcxovN*;45jLFPgRh&RIUdhl$w_x5_#G-4ZpQwh+&X|N91&nIk_u+!Yh z6_}OK!U7~D3}eU)Pmsa;G5}K{kBdvvvGjAXV?%Jff`cUJL5b(}=)@0VG_f+>aEDCe z`t9ZAm6mY*@-xNPBtZ36&qn*jge>w7m%?s(0l062;j!`kvzh$GBE$M{5A4Y)s2}75mz2`bEZ=3xe24tY}GHOW` z0jV19$Ys6s2%I&4gQme1rkv`J0<2A>p(2HsTH8xW4dlb788@V)R1C&X5<&y=v}o_$ zn7(edRRch|sOmv||7YumL|SnvPvwP+e1qi9im2T*fPW}OtbJh)tIF6r3m+cbm?J9J zxL0uk;92RbW>bgC`dZBkmZ5Gda)R1a?_X_yaK9t=tutUWaSvFj=-uWo$wlNufJH{! z;kqzvC7vi9zVo5guqNPmLy9s#IhX>v*S91$UROKs^I1F&ZQQRs6*J)UxLp37KFKs* zJ~A$p=esVjJ_c~_jbd~9!@mt72w`N(v~D(?{I!*TW*W!6m2U}6Z1s;uaK#6jJcE{I z5xOSXz4NR&77aB=KkEHjNZvqd$f#kG*f+BX(;-?OK3x3x=a~h+{E5JqP?sAtb$DKH z@t*wIDPW;q4Ne3`#k;`blC^iH5US8MuFyylBXBGr;AHFwf(i84zQM=*YCpw)AA$0p z6*jsgI1wufR0Plf;p|i}b=53q~iQ=P5uu z`$sSJ9+-KaXUW2i8C_;76}&n6fyfS zo)BEArTO&98EE(&q;+cN=uyyc&%AaPlvC_EUD-U;7pj35J8k4{SvzN(#4E?OcvI`j zy1s7dJsKa@J2e;}&Zxf7u_#_Xi0iriLG1PU7|;i0VbgM}7)9LSzrf0l8;LIx_d>!K zfuqY1i2InlxJRA+;z_{U5|XeA#@8Bg%EJTxQH${QZDj9SV%uH0!6ev}8K3 z!L2BCqYO9h{Hq9xyD7Zi@ClJ4!x9z1bhc zGJF#TFI#OfPVG(VUm78GRJrtQ0fqbydB5kh-XCUk;qxURUFqhFMuhQ|1`Ac$pEQyo^pX zG>}E9Xf2n9lhdn60QO8OB1bgJ&>(@-k?}Ly{d=Vtrs^0WnWM_L#=|h58!PCv@mUZ- z{e1JOY2z3hZGocw%}h6m?ACvulZnCATA*JbY1htMGCvJ1Dw=+B=*{V`Z^!^~;W5bl z>uraxw=|#IoD|Owq)8taG?_dpmnBfX8y@Lj#<#(`Lz^4-z(;LG}jA5DOKR@^T-2J^%jIn9zFaG8|sjhv{z!tGne+4!%il@y9&clE7eDq%yWR z<;AL!^uCm)!$>^wBkKAsY*m)(>DN=pa08400&efC>hMgNqs^86V*obQD`n>hUlLP)gfV1dd?3NYYe97Qqx7u-PQkbx8x+}V zD|a-McEo7O6!ko+xS=8vxWQTUv1LE_;FVl9c{xLskEdadG+vdGFo=Q7NP(7%f2NB} zzmF4vGa39o;cT)uj-GyvBsjlvB|nd||BN^G+(sLJv}Kfp@83c^P(Xd!`0V6vH>FFp z1%B4AVy?B8QP|3e`WmE8(&g43xn<;`iPeV>^tF5s3Ml98(GOjDqJUfYq-~RE@HCN9 z_;uiUXW3f zWNgoNQT;&4R5;QLJ3Kp+FBnLwiL}f_p~8nF}*I{POU8b=v3?KU1-E7X*Q)A0+Pyt4a_ zuP2T1NM#{C>$(=wEygH2Fe?{UQ(F`$<-C5XdHpg)r)Mp%9ik zt8FTQ1J6cN*h>d1b3}RkgJ5cG3BYe=Z}*hg5~8i8%&t-5t&yJo_hSsghd|Ze7|e<@ zJ3>w(XJ{rOb~HuB*{JlDxcuhu8{iWGBEMb32GV+d;bpxC6w~32e(z(c^W>ZR!Zyw0t2Oib+lmAe_^aJ zCbzvU1ze~y8=plnNPoQ()I0MXZ-Ogb2VFW7c?%N@QXgx#>OtQN$Do~O^I>K<90HL9 z!aX~BP#m6MUhnlHq`TZ}5f#KIcyG&wg9RcgPy0gu zU!Ojm46^jx^Xx63LrS>3_55UIIShfAPRspX82PEBcTC0f0CgT2kqsm!@riao%fz7S~Cl!M$y&D#*w@(P}suP!`M;EomnvPpqi`lY=xTk!}m2+oWogx0KZufh1tUoA5HLC`t>(=LyE`}}!niZhzH(J|-^!!|Zvqta?!=-rygakD{~ABK6V*3jx?^|^ zhx4xiPoLupI6ruU8vE+cK@Jk>XZ02sKvso1a1$z(T~WC__-2^3VQ6G!QeV^1v6jD? zFc)1~CJht<-!Zh@&CoWuwEbl%6YGIU@-R>g0T1uh^1(1mv{6jfCE;nRhS3=yMBxbg zVz#GlV*0j8aTM>uzoxSjYxwXZLNMEa{L|d|GMYXiv|ZV(+&znSGi&Zk*4=N(lh^$N z=V)o^UB+h5Y*#EVEXhh&8NXV8XZvVc(=!YET3EQmMD0*LEG;tT{GPP8bI+4*Vs6LN zt>-Z`4FDG4VGV0r-CAX8u(J+CUx+0lyR}{EKE0sX$c6w2VE@_o-?{(*1=M%bD}-u! zt{7r^ZGUC@3v!{Gs_$99#g#Ev-FePl2$hq&Zf-t8KhLOa`J<+rCdt}sxYFDQCI$($(b$^yI^=4#2C$D)jx;($U zaQ+7t8p8N>&j{CXc3X+7YcD@uC{H?f?1~T+Ef(Cpd8_~H?Z`o56K6-x<}x`SpDaNw z8Gr~F?xq;G{FC4Y45N2`!O_@983%0YVdar-z}Ho;t$C%;&&wR}Wp(6(@xN3;$viJ7>DmM-nd5&P0asQ>d}m`?_T?_=gXg8wm0lfCkO7xb#$wC8pd~LqskE^ z5T>fbg&eWdi`%a%^82A=9J7#>)tJhDN!#0E9(YRY<6RFai-tRDKV2#bU`fN)aV+)W zQ@?91?D4|*AdhLNhTPIcM#M=u`3jr#Z{k23cAH#gl_Xq90y1cj_#S}*ndu*H8pK>P z3rE1;M>?RKeS zi$~kD?%Br#g zG$%oh9pB6Wo#)|~RralF`*Bu=YqPf_V=bYVPmy){G=F7( zD697IW-LKFxzl;z*wg!&d^_$`yvP;@Ba0Po%*W-lJfWK(r|v_GP$Mgc0t0z#;e4d! z)@kk3lm*A4z3iWk;DI@ZS()*!3z?~}dFBn7A4tihaj`a(VlG+T{a)6xK2UNJavDH3 zG4qdl9B!o{7tV*~1%eh>?yZK~$iGUTUH5>KLgt6Ttm|{*PT2}Pa0by1^ZsR`l&X&?%lxbH=ft@hhx$jT*V3r&YtChgX`QcIov)yf~lmm z-83sU1{w<#G^`$zI;C_s&7C*5lIzX31@7hM2{wDpe0nu-YCNKO6{MnRYDTAHj!Pza za(US&{XW`;aL|^OJ_v9AeuJ%fQJNS|>5yNQ^I_Cg1o1VgP#*;Ur~0eCXjzqsw5!AdJ{^r6TrlmzcM&N)f{Cn!O3!97+2-#^|lio zX^yJDrJKSR82~#aPx5yLa5^)a*|?kpoW?#sizxSewz!@npz-B3X>kWRr&4ao|APLY zD@NT1-8;(?l_YtM>vp+p+1#u=pLG%1z|L@JBV{5$u{(hGHJ*h#zuNsphzJ2ky z0O!V%T~H*~i$Oq= zS@i{AF^fLzd5vOWkk<_Jy41~5xofF=z9qbK?YX_#l%^}dn`RbSH5%%PP50>}&y_wS zz{imb9)1lirC1C{DM`(}Sxo(VrKC$F@%bJ$EDj1Dr)A_)*dWscVgRz%6NOy&yvyhf zQ={nGCId|e$9PfqGbeq2?FrTaD=*%|7h7*n%UYF!vj;a}Q#5s0dPl?L^D~@Q{-%r9 zI&K>WGl^vD7}U3A;)INMFMOLiNYVyh{iCdb%U0S}&F$ss33ud4vmiy1hCJ?;4#5tV zom($d7B6kY?^Vn^QC=9Z!v+ZGt_jH*o>--CdA=~~0SzCT<@tAIG zti|Im8EiC|SO8*j(l6ee%-CnX52&`AyDc-K89)@qsnTXpvN0bDNg3BoUVzXHbIohk z^5EiTGdZ9=v=Sd2GbYr+{GVTgHg^5`rn&Q&*y>if$YrmIkcPQzzzg#2 zBgp`cgh3TOb9Yg`KqQ{Wkm($f^+(q9HiGxZV;L%5a=tMeyphq^BNJcqEI9aMVfC%A z`UqW_-a`lq{yT5+Kfcq0eOB4V`Bl9cR6l4rhxtchbSD51M( z<&i|tl^S_w@E(?~o||oq&9^1f6~2ViJtlww$ql*T_5Pq=*_NVz|AgPT%wsiuGD~PYJ)W zn86nGVe|A9VR~7CRik{%-iW3MqP;pWqudGDaO!@U9DUj@08@N7gR0!%MNgdHPki># zL)s*}p~2B`fU68l<80N`a~-_`&!mtiM_TeQga#rM|A|7EihVL-E?*DMc{UprA z^bgFr-Au_KJq4|*zzcTPuJHyN_p>q-uO*b?smswANxwYXexF*G$Pkm5|3d;Df}qrEAm3xxQ;He;0 z2Q-IxL$Q4BMI%@sl(z_n2j&gfgt0wuRB@a{5+uNv!n`i>lzxo!m(TMyw;X`&n6be& z@)zQ-mA;R)wjMXPSe5=EKD!OwEihVZVY3>BKtg2|Y5GLO-WdWHFW=e86J5OUb(6?$ zC=QjnAYow$0rORCJ8yU*GWx#aqfXH8G}c~G_^;0?7S|y#az<*AeD}LTo$JcjSxlc~ z;#psC;AN0gA~3kkrZtz-08Msyzs9o?JOnt{yLY&Mfh#-L(UD4x#t69XW^DwX8l0HJDge>MAg@dP zEY)6a2PhIB$3TX$$0u!UIeyL_99Kj=er?c1&ygf7p>(=&5Pd_)H@7~D&fuO64alVo zz*DKp@;;ac=O4#CVibqJH(*jT>buy?aTJn;`JBoccdqQ~n_=k-uAHsh6cDZ?8KcgV z2I4p(#%U~m)gj@4D*mhIgK@ArHEVjXjVbZ!o-+S)9s~ygvGOb{ z-fGneH^E?D=b74IPw>6%4?cII=`Mg~%-yW&O3yXLo~um&0}+Ftq03~*1f=(2AdUJ| zECW{8SHs|<#`;c(r0T)3X@{He<$rxd1_ITCuJ+q$Ep>p7TzFkKLYMSGix256tpXGg zmc6vYDS@D1n8sI0Lv+~+(u!LUv8Xl{1lwWS-DUEv5tx;rIWrzV$%D-`B2R{=$nihM z1hnR1HYD%~K2O!OCePmSStG(|;G$pLe@pV^84f37Q{JUc-X?fgJh2EG54AKR=zbeQ zF};X)sIQS7mFMQd&CkxA%-PYZrrCeye~bvtU`or9e{uO}O;z65qPXPfPV+g4MQcfs zP5k_YIlx{hN8u^1RE9jw1t-3b00+WZ1|h+imX0uwQvo`MG&{?=xAr;D(f`Mr!ao+Z zDQm5ky;*-}{Gq=rAlm5hR(5LvDexbV--H&b{bnubZjw~4Jm!_xIfw;iOOg05pT6NoXcS6*`#b#9Db~_sEStVBBQ- zrN9Z|eNA`*EN6s3VDk`K0Jyl7-CiJglj|>}LJeQi58h3CK8nTeVUpT-3@WPJWf-9A zVxt)XLa$7J^nv7DHb^Fnv``DyG7Z%7)IbfW)1!-@ld%t;DrZ$;75%Mpo!!ABsiU?lbf%WXZ=*z)O=2 z$2t#=DB#kdo5c0BhQ1W?X$+uNnNQ+X;g#6^{l27L_Au{5?SRtuPb_(k^!Z9*k1IzQgk-knaH-~|o*oX$ZFk}AdU39| zb$d%IIE4D$CEh-Rn1$onpb0mGtvV(BY=kW4{W?UqCPnGb=Fv6L0DSxt!A(Ca1Vzpg zKg`lDh4ePi(TC|}w1Dvrl3LEU&&6*dVk3yLz;Da$fkyMqU*Rpj5Nr7=CkNM~irVM! z`^(5DPXs>2=j3o8K`Lb)n=Gms5t?0@|kM_yl?- z|3NMzo=*nHY>+LwmY|tvcoqf>5l_EG-|XhhEq7MfQl}l7#Dn+89ruZo@6b>u?kGsmibQ{#YN%9T`zmwntmG>z^J@;DYUO2dlHrr*)W-*b*reWpXZK^_odX}ZEYh)OiI`pc)C;A+ z#6)Vi65E(1KnD5%DLU*brAJ_m0hjKn+F_OEk-nANk!m)D$<@wIwk}ILo;RW20?*V5 z6a)-8DDDCgQ!Rcyw_j;wTS97@9z5LPR;>u<3PM3F|kqfpCmWfU}hLFTD9)9Fl7 z8fj?hiQyZeP|$U!6$#qh-Tx{c-Y+^+1&jhz&dR#p-$PBVc@+Uc1HyxiPnzZf8S;Dg zAb7JsO%2J3gFqp3tJOM4Tcxbg4S%0_IGVHa6+yY!0p&^p%`Vs>!IH;4_xdlajwy@H z#X-#~kIkTAJ%}N%s;c%rFe`K~2s{oI=UoARwt>D4&6m#%CsE)<21!c{j-Tddb&M=_ zy@tdczO?jY5{O7a0!C5dnaUT1*>TH>(Y|$he2T|`hEVBCsiYK-W`=TL3;*2g>&b4xpIy)_gyq};{ zj99w57*aG9BkZ9|Yu-DfOHRod6QwMf^x_~Su1xV{hs z1ps3-!0Y~cJ#BG@hP(f@UfqeN#9@1MI^waPVe_^yI z8ajvB@nty~NsIP^xSdldO~{HKEycsfSMekW85ON=ev%?mtbJNO3PS5CzC`~8^OWXT zk;x2!2#9!miEo7|+ookR9#?fxcxpZDAUjwlTLKpRLpEBoJLFu#Jk)LLy%BWl*=4h zR!n0J^R}AE0q>{L?>F3sGF3OOc>5V|=3o@#p95S4Q}}cKct- z$jV}%rr?6X=HcqC;)uuotdKZ#g9fRoAvgz7h$T#bJmb3TOIx*}+9ObV06;Z)>rGUI zb)T_|S>>h9c3v8GWr5Gf3mYi7HJIRGw~GuX(=Eo*R!Fx-q$JNlVje`E-?`BH*F;rb z*^C9j3t?B>mq+_^kQ0VNjh0w9K+>Ucn(FJ1??#4(y4ocFl1auT4<)xL#aLNM7XJ3k zoErc~Z#m65bThNM#L}No{d{1wA<{Q3wq4k4ciZzSC6?PM%Q1MDPcTNy3^_`+y}=$IkXz~bDAa<&ep^o3faFxgh0@MC=^&%A>-d9>$q=yMM_QQ>N#Gm%Ez$9X_VJkW7{2p#iH;2l1fK+VRwNY!AoNu~r~ z74SpfI;$_RLT8Yn%x?mjYS7`g1JFPFX%L;V+5n1T&kvShQbdxpGXj*r@f-T180p&~ zEf=4r@QhzwZ2i51rQ5TCSN1QW#G~lEKC|AgE9@4dm{A^Y{N?-odzPmdfGwgV?tz*X zKSq1@mSO1vHsdk+NcoKo-RJ2WygYiu^5+K65M@dRJ0~=6GfwvXWw_C#p+7tQj+8Oa zBnH|t%uyagxXW-JFB{Hmj>a$V7rApYOvENe{;)OkzN? z0Xy!QbqaU81e6MB0ANzCU5TSS&qvqVE*DJXVTp&&EEz@j=`97AzH3`nD=wX^Z|3Ke|kuzxM>l;Aa}%tt)} zc<<$cp51Li6a>T2tTv2|Np-f#H)c7Z(H$n#EO@r;j<`o%fVbPhvJTH8tD|ReCF_u0EuS9e(`XB^2o8Iq92g zV@Q3Su@q2VZ-dqNoOV4;V}FuD>-y01+x$Fx1;W))D9{JA@=-+C4$%%9iK|<%(eS<& zr3?v*VU#Dp-?uupT7gnD&8zKk(t;;Aob_6Xl&5 z&eF_3=>rLl4ECx>-5^C*05cwdVXiwo3CUIm+M;A4y|l}{Ba-K>r=NB&N3LfzX+sYW=95j!#9Kze^=%W=@ z43}fqFvVBA<2@_VyRo(Rc+A%}^~}2|mWvlpz@>VxsnL=APW&$Mku!Xtt_5L{2x>AT zYfFg{G!mLRo_1BKRHn{b$}jlMR$UNxc|uL^$=XtHE?{p;X)Fy))kCbQo245~-5crkT$U~)#*GRruECQnq}jFRsTl9gkKc!s3X?>#3>=iY zh14KwLZLpqgBl+CX6UEVtn6wBuYAiu!YOoFY@6OVOirs1pd1|@UqUd4J6yFfX}%%w z#C~A=VL`N^Yu-a$0?eH83l9(}Ec_@3kr~nELn#DSWptEVr9P-SfTxgsHuwNQjL>BB zW;V1ueh9oB3r!}0QV{g>pW?l$KtzHLaw#Mn4Z+e+&O4Up*QB}BZ$k%vGlJ~O`UyqC zV|*Lt?x|$;^oUsEz^tOnR{ArZS#YOEyGP&^;O7Np2ItF%7vQ!V7u{@&?JIHM7d!-E z1?cgPd6D)67a6HiXc%VR`cwtQH|Lz4wCwoyIB(G!$<{`G%*jCvkbmJ45@3x&fizgh zvUK&7WG2;Tehg49C0A$|>)^p6U8^ISjC%vwK@JM`)hqsJ58^J$2;ZH=QofuHYxjnJ zRS`ZuSHHf4f&%?HS3+vIUV!PxO!C2o)J6(JP`7k z!*}&9!9e}jD4Jf!J-{lxa0$QLdl?rPvR~;8 z%9A668)9JEFJbGh4e>yo6^WNWBJOudV2e> z%v6DV41pij?_f+>a#}xrwDHp5DJbPw*@^MVH%;zcm#T;34n%m3ATTQxEp^X7{j%&% zT+Q3DW@%!Gg7|-=tOlAI@CE&zj-1eEs-g=loz|^-82M54;8T4-x<2bO zJ91$>=0DiJb)tXL_F^pr*_h!$>ht>RAc=Xhbq>zB=Fvn?NI5IXDXYq_pp%z8RO-9= zdpg!Xh41;3gHrEOL_?MBib8n%28I?)838FXl9nnEj;FNf?A>VNZD6z_H4KVVZDP=R~ANW@_{Jf^f%}KvDoE;DS z!BVHaQj$0ws=Q*S$&wKI2w1U@{4xUxTo3^u;h(?@3O$cc&v9CdNg+H~X~tD@x*6!Q z;di;acod{5P+3_lSwZ7>7Tyxzq(9%%Ic^sDhhQd(`=kkL2OYJsQLg-PYE=L7hn=c- z-Ovc@9_Ysmxx6YWX*kl1x@aLcyA1?@`G9V$wDBD{yY9et*}nV!Vz{iu;LdHlu7=97 zMr4@(oBw@LTUuPbIAa(t73o++Tg2~^5JQ6&Rp&W5n3c9!ZzzJnm;eim4C97IIGc})(dbKsP z0940_;S9q1P$<|eWUej4rxT+^;V4IZuoGTRsO-BxS*7EpL)FzGUkmCnyxz#iY77)d zILNgNaPdMBlu1!D$&u&I16kgWLP2b421Uh9o5DQ}vtm~v;J8~@lVLq0^*ypn=#xAc zdjPsDGc-_92lahp-&BB`A6y{UA7l@Kcc^={j?DA+_D2o2E(!-=G z;Dqd_e!x9`TK}}egam;>A?;^wa%_e=KI2I(VliP3L$%tF+C+>J2U{3OQdF@w+M!74 zS%SVISL_MKPxDPV5&IyiXv(^$$2IH$7XHIT7LdiSWeVg@lw@#pXSGbOYNAAan<9SR z0R!uqR|c3yO~{=D+a?MHYW3=33?ejEq=x$t|L$V=jDF}!6~I+Zz(1f+U=;!3lF#fm z6hQ$WTIKg`>WATKz@1so;fp?>t_;AnBVJpF{$9|$SCJiq86A?&AOj3` z459I~EZ3cfSr5Lf!X5}~Zg8~;dJx^*6=;mo!oL9`UOXT9j3Dp~j)rOI6OFj_Vad-Q zJS)DG%#~!fHiG@@=K3e`i=4;T(!fZ>pMs03I=hr=$Cgi_;3_@n_cyoOGC;Beny^*b zCwr%Q7Uv~1lkp%(2DKC4e&f||r0yG3dCPfR-HO#z=uDV?S1{az~X!wqdAubCF zBay-rAMe_aEpFk5QIi}_-oEr2)coJdVBeh+wJyF3B;{OEzoGb{-%Zag0SNGq{v=7#M*WIb5;WEcw|WsQPCVCn2thR7lWaCAdM*% zOn_0Cja3Hu2m-5Ip7Uf=BAK4lFUz}d+W+I(o`9>lSxd!KJ3hI360Vay(dXIaxpux8 zVu@o@9i(>6!B|i+DRkv>ZU25tg8NC%;)34aKUbLV6z&uy|}#40xAkbOtB=( z`YP&vXVGOuAr2G^fTf^1dTgO^4WRq zjEH5 z$sD!zOJA@x1X}0jF8n?2z1S(?y+(+dDE_lBbN5m#>^CyyU1E7vd00bVa+Jq2VMPMT zfB9ZH_5Iv*ubiI_KQz(84WX43?@RY>Ppd(R4;Ny|s>tH)>QaLQ5!5z@a7h3r_v}S^ z0)U|QTPQTZ&u-m{#49J3yW*;MUw=MeA5}o76(=hee_tXgJDY;KyVW!1?l-bfA#+}f zvT^F7(rrJF%Tj=&5FDe0KSS*SzVqh5%TX(tF}mAdyU2#&@#1S_&E@;hI2?mIK$Lhm zWYOrX1F8V52y$bdx8+)mFqHL(i}F(}x~5J=pvc=t15>Ni&%gvZ3*nG;>4PkEbd5|N zH*$WjXiSxSA@;a)DHj;&iBVIrro2U4J4h+B0{xybFRMP1M*wkYa97a@ly)fWO7>90 zfaG)5n6`yi3^asKwqhWdIRHO5+jgZ6{F9SgZUDaBzGro@Q@$OYg_rN|MS+hJ2lRrm zsW#ypl2wGdum6@pZ4)$qlxcapO58wPo?q7o-O}*Va+&@eXav*3r4pN_bYg$I_-Zo? zOth$-&50Dkm~n59<^L7%v~9duE;{h{+QvVPT&$R|e5$ie2{nZk6EEbU%w_k`u)OQR zNHd3yhiLV@`Tgi1$9r+MP+t{m(-a3KXDW;XXNqxF5pxC`78qF+64 zf`jJgTA)c%j;1>cyV9*txG&1_r~M7-0d=rf#t}$jtCi{eeD>x9*`zVZ5gZgfz#P`# zvmUg)B|%}rN1}NC@3m_#0UrIQkiYejY#jxS{0OCzPlTFbA>tsh>4wT>S&su?2kvRE z!JigioR4q+?kl8Hm;hxk50Xf_hyeH}QP7kD=$SMIIx6so6bU6n3xxrYL#C_f_@7Vg zLvX1=pryG`ttb?Pd3(Plfl4oLM0v31?32;V50(&I!s2oX6Ru7CS$;~LBY<5Sxg!Bi z8&q|*>d+UP{ZF|hhO6Y|vO{vax?j&u`bs>~18v*!kK7sZVoV5vmE)FTcBo-uA6`;$ z{rmIfm;ewq{c8MJ{gc=^p+EjJ+<_BQ-t7ffM7?hKeF6me-Fixfr!v#1t_R^AaCMo6 znfN*GKmOA3tw4{@jvGV5pj=c+PY{#p-?J>z4n&2QRV6a3B)A=B=<@Si8q zgeRG$!Gcfxf7tHGG`qzA^Hpai3_CDvvDZ$H%KE+~7gb#h>YVq3W|z zJr^ebVd7Elqk+XXXt;Lw^n^6*)i$(@;}6EeeKpl&ycXy){C{a7jlX6-qgUfOb?Ot| zUsspv-pWYi;y|%#(e-J#(*F8Wm4(I!MBB}b^$1e6HRiVl7HKlAFmk){O6fo-06MsY zI1IA8(-|1>@wd?%^QLyCLyz^XExZ0g6Q8~1N>wo(GCJYp<7V$%lrb0u$mzNPzvx_G z0X|BC%)i|#p!fit{rL5fce??NC;!K!}sKI}5r){)=Yggb3fs zlRJ1FiBuAea0>(x{H3L)YzUrUWPdGkl=gp<;l#=<$r$OlO)KE)!Nq9wh??(&B9|bOLk}%{7GD#SduCq%hW}sU3mM! zGXkVj2`>+B_o{MSWXEsczKsNd<9(jtgYkQ#!zmbdZSL+HGML?Rd}k)CT(ITVLM&(LORQ$1!+MS+h@HRP4$&kbK!-ILtUWjqMM z69^#;u$2>(m1TiDz)M|g;UUY*$!QG?YJ_pAwbEG38Q5s|xcGQNa6d{*V*%U7yuO9+ zZ{T1t-%;qk!EAo}1T%9ajINN@2}9dP152~KyjtJl|p-!)6WyN{8lZj`B z`DcU7UHaM1OwI*v{S+2+k88%p5pcSuI1eW|A?!82Jd>t^>4_%BdfXAV^LO}Ku{rMN zm~ugXv4&VWY)`);0DfCG6{eGGJ}Q#(+1vr^Sk9f0{=ohAK=L&Q!RIc+<;Rc-cNrYm z3az}GJAL}}+UzqoLIi#%RoReyQVUkJXMCKhaqC4qpH_=yLHBll z%PGjqLnh4Fv$`GhOidqU9xU0*-1!lKXSBn1N3hG?o2Z`4P<3%}5e(P10XKE$L+d~= z;WB>bVuI%HALx>xrcvfF%A&O0M5SkG83WT%TrjZ3C}P=K9I$a348NPLMJK+GAKuFg z*A^d{VEEb5aRt8116gIl>&h!SSaYNblhg|Kqk=)FjowUvsXZZrhJNssSI3uw1YIgB zE3u`eG)ULzC6Pg5x_cMF z-L@Jnv!}`kJiK3J{)T&?ys~myx?zVRhV6Xt_~B;d2%#$X+TJ*R3_oX|iIIB9`@)@` zl{G5aDuB6RyG;bVJ@*MvZ**`L4>K>j|NE9dbSp33x^*i;Lu=CZaDN9)5wM@`+c+D@ zZweD$oc=h15m#v>tFqGjRJbNReI*$!=Hd6{57PB(6cB(Tf%t<#7>mjaSF>?(uQ-7e znpe}h)on1KrRt2?JMz=l3mef8LhHrhm{D=vCd-xQGBmFzuck1`&%A|YK!?3vbUQ%5 zSY%?p8(ZbSbI#IlSrVKynGyGcT`zw~YMX9-JZY!k0Q$_Odm~77b@l%JE8P0#=8wTS z3h&e#D;szyKZe<|a4pW&s>JNwgYtzS0k{y(ZKG?WuOT-p?RLlR2kw49Qd!;>-#XC8RJrNd*{{#S@!PR@oV2Tw-{u)GrDpxaZNTIU<^SL`K_*Ng z(D>C*3oVa&kGDapc0}hPhlYl>hdKkova@;2-}}&@(|4t?DR=MQoq)70hHf*-zC=D) zo2~uS4P-$e5r)G6i|u$l26Z?COTHLRr5H%y2~{4`VS!2O)?MoKPLKp$^&xPr4u2&h zK@hVA%CSBIX{SJ8`S{O2O{kHmLOrJM^|g;5KOz%y6b4Ucgjl#ku`r5=&-4bH>zCTe zWA~!@w6=E#8dltJyPMzLbcePhkZmI~XrfGv`K?mG`5u8(JRQntc_A=R>CT-yxP^jZ zTS%s0a~tvKQ8SvcGVwc^Xw5~oXFsB4fty3}>s_neZ^`>+#03QfYi%L{G+-^*maQIz zTPo|!uBu`}ty%X^8-{E&@uZJWD6Gjk<m%BRlG_Px~xD&$c2W(YI6en^oNR~mUZ6(`BW zGpca26PgkxBz^=i$`v~(FyDXetc8UI`5T8%d;$VgJECNWtel*_iOI2vS*L((JydSx zE3w+2Vm!3I3-+$cMwM0O!uacjP_R@B`mPKy3GIV9UKf7uIHR!j&M$BnK70B!(Zunc z$lPGajiJXGA(C1qcZp~(Ghn-qHZrb!U*5D)(sOcfK#)AcQ}_WIp&~Hygbb(R=_#J! zw{rf*ty?7TS3bt^9`+E2o1N8^NE)1m1=(uAV-lY}rQB=i4A?9?T%=-?LCt&Lyvx?| za^}K8nIfrM`6@lWUhL28Qllkd0w7_|N_k_T;ZSnEzZa103mBM1+#wlV8QKya`}J0w>sN$EbK@0SeY1zh>luiV7hh z!}Blga&Xu_r#0!o-_nSG=0pIPEH^CkY1fX%phl{Qzk)gVIKRl};1A6Q6uF20GVw@d z=5TLyi*#S>zJ}vIPdd>t zxxm18f0irZL8MA_k|u0sP7dEbxZ(1mvqspmGCG(CYhQz-Y&-KQ37jou!1EGyz(zd| z0l=d6f|YGui6|a+$4w|FCMHfa;P)ClAj!+GwbDVUuL?MjMQ``TyLSu-4yJvX`4fa% zd2`+MuU(tLdtSeOeV#iUteYypm34-e* zh?LvdV^U6k(N=73>dNZj$|_1Nu=)ANVih)-6t}87xkW^%;OLx4 zL!WXrX{}!um3U5OXQ7p30xB(jxb}Bm-X}%M39CePLs6O&24e! z#M5I9nVzz99rAE8-B!$Hel6Y)&^;QI_A+?HF@)uFAb!sZwL&BdRdMzMpkC^`;Z#*! zEqUk0I$y2dmN3H^;~=HOZB_eB?{N%c6vNrzxHx@Y4GrD5x=IK1koA$E!9nA@t7CPM zyx9UKIy&Uni%dfwJt9lTe28I>l63Bm5HE~miC-8OA13EiWCOQp!|ykrL(Y4p74U5d z47=XAaU-j|oF4W?qaom6VsWwH`p5JFPfAmQno{-kozXLt6Sj>F< z|1Bqd&hyBQMg2IsGdu@76Oij~L%6%aj;9v^a7Usy^@7^UwT3cHu zCYnM_J5LZ3z>N_hUsA#+hv%wugzeQ3z9m>Hzq_!zzG)aE16dAie$1ZdJeB|7R_I5kamw*TIT)K3r ztJLlyIlC-95DwK!*HMW#C*w3rY}?3?#?9sL-3@_(55vP%dEzEu!Sf0W)wWhf+d4-9 z-n3WOWkWE)-%c+Ll~vvU%}-Buq_C){yVy#psCU?4>lGDh_>(K|J(jZ?<9ubl-@R_nbQmY*-wk*S1gaZ|Oc^(}n`M%}rWSlBRLxpag>;p z)b8UGZr|xX$<4XUz!>;e^t=6if6y(bLAqiRwML{|M~*_8FCFdZpv3HLssjW@87brv z$o#r<6`M;#yd1h|5?aog#D}V#ZgNzC6 zo2OszYW$^_@L-yntt}7wu+!IP($b&KmF49LLiW4rZvojLF_Hf6^6)i(5q^G>2M-=h zo#DF{8X78i=1eQ#*V5bHrBK33L_+Sfwd z`~v7Mq-I7S67Kizg%uQ>h9kH(U=@hQHw3T`Zh~O{;J{;jUf4tziOtC&2DU5I+|uG@ z>HIZK@?%Gb1{_iJN7t9XGs5MzXOgX+#K#BwZLOes_%>w!u7*Io?6^Uw3W1W6Qfpfq z0ivU$BO0*pgRVkuZZ0Yc@XTYTUhBK757^ihs__mjnnf?E-O+YZeyK5K06= zGPTP#SM%wA&vl0D@V7w9TpM;Se&My)FXO$!1_tchM+rgCqoJk_N=acdG%~XLmZQZa z;XsXWNl7uZ$FLJ2#7B#U+j##_7QNb6$D0VozGC=Vufi(R3 zbq7`?3nFaw{qL9#zp9&`8E)9yV{dv= z2t`&tejXm*;A238d-H;WgNY$&G>wf}9wj4tJ>ZG)_iw!i-?0H)%Yu0n6?MeO$f#7; z!68p0Ggj%)*u|>ezZz7yH{ElfTLeFQ_H0mun_Ia^L0a1A?9&&j;bbNzCQg`7pFf9! zQlG!^dvlZF#<^5erBogM$4p`>O9{!zulhOY=nNJsK&=dgIEci^%gaB6P)^?xfvXgq zm5?xWVLg8*$9?A0iD*Bbgnu47_T)w$0IbgHw=^|91yO5MQd!AJLdll5QBM}k#mCS8 z$j)W3MA`mvP5t!5gmr~to!=HiM}|T{w+I*4g{3mb9{%V}X7O7Q%TR(Uvh)NccK3JY zGMh_mTG0D{VN`6XqM;FHy7#l^RiCYgrzag58JWa~p=!@pgZ5on%BIDVzUvO?GQoC~ zYnWM}og}BV=dxaxoRvaVxF54fUP3>T@8*&asOgT&MtXW&-Stk+EgLo2n#C$f^*7E+<4B>57G7I`FR8XqUQuTiJ6(PqqW{dc_x(Wa8E6ewe6mx z2nH~k?OE2F7*>m?!WOT^uyyoQjVjW|5%eD_-}WoVdFkqKqdbV{pF zyi=Z+Lrz8p)wz3ybt_i%w6r2SQb@>|vMM{CEL?pD@p#Q%=^C^QpmGr-K&#aDzYH52 z8#lJMU-ZX-?ptvWGV7YL*_5(yGB4!B3t#Q^>y)VcrlY0H>p^++tN1ZKSZY_X+2gGX zx(OGDZic*xJ^6*pw%R77LeO>qo;tU2cT^6xy{_}7YS-3tfT7%qiW~rDD)?{Ic&`d` zC_$l$o7Nk1hKE-w=2ThxPWpbjSwPVSyNaqeu+$)W(g1#0n{F@IFlh)-0JI5gruBvg zBr2$o6Dun*ps%#FwH-kpjLi%@9Lib)HuJlc1?6~O6VubvJEXL=wbzVI3@-kJTtXcS z5UY*vkuc=u6R|x#JrxXotNlgKI&8ncKK2w4==C`-L+`6VqWXv2fC%h1w*?UpS>Lz; zkmyNT+Uv^Vcqb(baW_K>c3H!bBe+Rtawbs(ZHuDV<>5nONUsHbj*vT_r0jP|e;7W`J@?uRkZy^8W8u z)Wj(%DG?R3Y2N^@Q!_G>dU|?-o~HM>v$IpTF!L6WcEGb!AckpbYBmBYFk9>t1d4p> z542!00V2_6r+hQKu14s2Fe#;Lc)a6lk@F^2>j#hXK7IYF4r&)j zq>llj@`6|cZ9h{x|K7}>!NG)UY^ge7AbV7H%rP)BVryzlR|`LemET)7MH&ITbmtog zAdR3$wC41ooz;Fbw-=x?qfHF}9z=lA0Bhu*d=CX}ee$X@96ycR5fAdN=gWrA_O@1? z*)KjDrZubiIP+2W8ZG8LPuz;Wl1>#qM>7@zu*53)yKpT~Fs1dh_Wt9y2}I z-uB7K$>EZgW`q>k4kxfpv7%4b$)U)!BG_Cwyt?{+da|LE07&0BP$?GA0r0eG3?cw# z|FEd&EJV!Y!~`NPE)I9xSKWngG0cpm;nnCh9T}q*q$l$bS^|}{E%ok+6DMS#qk}{| z;}Y~*=snh{xOWB^4WP!W&Z81ASa$Mn(553P9T{ZWXU%&gZu`*g~d;8pNEt?rXFNxR^AXFxGB{Q zgBp)funB1Y{*Tm$VVWC5R_|O=$N1)e8Nscj%X-l!1Othvx-;`=><~coafUM&tgX2M zfXBAiuT0kj;uWQC55oiOz!`_{d>(d6R9N`t0J{-{N@Lj?_O7lmHZ5TUc_we)0n(C9 zue58Y^pI-7O}1<>oWsj~c+AYpaZy7fJ1gsg{rKl(5net%V%kVzn8mOtl1ya*Nu;pD z#3+~x9mtq+?(2N5i!O9K7R-&V$L)(go6K%>TPt$UT z2gm66dxhSF<@mZmw1y!!q(pYBPco%LUZ5*K4rAV>vX{W+QszRxMRUVz=6e@Zo0VaYs$qc zyv|VYd(@UYa|gu;;n3`HT(1TG1h1Lk`vARTi#dxGmT!>mQOF)WW7gN#w+#~^5c-5! zxfxFq5)9HmNC0g-u6H~*s4a@o*`ohs^5;^YHQSMT3@d_4wRHoyU%zPE;m>l2HE?m+ zWJal6Xabu&?@I>s2E8oDvwRt#0nB_(jw-hQ3U38rCF<2kEj)YnEau1P9!JtbyyeZN zweXv_ZrQ?1Fr2(PPn|j?v767ITJcrmnV4PJ;2f|><4V``{vRU%kZ@3^2=cVT@)^|K z$brlNTIEEL!^1KXO(GWoy|2T68coi?82*A!q^nSN5d`db!IX^#`wT6N4 z#I3KlLZOO_i+cd)6;MV6yuySYQ1jBIX22jGzh^J&q=*tsx5r+@A0F5^IXwcY2c*h~ z4L{()s7_L6w>jTUbmhtwO)V`N`yNRz{d*??0s&7TND$D~aC8)cxggPdOU63T{)U%R z@T`nC#6md-?c}149yyx~bx-bP=aLk-+!Hvt)#fp=in~W)(H^Fz4vi1@=_X5$`_lo= zLr~_r573=S{1!RTf9|A99=q#~e0+QnrPcyI*QE z7FyP^z?M)G6L52L|7pzx9(dhd?5BW}4wr@RRLOL}z*hGwUCwCdo!wm?C`m|(`=rVl z)ABsa6;cFMIPO%l!NbiU%~$m+j*X3>tQznS2`4A#`<>ZmAXV9R?@+SKTEn|iHW)yL zmkj(Pl}pCm|2^9@Jj`MhuyYcS3n-uKD}wzXMxxJb!_<^hNPGVaRZ_ri*8jwxgf(me zPE`W=qX+{~j)tDT*ZOk{M<2W*1_oe)L5VD7h&=^UATIDC1UPJdIUZ(^0Dva|ALlxC ziVSjV1f*op3N)-385s-BD&Jg@Q-VRgM`ewmZ4L;IyIBkk5A(>&n?JYVPr9^rur=d^}IUIH;E;Wo1N&kB?6yup-XDeKA;rbK>G{ z>3fFEtgLo0dSP7UZle$|JUlZy3m=M2Lqh|%wKXub8ZAB6h(;hxyMW%oefI2S7jbFn z_T7y|R+m(9RL=gpz|hAMfo_N`geR&pKnz7fGX`)MsYBoE6A&j*d4u$^8ci)NL{P1r z$_Hh=S000S4iIP(ehh7`7@i7Md2u0}lD(_e+YLUfHL8PXho!|QC7s~m<)vX@2!maj zg5T``>LVK)8|b|c(6EFNl32-iAe9ml5sAT}`WMguRD*B>?fL;0%L{05a%$@8oL_cL zB22zm6b)8z`vAqmeHLl3hEI*^!Klgvni(P)+P1f`7_ODTZv**@g5&dIaRX(j%7f+a zl~HBXkf!vnUJVAxy>D*=eTxzl9d1}N5O@WJg*5=k+T6T(L{3ib{pPSU4u=b>u09VU zN!;`2kD=&H!%UaqclpUw`wV61Mt~;aKG}R^q#=+KMda`s+FDQO87w*hc?O-MF_KOI zgrXT0s<9Viees1ALnSsuAg74|U-LMhB@D`0i?&GJLrD4NFgAI6 zxcos>)Nxda+`JDkbD>$&VV;lx!d#6)hbPV&rjL+?!25=($0;cp?8CJ9;z_JXE`1%} z`8_u|GZRXsxSiF#XpJ^!q#5jikn=e4z)%!0M#Sl9w3ws%2Dg9!DS}(?(Utb#gNp`o z{_SyHzVJfN8lCa@Bg$kdqP@S_3&P3MFz1Ob2VlAG-brZt9NaGa)1&FtggMF>P(GSI zd>{dW@`oM{e*fMwI$9Tr#m+X4k8^_7VqAN#sL3S-9(Y~%7X9}hLdJ3j#Rru7h*c9~ zV^j#BsA1()I1f6&BS`DIMKbd6SfCO!J`}z0Ud=CGzKFUEiav*Tv4FOR3YnRioZhBb z-xhmVqBPc(Q(z4B_lqR5Z3vZ{(69di?Kree_W#`RJD}pe<&@|z_-+G%FD>;eYHw7m GgZ~SnNaG>^ literal 0 HcmV?d00001 diff --git a/_images/742e52f2f15e414e0800497e497fc87898dd248f94b2d57c9b41dff58cddb700.svg b/_images/742e52f2f15e414e0800497e497fc87898dd248f94b2d57c9b41dff58cddb700.svg new file mode 100644 index 0000000..cd81abe --- /dev/null +++ b/_images/742e52f2f15e414e0800497e497fc87898dd248f94b2d57c9b41dff58cddb700.svg @@ -0,0 +1,1535 @@ + + + + + + + + 2024-03-24T10:38:55.438321 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/74d77756b41ba60d1d38df8eb5c447ae35917a080683db10ca1bc7d8ce08553a.svg b/_images/74d77756b41ba60d1d38df8eb5c447ae35917a080683db10ca1bc7d8ce08553a.svg new file mode 100644 index 0000000..dd0c7e6 --- /dev/null +++ b/_images/74d77756b41ba60d1d38df8eb5c447ae35917a080683db10ca1bc7d8ce08553a.svg @@ -0,0 +1,660 @@ + + + + + + + + 2024-03-24T10:38:58.287093 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/76bf4d7e9e99ea46847f8586f512ff8ec1f2ea62f6944606c24af42f8de3858e.svg b/_images/76bf4d7e9e99ea46847f8586f512ff8ec1f2ea62f6944606c24af42f8de3858e.svg new file mode 100644 index 0000000..1160fc6 --- /dev/null +++ b/_images/76bf4d7e9e99ea46847f8586f512ff8ec1f2ea62f6944606c24af42f8de3858e.svg @@ -0,0 +1,1205 @@ + + + + + + + + 2024-03-24T10:33:08.647323 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/7b7e99bf4906a1384544b899b364f7758a758818bda6388d1e7eb4584e67dee0.svg b/_images/7b7e99bf4906a1384544b899b364f7758a758818bda6388d1e7eb4584e67dee0.svg new file mode 100644 index 0000000..c4fd49f --- /dev/null +++ b/_images/7b7e99bf4906a1384544b899b364f7758a758818bda6388d1e7eb4584e67dee0.svg @@ -0,0 +1,1205 @@ + + + + + + + + 2024-03-24T10:38:56.154302 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/7db0816439096a978a06e650f7414daf7d2b77b8c53439eeefc54d2c253929a3.svg b/_images/7db0816439096a978a06e650f7414daf7d2b77b8c53439eeefc54d2c253929a3.svg new file mode 100644 index 0000000..3b703f4 --- /dev/null +++ b/_images/7db0816439096a978a06e650f7414daf7d2b77b8c53439eeefc54d2c253929a3.svg @@ -0,0 +1,1171 @@ + + + + + + + + 2024-03-24T10:39:04.491798 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/80a682d83a913f6a7e37e956347c068952eb427c7df67dbde659547a8d9390e3.svg b/_images/80a682d83a913f6a7e37e956347c068952eb427c7df67dbde659547a8d9390e3.svg new file mode 100644 index 0000000..3745c82 --- /dev/null +++ b/_images/80a682d83a913f6a7e37e956347c068952eb427c7df67dbde659547a8d9390e3.svg @@ -0,0 +1,1526 @@ + + + + + + + + 2024-03-24T10:33:06.317865 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/854500845595589798ae7e5b04edcc666c61b018d8f4085a37ff53f652fd8a5b.svg b/_images/854500845595589798ae7e5b04edcc666c61b018d8f4085a37ff53f652fd8a5b.svg new file mode 100644 index 0000000..fee4a29 --- /dev/null +++ b/_images/854500845595589798ae7e5b04edcc666c61b018d8f4085a37ff53f652fd8a5b.svg @@ -0,0 +1,1526 @@ + + + + + + + + 2024-03-24T10:38:53.821764 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/8933913098a6b843249b0a6db427cc2a6a40bd8cc7533415df2681164a024b5c.svg b/_images/8933913098a6b843249b0a6db427cc2a6a40bd8cc7533415df2681164a024b5c.svg new file mode 100644 index 0000000..9661076 --- /dev/null +++ b/_images/8933913098a6b843249b0a6db427cc2a6a40bd8cc7533415df2681164a024b5c.svg @@ -0,0 +1,1147 @@ + + + + + + + + 2024-03-24T10:33:08.577951 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/8cdd5dc269eea22a7fbd938e752ccac2e7de25e57f229d660947631bb0bac106.svg b/_images/8cdd5dc269eea22a7fbd938e752ccac2e7de25e57f229d660947631bb0bac106.svg new file mode 100644 index 0000000..40c18d9 --- /dev/null +++ b/_images/8cdd5dc269eea22a7fbd938e752ccac2e7de25e57f229d660947631bb0bac106.svg @@ -0,0 +1,43 @@ + + + + + + + + 2024-03-24T10:38:58.925464 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/943798fa59e966bad758bd7f0c0a0a95b8ae16446a4e0fcb054b8d9b6be7a3d4.svg b/_images/943798fa59e966bad758bd7f0c0a0a95b8ae16446a4e0fcb054b8d9b6be7a3d4.svg new file mode 100644 index 0000000..c78788b --- /dev/null +++ b/_images/943798fa59e966bad758bd7f0c0a0a95b8ae16446a4e0fcb054b8d9b6be7a3d4.svg @@ -0,0 +1,5494 @@ + + + + + + + + 2024-03-24T10:33:04.965796 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/974a26595e62b5728787f74dee1ef79d41be9242a93b1958448296289f5f783b.svg b/_images/974a26595e62b5728787f74dee1ef79d41be9242a93b1958448296289f5f783b.svg new file mode 100644 index 0000000..39d69de --- /dev/null +++ b/_images/974a26595e62b5728787f74dee1ef79d41be9242a93b1958448296289f5f783b.svg @@ -0,0 +1,2473 @@ + + + + + + + + 2024-03-24T10:38:55.817345 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/a7419f0ef175fe401fea4e14ea78a3828edc14fdd875662dd117f46f2982d58e.svg b/_images/a7419f0ef175fe401fea4e14ea78a3828edc14fdd875662dd117f46f2982d58e.svg new file mode 100644 index 0000000..1d02728 --- /dev/null +++ b/_images/a7419f0ef175fe401fea4e14ea78a3828edc14fdd875662dd117f46f2982d58e.svg @@ -0,0 +1,854 @@ + + + + + + + + 2024-03-24T10:38:55.977260 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/aa19cdc6e30ec55d3405e116c8c8ac517a19885725dcd2fe117121d3a845985e.svg b/_images/aa19cdc6e30ec55d3405e116c8c8ac517a19885725dcd2fe117121d3a845985e.svg new file mode 100644 index 0000000..dcb86c2 --- /dev/null +++ b/_images/aa19cdc6e30ec55d3405e116c8c8ac517a19885725dcd2fe117121d3a845985e.svg @@ -0,0 +1,9567 @@ + + + + + + + + 2024-03-24T10:39:04.139997 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/ad5dd6f1b26f2ff31fa49163cdd80cb83eba36ad62ce0d74921eabb820a56dcc.svg b/_images/ad5dd6f1b26f2ff31fa49163cdd80cb83eba36ad62ce0d74921eabb820a56dcc.svg new file mode 100644 index 0000000..0588787 --- /dev/null +++ b/_images/ad5dd6f1b26f2ff31fa49163cdd80cb83eba36ad62ce0d74921eabb820a56dcc.svg @@ -0,0 +1,1628 @@ + + + + + + + + 2024-03-24T10:38:49.131638 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/b1e180df3ad94883f1f180db4d65257bfbe86399d1ef425830cef9b77193f3b6.svg b/_images/b1e180df3ad94883f1f180db4d65257bfbe86399d1ef425830cef9b77193f3b6.svg new file mode 100644 index 0000000..e3fb947 --- /dev/null +++ b/_images/b1e180df3ad94883f1f180db4d65257bfbe86399d1ef425830cef9b77193f3b6.svg @@ -0,0 +1,4543 @@ + + + + + + + + 2024-03-24T10:33:17.976802 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/b5bb1970e8409c7c70449aeac4c97e30c6b7c5e646f8118ed2bf0de5b210c108.svg b/_images/b5bb1970e8409c7c70449aeac4c97e30c6b7c5e646f8118ed2bf0de5b210c108.svg new file mode 100644 index 0000000..d454704 --- /dev/null +++ b/_images/b5bb1970e8409c7c70449aeac4c97e30c6b7c5e646f8118ed2bf0de5b210c108.svg @@ -0,0 +1,1147 @@ + + + + + + + + 2024-03-24T10:38:56.083632 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/b80c32eaa2120597526b15cf892f4d73b33feb45653bf8898c6e10b212d0fcf8.svg b/_images/b80c32eaa2120597526b15cf892f4d73b33feb45653bf8898c6e10b212d0fcf8.svg new file mode 100644 index 0000000..46b9abf --- /dev/null +++ b/_images/b80c32eaa2120597526b15cf892f4d73b33feb45653bf8898c6e10b212d0fcf8.svg @@ -0,0 +1,1394 @@ + + + + + + + + 2024-03-24T10:33:06.045523 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/b962af7b1040a82ca44a80e517a6d070661b553abcb5ca134e6ae5f12dd1a2a9.svg b/_images/b962af7b1040a82ca44a80e517a6d070661b553abcb5ca134e6ae5f12dd1a2a9.svg new file mode 100644 index 0000000..cbaf0a0 --- /dev/null +++ b/_images/b962af7b1040a82ca44a80e517a6d070661b553abcb5ca134e6ae5f12dd1a2a9.svg @@ -0,0 +1,837 @@ + + + + + + + + 2024-03-24T10:33:10.653814 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/b9e6c1a362c28c67deb9a93552a28e50c61d4a10c727f01b13c2f768e90a2ed6.svg b/_images/b9e6c1a362c28c67deb9a93552a28e50c61d4a10c727f01b13c2f768e90a2ed6.svg new file mode 100644 index 0000000..908c2ca --- /dev/null +++ b/_images/b9e6c1a362c28c67deb9a93552a28e50c61d4a10c727f01b13c2f768e90a2ed6.svg @@ -0,0 +1,43 @@ + + + + + + + + 2024-03-24T10:33:13.762720 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/bff2452f5034a97282bffce9c763531e7847d93838c5ec994d0f6ee3f6d244f4.svg b/_images/bff2452f5034a97282bffce9c763531e7847d93838c5ec994d0f6ee3f6d244f4.svg new file mode 100644 index 0000000..d60f1f3 --- /dev/null +++ b/_images/bff2452f5034a97282bffce9c763531e7847d93838c5ec994d0f6ee3f6d244f4.svg @@ -0,0 +1,1728 @@ + + + + + + + + 2024-03-24T10:33:06.930104 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/c01f81c5303c927a6f9a74a0067cd29d2c4258910cb2c572141c1ffb013d2104.svg b/_images/c01f81c5303c927a6f9a74a0067cd29d2c4258910cb2c572141c1ffb013d2104.svg new file mode 100644 index 0000000..0028a81 --- /dev/null +++ b/_images/c01f81c5303c927a6f9a74a0067cd29d2c4258910cb2c572141c1ffb013d2104.svg @@ -0,0 +1,1171 @@ + + + + + + + + 2024-03-24T10:33:16.523812 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/c0285b269063bb81e9cdac0db7d9b21d3dbf0207ae3eaf0857e9f251977c3f1f.svg b/_images/c0285b269063bb81e9cdac0db7d9b21d3dbf0207ae3eaf0857e9f251977c3f1f.svg new file mode 100644 index 0000000..1ac1e1f --- /dev/null +++ b/_images/c0285b269063bb81e9cdac0db7d9b21d3dbf0207ae3eaf0857e9f251977c3f1f.svg @@ -0,0 +1,2830 @@ + + + + + + + + 2024-03-24T10:39:06.467220 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/c259f8638588ac929f2ea3176409da179471f77a800fe2ad5ba96fbf6b78a30d.svg b/_images/c259f8638588ac929f2ea3176409da179471f77a800fe2ad5ba96fbf6b78a30d.svg new file mode 100644 index 0000000..944ce3a --- /dev/null +++ b/_images/c259f8638588ac929f2ea3176409da179471f77a800fe2ad5ba96fbf6b78a30d.svg @@ -0,0 +1,1490 @@ + + + + + + + + 2024-03-24T10:30:18.947139 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/c2cbef828f554310f93699cff0c5088b91e19826937fc47851ff886020d48c41.svg b/_images/c2cbef828f554310f93699cff0c5088b91e19826937fc47851ff886020d48c41.svg new file mode 100644 index 0000000..b59b8ea --- /dev/null +++ b/_images/c2cbef828f554310f93699cff0c5088b91e19826937fc47851ff886020d48c41.svg @@ -0,0 +1,466 @@ + + + + + + + + 2024-03-24T10:39:04.534615 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/c5a748b6347a4855086423ab8bae1068adef55eb1f96361e7295c1fa8d8561b6.svg b/_images/c5a748b6347a4855086423ab8bae1068adef55eb1f96361e7295c1fa8d8561b6.svg new file mode 100644 index 0000000..1cef340 --- /dev/null +++ b/_images/c5a748b6347a4855086423ab8bae1068adef55eb1f96361e7295c1fa8d8561b6.svg @@ -0,0 +1,9567 @@ + + + + + + + + 2024-03-24T10:33:16.185786 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/c9521687efd6a517b74d320964ea342fe6781d7605bd84802370a6ff9e19fb87.svg b/_images/c9521687efd6a517b74d320964ea342fe6781d7605bd84802370a6ff9e19fb87.svg new file mode 100644 index 0000000..71ce417 --- /dev/null +++ b/_images/c9521687efd6a517b74d320964ea342fe6781d7605bd84802370a6ff9e19fb87.svg @@ -0,0 +1,466 @@ + + + + + + + + 2024-03-24T10:33:16.565208 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/c987bc1150ca9ca2141036161a17ba9ea69c02459957f2bb1455961af77fb2c3.svg b/_images/c987bc1150ca9ca2141036161a17ba9ea69c02459957f2bb1455961af77fb2c3.svg new file mode 100644 index 0000000..54b5ae6 --- /dev/null +++ b/_images/c987bc1150ca9ca2141036161a17ba9ea69c02459957f2bb1455961af77fb2c3.svg @@ -0,0 +1,792 @@ + + + + + + + + 2024-03-24T10:33:08.409371 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/cc82890f49ca18b7a5e466d37dd7ae0749fa2313ec5dcf8243cdb8c6406254e0.svg b/_images/cc82890f49ca18b7a5e466d37dd7ae0749fa2313ec5dcf8243cdb8c6406254e0.svg new file mode 100644 index 0000000..e0c56c2 --- /dev/null +++ b/_images/cc82890f49ca18b7a5e466d37dd7ae0749fa2313ec5dcf8243cdb8c6406254e0.svg @@ -0,0 +1,1435 @@ + + + + + + + + 2024-03-24T10:30:19.105633 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/ccc09b63ab0808dc362c14666cabbab135656d0f0395cdc609edccd9e6faf439.svg b/_images/ccc09b63ab0808dc362c14666cabbab135656d0f0395cdc609edccd9e6faf439.svg new file mode 100644 index 0000000..842e528 --- /dev/null +++ b/_images/ccc09b63ab0808dc362c14666cabbab135656d0f0395cdc609edccd9e6faf439.svg @@ -0,0 +1,1512 @@ + + + + + + + + 2024-03-24T10:33:07.366193 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/ce18badf8e8bfe16c03e1ed8abd56b3bab2bb809df95a3dcb6380cf094598350.svg b/_images/ce18badf8e8bfe16c03e1ed8abd56b3bab2bb809df95a3dcb6380cf094598350.svg new file mode 100644 index 0000000..1208371 --- /dev/null +++ b/_images/ce18badf8e8bfe16c03e1ed8abd56b3bab2bb809df95a3dcb6380cf094598350.svg @@ -0,0 +1,1311 @@ + + + + + + + + 2024-03-24T10:33:04.739503 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/d44a180192945a345f841f66d7a7386b2510df56428286bc3a5dd016e330c04d.svg b/_images/d44a180192945a345f841f66d7a7386b2510df56428286bc3a5dd016e330c04d.svg new file mode 100644 index 0000000..e3332dd --- /dev/null +++ b/_images/d44a180192945a345f841f66d7a7386b2510df56428286bc3a5dd016e330c04d.svg @@ -0,0 +1,957 @@ + + + + + + + + 2024-03-24T10:38:50.166645 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/de62c3124743a8142fdd1eadac9f0effca6c62b503c092a12ac0aa29f26a558e.svg b/_images/de62c3124743a8142fdd1eadac9f0effca6c62b503c092a12ac0aa29f26a558e.svg new file mode 100644 index 0000000..90585a7 --- /dev/null +++ b/_images/de62c3124743a8142fdd1eadac9f0effca6c62b503c092a12ac0aa29f26a558e.svg @@ -0,0 +1,1728 @@ + + + + + + + + 2024-03-24T10:38:54.428335 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/e6e4c928d7cefa6c4da9201bdb38a78a8446760d49049aa72813f0fa4e1be3ed.svg b/_images/e6e4c928d7cefa6c4da9201bdb38a78a8446760d49049aa72813f0fa4e1be3ed.svg new file mode 100644 index 0000000..78290b4 --- /dev/null +++ b/_images/e6e4c928d7cefa6c4da9201bdb38a78a8446760d49049aa72813f0fa4e1be3ed.svg @@ -0,0 +1,1470 @@ + + + + + + + + 2024-03-24T10:38:53.240497 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/e83efcfaed61a765c2d2afaa8cb64fdd9819a1442d9516dfc17ba1b0adf5a2f4.svg b/_images/e83efcfaed61a765c2d2afaa8cb64fdd9819a1442d9516dfc17ba1b0adf5a2f4.svg new file mode 100644 index 0000000..36bc423 --- /dev/null +++ b/_images/e83efcfaed61a765c2d2afaa8cb64fdd9819a1442d9516dfc17ba1b0adf5a2f4.svg @@ -0,0 +1,1225 @@ + + + + + + + + 2024-03-24T10:38:56.303804 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/e9b64610fee0ca2b9d2d1a7facd21e718e8eb1cea83a6c6b0bc02d4d4615d05b.svg b/_images/e9b64610fee0ca2b9d2d1a7facd21e718e8eb1cea83a6c6b0bc02d4d4615d05b.svg new file mode 100644 index 0000000..7255369 --- /dev/null +++ b/_images/e9b64610fee0ca2b9d2d1a7facd21e718e8eb1cea83a6c6b0bc02d4d4615d05b.svg @@ -0,0 +1,1441 @@ + + + + + + + + 2024-03-24T10:38:57.354780 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/ebbe19550dbe886de532f3120e74e628ae69166229f3b9594fa34ba3763ea058.svg b/_images/ebbe19550dbe886de532f3120e74e628ae69166229f3b9594fa34ba3763ea058.svg new file mode 100644 index 0000000..75152af --- /dev/null +++ b/_images/ebbe19550dbe886de532f3120e74e628ae69166229f3b9594fa34ba3763ea058.svg @@ -0,0 +1,1535 @@ + + + + + + + + 2024-03-24T10:33:07.942327 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/f2d6a5dbc976c6e7f2da418be3dfea70bd12f86e013771b04dd99fe655aae02e.svg b/_images/f2d6a5dbc976c6e7f2da418be3dfea70bd12f86e013771b04dd99fe655aae02e.svg new file mode 100644 index 0000000..fa7df43 --- /dev/null +++ b/_images/f2d6a5dbc976c6e7f2da418be3dfea70bd12f86e013771b04dd99fe655aae02e.svg @@ -0,0 +1,1435 @@ + + + + + + + + 2024-03-24T10:38:49.931064 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/fa37f789ff050f0402122cb8619eefb3e336d334779b248f198f968921889bd2.svg b/_images/fa37f789ff050f0402122cb8619eefb3e336d334779b248f198f968921889bd2.svg new file mode 100644 index 0000000..2bc3505 --- /dev/null +++ b/_images/fa37f789ff050f0402122cb8619eefb3e336d334779b248f198f968921889bd2.svg @@ -0,0 +1,1311 @@ + + + + + + + + 2024-03-24T10:30:22.071976 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/fbfd4aa4278184cf9f6eb12851515e9e5ea53aadd42448a4a916c1a110ed45b3.svg b/_images/fbfd4aa4278184cf9f6eb12851515e9e5ea53aadd42448a4a916c1a110ed45b3.svg new file mode 100644 index 0000000..d3677c1 --- /dev/null +++ b/_images/fbfd4aa4278184cf9f6eb12851515e9e5ea53aadd42448a4a916c1a110ed45b3.svg @@ -0,0 +1,660 @@ + + + + + + + + 2024-03-24T10:33:10.769906 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/fcd30e864a5f55290b942766a0f4c2ea02a691e37e2e81f15d478b918a4f3824.svg b/_images/fcd30e864a5f55290b942766a0f4c2ea02a691e37e2e81f15d478b918a4f3824.svg new file mode 100644 index 0000000..e45f889 --- /dev/null +++ b/_images/fcd30e864a5f55290b942766a0f4c2ea02a691e37e2e81f15d478b918a4f3824.svg @@ -0,0 +1,1513 @@ + + + + + + + + 2024-03-24T10:38:55.625982 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_images/fcec49fb6f6a40d95a5a238e7906aa807de3ccb5b72a006f2908a1477e62db1c.svg b/_images/fcec49fb6f6a40d95a5a238e7906aa807de3ccb5b72a006f2908a1477e62db1c.svg new file mode 100644 index 0000000..828c1c4 --- /dev/null +++ b/_images/fcec49fb6f6a40d95a5a238e7906aa807de3ccb5b72a006f2908a1477e62db1c.svg @@ -0,0 +1,1490 @@ + + + + + + + + 2024-03-24T10:33:02.447096 + image/svg+xml + + + Matplotlib v3.8.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/_sources/coming-from-stata.ipynb b/_sources/coming-from-stata.ipynb index 7aea410..b6f5892 100644 --- a/_sources/coming-from-stata.ipynb +++ b/_sources/coming-from-stata.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "4dffb98d", + "id": "ca3b8dd7", "metadata": {}, "source": [ "# Coming from Stata\n", @@ -59,28 +59,28 @@ "| `merge 1:1 vars using filename` | `df = pd.merge(df1, df2, on=vars)` but there are very rich options for merging dataframes (Python is similar to SQL in this respect) and you should check the [full documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html). |\n", "| `reshape , i() j()` | **pandas** has several reshaping functions, including `df.unstack('level')` for going to wide, `df.stack('column_level')` for going to long, `pd.melt`, and `df.pivot`. It's best to check the excellent [reshaping](https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html) documentation to find what best suits your needs. |\n", "| `xi: i.var` | `pd.get_dummies(df['var'])`|\n", - "| `reg yvar xvar if condition, r` | from pyfixest.estimation import feols
fit = feols(\"yvar ~ xvar\", data=df[\"condition\"], vcov=\"HC2\") |\n", - "| `reg yvar xvar if condition, vce(cluster clustervar)` | from pyfixest.estimation import feols
fit = feols(\"yvar ~ xvar\", data=df[\"condition\"], vcov={\"CRV1\": \"clustervar\"}) |\n", - "| `areg yvar xvar, absorb(fe_var)` | from pyfixest.estimation import feols
fit = feols(\"yvar ~ xvar \\| fe_var\", data=df) |\n", - "| `_b[var], _se[var]` | `results_sw.coef()[\"var\"], results_sw.se()[\"var\"]` following creation of `results_sw` via `results_sw = feols(...)` |\n", - "| `ivreg2 lwage exper expersq (educ=age)` | feols(\"lwage ~ exper + expersq \\| educ ~ age\", data=dfiv) |\n", - "| `outreg2` | `results = feols(...)` then `results.tidy()` |\n", + "| `reg yvar xvar if condition, r` | import pyfixest as pf
fit = pf.feols(\"yvar ~ xvar\", data=df[\"condition\"], vcov=\"HC2\") |\n", + "| `reg yvar xvar if condition, vce(cluster clustervar)` | import pyfixest as pf
fit = pf.feols(\"yvar ~ xvar\", data=df[\"condition\"], vcov={\"CRV1\": \"clustervar\"}) |\n", + "| `areg yvar xvar, absorb(fe_var)` | import pyfixest as pf
fit = pf.feols(\"yvar ~ xvar \\| fe_var\", data=df) |\n", + "| `_b[var], _se[var]` | `results_sw.coef()[\"var\"], results_sw.se()[\"var\"]` following creation of `results_sw` via `results_sw = pf.feols(...)` |\n", + "| `ivreg2 lwage exper expersq (educ=age)` | pf.feols(\"lwage ~ exper + expersq \\| educ ~ age\", data=dfiv) |\n", + "| `outreg2` | `results = pf.feols(...)` then `results.tidy()` |\n", "| `binscatter` | `binsreg` from the [**binsreg**](https://pypi.org/project/binsreg/) package; see {ref}`regression-diagnostics`. |\n", "| `twoway scatter var1 var2` | `df.scatter(var2, var1)` |\n", "\n", "The table below presents further examples of doing regression with both the **statsmodels** and [**pyfixest**](https://s3alfisc.github.io/pyfixest/) packages.\n", "\n", - "Note that, in the below, you need only import `feols` once in each Python session, and the syntax for looking at results is `results = feols(...)` and then `results.summary()`.\n", + "Note that, in the below, you need only import `pf.feols` once in each Python session, and the syntax for looking at results is `results = pf.feols(...)` and then `results.summary()`.\n", "\n", "| Command | Stata | Python |\n", "| ----------- | ----------- | ----------- |\n", - "| Fixed Effects (absorbing) | `reghdfe y x, absorb(fe)` | from pyfixest.estimation import feols
fit = feols(\"y ~ x \\| fe\", data=df) |\n", - "| Categorical regression | `reghdfe y x i.cat` | from pyfixest.estimation import feols
fit = feols(\"y ~ x + C(cat)\", data=df)
But if `cat` is of type categorical it can be run with `y ~ x + cat`|\n", - "| Interacting categoricals | `reghdfe y x i.cat#i.cat2` | from pyfixest.estimation import feols
fit = feols(\"yvar ~ xvar + C(cat):C(cat2)\", data=df)
Note that `a*b` is a short-hand for `a + b + a:b`, with the last term representing the interaction.|\n", - "| Robust standard errors | `reghdfe y x, r` | from pyfixest.estimation import feols
fit = feols(\"y ~ x, data=df, vcov=\"HC1\")
Note that a range of heteroskedasticity robust standard errors are available: see {ref}`regression` for more.|\n", - "| Clustered standard errors | `reghdfe y x, cluster(clust)` | from pyfixest.estimation import feols
fit = feols(\"y ~ x\", data=df, vcov={\"CRV1\": \"clust\"}) |\n", - "| Two-way clustered standard errors | `reghdfe y x, cluster(clust1 clust2)` |from pyfixest.estimation import feols
fit = feols(\"y ~ x\", data=df, vcov={\"CRV1\": \"clust1 + clust2\"}) |\n", - "| Instrumental variables | `ivreghdfe 2sls y exog (endog = instrument)` | from pyfixest.estimation import feols
fit = feols(\"y ~ exog \\| endog ~ instrument\", data=df) |" + "| Fixed Effects (absorbing) | `reghdfe y x, absorb(fe)` | import pyfixest as pf
fit = pf.feols(\"y ~ x \\| fe\", data=df) |\n", + "| Categorical regression | `reghdfe y x i.cat` | import pyfixest as pf
fit = pf.feols(\"y ~ x + C(cat)\", data=df)
But if `cat` is of type categorical it can be run with `y ~ x + cat`|\n", + "| Interacting categoricals | `reghdfe y x i.cat#i.cat2` | import pyfixest as pf
fit = pf.feols(\"yvar ~ xvar + C(cat):C(cat2)\", data=df)
Note that `a*b` is a short-hand for `a + b + a:b`, with the last term representing the interaction.|\n", + "| Robust standard errors | `reghdfe y x, r` | import pyfixest as pf
fit = pf.feols(\"y ~ x, data=df, vcov=\"HC1\")
Note that a range of heteroskedasticity robust standard errors are available: see {ref}`regression` for more.|\n", + "| Clustered standard errors | `reghdfe y x, cluster(clust)` | import pyfixest as pf
fit = pf.feols(\"y ~ x\", data=df, vcov={\"CRV1\": \"clust\"}) |\n", + "| Two-way clustered standard errors | `reghdfe y x, cluster(clust1 clust2)` |import pyfixest as pf
fit = pf.feols(\"y ~ x\", data=df, vcov={\"CRV1\": \"clust1 + clust2\"}) |\n", + "| Instrumental variables | `ivreghdfe 2sls y exog (endog = instrument)` | import pyfixest as pf
fit = pf.feols(\"y ~ exog \\| endog ~ instrument\", data=df) |" ] } ], diff --git a/_sources/coming-from-stata.md b/_sources/coming-from-stata.md index 55765f2..8db3556 100644 --- a/_sources/coming-from-stata.md +++ b/_sources/coming-from-stata.md @@ -67,25 +67,25 @@ You can find more on (frequentist) regressions in {ref}`regression`, Bayesian re | `merge 1:1 vars using filename` | `df = pd.merge(df1, df2, on=vars)` but there are very rich options for merging dataframes (Python is similar to SQL in this respect) and you should check the [full documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html). | | `reshape , i() j()` | **pandas** has several reshaping functions, including `df.unstack('level')` for going to wide, `df.stack('column_level')` for going to long, `pd.melt`, and `df.pivot`. It's best to check the excellent [reshaping](https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html) documentation to find what best suits your needs. | | `xi: i.var` | `pd.get_dummies(df['var'])`| -| `reg yvar xvar if condition, r` | from pyfixest.estimation import feols
fit = feols("yvar ~ xvar", data=df["condition"], vcov="HC2") | -| `reg yvar xvar if condition, vce(cluster clustervar)` | from pyfixest.estimation import feols
fit = feols("yvar ~ xvar", data=df["condition"], vcov={"CRV1": "clustervar"}) | -| `areg yvar xvar, absorb(fe_var)` | from pyfixest.estimation import feols
fit = feols("yvar ~ xvar \| fe_var", data=df) | -| `_b[var], _se[var]` | `results_sw.coef()["var"], results_sw.se()["var"]` following creation of `results_sw` via `results_sw = feols(...)` | -| `ivreg2 lwage exper expersq (educ=age)` | feols("lwage ~ exper + expersq \| educ ~ age", data=dfiv) | -| `outreg2` | `results = feols(...)` then `results.tidy()` | +| `reg yvar xvar if condition, r` | import pyfixest as pf
fit = pf.feols("yvar ~ xvar", data=df["condition"], vcov="HC2") | +| `reg yvar xvar if condition, vce(cluster clustervar)` | import pyfixest as pf
fit = pf.feols("yvar ~ xvar", data=df["condition"], vcov={"CRV1": "clustervar"}) | +| `areg yvar xvar, absorb(fe_var)` | import pyfixest as pf
fit = pf.feols("yvar ~ xvar \| fe_var", data=df) | +| `_b[var], _se[var]` | `results_sw.coef()["var"], results_sw.se()["var"]` following creation of `results_sw` via `results_sw = pf.feols(...)` | +| `ivreg2 lwage exper expersq (educ=age)` | pf.feols("lwage ~ exper + expersq \| educ ~ age", data=dfiv) | +| `outreg2` | `results = pf.feols(...)` then `results.tidy()` | | `binscatter` | `binsreg` from the [**binsreg**](https://pypi.org/project/binsreg/) package; see {ref}`regression-diagnostics`. | | `twoway scatter var1 var2` | `df.scatter(var2, var1)` | The table below presents further examples of doing regression with both the **statsmodels** and [**pyfixest**](https://s3alfisc.github.io/pyfixest/) packages. -Note that, in the below, you need only import `feols` once in each Python session, and the syntax for looking at results is `results = feols(...)` and then `results.summary()`. +Note that, in the below, you need only import `pf.feols` once in each Python session, and the syntax for looking at results is `results = pf.feols(...)` and then `results.summary()`. | Command | Stata | Python | | ----------- | ----------- | ----------- | -| Fixed Effects (absorbing) | `reghdfe y x, absorb(fe)` | from pyfixest.estimation import feols
fit = feols("y ~ x \| fe", data=df) | -| Categorical regression | `reghdfe y x i.cat` | from pyfixest.estimation import feols
fit = feols("y ~ x + C(cat)", data=df)
But if `cat` is of type categorical it can be run with `y ~ x + cat`| -| Interacting categoricals | `reghdfe y x i.cat#i.cat2` | from pyfixest.estimation import feols
fit = feols("yvar ~ xvar + C(cat):C(cat2)", data=df)
Note that `a*b` is a short-hand for `a + b + a:b`, with the last term representing the interaction.| -| Robust standard errors | `reghdfe y x, r` | from pyfixest.estimation import feols
fit = feols("y ~ x, data=df, vcov="HC1")
Note that a range of heteroskedasticity robust standard errors are available: see {ref}`regression` for more.| -| Clustered standard errors | `reghdfe y x, cluster(clust)` | from pyfixest.estimation import feols
fit = feols("y ~ x", data=df, vcov={"CRV1": "clust"}) | -| Two-way clustered standard errors | `reghdfe y x, cluster(clust1 clust2)` |from pyfixest.estimation import feols
fit = feols("y ~ x", data=df, vcov={"CRV1": "clust1 + clust2"}) | -| Instrumental variables | `ivreghdfe 2sls y exog (endog = instrument)` | from pyfixest.estimation import feols
fit = feols("y ~ exog \| endog ~ instrument", data=df) | \ No newline at end of file +| Fixed Effects (absorbing) | `reghdfe y x, absorb(fe)` | import pyfixest as pf
fit = pf.feols("y ~ x \| fe", data=df) | +| Categorical regression | `reghdfe y x i.cat` | import pyfixest as pf
fit = pf.feols("y ~ x + C(cat)", data=df)
But if `cat` is of type categorical it can be run with `y ~ x + cat`| +| Interacting categoricals | `reghdfe y x i.cat#i.cat2` | import pyfixest as pf
fit = pf.feols("yvar ~ xvar + C(cat):C(cat2)", data=df)
Note that `a*b` is a short-hand for `a + b + a:b`, with the last term representing the interaction.| +| Robust standard errors | `reghdfe y x, r` | import pyfixest as pf
fit = pf.feols("y ~ x, data=df, vcov="HC1")
Note that a range of heteroskedasticity robust standard errors are available: see {ref}`regression` for more.| +| Clustered standard errors | `reghdfe y x, cluster(clust)` | import pyfixest as pf
fit = pf.feols("y ~ x", data=df, vcov={"CRV1": "clust"}) | +| Two-way clustered standard errors | `reghdfe y x, cluster(clust1 clust2)` |import pyfixest as pf
fit = pf.feols("y ~ x", data=df, vcov={"CRV1": "clust1 + clust2"}) | +| Instrumental variables | `ivreghdfe 2sls y exog (endog = instrument)` | import pyfixest as pf
fit = pf.feols("y ~ exog \| endog ~ instrument", data=df) | \ No newline at end of file diff --git a/_sources/data-sharing.ipynb b/_sources/data-sharing.ipynb index 38a34c5..61d7e53 100644 --- a/_sources/data-sharing.ipynb +++ b/_sources/data-sharing.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "fbf7e16c", + "id": "ce0fa25a", "metadata": {}, "source": [ "(data-sharing)=\n", @@ -82,8 +82,6 @@ "\n", "If you want to follow a full example of serving up data end-to-end, take a look at the [particulate matter datasette](https://github.com/aeturrell/datasette_particulate_matter) github repo. These data were constructed by downloading files of estimates of 2.5 micron particulate matter concentration in the UK from the DEFRA website and combining them into a CSV.\n", "\n", - "**You can see how the data in this repo get served up by datasette at [this link](https://particulatematter-fsx2r7puuq-nw.a.run.app/).**\n", - "\n", "```{admonition} Exercises\n", "1. Download the CSV file from the [particulate matter repo](https://github.com/aeturrell/datasette_particulate_matter) and run it as a datasette locally on your own computer.\n", "2. (Advanced) Run the same set of data via a datasette instance on the web using Google Cloud Run. (This requires a Google Cloud Account; you may be billed for excessive use of this service so remember to shut it down via the Google Cloud Console once you're done.)\n", diff --git a/_sources/data-sharing.md b/_sources/data-sharing.md index c5ebc11..822596b 100644 --- a/_sources/data-sharing.md +++ b/_sources/data-sharing.md @@ -90,8 +90,6 @@ This will create a docker container and deploy it to the Cloud Run service. You If you want to follow a full example of serving up data end-to-end, take a look at the [particulate matter datasette](https://github.com/aeturrell/datasette_particulate_matter) github repo. These data were constructed by downloading files of estimates of 2.5 micron particulate matter concentration in the UK from the DEFRA website and combining them into a CSV. -**You can see how the data in this repo get served up by datasette at [this link](https://particulatematter-fsx2r7puuq-nw.a.run.app/).** - ```{admonition} Exercises 1. Download the CSV file from the [particulate matter repo](https://github.com/aeturrell/datasette_particulate_matter) and run it as a datasette locally on your own computer. 2. (Advanced) Run the same set of data via a datasette instance on the web using Google Cloud Run. (This requires a Google Cloud Account; you may be billed for excessive use of this service so remember to shut it down via the Google Cloud Console once you're done.) diff --git a/_sources/econmt-regression.ipynb b/_sources/econmt-regression.ipynb index 4404cdf..46e5d3c 100644 --- a/_sources/econmt-regression.ipynb +++ b/_sources/econmt-regression.ipynb @@ -102,8 +102,7 @@ "from lets_plot import *\n", "import statsmodels.api as sm\n", "import statsmodels.formula.api as smf\n", - "from pyfixest.estimation import feols\n", - "from pyfixest.summarize import summary\n", + "import pyfixest as pf\n", "\n", "LetsPlot.setup_html()\n", "\n", @@ -132,7 +131,7 @@ "matplotlib_inline.backend_inline.set_matplotlib_formats(\"svg\")\n", "\n", "# Set max rows displayed for readability\n", - "pd.set_option(\"display.max_rows\", 6)" + "pd.set_option(\"display.max_rows\", 10)" ] }, { @@ -175,7 +174,7 @@ "metadata": {}, "outputs": [], "source": [ - "results_sw = feols(\"mass ~ height\", data=df)" + "results_sw = pf.feols(\"mass ~ height\", data=df)" ] }, { @@ -256,7 +255,7 @@ "metadata": {}, "outputs": [], "source": [ - "results_outlier_free = feols(\n", + "results_outlier_free = pf.feols(\n", " \"mass ~ height\", data=df[~df[\"name\"].str.contains(\"Jabba\")]\n", ")\n", "print(results_outlier_free.summary())" @@ -284,7 +283,7 @@ "metadata": {}, "outputs": [], "source": [ - "feols(\"mass ~ height\", data=df, vcov=\"HC2\").summary()" + "pf.feols(\"mass ~ height\", data=df, vcov=\"HC2\").summary()" ] }, { @@ -336,7 +335,7 @@ "outputs": [], "source": [ "xf = df.dropna(subset=[\"homeworld\", \"mass\", \"height\", \"species\"])\n", - "feols(\"mass ~ height\", data=xf, vcov={\"CRV1\": \"homeworld\"}).summary()" + "pf.feols(\"mass ~ height\", data=xf, vcov={\"CRV1\": \"homeworld\"}).summary()" ] }, { @@ -352,7 +351,7 @@ "metadata": {}, "outputs": [], "source": [ - "feols(\"mass ~ height\", data=xf, vcov={\"CRV1\": \"homeworld + species\"}).summary()" + "pf.feols(\"mass ~ height\", data=xf, vcov={\"CRV1\": \"homeworld + species\"}).summary()" ] }, { @@ -403,7 +402,7 @@ "metadata": {}, "outputs": [], "source": [ - "feols(\"mpg ~ hp + C(cyl)\", data=mpg).summary()" + "pf.feols(\"mpg ~ hp + C(cyl)\", data=mpg).summary()" ] }, { @@ -435,7 +434,7 @@ "metadata": {}, "outputs": [], "source": [ - "feols(\"mpg ~ hp + C(cyl) -1\", data=mpg).tidy()" + "pf.feols(\"mpg ~ hp + C(cyl) -1\", data=mpg).tidy()" ] }, { @@ -519,7 +518,7 @@ "metadata": {}, "outputs": [], "source": [ - "results_hdfe = feols(\"y ~ exog_0 + exog_1 | state_id + firm_id\", data=sim)\n", + "results_hdfe = pf.feols(\"y ~ exog_0 + exog_1 | state_id + firm_id\", data=sim)\n", "results_hdfe.summary()" ] }, @@ -555,7 +554,7 @@ "outputs": [], "source": [ "mpg[\"lnhp\"] = np.log(mpg[\"hp\"])\n", - "feols(\"mpg ~ lnhp\", data=mpg).tidy()" + "pf.feols(\"mpg ~ lnhp\", data=mpg).tidy()" ] }, { @@ -571,7 +570,7 @@ "metadata": {}, "outputs": [], "source": [ - "results_ln = feols(\"mpg ~ np.log(hp)\", data=mpg)\n", + "results_ln = pf.feols(\"mpg ~ np.log(hp)\", data=mpg)\n", "results_ln.tidy()" ] }, @@ -588,7 +587,7 @@ "metadata": {}, "outputs": [], "source": [ - "feols(\"mpg ~ np.arcsinh(hp)\", data=mpg).tidy()" + "pf.feols(\"mpg ~ np.arcsinh(hp)\", data=mpg).tidy()" ] }, { @@ -622,7 +621,7 @@ "metadata": {}, "outputs": [], "source": [ - "res_poly = feols(\"mpg ~ hp + np.power(hp, 2)\", data=mpg)\n", + "res_poly = pf.feols(\"mpg ~ hp + np.power(hp, 2)\", data=mpg)\n", "res_poly.tidy()" ] }, @@ -639,7 +638,7 @@ "metadata": {}, "outputs": [], "source": [ - "res_inter = feols(\"mpg ~ hp * disp\", data=mpg)\n", + "res_inter = pf.feols(\"mpg ~ hp * disp\", data=mpg)\n", "res_inter.tidy()" ] }, @@ -656,7 +655,7 @@ "metadata": {}, "outputs": [], "source": [ - "res_only_inter = feols(\"mpg ~ hp : disp\", data=mpg)\n", + "res_only_inter = pf.feols(\"mpg ~ hp : disp\", data=mpg)\n", "res_only_inter.tidy()" ] }, @@ -692,9 +691,7 @@ "metadata": {}, "outputs": [], "source": [ - "from pyfixest.summarize import etable\n", - "\n", - "etable([results_ln, res_poly, res_inter], type=\"df\")" + "pf.etable([results_ln, res_poly, res_inter], type=\"df\")" ] }, { @@ -710,7 +707,7 @@ "metadata": {}, "outputs": [], "source": [ - "etable([results_ln, res_poly, res_inter], type=\"md\")" + "pf.etable([results_ln, res_poly, res_inter], type=\"md\")" ] }, { @@ -760,7 +757,7 @@ "metadata": {}, "outputs": [], "source": [ - "feols(\"y ~ sw(x1, x2, x3)\", data=iris).summary()" + "pf.feols(\"y ~ sw(x1, x2, x3)\", data=iris).summary()" ] }, { @@ -776,7 +773,7 @@ "metadata": {}, "outputs": [], "source": [ - "feols(\"y ~ csw(x1, x2, x3)\", data=iris).summary()" + "pf.feols(\"y ~ csw(x1, x2, x3)\", data=iris).summary()" ] }, { @@ -792,7 +789,7 @@ "metadata": {}, "outputs": [], "source": [ - "reg_iris_csw = feols(\"y ~ csw(x1, x2, x3)\", data=iris)\n", + "reg_iris_csw = pf.feols(\"y ~ csw(x1, x2, x3)\", data=iris)\n", "reg_iris_csw.etable()" ] }, @@ -884,7 +881,7 @@ "metadata": {}, "outputs": [], "source": [ - "results_iv = feols(\"np.log(packs) ~ np.log(rincome) + 1 | year + state | np.log(rprice) ~ taxs \", data=dfiv, vcov={\"CRV1\": \"year\"})\n", + "results_iv = pf.feols(\"np.log(packs) ~ np.log(rincome) + 1 | year + state | np.log(rprice) ~ taxs \", data=dfiv, vcov={\"CRV1\": \"year\"})\n", "results_iv.summary()" ] }, @@ -901,7 +898,7 @@ "metadata": {}, "outputs": [], "source": [ - "results_cigs_ols = feols(\"np.log(packs) ~ np.log(rprice) + np.log(rincome) | year + state\", data=dfiv, vcov={\"CRV1\": \"year\"})\n", + "results_cigs_ols = pf.feols(\"np.log(packs) ~ np.log(rprice) + np.log(rincome) | year + state\", data=dfiv, vcov={\"CRV1\": \"year\"})\n", "results_cigs_ols.summary()" ] }, @@ -918,7 +915,7 @@ "metadata": {}, "outputs": [], "source": [ - "etable([results_cigs_ols, results_iv], type=\"md\")" + "pf.etable([results_cigs_ols, results_iv], type=\"md\")" ] }, { diff --git a/_sources/vis-common-plots.ipynb b/_sources/vis-common-plots.ipynb index 2ab2e4f..6fc29b1 100644 --- a/_sources/vis-common-plots.ipynb +++ b/_sources/vis-common-plots.ipynb @@ -22,9 +22,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import numpy as np\n", "import pandas as pd\n", @@ -55,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "tags": [ "remove-cell" @@ -87,9 +128,127 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
NameMiles_per_GallonCylindersDisplacementHorsepowerWeight_in_lbsAccelerationYearOrigin
0chevrolet chevelle malibu18.08307.0130.0350412.01970-01-01USA
1buick skylark 32015.08350.0165.0369311.51970-01-01USA
2plymouth satellite18.08318.0150.0343611.01970-01-01USA
3amc rebel sst16.08304.0150.0343312.01970-01-01USA
4ford torino17.08302.0140.0344910.51970-01-01USA
\n", + "
" + ], + "text/plain": [ + " Name Miles_per_Gallon Cylinders Displacement \\\n", + "0 chevrolet chevelle malibu 18.0 8 307.0 \n", + "1 buick skylark 320 15.0 8 350.0 \n", + "2 plymouth satellite 18.0 8 318.0 \n", + "3 amc rebel sst 16.0 8 304.0 \n", + "4 ford torino 17.0 8 302.0 \n", + "\n", + " Horsepower Weight_in_lbs Acceleration Year Origin \n", + "0 130.0 3504 12.0 1970-01-01 USA \n", + "1 165.0 3693 11.5 1970-01-01 USA \n", + "2 150.0 3436 11.0 1970-01-01 USA \n", + "3 150.0 3433 12.0 1970-01-01 USA \n", + "4 140.0 3449 10.5 1970-01-01 USA " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "cars = data.cars()\n", "cars.head()" @@ -104,9 +263,1649 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:18.375594\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots()\n", "for origin in cars[\"Origin\"].unique():\n", @@ -130,9 +1929,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": { + "image/png": { + "height": 378.25, + "width": 604.775 + } + }, + "output_type": "execute_result" + } + ], "source": [ "(so.Plot(cars, x=\"Horsepower\", y=\"Miles_per_Gallon\", color=\"Origin\").add(so.Dot()))" ] @@ -146,9 +1962,64 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "(\n", " ggplot(cars, aes(x=\"Horsepower\", y=\"Miles_per_Gallon\", color=\"Origin\"))\n", @@ -168,9 +2039,77 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "alt.Chart(cars).mark_circle(size=60).encode(\n", " x=\"Horsepower\",\n", @@ -191,9 +2130,3369 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "customdata": [ + [ + "chevrolet chevelle malibu", + "USA" + ], + [ + "buick skylark 320", + "USA" + ], + [ + "plymouth satellite", + "USA" + ], + [ + "amc rebel sst", + "USA" + ], + [ + "ford torino", + "USA" + ], + [ + "ford galaxie 500", + "USA" + ], + [ + "chevrolet impala", + "USA" + ], + [ + "plymouth fury iii", + "USA" + ], + [ + "pontiac catalina", + "USA" + ], + [ + "amc ambassador dpl", + "USA" + ], + [ + "chevrolet chevelle concours (sw)", + "USA" + ], + [ + "ford torino (sw)", + "USA" + ], + [ + "plymouth satellite (sw)", + "USA" + ], + [ + "amc rebel sst (sw)", + "USA" + ], + [ + "dodge challenger se", + "USA" + ], + [ + "plymouth 'cuda 340", + "USA" + ], + [ + "ford mustang boss 302", + "USA" + ], + [ + "chevrolet monte carlo", + "USA" + ], + [ + "buick estate wagon (sw)", + "USA" + ], + [ + "plymouth duster", + "USA" + ], + [ + "amc hornet", + "USA" + ], + [ + "ford maverick", + "USA" + ], + [ + "amc gremlin", + "USA" + ], + [ + "ford f250", + "USA" + ], + [ + "chevy c20", + "USA" + ], + [ + "dodge d200", + "USA" + ], + [ + "hi 1200d", + "USA" + ], + [ + "chevrolet vega 2300", + "USA" + ], + [ + "ford pinto", + "USA" + ], + [ + "amc gremlin", + "USA" + ], + [ + "plymouth satellite custom", + "USA" + ], + [ + "chevrolet chevelle malibu", + "USA" + ], + [ + "ford torino 500", + "USA" + ], + [ + "amc matador", + "USA" + ], + [ + "chevrolet impala", + "USA" + ], + [ + "pontiac catalina brougham", + "USA" + ], + [ + "ford galaxie 500", + "USA" + ], + [ + "plymouth fury iii", + "USA" + ], + [ + "dodge monaco (sw)", + "USA" + ], + [ + "ford country squire (sw)", + "USA" + ], + [ + "pontiac safari (sw)", + "USA" + ], + [ + "amc hornet sportabout (sw)", + "USA" + ], + [ + "chevrolet vega (sw)", + "USA" + ], + [ + "pontiac firebird", + "USA" + ], + [ + "ford mustang", + "USA" + ], + [ + "mercury capri 2000", + "USA" + ], + [ + "plymouth cricket", + "USA" + ], + [ + "dodge colt hardtop", + "USA" + ], + [ + "chevrolet vega", + "USA" + ], + [ + "ford pinto runabout", + "USA" + ], + [ + "chevrolet impala", + "USA" + ], + [ + "pontiac catalina", + "USA" + ], + [ + "plymouth fury iii", + "USA" + ], + [ + "ford galaxie 500", + "USA" + ], + [ + "amc ambassador sst", + "USA" + ], + [ + "mercury marquis", + "USA" + ], + [ + "buick lesabre custom", + "USA" + ], + [ + "oldsmobile delta 88 royale", + "USA" + ], + [ + "chrysler newport royal", + "USA" + ], + [ + "amc matador (sw)", + "USA" + ], + [ + "chevrolet chevelle concours (sw)", + "USA" + ], + [ + "ford gran torino (sw)", + "USA" + ], + [ + "plymouth satellite custom (sw)", + "USA" + ], + [ + "ford pinto (sw)", + "USA" + ], + [ + "dodge colt (sw)", + "USA" + ], + [ + "buick century 350", + "USA" + ], + [ + "amc matador", + "USA" + ], + [ + "chevrolet malibu", + "USA" + ], + [ + "ford gran torino", + "USA" + ], + [ + "dodge coronet custom", + "USA" + ], + [ + "mercury marquis brougham", + "USA" + ], + [ + "chevrolet caprice classic", + "USA" + ], + [ + "ford ltd", + "USA" + ], + [ + "plymouth fury gran sedan", + "USA" + ], + [ + "chrysler new yorker brougham", + "USA" + ], + [ + "buick electra 225 custom", + "USA" + ], + [ + "amc ambassador brougham", + "USA" + ], + [ + "plymouth valiant", + "USA" + ], + [ + "chevrolet nova custom", + "USA" + ], + [ + "amc hornet", + "USA" + ], + [ + "ford maverick", + "USA" + ], + [ + "plymouth duster", + "USA" + ], + [ + "chevrolet impala", + "USA" + ], + [ + "ford country", + "USA" + ], + [ + "plymouth custom suburb", + "USA" + ], + [ + "oldsmobile vista cruiser", + "USA" + ], + [ + "amc gremlin", + "USA" + ], + [ + "chevrolet vega", + "USA" + ], + [ + "ford pinto", + "USA" + ], + [ + "mercury capri v6", + "USA" + ], + [ + "chevrolet monte carlo s", + "USA" + ], + [ + "pontiac grand prix", + "USA" + ], + [ + "dodge dart custom", + "USA" + ], + [ + "oldsmobile omega", + "USA" + ], + [ + "plymouth duster", + "USA" + ], + [ + "ford maverick", + "USA" + ], + [ + "amc hornet", + "USA" + ], + [ + "chevrolet nova", + "USA" + ], + [ + "ford pinto", + "USA" + ], + [ + "chevrolet vega", + "USA" + ], + [ + "chevrolet chevelle malibu classic", + "USA" + ], + [ + "amc matador", + "USA" + ], + [ + "plymouth satellite sebring", + "USA" + ], + [ + "ford gran torino", + "USA" + ], + [ + "buick century luxus (sw)", + "USA" + ], + [ + "dodge coronet custom (sw)", + "USA" + ], + [ + "ford gran torino (sw)", + "USA" + ], + [ + "amc matador (sw)", + "USA" + ], + [ + "dodge colt", + "USA" + ], + [ + "plymouth valiant custom", + "USA" + ], + [ + "chevrolet nova", + "USA" + ], + [ + "mercury monarch", + "USA" + ], + [ + "ford maverick", + "USA" + ], + [ + "pontiac catalina", + "USA" + ], + [ + "chevrolet bel air", + "USA" + ], + [ + "plymouth grand fury", + "USA" + ], + [ + "ford ltd", + "USA" + ], + [ + "buick century", + "USA" + ], + [ + "chevroelt chevelle malibu", + "USA" + ], + [ + "amc matador", + "USA" + ], + [ + "plymouth fury", + "USA" + ], + [ + "buick skyhawk", + "USA" + ], + [ + "chevrolet monza 2+2", + "USA" + ], + [ + "ford mustang ii", + "USA" + ], + [ + "ford pinto", + "USA" + ], + [ + "amc gremlin", + "USA" + ], + [ + "pontiac astro", + "USA" + ], + [ + "ford pinto", + "USA" + ], + [ + "amc pacer", + "USA" + ], + [ + "capri ii", + "USA" + ], + [ + "dodge colt", + "USA" + ], + [ + "chevrolet chevelle malibu classic", + "USA" + ], + [ + "dodge coronet brougham", + "USA" + ], + [ + "amc matador", + "USA" + ], + [ + "ford gran torino", + "USA" + ], + [ + "plymouth valiant", + "USA" + ], + [ + "chevrolet nova", + "USA" + ], + [ + "ford maverick", + "USA" + ], + [ + "amc hornet", + "USA" + ], + [ + "chevrolet chevette", + "USA" + ], + [ + "chevrolet woody", + "USA" + ], + [ + "dodge aspen se", + "USA" + ], + [ + "ford granada ghia", + "USA" + ], + [ + "pontiac ventura sj", + "USA" + ], + [ + "amc pacer d/l", + "USA" + ], + [ + "ford pinto", + "USA" + ], + [ + "plymouth volare premier v8", + "USA" + ], + [ + "cadillac seville", + "USA" + ], + [ + "chevy c10", + "USA" + ], + [ + "ford f108", + "USA" + ], + [ + "dodge d100", + "USA" + ], + [ + "buick opel isuzu deluxe", + "USA" + ], + [ + "plymouth arrow gs", + "USA" + ], + [ + "chevrolet caprice classic", + "USA" + ], + [ + "oldsmobile cutlass supreme", + "USA" + ], + [ + "dodge monaco brougham", + "USA" + ], + [ + "mercury cougar brougham", + "USA" + ], + [ + "chevrolet concours", + "USA" + ], + [ + "buick skylark", + "USA" + ], + [ + "plymouth volare custom", + "USA" + ], + [ + "ford granada", + "USA" + ], + [ + "pontiac grand prix lj", + "USA" + ], + [ + "chevrolet monte carlo landau", + "USA" + ], + [ + "chrysler cordoba", + "USA" + ], + [ + "ford thunderbird", + "USA" + ], + [ + "pontiac sunbird coupe", + "USA" + ], + [ + "ford mustang ii 2+2", + "USA" + ], + [ + "chevrolet chevette", + "USA" + ], + [ + "dodge colt m/m", + "USA" + ], + [ + "ford fiesta", + "USA" + ], + [ + "oldsmobile cutlass salon brougham", + "USA" + ], + [ + "dodge diplomat", + "USA" + ], + [ + "mercury monarch ghia", + "USA" + ], + [ + "pontiac phoenix lj", + "USA" + ], + [ + "chevrolet malibu", + "USA" + ], + [ + "ford fairmont (auto)", + "USA" + ], + [ + "ford fairmont (man)", + "USA" + ], + [ + "plymouth volare", + "USA" + ], + [ + "amc concord", + "USA" + ], + [ + "buick century special", + "USA" + ], + [ + "mercury zephyr", + "USA" + ], + [ + "dodge aspen", + "USA" + ], + [ + "amc concord d/l", + "USA" + ], + [ + "chevrolet monte carlo landau", + "USA" + ], + [ + "buick regal sport coupe (turbo)", + "USA" + ], + [ + "ford futura", + "USA" + ], + [ + "dodge magnum xe", + "USA" + ], + [ + "chevrolet chevette", + "USA" + ], + [ + "dodge omni", + "USA" + ], + [ + "plymouth sapporo", + "USA" + ], + [ + "oldsmobile starfire sx", + "USA" + ], + [ + "pontiac lemans v6", + "USA" + ], + [ + "mercury zephyr 6", + "USA" + ], + [ + "ford fairmont 4", + "USA" + ], + [ + "amc concord dl 6", + "USA" + ], + [ + "dodge aspen 6", + "USA" + ], + [ + "chevrolet caprice classic", + "USA" + ], + [ + "ford ltd landau", + "USA" + ], + [ + "mercury grand marquis", + "USA" + ], + [ + "dodge st. regis", + "USA" + ], + [ + "buick estate wagon (sw)", + "USA" + ], + [ + "ford country squire (sw)", + "USA" + ], + [ + "chevrolet malibu classic (sw)", + "USA" + ], + [ + "chrysler lebaron town @ country (sw)", + "USA" + ], + [ + "dodge colt hatchback custom", + "USA" + ], + [ + "amc spirit dl", + "USA" + ], + [ + "cadillac eldorado", + "USA" + ], + [ + "oldsmobile cutlass salon brougham", + "USA" + ], + [ + "plymouth horizon", + "USA" + ], + [ + "plymouth horizon tc3", + "USA" + ], + [ + "buick skylark limited", + "USA" + ], + [ + "chevrolet citation", + "USA" + ], + [ + "oldsmobile omega brougham", + "USA" + ], + [ + "pontiac phoenix", + "USA" + ], + [ + "chevrolet chevette", + "USA" + ], + [ + "chevrolet citation", + "USA" + ], + [ + "ford fairmont", + "USA" + ], + [ + "amc concord", + "USA" + ], + [ + "dodge aspen", + "USA" + ], + [ + "dodge colt", + "USA" + ], + [ + "ford mustang cobra", + "USA" + ], + [ + "plymouth reliant", + "USA" + ], + [ + "buick skylark", + "USA" + ], + [ + "dodge aries wagon (sw)", + "USA" + ], + [ + "chevrolet citation", + "USA" + ], + [ + "plymouth reliant", + "USA" + ], + [ + "plymouth champ", + "USA" + ], + [ + "plymouth horizon 4", + "USA" + ], + [ + "ford escort 4w", + "USA" + ], + [ + "ford escort 2h", + "USA" + ], + [ + "buick century", + "USA" + ], + [ + "oldsmobile cutlass ls", + "USA" + ], + [ + "ford granada gl", + "USA" + ], + [ + "chrysler lebaron salon", + "USA" + ], + [ + "chevrolet cavalier", + "USA" + ], + [ + "chevrolet cavalier wagon", + "USA" + ], + [ + "chevrolet cavalier 2-door", + "USA" + ], + [ + "pontiac j2000 se hatchback", + "USA" + ], + [ + "dodge aries se", + "USA" + ], + [ + "pontiac phoenix", + "USA" + ], + [ + "ford fairmont futura", + "USA" + ], + [ + "amc concord dl", + "USA" + ], + [ + "plymouth horizon miser", + "USA" + ], + [ + "mercury lynx l", + "USA" + ], + [ + "buick century limited", + "USA" + ], + [ + "oldsmobile cutlass ciera (diesel)", + "USA" + ], + [ + "chrysler lebaron medallion", + "USA" + ], + [ + "ford granada l", + "USA" + ], + [ + "dodge charger 2.2", + "USA" + ], + [ + "chevrolet camaro", + "USA" + ], + [ + "ford mustang gl", + "USA" + ], + [ + "dodge rampage", + "USA" + ], + [ + "ford ranger", + "USA" + ], + [ + "chevy s-10", + "USA" + ] + ], + "hovertemplate": "Origin=%{customdata[1]}
Horsepower=%{x}
Miles_per_Gallon=%{y}
Name=%{customdata[0]}", + "legendgroup": "USA", + "marker": { + "color": "#636efa", + "symbol": "circle" + }, + "mode": "markers", + "name": "USA", + "orientation": "v", + "showlegend": true, + "type": "scatter", + "x": [ + 130, + 165, + 150, + 150, + 140, + 198, + 220, + 215, + 225, + 190, + 165, + 153, + 175, + 175, + 170, + 160, + 140, + 150, + 225, + 95, + 97, + 85, + 90, + 215, + 200, + 210, + 193, + 90, + null, + 100, + 105, + 100, + 88, + 100, + 165, + 175, + 153, + 150, + 180, + 170, + 175, + 110, + 72, + 100, + 88, + 86, + 70, + 80, + 90, + 86, + 165, + 175, + 150, + 153, + 150, + 208, + 155, + 160, + 190, + 150, + 130, + 140, + 150, + 86, + 80, + 175, + 150, + 145, + 137, + 150, + 198, + 150, + 158, + 150, + 215, + 225, + 175, + 105, + 100, + 100, + 88, + 95, + 150, + 167, + 170, + 180, + 100, + 72, + 85, + 107, + 145, + 230, + 150, + 180, + 95, + null, + 100, + 100, + 80, + 75, + 100, + 110, + 105, + 140, + 150, + 150, + 140, + 150, + 75, + 95, + 105, + 72, + 72, + 170, + 145, + 150, + 148, + 110, + 105, + 110, + 95, + 110, + 110, + 129, + 83, + 100, + 78, + 97, + 90, + 92, + 79, + 140, + 150, + 120, + 152, + 100, + 105, + 81, + 90, + 52, + 60, + 100, + 78, + 110, + 95, + 72, + 150, + 180, + 145, + 130, + 150, + 80, + 96, + 145, + 110, + 145, + 130, + 110, + 105, + 100, + 98, + 180, + 170, + 190, + 149, + 88, + 89, + 63, + 83, + 66, + 110, + 140, + 139, + 105, + 95, + 85, + 88, + 100, + 90, + 105, + 85, + 110, + 120, + 145, + 165, + 139, + 140, + 68, + 75, + 105, + 85, + 115, + 85, + 88, + 90, + 110, + 130, + 129, + 138, + 135, + 155, + 142, + 125, + 150, + 80, + 80, + 125, + 90, + 70, + 70, + 90, + 115, + 115, + 90, + 70, + 90, + 88, + 90, + 90, + 105, + null, + 84, + 84, + 92, + 110, + 84, + 64, + 63, + 65, + 65, + 110, + 105, + 88, + 85, + 88, + 88, + 88, + 85, + 84, + 90, + 92, + null, + 63, + 70, + 110, + 85, + 92, + 112, + 84, + 90, + 86, + 84, + 79, + 82 + ], + "xaxis": "x", + "y": [ + 18, + 15, + 18, + 16, + 17, + 15, + 14, + 14, + 14, + 15, + null, + null, + null, + null, + 15, + 14, + null, + 15, + 14, + 22, + 18, + 21, + 21, + 10, + 10, + 11, + 9, + 28, + 25, + 19, + 16, + 17, + 19, + 18, + 14, + 14, + 14, + 14, + 12, + 13, + 13, + 18, + 22, + 19, + 18, + 23, + 26, + 25, + 20, + 21, + 13, + 14, + 15, + 14, + 17, + 11, + 13, + 12, + 13, + 15, + 13, + 13, + 14, + 22, + 28, + 13, + 14, + 13, + 14, + 15, + 12, + 13, + 13, + 14, + 13, + 12, + 13, + 18, + 16, + 18, + 18, + 23, + 11, + 12, + 13, + 12, + 18, + 21, + 19, + 21, + 15, + 16, + 15, + 11, + 20, + 21, + 19, + 15, + 26, + 25, + 16, + 16, + 18, + 16, + 13, + 14, + 14, + 14, + 28, + 19, + 18, + 15, + 15, + 16, + 15, + 16, + 14, + 17, + 16, + 15, + 18, + 21, + 20, + 13, + 23, + 20, + 23, + 18, + 19, + 25, + 26, + 17.5, + 16, + 15.5, + 14.5, + 22, + 22, + 24, + 22.5, + 29, + 24.5, + 20, + 18, + 18.5, + 17.5, + 26.5, + 13, + 16.5, + 13, + 13, + 13, + 30, + 25.5, + 17.5, + 17, + 15.5, + 15, + 17.5, + 20.5, + 19, + 18.5, + 16, + 15.5, + 15.5, + 16, + 24.5, + 25.5, + 30.5, + 33.5, + 36.1, + 19.9, + 19.4, + 20.2, + 19.2, + 20.5, + 20.2, + 25.1, + 20.5, + 19.4, + 20.6, + 20.8, + 18.6, + 18.1, + 19.2, + 17.7, + 18.1, + 17.5, + 30, + 30.9, + 23.2, + 23.8, + 21.5, + 19.8, + 22.3, + 20.2, + 20.6, + 17, + 17.6, + 16.5, + 18.2, + 16.9, + 15.5, + 19.2, + 18.5, + 35.7, + 27.4, + 23, + 23.9, + 34.2, + 34.5, + 28.4, + 28.8, + 26.8, + 33.5, + 32.1, + 28, + 26.4, + 24.3, + 19.1, + 27.9, + 23.6, + 27.2, + 26.6, + 25.8, + 23.5, + 30, + 39, + 34.7, + 34.4, + 29.9, + 22.4, + 26.6, + 20.2, + 17.6, + 28, + 27, + 34, + 31, + 29, + 27, + 24, + 23, + 38, + 36, + 25, + 38, + 26, + 22, + 36, + 27, + 27, + 32, + 28, + 31 + ], + "yaxis": "y" + }, + { + "customdata": [ + [ + "citroen ds-21 pallas", + "Europe" + ], + [ + "volkswagen 1131 deluxe sedan", + "Europe" + ], + [ + "peugeot 504", + "Europe" + ], + [ + "audi 100 ls", + "Europe" + ], + [ + "saab 99e", + "Europe" + ], + [ + "bmw 2002", + "Europe" + ], + [ + "volkswagen super beetle 117", + "Europe" + ], + [ + "opel 1900", + "Europe" + ], + [ + "peugeot 304", + "Europe" + ], + [ + "fiat 124b", + "Europe" + ], + [ + "volkswagen model 111", + "Europe" + ], + [ + "volkswagen type 3", + "Europe" + ], + [ + "volvo 145e (sw)", + "Europe" + ], + [ + "volkswagen 411 (sw)", + "Europe" + ], + [ + "peugeot 504 (sw)", + "Europe" + ], + [ + "renault 12 (sw)", + "Europe" + ], + [ + "volkswagen super beetle", + "Europe" + ], + [ + "fiat 124 sport coupe", + "Europe" + ], + [ + "fiat 128", + "Europe" + ], + [ + "opel manta", + "Europe" + ], + [ + "audi 100ls", + "Europe" + ], + [ + "volvo 144ea", + "Europe" + ], + [ + "saab 99le", + "Europe" + ], + [ + "audi fox", + "Europe" + ], + [ + "volkswagen dasher", + "Europe" + ], + [ + "opel manta", + "Europe" + ], + [ + "fiat 128", + "Europe" + ], + [ + "fiat 124 tc", + "Europe" + ], + [ + "fiat x1.9", + "Europe" + ], + [ + "volkswagen dasher", + "Europe" + ], + [ + "volkswagen rabbit", + "Europe" + ], + [ + "audi 100ls", + "Europe" + ], + [ + "peugeot 504", + "Europe" + ], + [ + "volvo 244dl", + "Europe" + ], + [ + "saab 99le", + "Europe" + ], + [ + "fiat 131", + "Europe" + ], + [ + "opel 1900", + "Europe" + ], + [ + "renault 12tl", + "Europe" + ], + [ + "vw rabbit", + "Europe" + ], + [ + "volkswagen rabbit", + "Europe" + ], + [ + "volvo 245", + "Europe" + ], + [ + "peugeot 504", + "Europe" + ], + [ + "mercedes-benz 280s", + "Europe" + ], + [ + "renault 5 gtl", + "Europe" + ], + [ + "volkswagen rabbit custom", + "Europe" + ], + [ + "volkswagen dasher", + "Europe" + ], + [ + "bmw 320i", + "Europe" + ], + [ + "volkswagen rabbit custom diesel", + "Europe" + ], + [ + "audi 5000", + "Europe" + ], + [ + "volvo 264gl", + "Europe" + ], + [ + "saab 99gle", + "Europe" + ], + [ + "peugeot 604sl", + "Europe" + ], + [ + "volkswagen scirocco", + "Europe" + ], + [ + "vw rabbit custom", + "Europe" + ], + [ + "mercedes benz 300d", + "Europe" + ], + [ + "peugeot 504", + "Europe" + ], + [ + "fiat strada custom", + "Europe" + ], + [ + "vw rabbit", + "Europe" + ], + [ + "audi 4000", + "Europe" + ], + [ + "vw rabbit c (diesel)", + "Europe" + ], + [ + "vw dasher (diesel)", + "Europe" + ], + [ + "audi 5000s (diesel)", + "Europe" + ], + [ + "mercedes-benz 240d", + "Europe" + ], + [ + "renault lecar deluxe", + "Europe" + ], + [ + "vokswagen rabbit", + "Europe" + ], + [ + "triumph tr7 coupe", + "Europe" + ], + [ + "volkswagen jetta", + "Europe" + ], + [ + "renault 18i", + "Europe" + ], + [ + "peugeot 505s turbo diesel", + "Europe" + ], + [ + "saab 900s", + "Europe" + ], + [ + "volvo diesel", + "Europe" + ], + [ + "volkswagen rabbit l", + "Europe" + ], + [ + "vw pickup", + "Europe" + ] + ], + "hovertemplate": "Origin=%{customdata[1]}
Horsepower=%{x}
Miles_per_Gallon=%{y}
Name=%{customdata[0]}", + "legendgroup": "Europe", + "marker": { + "color": "#EF553B", + "symbol": "circle" + }, + "mode": "markers", + "name": "Europe", + "orientation": "v", + "showlegend": true, + "type": "scatter", + "x": [ + 115, + 46, + 87, + 90, + 95, + 113, + 48, + 90, + 70, + 76, + 60, + 54, + 112, + 76, + 87, + 69, + 46, + 90, + 49, + 75, + 91, + 112, + 110, + 83, + 67, + 78, + 75, + 75, + 67, + 71, + 70, + 95, + 88, + 98, + 115, + 86, + 81, + 83, + 70, + 71, + 102, + 88, + 120, + 58, + 78, + 78, + 110, + 48, + 103, + 125, + 115, + 133, + 71, + 71, + 77, + 71, + 69, + 76, + 78, + 48, + 48, + 67, + 67, + null, + 62, + 88, + 74, + null, + 80, + 110, + 76, + 74, + 52 + ], + "xaxis": "x", + "y": [ + null, + 26, + 25, + 24, + 25, + 26, + null, + 28, + 30, + 30, + 27, + 23, + 18, + 22, + 21, + 26, + 26, + 26, + 29, + 24, + 20, + 19, + 24, + 29, + 26, + 26, + 24, + 26, + 31, + 25, + 29, + 23, + 23, + 22, + 25, + 28, + 25, + 27, + 29, + 29.5, + 20, + 19, + 16.5, + 36, + 29, + 30.5, + 21.5, + 43.1, + 20.3, + 17, + 21.6, + 16.2, + 31.5, + 31.9, + 25.4, + 27.2, + 37.3, + 41.5, + 34.3, + 44.3, + 43.4, + 36.4, + 30, + 40.9, + 29.8, + 35, + 33, + 34.5, + 28.1, + null, + 30.7, + 36, + 44 + ], + "yaxis": "y" + }, + { + "customdata": [ + [ + "toyota corona mark ii", + "Japan" + ], + [ + "datsun pl510", + "Japan" + ], + [ + "datsun pl510", + "Japan" + ], + [ + "toyota corona", + "Japan" + ], + [ + "toyota corolla 1200", + "Japan" + ], + [ + "datsun 1200", + "Japan" + ], + [ + "toyota corona hardtop", + "Japan" + ], + [ + "mazda rx2 coupe", + "Japan" + ], + [ + "datsun 510 (sw)", + "Japan" + ], + [ + "toyouta corona mark ii (sw)", + "Japan" + ], + [ + "toyota corolla 1600 (sw)", + "Japan" + ], + [ + "toyota carina", + "Japan" + ], + [ + "datsun 610", + "Japan" + ], + [ + "maxda rx3", + "Japan" + ], + [ + "toyota mark ii", + "Japan" + ], + [ + "datsun b210", + "Japan" + ], + [ + "toyota corolla 1200", + "Japan" + ], + [ + "toyota corona", + "Japan" + ], + [ + "datsun 710", + "Japan" + ], + [ + "honda civic", + "Japan" + ], + [ + "subaru", + "Japan" + ], + [ + "toyota corolla", + "Japan" + ], + [ + "toyota corona", + "Japan" + ], + [ + "datsun 710", + "Japan" + ], + [ + "honda civic cvcc", + "Japan" + ], + [ + "honda civic", + "Japan" + ], + [ + "datsun b-210", + "Japan" + ], + [ + "toyota corolla", + "Japan" + ], + [ + "toyota mark ii", + "Japan" + ], + [ + "honda Accelerationord cvcc", + "Japan" + ], + [ + "datsun f-10 hatchback", + "Japan" + ], + [ + "toyota corolla liftback", + "Japan" + ], + [ + "subaru dl", + "Japan" + ], + [ + "datsun 810", + "Japan" + ], + [ + "mazda rx-4", + "Japan" + ], + [ + "mazda glc deluxe", + "Japan" + ], + [ + "datsun b210 gx", + "Japan" + ], + [ + "honda civic cvcc", + "Japan" + ], + [ + "toyota corona", + "Japan" + ], + [ + "datsun 510", + "Japan" + ], + [ + "toyota celica gt liftback", + "Japan" + ], + [ + "datsun 200-sx", + "Japan" + ], + [ + "honda Accelerationord lx", + "Japan" + ], + [ + "maxda glc deluxe", + "Japan" + ], + [ + "datsun 210", + "Japan" + ], + [ + "toyota corolla tercel", + "Japan" + ], + [ + "datsun 310", + "Japan" + ], + [ + "toyota corona liftback", + "Japan" + ], + [ + "mazda 626", + "Japan" + ], + [ + "datsun 510 hatchback", + "Japan" + ], + [ + "toyota corolla", + "Japan" + ], + [ + "mazda glc", + "Japan" + ], + [ + "datsun 210", + "Japan" + ], + [ + "honda civic 1500 gl", + "Japan" + ], + [ + "subaru dl", + "Japan" + ], + [ + "datsun 280-zx", + "Japan" + ], + [ + "mazda rx-7 gs", + "Japan" + ], + [ + "honda Accelerationord", + "Japan" + ], + [ + "toyota starlet", + "Japan" + ], + [ + "honda civic 1300", + "Japan" + ], + [ + "subaru", + "Japan" + ], + [ + "datsun 210", + "Japan" + ], + [ + "toyota tercel", + "Japan" + ], + [ + "mazda glc 4", + "Japan" + ], + [ + "honda prelude", + "Japan" + ], + [ + "toyota corolla", + "Japan" + ], + [ + "datsun 200sx", + "Japan" + ], + [ + "mazda 626", + "Japan" + ], + [ + "toyota cressida", + "Japan" + ], + [ + "datsun 810 maxima", + "Japan" + ], + [ + "mazda glc custom l", + "Japan" + ], + [ + "mazda glc custom", + "Japan" + ], + [ + "nissan stanza xe", + "Japan" + ], + [ + "honda Accelerationord", + "Japan" + ], + [ + "toyota corolla", + "Japan" + ], + [ + "honda civic", + "Japan" + ], + [ + "honda civic (auto)", + "Japan" + ], + [ + "datsun 310 gx", + "Japan" + ], + [ + "toyota celica gt", + "Japan" + ] + ], + "hovertemplate": "Origin=%{customdata[1]}
Horsepower=%{x}
Miles_per_Gallon=%{y}
Name=%{customdata[0]}", + "legendgroup": "Japan", + "marker": { + "color": "#00cc96", + "symbol": "circle" + }, + "mode": "markers", + "name": "Japan", + "orientation": "v", + "showlegend": true, + "type": "scatter", + "x": [ + 95, + 88, + 88, + 95, + 65, + 69, + 95, + 97, + 92, + 97, + 88, + 88, + 94, + 90, + 122, + 67, + 65, + 52, + 61, + 97, + 93, + 75, + 96, + 97, + 53, + 53, + 70, + 75, + 108, + 68, + 70, + 75, + 67, + 97, + 110, + 52, + 70, + 60, + 95, + 97, + 95, + 97, + 68, + 65, + 65, + 60, + 65, + 90, + 75, + 92, + 75, + 65, + 65, + 67, + 67, + 132, + 100, + 72, + 58, + 60, + 67, + 65, + 62, + 68, + 75, + 75, + 100, + 74, + 116, + 120, + 68, + 68, + 88, + 75, + 70, + 67, + 67, + 67, + 96 + ], + "xaxis": "x", + "y": [ + 24, + 27, + 27, + 25, + 31, + 35, + 24, + 19, + 28, + 23, + 27, + 20, + 22, + 18, + 20, + 31, + 32, + 31, + 32, + 24, + 26, + 29, + 24, + 24, + 33, + 33, + 32, + 28, + 19, + 31.5, + 33.5, + 26, + 30, + 22, + 21.5, + 32.8, + 39.4, + 36.1, + 27.5, + 27.2, + 21.1, + 23.9, + 29.5, + 34.1, + 31.8, + 38.1, + 37.2, + 29.8, + 31.3, + 37, + 32.2, + 46.6, + 40.8, + 44.6, + 33.8, + 32.7, + 23.7, + 32.4, + 39.1, + 35.1, + 32.3, + 37, + 37.7, + 34.1, + 33.7, + 32.4, + 32.9, + 31.6, + 25.4, + 24.2, + 37, + 31, + 36, + 36, + 34, + 38, + 32, + 38, + 32 + ], + "yaxis": "y" + } + ], + "layout": { + "legend": { + "title": { + "text": "Origin" + }, + "tracegroupgap": 0 + }, + "margin": { + "t": 60 + }, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Horsepower" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Miles_per_Gallon" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig = px.scatter(\n", " cars,\n", @@ -218,9 +5517,108 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
total_billtipsexsmokerdaytimesize
016.991.01FemaleNoSunDinner2
110.341.66MaleNoSunDinner3
221.013.50MaleNoSunDinner3
323.683.31MaleNoSunDinner2
424.593.61FemaleNoSunDinner4
\n", + "
" + ], + "text/plain": [ + " total_bill tip sex smoker day time size\n", + "0 16.99 1.01 Female No Sun Dinner 2\n", + "1 10.34 1.66 Male No Sun Dinner 3\n", + "2 21.01 3.50 Male No Sun Dinner 3\n", + "3 23.68 3.31 Male No Sun Dinner 2\n", + "4 24.59 3.61 Female No Sun Dinner 4" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df = sns.load_dataset(\"tips\")\n", "df.head()" @@ -239,9 +5637,1511 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:19.078871\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, axes = plt.subplots(nrows=1, ncols=4, sharex=True, sharey=True)\n", "flat_axes = axes.flatten() # Not needed with 1 row or 1 col, but good to be aware of\n", @@ -276,9 +7176,1456 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:19.259104\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# This part just to get some colours\n", "colormap = plt.cm.Dark2\n", @@ -314,9 +8661,978 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:19.519859\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "axd = plt.figure(constrained_layout=True).subplot_mosaic(\n", " \"\"\"\n", @@ -341,9 +9657,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": { + "image/png": { + "height": 378.25, + "width": 589.9 + } + }, + "output_type": "execute_result" + } + ], "source": [ "(\n", " so.Plot(df, x=\"tip\", y=\"total_bill\", color=\"day\")\n", @@ -361,9 +9694,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": { + "image/png": { + "height": 378.25, + "width": 589.9 + } + }, + "output_type": "execute_result" + } + ], "source": [ "(\n", " so.Plot(df, x=\"tip\", y=\"total_bill\", color=\"day\")\n", @@ -381,9 +9731,65 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "(\n", " ggplot(df, aes(x=\"tip\", y=\"total_bill\", color=\"smoker\"))\n", @@ -402,9 +9808,77 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "alt.Chart(df).mark_point().encode(\n", " x=\"tip:Q\",\n", @@ -426,9 +9900,1714 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "smoker=No
day=Sun
tip=%{x}
total_bill=%{y}", + "legendgroup": "No", + "marker": { + "color": "#636efa", + "symbol": "circle" + }, + "mode": "markers", + "name": "No", + "orientation": "v", + "showlegend": true, + "type": "scatter", + "x": [ + 1.01, + 1.66, + 3.5, + 3.31, + 3.61, + 4.71, + 2, + 3.12, + 1.96, + 3.23, + 1.71, + 5, + 1.57, + 3, + 3.02, + 3.92, + 1.67, + 3.71, + 3.5, + 2.54, + 3.06, + 1.32, + 5.6, + 3, + 5, + 6, + 2.05, + 3, + 2.5, + 2.6, + 5.2, + 1.56, + 4.34, + 3.51, + 4, + 2.55, + 4, + 3.5, + 5.07, + 2.5, + 2, + 2.74, + 2, + 2, + 5.14, + 5, + 3.75, + 2.61, + 2, + 3.5, + 2.5, + 2, + 2, + 3.48, + 2.24, + 4.5, + 5 + ], + "xaxis": "x5", + "y": [ + 16.99, + 10.34, + 21.01, + 23.68, + 24.59, + 25.29, + 8.77, + 26.88, + 15.04, + 14.78, + 10.27, + 35.26, + 15.42, + 18.43, + 14.83, + 21.58, + 10.33, + 16.29, + 16.97, + 17.46, + 13.94, + 9.68, + 30.4, + 18.29, + 22.23, + 32.4, + 28.55, + 18.04, + 12.54, + 10.29, + 34.81, + 9.94, + 25.56, + 19.49, + 38.07, + 23.95, + 25.71, + 17.31, + 29.93, + 14.07, + 13.13, + 17.26, + 24.55, + 19.77, + 29.85, + 48.17, + 25, + 13.39, + 16.49, + 21.5, + 12.66, + 16.21, + 13.81, + 24.52, + 20.76, + 31.71, + 20.69 + ], + "yaxis": "y5" + }, + { + "hovertemplate": "smoker=No
day=Sat
tip=%{x}
total_bill=%{y}", + "legendgroup": "No", + "marker": { + "color": "#636efa", + "symbol": "circle" + }, + "mode": "markers", + "name": "No", + "orientation": "v", + "showlegend": false, + "type": "scatter", + "x": [ + 3.35, + 4.08, + 2.75, + 2.23, + 7.58, + 3.18, + 2.34, + 2, + 2, + 4.3, + 3, + 1.45, + 2.5, + 3, + 2.45, + 3.27, + 3.6, + 2, + 3.07, + 2.31, + 5, + 2.24, + 1.5, + 6.73, + 2.64, + 3.15, + 2.47, + 2.01, + 1.97, + 3, + 2.2, + 1.25, + 4.08, + 3.76, + 3, + 1, + 9, + 3, + 2.72, + 3.39, + 1.47, + 1.25, + 4.67, + 5.92, + 1.75 + ], + "xaxis": "x6", + "y": [ + 20.65, + 17.92, + 20.29, + 15.77, + 39.42, + 19.82, + 17.81, + 13.37, + 12.69, + 21.7, + 19.65, + 9.55, + 18.35, + 15.06, + 20.69, + 17.78, + 24.06, + 16.31, + 16.93, + 18.69, + 31.27, + 16.04, + 26.41, + 48.27, + 17.59, + 20.08, + 16.45, + 20.23, + 12.02, + 17.07, + 14.73, + 10.51, + 20.92, + 18.24, + 14, + 7.25, + 48.33, + 20.45, + 13.28, + 11.61, + 10.77, + 10.07, + 35.83, + 29.03, + 17.82 + ], + "yaxis": "y6" + }, + { + "hovertemplate": "smoker=No
day=Thur
tip=%{x}
total_bill=%{y}", + "legendgroup": "No", + "marker": { + "color": "#636efa", + "symbol": "circle" + }, + "mode": "markers", + "name": "No", + "orientation": "v", + "showlegend": false, + "type": "scatter", + "x": [ + 4, + 3, + 2.71, + 3.4, + 1.83, + 2.03, + 5.17, + 2, + 4, + 5.85, + 3, + 1.5, + 1.8, + 2.92, + 2.31, + 1.68, + 2.5, + 2, + 2.52, + 4.2, + 1.48, + 2, + 2, + 2.18, + 1.5, + 2.83, + 1.5, + 2, + 3.25, + 1.25, + 2, + 2, + 2.75, + 3.5, + 6.7, + 5, + 5, + 2.3, + 1.5, + 1.36, + 1.63, + 1.73, + 2, + 1.44, + 3 + ], + "xaxis": "x7", + "y": [ + 27.2, + 22.76, + 17.29, + 16.66, + 10.07, + 15.98, + 34.83, + 13.03, + 18.28, + 24.71, + 21.16, + 10.65, + 12.43, + 24.08, + 11.69, + 13.42, + 14.26, + 15.95, + 12.48, + 29.8, + 8.52, + 14.52, + 11.38, + 22.82, + 19.08, + 20.27, + 11.17, + 12.26, + 18.26, + 8.51, + 10.33, + 14.15, + 13.16, + 17.47, + 34.3, + 41.19, + 27.05, + 16.43, + 8.35, + 18.64, + 11.87, + 9.78, + 7.51, + 7.56, + 18.78 + ], + "yaxis": "y7" + }, + { + "hovertemplate": "smoker=No
day=Fri
tip=%{x}
total_bill=%{y}", + "legendgroup": "No", + "marker": { + "color": "#636efa", + "symbol": "circle" + }, + "mode": "markers", + "name": "No", + "orientation": "v", + "showlegend": false, + "type": "scatter", + "x": [ + 3.5, + 3.25, + 1.5, + 3 + ], + "xaxis": "x8", + "y": [ + 22.49, + 22.75, + 12.46, + 15.98 + ], + "yaxis": "y8" + }, + { + "hovertemplate": "smoker=Yes
day=Sun
tip=%{x}
total_bill=%{y}", + "legendgroup": "Yes", + "marker": { + "color": "#EF553B", + "symbol": "circle" + }, + "mode": "markers", + "name": "Yes", + "orientation": "v", + "showlegend": true, + "type": "scatter", + "x": [ + 3, + 5.15, + 3.18, + 4, + 3.11, + 2, + 2, + 4, + 3.55, + 3.68, + 5.65, + 3.5, + 6.5, + 3, + 3.5, + 2, + 3.5, + 4, + 1.5 + ], + "xaxis": "x", + "y": [ + 17.51, + 7.25, + 31.85, + 16.82, + 32.9, + 17.89, + 14.48, + 9.6, + 34.63, + 34.65, + 23.33, + 45.35, + 23.17, + 40.55, + 20.9, + 30.46, + 18.15, + 23.1, + 15.69 + ], + "yaxis": "y" + }, + { + "hovertemplate": "smoker=Yes
day=Sat
tip=%{x}
total_bill=%{y}", + "legendgroup": "Yes", + "marker": { + "color": "#EF553B", + "symbol": "circle" + }, + "mode": "markers", + "name": "Yes", + "orientation": "v", + "showlegend": false, + "type": "scatter", + "x": [ + 3, + 1.76, + 3.21, + 2, + 1.98, + 3.76, + 1, + 2.09, + 3.14, + 5, + 3.08, + 2.5, + 3.48, + 1.64, + 4.06, + 4.29, + 4, + 1.61, + 2, + 10, + 3.16, + 3.41, + 3, + 2.03, + 2.23, + 2, + 5.16, + 2.5, + 6.5, + 1.1, + 3, + 1.5, + 1.44, + 3.09, + 2.88, + 2, + 3, + 3, + 1, + 1.17, + 2, + 2 + ], + "xaxis": "x2", + "y": [ + 38.01, + 11.24, + 20.29, + 13.81, + 11.02, + 18.29, + 3.07, + 15.01, + 26.86, + 25.28, + 17.92, + 44.3, + 22.42, + 15.36, + 20.49, + 25.21, + 14.31, + 10.59, + 10.63, + 50.81, + 15.81, + 26.59, + 38.73, + 24.27, + 12.76, + 30.06, + 25.89, + 13.27, + 28.17, + 12.9, + 28.15, + 11.59, + 7.74, + 30.14, + 22.12, + 24.01, + 15.69, + 15.53, + 12.6, + 32.83, + 27.18, + 22.67 + ], + "yaxis": "y2" + }, + { + "hovertemplate": "smoker=Yes
day=Thur
tip=%{x}
total_bill=%{y}", + "legendgroup": "Yes", + "marker": { + "color": "#EF553B", + "symbol": "circle" + }, + "mode": "markers", + "name": "Yes", + "orientation": "v", + "showlegend": false, + "type": "scatter", + "x": [ + 3, + 5, + 2, + 4.19, + 2.56, + 2.02, + 4, + 2, + 5, + 2, + 2, + 4, + 2.01, + 2, + 2.5, + 4, + 3.23 + ], + "xaxis": "x3", + "y": [ + 19.44, + 32.68, + 16, + 19.81, + 28.44, + 15.48, + 16.58, + 10.34, + 43.11, + 13, + 13.51, + 18.71, + 12.74, + 13, + 16.4, + 20.53, + 16.47 + ], + "yaxis": "y3" + }, + { + "hovertemplate": "smoker=Yes
day=Fri
tip=%{x}
total_bill=%{y}", + "legendgroup": "Yes", + "marker": { + "color": "#EF553B", + "symbol": "circle" + }, + "mode": "markers", + "name": "Yes", + "orientation": "v", + "showlegend": false, + "type": "scatter", + "x": [ + 3, + 1, + 4.3, + 4.73, + 4, + 1.5, + 3, + 2.5, + 3, + 2.2, + 3.48, + 1.92, + 1.58, + 2.5, + 2 + ], + "xaxis": "x4", + "y": [ + 28.97, + 5.75, + 16.32, + 40.17, + 27.28, + 12.03, + 21.01, + 11.35, + 15.38, + 12.16, + 13.42, + 8.58, + 13.42, + 16.27, + 10.09 + ], + "yaxis": "y4" + } + ], + "layout": { + "annotations": [ + { + "font": {}, + "showarrow": false, + "text": "day=Sun", + "x": 0.11499999999999999, + "xanchor": "center", + "xref": "paper", + "y": 1, + "yanchor": "bottom", + "yref": "paper" + }, + { + "font": {}, + "showarrow": false, + "text": "day=Sat", + "x": 0.365, + "xanchor": "center", + "xref": "paper", + "y": 1, + "yanchor": "bottom", + "yref": "paper" + }, + { + "font": {}, + "showarrow": false, + "text": "day=Thur", + "x": 0.615, + "xanchor": "center", + "xref": "paper", + "y": 1, + "yanchor": "bottom", + "yref": "paper" + }, + { + "font": {}, + "showarrow": false, + "text": "day=Fri", + "x": 0.865, + "xanchor": "center", + "xref": "paper", + "y": 1, + "yanchor": "bottom", + "yref": "paper" + }, + { + "font": {}, + "showarrow": false, + "text": "smoker=Yes", + "textangle": 90, + "x": 0.98, + "xanchor": "left", + "xref": "paper", + "y": 0.2425, + "yanchor": "middle", + "yref": "paper" + }, + { + "font": {}, + "showarrow": false, + "text": "smoker=No", + "textangle": 90, + "x": 0.98, + "xanchor": "left", + "xref": "paper", + "y": 0.7575000000000001, + "yanchor": "middle", + "yref": "paper" + } + ], + "legend": { + "title": { + "text": "smoker" + }, + "tracegroupgap": 0 + }, + "margin": { + "t": 60 + }, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 0.22999999999999998 + ], + "title": { + "text": "tip" + } + }, + "xaxis2": { + "anchor": "y2", + "domain": [ + 0.24999999999999997, + 0.48 + ], + "matches": "x", + "title": { + "text": "tip" + } + }, + "xaxis3": { + "anchor": "y3", + "domain": [ + 0.49999999999999994, + 0.73 + ], + "matches": "x", + "title": { + "text": "tip" + } + }, + "xaxis4": { + "anchor": "y4", + "domain": [ + 0.75, + 0.98 + ], + "matches": "x", + "title": { + "text": "tip" + } + }, + "xaxis5": { + "anchor": "y5", + "domain": [ + 0, + 0.22999999999999998 + ], + "matches": "x", + "showticklabels": false + }, + "xaxis6": { + "anchor": "y6", + "domain": [ + 0.24999999999999997, + 0.48 + ], + "matches": "x", + "showticklabels": false + }, + "xaxis7": { + "anchor": "y7", + "domain": [ + 0.49999999999999994, + 0.73 + ], + "matches": "x", + "showticklabels": false + }, + "xaxis8": { + "anchor": "y8", + "domain": [ + 0.75, + 0.98 + ], + "matches": "x", + "showticklabels": false + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 0.485 + ], + "title": { + "text": "total_bill" + } + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0, + 0.485 + ], + "matches": "y", + "showticklabels": false + }, + "yaxis3": { + "anchor": "x3", + "domain": [ + 0, + 0.485 + ], + "matches": "y", + "showticklabels": false + }, + "yaxis4": { + "anchor": "x4", + "domain": [ + 0, + 0.485 + ], + "matches": "y", + "showticklabels": false + }, + "yaxis5": { + "anchor": "x5", + "domain": [ + 0.515, + 1 + ], + "matches": "y", + "title": { + "text": "total_bill" + } + }, + "yaxis6": { + "anchor": "x6", + "domain": [ + 0.515, + 1 + ], + "matches": "y", + "showticklabels": false + }, + "yaxis7": { + "anchor": "x7", + "domain": [ + 0.515, + 1 + ], + "matches": "y", + "showticklabels": false + }, + "yaxis8": { + "anchor": "x8", + "domain": [ + 0.515, + 1 + ], + "matches": "y", + "showticklabels": false + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig = px.scatter(\n", " df, x=\"tip\", y=\"total_bill\", color=\"smoker\", facet_row=\"smoker\", facet_col=\"day\"\n", @@ -447,9 +11626,98 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 137, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
VacanciesUnemploymentLabourForceYear
DATE
2001-01-013.0282394.741667143768.9166672001
2002-01-012.3872545.783333144856.0833332002
2003-01-012.2122375.991667146499.5000002003
2004-01-012.4702095.541667147379.5833332004
2005-01-012.7533255.083333149289.1666672005
\n", + "
" + ], + "text/plain": [ + " Vacancies Unemployment LabourForce Year\n", + "DATE \n", + "2001-01-01 3.028239 4.741667 143768.916667 2001\n", + "2002-01-01 2.387254 5.783333 144856.083333 2002\n", + "2003-01-01 2.212237 5.991667 146499.500000 2003\n", + "2004-01-01 2.470209 5.541667 147379.583333 2004\n", + "2005-01-01 2.753325 5.083333 149289.166667 2005" + ] + }, + "execution_count": 137, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import pandas_datareader.data as web\n", "import datetime\n", @@ -484,9 +11752,1332 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:21.493062\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.close(\"all\")\n", "fig, ax = plt.subplots()\n", @@ -537,9 +13128,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": { + "image/png": { + "height": 378.25, + "width": 509.15 + } + }, + "output_type": "execute_result" + } + ], "source": [ "(\n", " so.Plot(df, x=\"Unemployment\", y=\"Vacancies\")\n", @@ -556,14 +13164,113 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Lets-Plot" + "### Lets-Plot\n", + "\n", + "You can also use `geom_curve()` in place of `geom_segment()` below to get curved lines instead of straight lines." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 139, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 139, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# This is a convencience and creates a dataframe of the form\n", "# Vacancies_from\tUnemployment_from\tLabourForce_from\tYear_from\tVacancies_to\tUnemployment_to\tLabourForce_to\tYear_to\n", @@ -588,7 +13295,8 @@ " data=path_df,\n", " size=1,\n", " color=\"gray\",\n", - " arrow=arrow(type=\"closed\", length=20, angle=15),\n", + " arrow=arrow(type=\"closed\", length=15, angle=15),\n", + " spacer=5+1 # Avoids arrowheads being sunk into points (+1 as circles are size 1)\n", " )\n", " + geom_point(shape=21, color=\"gray\", fill=\"#c28dc3\", size=5)\n", " + geom_text(\n", @@ -619,9 +13327,5515 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:21.735285\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots()\n", "scat = ax.scatter(\n", @@ -648,9 +18862,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": { + "image/png": { + "height": 378.25, + "width": 612 + } + }, + "output_type": "execute_result" + } + ], "source": [ "(\n", " so.Plot(cars, x=\"Horsepower\", y=\"Miles_per_Gallon\", pointsize=\"Displacement\").add(\n", @@ -668,9 +18899,61 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "(\n", " ggplot(cars, aes(x=\"Horsepower\", y=\"Miles_per_Gallon\", size=\"Displacement\"))\n", @@ -687,9 +18970,77 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "alt.Chart(cars).mark_circle().encode(\n", " x=\"Horsepower\", y=\"Miles_per_Gallon\", size=\"Displacement\"\n", @@ -705,9 +19056,2181 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "Displacement_Size=(261.5, 358.25]
Horsepower=%{x}
Miles_per_Gallon=%{y}
Displacement=%{marker.size}", + "legendgroup": "(261.5, 358.25]", + "marker": { + "color": "#636efa", + "size": [ + 307, + 350, + 318, + 304, + 302, + 350, + 351, + 340, + 302, + 307, + 318, + 304, + 350, + 351, + 318, + 350, + 318, + 351, + 304, + 350, + 350, + 304, + 307, + 302, + 318, + 350, + 304, + 350, + 302, + 318, + 351, + 318, + 350, + 350, + 318, + 350, + 302, + 350, + 318, + 302, + 304, + 350, + 318, + 351, + 262, + 302, + 305, + 318, + 304, + 351, + 318, + 350, + 350, + 302, + 318, + 305, + 318, + 302, + 350, + 351, + 318, + 302, + 305, + 302, + 318, + 305, + 302, + 351, + 318, + 350, + 351, + 267, + 350, + 350, + 262 + ], + "sizemode": "area", + "sizeref": 1.1375, + "symbol": "circle" + }, + "mode": "markers", + "name": "(261.5, 358.25]", + "orientation": "v", + "showlegend": true, + "type": "scatter", + "x": [ + 130, + 165, + 150, + 150, + 140, + 165, + 153, + 160, + 140, + 200, + 210, + 193, + 165, + 153, + 150, + 165, + 150, + 153, + 150, + 155, + 160, + 150, + 130, + 140, + 150, + 175, + 150, + 145, + 137, + 150, + 158, + 150, + 180, + 145, + 150, + 180, + 140, + 150, + 150, + 140, + 150, + 145, + 150, + 148, + 110, + 129, + 140, + 150, + 120, + 152, + 150, + 180, + 145, + 130, + 150, + 145, + 145, + 130, + 170, + 149, + 140, + 139, + 145, + 139, + 140, + 130, + 129, + 138, + 135, + 155, + 142, + 125, + 125, + 105, + 85 + ], + "xaxis": "x", + "y": [ + 18, + 15, + 18, + 16, + 17, + null, + null, + 14, + null, + 10, + 11, + 9, + 14, + 14, + 14, + 13, + 15, + 14, + 17, + 13, + 12, + 15, + 13, + 13, + 14, + 13, + 14, + 13, + 14, + 15, + 13, + 14, + 12, + 15, + 15, + 11, + 16, + 13, + 14, + 14, + 14, + 15, + 16, + 14, + 20, + 13, + 17.5, + 16, + 15.5, + 14.5, + 13, + 16.5, + 13, + 13, + 13, + 17.5, + 15.5, + 15, + 15.5, + 16, + 19.4, + 20.2, + 19.2, + 18.1, + 17.5, + 17, + 17.6, + 16.5, + 18.2, + 16.9, + 15.5, + 19.2, + 23, + 26.6, + 38 + ], + "yaxis": "y" + }, + { + "hovertemplate": "Displacement_Size=(358.25, 455.0]
Horsepower=%{x}
Miles_per_Gallon=%{y}
Displacement=%{marker.size}", + "legendgroup": "(358.25, 455.0]", + "marker": { + "color": "#EF553B", + "size": [ + 429, + 454, + 440, + 455, + 390, + 383, + 360, + 383, + 400, + 455, + 360, + 400, + 383, + 400, + 400, + 400, + 429, + 400, + 429, + 400, + 440, + 455, + 360, + 400, + 400, + 360, + 400, + 400, + 400, + 400, + 360 + ], + "sizemode": "area", + "sizeref": 1.1375, + "symbol": "circle" + }, + "mode": "markers", + "name": "(358.25, 455.0]", + "orientation": "v", + "showlegend": true, + "type": "scatter", + "x": [ + 198, + 220, + 215, + 225, + 190, + 175, + 175, + 170, + 150, + 225, + 215, + 175, + 180, + 170, + 175, + 175, + 208, + 190, + 198, + 150, + 215, + 225, + 175, + 150, + 167, + 170, + 230, + 170, + 180, + 190, + 150 + ], + "xaxis": "x", + "y": [ + 15, + 14, + 14, + 14, + 15, + null, + null, + 15, + 15, + 14, + 10, + 14, + 12, + 13, + 13, + 14, + 11, + 13, + 12, + 13, + 13, + 12, + 13, + 11, + 12, + 13, + 16, + 16, + 16, + 15.5, + 18.5 + ], + "yaxis": "y" + }, + { + "hovertemplate": "Displacement_Size=(67.613, 164.75]
Horsepower=%{x}
Miles_per_Gallon=%{y}
Displacement=%{marker.size}", + "legendgroup": "(67.613, 164.75]", + "marker": { + "color": "#00cc96", + "size": [ + 133, + 113, + 97, + 97, + 110, + 107, + 104, + 121, + 97, + 140, + 113, + 98, + 97, + 140, + 122, + 116, + 79, + 88, + 71, + 72, + 97, + 91, + 113, + 97.5, + 97, + 140, + 122, + 70, + 121, + 121, + 120, + 96, + 122, + 97, + 120, + 98, + 97, + 97, + 97, + 140, + 108, + 70, + 122, + 155, + 98, + 68, + 116, + 114, + 121, + 121, + 156, + 79, + 122, + 71, + 140, + 98, + 79, + 97, + 76, + 83, + 90, + 90, + 116, + 120, + 108, + 79, + 97, + 140, + 140, + 134, + 90, + 119, + 90, + 115, + 120, + 121, + 121, + 91, + 107, + 116, + 140, + 98, + 101, + 85, + 98, + 90, + 91, + 97, + 85, + 97, + 140, + 130, + 120, + 156, + 98, + 111, + 79, + 122, + 85, + 97, + 151, + 97, + 140, + 98, + 98, + 97, + 97, + 146, + 121, + 80, + 90, + 98, + 78, + 85, + 91, + 140, + 98, + 134, + 119, + 105, + 134, + 156, + 151, + 119, + 131, + 163, + 121, + 163, + 89, + 98, + 140, + 89, + 86, + 98, + 121, + 141, + 105, + 105, + 85, + 91, + 151, + 151, + 98, + 89, + 98, + 86, + 151, + 140, + 151, + 97, + 134, + 120, + 119, + 108, + 86, + 156, + 85, + 90, + 90, + 121, + 146, + 91, + 85, + 97, + 89, + 70, + 122, + 140, + 107, + 135, + 151, + 156, + 135, + 79, + 86, + 81, + 97, + 85, + 89, + 91, + 105, + 98, + 98, + 105, + 100, + 107, + 108, + 119, + 120, + 141, + 121, + 145, + 146, + 112, + 112, + 112, + 112, + 135, + 151, + 140, + 151, + 105, + 91, + 91, + 105, + 98, + 120, + 107, + 108, + 91, + 91, + 91, + 156, + 144, + 135, + 151, + 140, + 97, + 135, + 120, + 119 + ], + "sizemode": "area", + "sizeref": 1.1375, + "symbol": "circle" + }, + "mode": "markers", + "name": "(67.613, 164.75]", + "orientation": "v", + "showlegend": true, + "type": "scatter", + "x": [ + 115, + 95, + 88, + 46, + 87, + 90, + 95, + 113, + 88, + 90, + 95, + null, + 48, + 72, + 86, + 90, + 70, + 76, + 65, + 69, + 60, + 70, + 95, + 80, + 54, + 90, + 86, + 97, + 112, + 76, + 87, + 69, + 86, + 92, + 97, + 80, + 88, + 46, + 88, + 72, + 94, + 90, + 85, + 107, + 90, + 49, + 75, + 91, + 112, + 110, + 122, + 67, + 80, + 65, + 75, + 83, + 67, + 78, + 52, + 61, + 75, + 75, + 75, + 97, + 93, + 67, + 75, + 83, + 78, + 96, + 71, + 97, + 70, + 95, + 88, + 98, + 115, + 53, + 86, + 81, + 92, + 79, + 83, + 52, + 60, + 70, + 53, + 71, + 70, + 75, + 72, + 102, + 88, + 108, + 68, + 80, + 58, + 96, + 70, + 78, + 88, + 75, + 89, + 63, + 83, + 67, + 78, + 97, + 110, + 110, + 48, + 66, + 52, + 70, + 60, + 88, + 68, + 95, + 97, + 75, + 95, + 105, + 85, + 97, + 103, + 125, + 115, + 133, + 71, + 68, + 88, + 71, + 65, + 80, + 80, + 71, + 70, + 70, + 65, + 69, + 90, + 90, + 76, + 60, + 70, + 65, + 90, + 88, + 90, + 78, + 90, + 75, + 92, + 75, + 65, + 105, + 65, + 48, + 48, + 67, + 67, + 67, + null, + 67, + 62, + 100, + 88, + null, + 72, + 84, + 84, + 92, + 84, + 58, + 64, + 60, + 67, + 65, + 62, + 68, + 63, + 65, + 65, + 74, + null, + 75, + 75, + 100, + 74, + 80, + 110, + 76, + 120, + 88, + 88, + 88, + 85, + 84, + 90, + 92, + null, + 74, + 68, + 68, + 63, + 70, + 88, + 75, + 70, + 67, + 67, + 67, + 92, + 96, + 84, + 90, + 86, + 52, + 84, + 79, + 82 + ], + "xaxis": "x", + "y": [ + null, + 24, + 27, + 26, + 25, + 24, + 25, + 26, + 27, + 28, + 25, + 25, + null, + 22, + 23, + 28, + 30, + 30, + 31, + 35, + 27, + 26, + 24, + 25, + 23, + 20, + 21, + 19, + 18, + 22, + 21, + 26, + 22, + 28, + 23, + 28, + 27, + 26, + 20, + 21, + 22, + 18, + 19, + 21, + 26, + 29, + 24, + 20, + 19, + 24, + 20, + 31, + 26, + 32, + 25, + 29, + 26, + 26, + 31, + 32, + 28, + 24, + 26, + 24, + 26, + 31, + 29, + 23, + 23, + 24, + 25, + 24, + 29, + 23, + 23, + 22, + 25, + 33, + 28, + 25, + 25, + 26, + 27, + 29, + 24.5, + 29, + 33, + 29.5, + 32, + 28, + 26.5, + 20, + 19, + 19, + 31.5, + 30, + 36, + 25.5, + 33.5, + 29, + 24.5, + 26, + 25.5, + 30.5, + 33.5, + 30, + 30.5, + 22, + 21.5, + 21.5, + 43.1, + 36.1, + 32.8, + 39.4, + 36.1, + 25.1, + 30, + 27.5, + 27.2, + 30.9, + 21.1, + 23.2, + 23.8, + 23.9, + 20.3, + 17, + 21.6, + 16.2, + 31.5, + 29.5, + 22.3, + 31.9, + 34.1, + 35.7, + 27.4, + 27.2, + 34.2, + 34.5, + 31.8, + 37.3, + 28.4, + 33.5, + 41.5, + 38.1, + 32.1, + 37.2, + 28, + 26.4, + 24.3, + 34.3, + 29.8, + 31.3, + 37, + 32.2, + 46.6, + 27.9, + 40.8, + 44.3, + 43.4, + 36.4, + 30, + 44.6, + 40.9, + 33.8, + 29.8, + 23.7, + 35, + 23.6, + 32.4, + 27.2, + 26.6, + 25.8, + 30, + 39.1, + 39, + 35.1, + 32.3, + 37, + 37.7, + 34.1, + 34.7, + 34.4, + 29.9, + 33, + 34.5, + 33.7, + 32.4, + 32.9, + 31.6, + 28.1, + null, + 30.7, + 24.2, + 28, + 27, + 34, + 31, + 29, + 27, + 24, + 23, + 36, + 37, + 31, + 38, + 36, + 36, + 36, + 34, + 38, + 32, + 38, + 26, + 32, + 36, + 27, + 27, + 44, + 32, + 28, + 31 + ], + "yaxis": "y" + }, + { + "hovertemplate": "Displacement_Size=(164.75, 261.5]
Horsepower=%{x}
Miles_per_Gallon=%{y}
Displacement=%{marker.size}", + "legendgroup": "(164.75, 261.5]", + "marker": { + "color": "#ab63fa", + "size": [ + 198, + 199, + 200, + 199, + 232, + 225, + 250, + 250, + 232, + 258, + 250, + 250, + 225, + 250, + 232, + 250, + 198, + 232, + 198, + 200, + 232, + 250, + 250, + 258, + 225, + 225, + 250, + 250, + 250, + 231, + 250, + 258, + 225, + 231, + 232, + 171, + 232, + 225, + 250, + 200, + 232, + 225, + 250, + 250, + 258, + 168, + 260, + 250, + 231, + 225, + 250, + 260, + 231, + 200, + 200, + 225, + 232, + 231, + 200, + 225, + 258, + 231, + 231, + 200, + 232, + 225, + 183, + 260, + 173, + 173, + 225, + 168, + 173, + 168, + 231, + 200, + 225, + 181, + 232 + ], + "sizemode": "area", + "sizeref": 1.1375, + "symbol": "circle" + }, + "mode": "markers", + "name": "(164.75, 261.5]", + "orientation": "v", + "showlegend": true, + "type": "scatter", + "x": [ + 95, + 97, + 85, + 90, + 100, + 105, + 100, + 88, + 100, + 110, + 100, + 88, + 105, + 100, + 100, + 88, + 95, + 100, + 95, + null, + 100, + 100, + 100, + 110, + 105, + 95, + 105, + 72, + 72, + 110, + 105, + 110, + 95, + 110, + 100, + 97, + 90, + 100, + 105, + 81, + 90, + 100, + 78, + 110, + 95, + 120, + 110, + 110, + 105, + 100, + 98, + 110, + 105, + 95, + 85, + 100, + 90, + 105, + 85, + 110, + 120, + 165, + 115, + 85, + 90, + 110, + 77, + 90, + 115, + 115, + 90, + 132, + 110, + 116, + 110, + 88, + 85, + 110, + 112 + ], + "xaxis": "x", + "y": [ + 22, + 18, + 21, + 21, + 19, + 16, + 17, + 19, + 18, + 18, + 19, + 18, + 18, + 16, + 18, + 18, + 23, + 18, + 20, + 21, + 19, + 15, + 16, + 16, + 18, + 19, + 18, + 15, + 15, + 17, + 16, + 15, + 18, + 21, + 20, + 18, + 19, + 22, + 22, + 24, + 22.5, + 20, + 18, + 18.5, + 17.5, + 16.5, + 17, + 17.5, + 20.5, + 19, + 18.5, + 19.9, + 19.2, + 20.5, + 20.2, + 20.5, + 19.4, + 20.6, + 20.8, + 18.6, + 18.1, + 17.7, + 21.5, + 19.8, + 20.2, + 20.6, + 25.4, + 23.9, + 28.8, + 26.8, + 19.1, + 32.7, + 23.5, + 25.4, + 22.4, + 20.2, + 17.6, + 25, + 22 + ], + "yaxis": "y" + } + ], + "layout": { + "legend": { + "itemsizing": "constant", + "title": { + "text": "Displacement_Size" + }, + "tracegroupgap": 0 + }, + "margin": { + "t": 60 + }, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Horsepower" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Miles_per_Gallon" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Adding a new col is easiest way to get displacement into legend with plotly:\n", "cars[\"Displacement_Size\"] = pd.cut(cars[\"Displacement\"], bins=4)\n", @@ -732,9 +21255,84 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
CountryReal GDP growth, %
Date
1999-01-01USNaN
1999-04-01USNaN
1999-07-01USNaN
1999-10-01USNaN
2000-01-01US4.224745
\n", + "
" + ], + "text/plain": [ + " Country Real GDP growth, %\n", + "Date \n", + "1999-01-01 US NaN\n", + "1999-04-01 US NaN\n", + "1999-07-01 US NaN\n", + "1999-10-01 US NaN\n", + "2000-01-01 US 4.224745" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "todays_date = datetime.datetime.now().strftime(\"%Y-%m-%d\")\n", "fred_df = web.DataReader([\"GDPC1\", \"NGDPRSAXDCGBQ\"], \"fred\", \"1999-01-01\", \"2021-12-31\")\n", @@ -772,9 +21370,1440 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:22.887652\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots()\n", "for i, country in enumerate(df[\"Country\"].unique()):\n", @@ -799,9 +22828,1491 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:23.003688\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots()\n", "y_var = \"Real GDP growth, %\"\n", @@ -816,9 +24327,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": { + "image/png": { + "height": 378.25, + "width": 581.4 + } + }, + "output_type": "execute_result" + } + ], "source": [ "(\n", " so.Plot(df.reset_index(), x=\"Date\", y=\"Real GDP growth, %\", color=\"Country\").add(\n", @@ -836,9 +24364,62 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "(\n", " ggplot(df.reset_index(), aes(x=\"Date\", y=\"Real GDP growth, %\", color=\"Country\"))\n", @@ -855,9 +24436,77 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "alt.Chart(df.reset_index()).mark_line().encode(\n", " x=\"Date:T\",\n", @@ -876,9 +24525,1282 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "Country=US
Date=%{x}
Real GDP growth, %=%{y}", + "legendgroup": "US", + "line": { + "color": "#636efa", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines", + "name": "US", + "orientation": "v", + "showlegend": true, + "type": "scatter", + "x": [ + "1999-01-01T00:00:00", + "1999-04-01T00:00:00", + "1999-07-01T00:00:00", + "1999-10-01T00:00:00", + "2000-01-01T00:00:00", + "2000-04-01T00:00:00", + "2000-07-01T00:00:00", + "2000-10-01T00:00:00", + "2001-01-01T00:00:00", + "2001-04-01T00:00:00", + "2001-07-01T00:00:00", + "2001-10-01T00:00:00", + "2002-01-01T00:00:00", + "2002-04-01T00:00:00", + "2002-07-01T00:00:00", + "2002-10-01T00:00:00", + "2003-01-01T00:00:00", + "2003-04-01T00:00:00", + "2003-07-01T00:00:00", + "2003-10-01T00:00:00", + "2004-01-01T00:00:00", + "2004-04-01T00:00:00", + "2004-07-01T00:00:00", + "2004-10-01T00:00:00", + "2005-01-01T00:00:00", + "2005-04-01T00:00:00", + "2005-07-01T00:00:00", + "2005-10-01T00:00:00", + "2006-01-01T00:00:00", + "2006-04-01T00:00:00", + "2006-07-01T00:00:00", + "2006-10-01T00:00:00", + "2007-01-01T00:00:00", + "2007-04-01T00:00:00", + "2007-07-01T00:00:00", + "2007-10-01T00:00:00", + "2008-01-01T00:00:00", + "2008-04-01T00:00:00", + "2008-07-01T00:00:00", + "2008-10-01T00:00:00", + "2009-01-01T00:00:00", + "2009-04-01T00:00:00", + "2009-07-01T00:00:00", + "2009-10-01T00:00:00", + "2010-01-01T00:00:00", + "2010-04-01T00:00:00", + "2010-07-01T00:00:00", + "2010-10-01T00:00:00", + "2011-01-01T00:00:00", + "2011-04-01T00:00:00", + "2011-07-01T00:00:00", + "2011-10-01T00:00:00", + "2012-01-01T00:00:00", + "2012-04-01T00:00:00", + "2012-07-01T00:00:00", + "2012-10-01T00:00:00", + "2013-01-01T00:00:00", + "2013-04-01T00:00:00", + "2013-07-01T00:00:00", + "2013-10-01T00:00:00", + "2014-01-01T00:00:00", + "2014-04-01T00:00:00", + "2014-07-01T00:00:00", + "2014-10-01T00:00:00", + "2015-01-01T00:00:00", + "2015-04-01T00:00:00", + "2015-07-01T00:00:00", + "2015-10-01T00:00:00", + "2016-01-01T00:00:00", + "2016-04-01T00:00:00", + "2016-07-01T00:00:00", + "2016-10-01T00:00:00", + "2017-01-01T00:00:00", + "2017-04-01T00:00:00", + "2017-07-01T00:00:00", + "2017-10-01T00:00:00", + "2018-01-01T00:00:00", + "2018-04-01T00:00:00", + "2018-07-01T00:00:00", + "2018-10-01T00:00:00", + "2019-01-01T00:00:00", + "2019-04-01T00:00:00", + "2019-07-01T00:00:00", + "2019-10-01T00:00:00", + "2020-01-01T00:00:00", + "2020-04-01T00:00:00", + "2020-07-01T00:00:00", + "2020-10-01T00:00:00", + "2021-01-01T00:00:00", + "2021-04-01T00:00:00", + "2021-07-01T00:00:00", + "2021-10-01T00:00:00" + ], + "xaxis": "x", + "y": [ + null, + null, + null, + null, + 4.224744861233032, + 5.2444568111355006, + 3.9731723525519014, + 2.9055943095086967, + 2.1975051856706784, + 0.9962983270431103, + 0.48923629256110157, + 0.16731829373148877, + 1.3372586567694578, + 1.3253787532159889, + 2.1465169830615416, + 1.9925248221954739, + 1.6792570124718242, + 1.9550651524723905, + 3.2311353875128956, + 4.300440651057458, + 4.341952085996703, + 4.227491217016888, + 3.494860356231766, + 3.3511049439547858, + 3.9088432572524257, + 3.6177355801611855, + 3.448598847170792, + 2.9727366937913002, + 3.2131067957182013, + 2.9729038556202125, + 2.325277937424497, + 2.6345603763438197, + 1.5767249700146868, + 1.9343747200181172, + 2.3680360773066234, + 2.133364682457861, + 1.3924712055185307, + 1.3760107660276244, + 0.26596727212868654, + -2.5411536884212604, + -3.234227293078318, + -3.9788374053187248, + -3.1328157006863644, + 0.10557252348297741, + 1.7452658449103264, + 2.91359502911801, + 3.344209253779229, + 2.7759609093124693, + 2.037797199613345, + 1.7437088645501975, + 0.9426534645672335, + 1.543075871606514, + 2.6380693108862374, + 2.403335839137921, + 2.5737123144880414, + 1.551852007332477, + 1.700868485709428, + 1.5199382041070075, + 2.237022493295515, + 3.0091449601746367, + 1.6508175559380112, + 2.6891521587272926, + 3.059782928201682, + 2.6858351011456394, + 3.9692994988184793, + 3.2790878609270546, + 2.4473213664345517, + 2.119861312664817, + 1.7950168913805964, + 1.493250832714721, + 1.8057615310855146, + 2.1815663030564147, + 2.0874616941966906, + 2.3304137937582903, + 2.410821412174591, + 2.9941434967617697, + 3.328810094759116, + 3.2989628465220244, + 3.130030640718684, + 2.125110751484538, + 1.8512850454525775, + 2.1540533335291823, + 2.6702647849291328, + 3.182556070035214, + 1.2265547889679818, + -7.528460210503718, + -1.4689314767167172, + -1.0832850303525987, + 1.5726073238882199, + 11.950272211519607, + 4.735316794710931, + 5.421091782486576 + ], + "yaxis": "y" + }, + { + "hovertemplate": "Country=UK
Date=%{x}
Real GDP growth, %=%{y}", + "legendgroup": "UK", + "line": { + "color": "#EF553B", + "dash": "dot" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines", + "name": "UK", + "orientation": "v", + "showlegend": true, + "type": "scatter", + "x": [ + "1999-01-01T00:00:00", + "1999-04-01T00:00:00", + "1999-07-01T00:00:00", + "1999-10-01T00:00:00", + "2000-01-01T00:00:00", + "2000-04-01T00:00:00", + "2000-07-01T00:00:00", + "2000-10-01T00:00:00", + "2001-01-01T00:00:00", + "2001-04-01T00:00:00", + "2001-07-01T00:00:00", + "2001-10-01T00:00:00", + "2002-01-01T00:00:00", + "2002-04-01T00:00:00", + "2002-07-01T00:00:00", + "2002-10-01T00:00:00", + "2003-01-01T00:00:00", + "2003-04-01T00:00:00", + "2003-07-01T00:00:00", + "2003-10-01T00:00:00", + "2004-01-01T00:00:00", + "2004-04-01T00:00:00", + "2004-07-01T00:00:00", + "2004-10-01T00:00:00", + "2005-01-01T00:00:00", + "2005-04-01T00:00:00", + "2005-07-01T00:00:00", + "2005-10-01T00:00:00", + "2006-01-01T00:00:00", + "2006-04-01T00:00:00", + "2006-07-01T00:00:00", + "2006-10-01T00:00:00", + "2007-01-01T00:00:00", + "2007-04-01T00:00:00", + "2007-07-01T00:00:00", + "2007-10-01T00:00:00", + "2008-01-01T00:00:00", + "2008-04-01T00:00:00", + "2008-07-01T00:00:00", + "2008-10-01T00:00:00", + "2009-01-01T00:00:00", + "2009-04-01T00:00:00", + "2009-07-01T00:00:00", + "2009-10-01T00:00:00", + "2010-01-01T00:00:00", + "2010-04-01T00:00:00", + "2010-07-01T00:00:00", + "2010-10-01T00:00:00", + "2011-01-01T00:00:00", + "2011-04-01T00:00:00", + "2011-07-01T00:00:00", + "2011-10-01T00:00:00", + "2012-01-01T00:00:00", + "2012-04-01T00:00:00", + "2012-07-01T00:00:00", + "2012-10-01T00:00:00", + "2013-01-01T00:00:00", + "2013-04-01T00:00:00", + "2013-07-01T00:00:00", + "2013-10-01T00:00:00", + "2014-01-01T00:00:00", + "2014-04-01T00:00:00", + "2014-07-01T00:00:00", + "2014-10-01T00:00:00", + "2015-01-01T00:00:00", + "2015-04-01T00:00:00", + "2015-07-01T00:00:00", + "2015-10-01T00:00:00", + "2016-01-01T00:00:00", + "2016-04-01T00:00:00", + "2016-07-01T00:00:00", + "2016-10-01T00:00:00", + "2017-01-01T00:00:00", + "2017-04-01T00:00:00", + "2017-07-01T00:00:00", + "2017-10-01T00:00:00", + "2018-01-01T00:00:00", + "2018-04-01T00:00:00", + "2018-07-01T00:00:00", + "2018-10-01T00:00:00", + "2019-01-01T00:00:00", + "2019-04-01T00:00:00", + "2019-07-01T00:00:00", + "2019-10-01T00:00:00", + "2020-01-01T00:00:00", + "2020-04-01T00:00:00", + "2020-07-01T00:00:00", + "2020-10-01T00:00:00", + "2021-01-01T00:00:00", + "2021-04-01T00:00:00", + "2021-07-01T00:00:00", + "2021-10-01T00:00:00" + ], + "xaxis": "x", + "y": [ + null, + null, + null, + null, + 4.820671440401902, + 5.093797532533384, + 4.104074644664979, + 3.2934955103606, + 2.9438593168212623, + 2.642777266474683, + 2.529529615654069, + 2.2065845812643037, + 1.5688021420928067, + 1.6171018950149163, + 1.759833127539956, + 2.2177347082651178, + 2.692987934231783, + 3.1428862844442573, + 3.3657301054367705, + 3.3776375007306125, + 3.0222917517733094, + 2.669046102332606, + 2.15654988488978, + 1.8752883493301287, + 2.2374524899009263, + 2.4894555407969143, + 2.8926458873956618, + 3.327013923421185, + 3.0975089469879924, + 2.6440421630529176, + 2.1215045485595274, + 1.7017903066058437, + 2.1338491792065595, + 2.4824605295376445, + 2.8934973817097642, + 2.9661661441741316, + 2.503018900304088, + 1.261546516411327, + -0.9642055210672296, + -3.635315430651209, + -6.05941815048705, + -5.906406831546251, + -4.375541811149642, + -2.0132256320072206, + 0.9645624368350703, + 2.400136066585401, + 2.903622991010635, + 2.6886089371151556, + 1.9968011572663924, + 1.013402724837409, + 0.7653552845304556, + 0.8182187502644878, + 1.4378908401731394, + 1.1834768585371913, + 1.8423204073869437, + 1.5866386556385503, + 0.9938996987300053, + 1.8648732548715685, + 1.7382098687726888, + 2.567355109815539, + 3.119637254356644, + 3.2742928435563368, + 3.1924819777607416, + 3.196190675695809, + 2.6566670395882896, + 2.3640479345029775, + 1.999838817787447, + 1.8710450604488393, + 1.9388926729590361, + 1.9103227853489058, + 1.887977799832341, + 1.9470416922133582, + 2.434647479157648, + 2.5192216421452196, + 2.7879072160268636, + 2.8745569291951156, + 2.072811173275979, + 1.5888300433317237, + 1.2830939991020474, + 0.6852681058941101, + 1.360811264807693, + 1.5187750637987607, + 1.9188808647863098, + 1.7660144285901014, + -1.7192028691809802, + -21.94415117105859, + -9.508954752962195, + -8.260513745830067, + -6.659860666389294, + 25.73185375346776, + 9.517121284051978, + 9.693487855930938 + ], + "yaxis": "y" + } + ], + "layout": { + "legend": { + "title": { + "text": "Country" + }, + "tracegroupgap": 0 + }, + "margin": { + "t": 60 + }, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Date" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Real GDP growth, %" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig = px.line(\n", " df.reset_index(),\n", @@ -901,9 +25823,77 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
yield
site
Crookston748.39997
Duluth559.93334
Grand Rapids498.63334
Morris708.00001
University Farm653.33335
\n", + "
" + ], + "text/plain": [ + " yield\n", + "site \n", + "Crookston 748.39997\n", + "Duluth 559.93334\n", + "Grand Rapids 498.63334\n", + "Morris 708.00001\n", + "University Farm 653.33335" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "barley = data.barley()\n", "barley = pd.DataFrame(barley.groupby([\"site\"])[\"yield\"].sum())\n", @@ -921,9 +25911,1415 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:23.399515\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots()\n", "ax.barh(barley[\"yield\"].index, barley[\"yield\"], 0.35)\n", @@ -942,9 +27338,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "" + ] + }, + "execution_count": 36, + "metadata": { + "image/png": { + "height": 378.25, + "width": 651.525 + } + }, + "output_type": "execute_result" + } + ], "source": [ "(\n", " so.Plot(barley.reset_index(), x=\"yield\", y=\"site\", color=\"site\").add(\n", @@ -964,9 +27377,64 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "(\n", " ggplot(barley.reset_index(), aes(x=\"site\", y=\"yield\", fill=\"site\"))\n", @@ -987,9 +27455,77 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "alt.Chart(barley.reset_index()).mark_bar().encode(\n", " y=\"site\",\n", @@ -1008,9 +27544,903 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "alignmentgroup": "True", + "hovertemplate": "yield=%{x}
site=%{y}", + "legendgroup": "", + "marker": { + "color": "#636efa", + "pattern": { + "shape": "" + } + }, + "name": "", + "offsetgroup": "", + "orientation": "h", + "showlegend": false, + "textposition": "auto", + "type": "bar", + "x": [ + 748.3999699999999, + 559.93334, + 498.63334000000003, + 708.00001, + 653.33335, + 962.16663 + ], + "xaxis": "x", + "y": [ + "Crookston", + "Duluth", + "Grand Rapids", + "Morris", + "University Farm", + "Waseca" + ], + "yaxis": "y" + } + ], + "layout": { + "barmode": "relative", + "legend": { + "tracegroupgap": 0 + }, + "margin": { + "t": 60 + }, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "yield" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "site" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig = px.bar(barley.reset_index(), y=\"site\", x=\"yield\")\n", "fig.show()" @@ -1026,9 +28456,84 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
siteyearyield
0Crookston1931436.59999
1Crookston1932311.79998
2Duluth1931302.93333
3Duluth1932257.00001
4Grand Rapids1931290.53335
\n", + "
" + ], + "text/plain": [ + " site year yield\n", + "0 Crookston 1931 436.59999\n", + "1 Crookston 1932 311.79998\n", + "2 Duluth 1931 302.93333\n", + "3 Duluth 1932 257.00001\n", + "4 Grand Rapids 1931 290.53335" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "barley = data.barley()\n", "barley = pd.DataFrame(barley.groupby([\"site\", \"year\"])[\"yield\"].sum()).reset_index()\n", @@ -1044,9 +28549,1547 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:23.717669\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "labels = barley[\"site\"].unique()\n", "y = np.arange(len(labels)) # the label locations\n", @@ -1073,9 +30116,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "" + ] + }, + "execution_count": 42, + "metadata": { + "image/png": { + "height": 378.25, + "width": 592.025 + } + }, + "output_type": "execute_result" + } + ], "source": [ "barley[\"year\"] = barley[\"year\"].astype(\"category\") # to force category\n", "\n", @@ -1095,9 +30155,71 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "(\n", " ggplot(barley, aes(x=\"site\", y=\"yield\", group=\"year\", fill=as_discrete(\"year\")))\n", @@ -1115,9 +30237,77 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "alt.Chart(barley.reset_index()).mark_bar().encode(\n", " y=\"year:O\", x=\"yield\", color=\"year:N\", row=\"site:N\"\n", @@ -1135,9 +30325,941 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "alignmentgroup": "True", + "hovertemplate": "year=1931
yield=%{x}
site=%{y}", + "legendgroup": "1931", + "marker": { + "color": "#636efa", + "pattern": { + "shape": "" + } + }, + "name": "1931", + "offsetgroup": "1931", + "orientation": "h", + "showlegend": true, + "textposition": "auto", + "type": "bar", + "x": [ + 436.59999, + 302.93333, + 290.53335000000004, + 292.86669, + 358.26666, + 543.46666 + ], + "xaxis": "x", + "y": [ + "Crookston", + "Duluth", + "Grand Rapids", + "Morris", + "University Farm", + "Waseca" + ], + "yaxis": "y" + }, + { + "alignmentgroup": "True", + "hovertemplate": "year=1932
yield=%{x}
site=%{y}", + "legendgroup": "1932", + "marker": { + "color": "#EF553B", + "pattern": { + "shape": "" + } + }, + "name": "1932", + "offsetgroup": "1932", + "orientation": "h", + "showlegend": true, + "textposition": "auto", + "type": "bar", + "x": [ + 311.79998, + 257.00001, + 208.09999, + 415.13332, + 295.06669, + 418.69997 + ], + "xaxis": "x", + "y": [ + "Crookston", + "Duluth", + "Grand Rapids", + "Morris", + "University Farm", + "Waseca" + ], + "yaxis": "y" + } + ], + "layout": { + "barmode": "group", + "legend": { + "title": { + "text": "year" + }, + "tracegroupgap": 0 + }, + "margin": { + "t": 60 + }, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "yield" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "site" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "px_barley = barley.reset_index()\n", "# This prevents plotly from using a continuous scale for year\n", @@ -1150,8 +31272,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Stacked bar chart\n", - "\n" + "## Stacked bar chart" ] }, { @@ -1163,9 +31284,1558 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:24.036860\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "labels = barley[\"site\"].unique()\n", "y = np.arange(len(labels)) # the label locations\n", @@ -1199,9 +32869,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "" + ] + }, + "execution_count": 47, + "metadata": { + "image/png": { + "height": 378.25, + "width": 592.025 + } + }, + "output_type": "execute_result" + } + ], "source": [ "barley[\"year\"] = barley[\"year\"].astype(\"category\") # to force category\n", "(\n", @@ -1221,9 +32908,69 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "(\n", " ggplot(barley, aes(x=\"site\", y=\"yield\", fill=as_discrete(\"year\")))\n", @@ -1241,9 +32988,77 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "alt.Chart(barley.reset_index()).mark_bar().encode(\n", " y=\"site\",\n", @@ -1263,14 +33078,2872 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "alignmentgroup": "True", + "hovertemplate": "year=1931
yield=%{x}
site=%{y}", + "legendgroup": "1931", + "marker": { + "color": "#636efa", + "pattern": { + "shape": "" + } + }, + "name": "1931", + "offsetgroup": "1931", + "orientation": "h", + "showlegend": true, + "textposition": "auto", + "type": "bar", + "x": [ + 436.59999, + 302.93333, + 290.53335000000004, + 292.86669, + 358.26666, + 543.46666 + ], + "xaxis": "x", + "y": [ + "Crookston", + "Duluth", + "Grand Rapids", + "Morris", + "University Farm", + "Waseca" + ], + "yaxis": "y" + }, + { + "alignmentgroup": "True", + "hovertemplate": "year=1932
yield=%{x}
site=%{y}", + "legendgroup": "1932", + "marker": { + "color": "#EF553B", + "pattern": { + "shape": "" + } + }, + "name": "1932", + "offsetgroup": "1932", + "orientation": "h", + "showlegend": true, + "textposition": "auto", + "type": "bar", + "x": [ + 311.79998, + 257.00001, + 208.09999, + 415.13332, + 295.06669, + 418.69997 + ], + "xaxis": "x", + "y": [ + "Crookston", + "Duluth", + "Grand Rapids", + "Morris", + "University Farm", + "Waseca" + ], + "yaxis": "y" + } + ], + "layout": { + "barmode": "relative", + "legend": { + "title": { + "text": "year" + }, + "tracegroupgap": 0 + }, + "margin": { + "t": 60 + }, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "yield" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "site" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig = px.bar(px_barley, y=\"site\", x=\"yield\", barmode=\"relative\", color=\"year\")\n", "fig.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Diverging stacked bar chart\n", + "\n", + "First, let's create some data to use in our examples." + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Strongly disagreeDisagreeNeither agree nor disagreeAgreeStrongly agree
Question 11015173226
Question 22622291013
Question 335377219
Question 4321191533
Question 521295540
Question 681953038
\n", + "
" + ], + "text/plain": [ + " Strongly disagree Disagree Neither agree nor disagree Agree \\\n", + "Question 1 10 15 17 32 \n", + "Question 2 26 22 29 10 \n", + "Question 3 35 37 7 2 \n", + "Question 4 32 11 9 15 \n", + "Question 5 21 29 5 5 \n", + "Question 6 8 19 5 30 \n", + "\n", + " Strongly agree \n", + "Question 1 26 \n", + "Question 2 13 \n", + "Question 3 19 \n", + "Question 4 33 \n", + "Question 5 40 \n", + "Question 6 38 " + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "category_names = ['Strongly disagree', 'Disagree',\n", + " 'Neither agree nor disagree', 'Agree', 'Strongly agree']\n", + "results = [\n", + " [10, 15, 17, 32, 26],\n", + " [26, 22, 29, 10, 13],\n", + " [35, 37, 7, 2, 19],\n", + " [32, 11, 9, 15, 33],\n", + " [21, 29, 5, 5, 40],\n", + " [8, 19, 5, 30, 38]\n", + "]\n", + "\n", + "likert_df = pd.DataFrame(results, columns=category_names, index=[f\"Question {i}\" for i in range(1, 7)])\n", + "likert_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:24.469786\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "middle_index = likert_df.shape[1]//2\n", + "offsets = likert_df.iloc[:, range(middle_index)].sum(axis=1) + likert_df.iloc[:, middle_index]/2\n", + "category_colors = plt.get_cmap('coolwarm_r')(\n", + " np.linspace(0.15, 0.85, likert_df.shape[1]))\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 5))\n", + " \n", + "# Plot Bars\n", + "for i, (colname, color) in enumerate(zip(likert_df.columns, category_colors)):\n", + " widths = likert_df.iloc[:, i]\n", + " starts = likert_df.cumsum(axis=1).iloc[:, i] - widths - offsets\n", + " rects = ax.barh(likert_df.index, widths, left=starts, height=0.5,\n", + " label=colname, color=color)\n", + "\n", + "# Add Zero Reference Line\n", + "ax.axvline(0, linestyle='--', color='black', alpha=1, zorder=0, lw=0.3)\n", + "\n", + "# X Axis\n", + "ax.set_xlim(-90, 90)\n", + "ax.set_xticks(np.arange(-90, 91, 10))\n", + "ax.xaxis.set_major_formatter(lambda x, pos: str(abs(int(x))))\n", + "\n", + "# Y Axis\n", + "ax.invert_yaxis()\n", + "\n", + "# Remove spines\n", + "ax.spines['right'].set_visible(False)\n", + "ax.spines['top'].set_visible(False)\n", + "ax.spines['left'].set_visible(False)\n", + "\n", + "# Legend\n", + "ax.legend(ncol=len(category_names), bbox_to_anchor=(0, 1),\n", + " loc='lower left', fontsize='small', frameon=False)\n", + "\n", + "# Set Background Color\n", + "fig.set_facecolor('#FFFFFF')\n", + "plt.show()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1282,9 +35955,126 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
caratcutcolorclaritydepthtablepricexyz
410740.41PremiumFVVS260.658.011924.844.792.92
348240.30Very GoodHVVS163.457.08784.314.272.72
52780.79Very GoodDVS158.860.037986.036.083.56
3540.74PremiumFVS261.956.028055.805.773.58
366570.30IdealEVVS261.357.09494.304.322.64
\n", + "
" + ], + "text/plain": [ + " carat cut color clarity depth table price x y z\n", + "41074 0.41 Premium F VVS2 60.6 58.0 1192 4.84 4.79 2.92\n", + "34824 0.30 Very Good H VVS1 63.4 57.0 878 4.31 4.27 2.72\n", + "5278 0.79 Very Good D VS1 58.8 60.0 3798 6.03 6.08 3.56\n", + "354 0.74 Premium F VS2 61.9 56.0 2805 5.80 5.77 3.58\n", + "36657 0.30 Ideal E VVS2 61.3 57.0 949 4.30 4.32 2.64" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "diamonds = sns.load_dataset(\"diamonds\").sample(1000)\n", "diamonds.head()" @@ -1309,9 +36099,3280 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:24.711177\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Note that there isn't a clear way to do this in the seaborn objects API yet\n", "sns.displot(diamonds, x=\"carat\", kind=\"kde\", hue=\"cut\", fill=True);" @@ -1327,9 +39388,59 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "(ggplot(diamonds, aes(x=\"carat\", fill=\"cut\", colour=\"cut\")) + geom_density(alpha=0.5))" ] @@ -1343,9 +39454,77 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "alt.Chart(diamonds).transform_density(\n", " density=\"carat\", as_=[\"carat\", \"density\"], groupby=[\"cut\"]\n", @@ -1365,9 +39544,5942 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 57, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "legendgroup": "Ideal", + "marker": { + "color": "rgb(31, 119, 180)" + }, + "mode": "lines", + "name": "Ideal", + "showlegend": true, + "type": "scatter", + "x": [ + 0.23, + 0.23406000000000002, + 0.23812, + 0.24218, + 0.24624000000000001, + 0.2503, + 0.25436000000000003, + 0.25842, + 0.26248, + 0.26654, + 0.2706, + 0.27466, + 0.27872, + 0.28278000000000003, + 0.28684, + 0.2909, + 0.29496, + 0.29902, + 0.30308, + 0.30713999999999997, + 0.31120000000000003, + 0.31526, + 0.31932, + 0.32338, + 0.32744, + 0.3315, + 0.33555999999999997, + 0.33962000000000003, + 0.34368, + 0.34774, + 0.3518, + 0.35585999999999995, + 0.35992, + 0.36397999999999997, + 0.36804000000000003, + 0.3721, + 0.37616, + 0.38022, + 0.38427999999999995, + 0.38834, + 0.39239999999999997, + 0.39646, + 0.40052, + 0.40458, + 0.40864, + 0.4127, + 0.41676, + 0.42081999999999997, + 0.42488000000000004, + 0.42894, + 0.43299999999999994, + 0.43706, + 0.44111999999999996, + 0.44518, + 0.44924, + 0.4533, + 0.45736, + 0.46142, + 0.46548, + 0.46953999999999996, + 0.47359999999999997, + 0.47766, + 0.4817199999999999, + 0.48578, + 0.48983999999999994, + 0.4939, + 0.49795999999999996, + 0.50202, + 0.50608, + 0.51014, + 0.5142, + 0.51826, + 0.52232, + 0.52638, + 0.53044, + 0.5345, + 0.5385599999999999, + 0.54262, + 0.5466799999999999, + 0.55074, + 0.5548, + 0.5588599999999999, + 0.56292, + 0.5669799999999999, + 0.57104, + 0.5751, + 0.57916, + 0.58322, + 0.58728, + 0.59134, + 0.5954, + 0.59946, + 0.60352, + 0.60758, + 0.61164, + 0.6157, + 0.61976, + 0.6238199999999999, + 0.62788, + 0.63194, + 0.6359999999999999, + 0.64006, + 0.6441199999999999, + 0.64818, + 0.6522399999999999, + 0.6563, + 0.66036, + 0.66442, + 0.66848, + 0.67254, + 0.6766, + 0.6806599999999999, + 0.68472, + 0.68878, + 0.69284, + 0.6969, + 0.70096, + 0.70502, + 0.7090799999999999, + 0.7131399999999999, + 0.7172, + 0.7212599999999999, + 0.72532, + 0.7293799999999999, + 0.7334399999999999, + 0.7374999999999999, + 0.7415599999999999, + 0.74562, + 0.7496799999999999, + 0.7537399999999999, + 0.7577999999999999, + 0.7618599999999999, + 0.7659199999999999, + 0.7699799999999999, + 0.77404, + 0.7780999999999999, + 0.78216, + 0.7862199999999999, + 0.79028, + 0.7943399999999999, + 0.7984, + 0.80246, + 0.80652, + 0.8105799999999999, + 0.8146399999999999, + 0.8186999999999999, + 0.8227599999999999, + 0.8268199999999999, + 0.83088, + 0.8349399999999999, + 0.8389999999999999, + 0.8430599999999999, + 0.8471199999999999, + 0.8511799999999999, + 0.8552399999999999, + 0.8593, + 0.8633599999999999, + 0.86742, + 0.8714799999999999, + 0.87554, + 0.8795999999999999, + 0.88366, + 0.8877199999999998, + 0.8917799999999999, + 0.8958399999999999, + 0.8998999999999999, + 0.9039599999999999, + 0.9080199999999999, + 0.9120799999999999, + 0.91614, + 0.9201999999999999, + 0.9242599999999999, + 0.9283199999999999, + 0.9323799999999999, + 0.9364399999999999, + 0.9404999999999999, + 0.94456, + 0.9486199999999999, + 0.95268, + 0.9567399999999999, + 0.9608, + 0.9648599999999998, + 0.9689199999999999, + 0.9729799999999998, + 0.9770399999999999, + 0.9810999999999999, + 0.9851599999999999, + 0.9892199999999999, + 0.9932799999999999, + 0.9973399999999999, + 1.0014, + 1.00546, + 1.00952, + 1.01358, + 1.0176399999999999, + 1.0217, + 1.02576, + 1.02982, + 1.03388, + 1.03794, + 1.0419999999999998, + 1.04606, + 1.05012, + 1.05418, + 1.0582399999999998, + 1.0623, + 1.06636, + 1.07042, + 1.0744799999999999, + 1.07854, + 1.0826, + 1.08666, + 1.09072, + 1.09478, + 1.09884, + 1.1029, + 1.10696, + 1.11102, + 1.11508, + 1.1191399999999998, + 1.1232, + 1.12726, + 1.1313199999999999, + 1.1353799999999998, + 1.13944, + 1.1435, + 1.14756, + 1.1516199999999999, + 1.15568, + 1.15974, + 1.1638, + 1.16786, + 1.17192, + 1.17598, + 1.18004, + 1.1841, + 1.1881599999999999, + 1.19222, + 1.1962799999999998, + 1.20034, + 1.2044, + 1.2084599999999999, + 1.2125199999999998, + 1.21658, + 1.22064, + 1.2247, + 1.2287599999999999, + 1.23282, + 1.2368799999999998, + 1.24094, + 1.2449999999999999, + 1.2490599999999998, + 1.2531199999999998, + 1.2571799999999997, + 1.26124, + 1.2652999999999999, + 1.2693599999999998, + 1.2734199999999998, + 1.2774799999999997, + 1.28154, + 1.2855999999999999, + 1.2896599999999998, + 1.2937199999999998, + 1.29778, + 1.3018399999999999, + 1.3058999999999998, + 1.3099599999999998, + 1.31402, + 1.31808, + 1.3221399999999999, + 1.3261999999999998, + 1.33026, + 1.33432, + 1.33838, + 1.3424399999999999, + 1.3465, + 1.35056, + 1.35462, + 1.3586799999999999, + 1.3627399999999998, + 1.3668, + 1.37086, + 1.37492, + 1.3789799999999999, + 1.38304, + 1.3871, + 1.3911599999999997, + 1.3952199999999997, + 1.3992799999999999, + 1.4033399999999998, + 1.4073999999999998, + 1.4114599999999997, + 1.41552, + 1.4195799999999998, + 1.4236399999999998, + 1.4276999999999997, + 1.43176, + 1.4358199999999999, + 1.4398799999999998, + 1.4439399999999998, + 1.4479999999999997, + 1.45206, + 1.4561199999999999, + 1.4601799999999998, + 1.4642399999999998, + 1.4683, + 1.47236, + 1.4764199999999998, + 1.4804799999999998, + 1.48454, + 1.4886, + 1.4926599999999999, + 1.4967199999999998, + 1.50078, + 1.50484, + 1.5089, + 1.5129599999999999, + 1.51702, + 1.52108, + 1.52514, + 1.5292, + 1.5332599999999998, + 1.53732, + 1.54138, + 1.5454399999999997, + 1.5494999999999997, + 1.5535599999999998, + 1.5576199999999998, + 1.5616799999999997, + 1.5657399999999997, + 1.5697999999999999, + 1.5738599999999998, + 1.5779199999999998, + 1.5819799999999997, + 1.58604, + 1.5900999999999998, + 1.5941599999999998, + 1.5982199999999998, + 1.60228, + 1.6063399999999999, + 1.6103999999999998, + 1.6144599999999998, + 1.6185199999999997, + 1.62258, + 1.6266399999999999, + 1.6306999999999998, + 1.6347599999999998, + 1.63882, + 1.64288, + 1.6469399999999998, + 1.6509999999999998, + 1.65506, + 1.65912, + 1.6631799999999999, + 1.6672399999999998, + 1.6713, + 1.67536, + 1.67942, + 1.6834799999999999, + 1.68754, + 1.6916, + 1.69566, + 1.6997199999999997, + 1.7037799999999996, + 1.7078399999999998, + 1.7118999999999998, + 1.7159599999999997, + 1.7200199999999997, + 1.7240799999999998, + 1.7281399999999998, + 1.7321999999999997, + 1.7362599999999997, + 1.7403199999999999, + 1.7443799999999998, + 1.7484399999999998, + 1.7524999999999997, + 1.75656, + 1.7606199999999999, + 1.7646799999999998, + 1.7687399999999998, + 1.7728, + 1.7768599999999999, + 1.7809199999999998, + 1.7849799999999998, + 1.78904, + 1.7931, + 1.7971599999999999, + 1.8012199999999998, + 1.8052799999999998, + 1.80934, + 1.8134, + 1.8174599999999999, + 1.8215199999999998, + 1.82558, + 1.82964, + 1.8336999999999999, + 1.8377599999999998, + 1.84182, + 1.84588, + 1.84994, + 1.8539999999999996, + 1.8580599999999998, + 1.8621199999999998, + 1.8661799999999997, + 1.8702399999999997, + 1.8742999999999999, + 1.8783599999999998, + 1.8824199999999998, + 1.8864799999999997, + 1.8905399999999997, + 1.8945999999999998, + 1.8986599999999998, + 1.9027199999999997, + 1.9067799999999997, + 1.9108399999999999, + 1.9148999999999998, + 1.9189599999999998, + 1.9230199999999997, + 1.92708, + 1.9311399999999999, + 1.9351999999999998, + 1.9392599999999998, + 1.94332, + 1.9473799999999999, + 1.9514399999999998, + 1.9554999999999998, + 1.95956, + 1.96362, + 1.9676799999999999, + 1.9717399999999998, + 1.9757999999999998, + 1.97986, + 1.98392, + 1.9879799999999999, + 1.9920399999999998, + 1.9961, + 2.00016, + 2.00422, + 2.0082799999999996, + 2.01234, + 2.0164, + 2.02046, + 2.02452, + 2.02858, + 2.03264, + 2.0366999999999997, + 2.0407599999999997, + 2.04482, + 2.04888, + 2.05294, + 2.057, + 2.06106, + 2.06512, + 2.06918, + 2.0732399999999997, + 2.0772999999999997, + 2.08136, + 2.08542, + 2.08948, + 2.09354, + 2.0976, + 2.10166, + 2.10572, + 2.1097799999999998, + 2.11384, + 2.1179, + 2.12196, + 2.12602, + 2.13008, + 2.13414, + 2.1382, + 2.14226, + 2.14632, + 2.15038, + 2.15444, + 2.1585, + 2.1625599999999996, + 2.16662, + 2.17068, + 2.17474, + 2.1788, + 2.18286, + 2.1869199999999998, + 2.1909799999999997, + 2.1950399999999997, + 2.1991, + 2.20316, + 2.20722, + 2.21128, + 2.21534, + 2.2194, + 2.2234599999999998, + 2.2275199999999997, + 2.2315799999999997, + 2.23564, + 2.2396999999999996, + 2.2437599999999995, + 2.2478199999999995, + 2.25188, + 2.25594 + ], + "xaxis": "x", + "y": [ + 0.8554517493594164, + 0.8778108667843494, + 0.9000577837492292, + 0.9221599324591037, + 0.9440848090873734, + 0.9658000888804413, + 0.9872737412763677, + 1.008474144354721, + 1.0293701979317853, + 1.0499314346178203, + 1.0701281281611472, + 1.089931398417747, + 1.1093133123042154, + 1.1282469801170798, + 1.1467066466313867, + 1.1646677764269955, + 1.1821071329309965, + 1.1990028507093689, + 1.215334500589781, + 1.2310831472497925, + 1.2462313989605038, + 1.2607634492340847, + 1.274665110184184, + 1.2879238374704325, + 1.3005287467616284, + 1.3124706217156956, + 1.3237419135381365, + 1.334336732243478, + 1.3442508298053324, + 1.3534815754403513, + 1.3620279233278965, + 1.369890373121062, + 1.3770709236548557, + 1.3835730203031995, + 1.3894014964780355, + 1.394562509800424, + 1.3990634735047842, + 1.4029129836634922, + 1.406120742839125, + 1.4086974807860893, + 1.4106548728316544, + 1.4120054565691176, + 1.4127625474922172, + 1.412940154190935, + 1.4125528937138647, + 1.4116159076824217, + 1.4101447797168651, + 1.4081554547042008, + 1.4056641604040196, + 1.4026873318500668, + 1.3992415389640842, + 1.3953434177541066, + 1.3910096054227845, + 1.3862566796629423, + 1.3811011023680058, + 1.3755591679347354, + 1.369646956285471, + 1.363380290687355, + 1.3567747003973323, + 1.3498453881146815, + 1.3426072021777826, + 1.3350746133993494, + 1.3272616963949064, + 1.3191821152230225, + 1.3108491131232836, + 1.3022755061093894, + 1.2934736801502287, + 1.2844555916515732, + 1.275232770935224, + 1.2658163284010824, + 1.2562169630504971, + 1.246444973046804, + 1.2365102679903626, + 1.2264223825910248, + 1.2161904914304138, + 1.2058234245192716, + 1.195329683371179, + 1.184717457333132, + 1.1739946399347052, + 1.1631688450413191, + 1.1522474226221637, + 1.1412374739700024, + 1.1301458662372077, + 1.1189792461801393, + 1.1077440530315437, + 1.0964465304478315, + 1.0850927375043282, + 1.0736885587367602, + 1.0622397132505816, + 1.050751762941538, + 1.0392301198901608, + 1.0276800530101056, + 1.0161066940446608, + 1.0045150430175496, + 0.9929099732531274, + 0.9812962360871016, + 0.9696784653920881, + 0.9580611820425871, + 0.9464487984414965, + 0.934845623225081, + 0.9232558662556054, + 0.9116836440008288, + 0.9001329853873739, + 0.888607838200961, + 0.8771120760909057, + 0.8656495062192853, + 0.8542238775773283, + 0.8428388899728495, + 0.8314982036736922, + 0.8202054496729693, + 0.8089642405232047, + 0.7977781816682346, + 0.7866508831843795, + 0.7755859718262439, + 0.7645871032576128, + 0.7536579743348196, + 0.7428023352985628, + 0.732024001720826, + 0.7213268660463813, + 0.710714908563403, + 0.7001922076352054, + 0.6897629490248783, + 0.679431434146838, + 0.6692020870839503, + 0.6590794602157635, + 0.6490682383126085, + 0.6391732409616482, + 0.6293994232042396, + 0.6197518742791497, + 0.6102358143829055, + 0.6008565893768544, + 0.5916196633898814, + 0.5825306092861824, + 0.5735950969886783, + 0.5648188796702578, + 0.5562077778469598, + 0.5477676614290499, + 0.5395044298075516, + 0.5314239900748933, + 0.5235322334985889, + 0.5158350103863415, + 0.508338103499073, + 0.5010472001851309, + 0.49396786342429555, + 0.4871055019836283, + 0.48046533989895657, + 0.474052385505467, + 0.4678714002486212, + 0.461926867512074, + 0.45622296170272836, + 0.4507635178342389, + 0.4455520018492255, + 0.44059148191742176, + 0.43588460094152953, + 0.43143355049538534, + 0.4272400464095864, + 0.4233053062086085, + 0.41963002859048554, + 0.416214375125569, + 0.41305795433476983, + 0.4101598082904811, + 0.40751840186467814, + 0.40513161472938247, + 0.4029967361942954, + 0.40111046294556846, + 0.39946889972836214, + 0.3980675629943796, + 0.39690138751385584, + 0.3959647359302345, + 0.3952514112145176, + 0.3947546719557734, + 0.3944672504042847, + 0.3943813731647709, + 0.3944887844189429, + 0.3947807715396639, + 0.3952481929431919, + 0.39588150801156596, + 0.3966708089042617, + 0.39760585406671956, + 0.398676103233677, + 0.3998707537169817, + 0.4011787777611684, + 0.40258896074539485, + 0.40408994000741527, + 0.40567024406400704, + 0.40731833200287515, + 0.4090226328232623, + 0.4107715845063512, + 0.412553672602065, + 0.41435746812584107, + 0.4161716645673794, + 0.4179851138231276, + 0.4197868608753006, + 0.42156617705232524, + 0.42331259171874147, + 0.42501592225658297, + 0.4266663022149999, + 0.42825420752024174, + 0.42977048065388757, + 0.43120635272333974, + 0.4325534633649378, + 0.43380387843626544, + 0.4349501054706219, + 0.43598510688242514, + 0.43690231092809234, + 0.4376956204419243, + 0.4383594193811599, + 0.4388885772280118, + 0.43927845130952403, + 0.4395248871081064, + 0.4396242166466477, + 0.43957325504192385, + 0.4393692953291207, + 0.4390101016676007, + 0.43849390104472163, + 0.4378193735995446, + 0.43698564169230575, + 0.4359922578481172, + 0.4348391917048972, + 0.433526816095668, + 0.43205589239453224, + 0.4304275552534924, + 0.4286432968541135, + 0.4267049507939996, + 0.42461467572284184, + 0.42237493883694965, + 0.4199884993344643, + 0.4174583919260728, + 0.414787910488151, + 0.41198059193682873, + 0.40904020039272604, + 0.4059707116971279, + 0.4027762983310725, + 0.39946131477976266, + 0.39603028337540447, + 0.3924878806426114, + 0.38883892416176014, + 0.3850883599571319, + 0.3812412504087958, + 0.37730276267950735, + 0.373278157641062, + 0.3691727792780434, + 0.3649920445414303, + 0.3607414336193876, + 0.35642648058853127, + 0.3520527644054954, + 0.34762590019599154, + 0.3431515307968861, + 0.33863531850569456, + 0.3340829369918058, + 0.32950006332431575, + 0.3248923700726809, + 0.3202655174384453, + 0.3156251453790789, + 0.3109768656882427, + 0.3063262540007476, + 0.30167884169486336, + 0.29704010766954164, + 0.2924154699792515, + 0.28781027731469666, + 0.28322980032344475, + 0.27867922277029333, + 0.274163632543254, + 0.2696880125168535, + 0.26525723129045603, + 0.2608760338248522, + 0.2565490320059765, + 0.25228069516968893, + 0.2480753406264675, + 0.2439371242291784, + 0.23987003103112717, + 0.23587786608497027, + 0.2319642454359317, + 0.22813258736509956, + 0.22438610394022504, + 0.22072779293245162, + 0.21716043015785474, + 0.2136865623023182, + 0.21030850028741443, + 0.2070283132333462, + 0.20384782307280636, + 0.20076859986681922, + 0.19779195787020848, + 0.19491895239041615, + 0.19215037747894992, + 0.18948676448982432, + 0.1869283815340578, + 0.18447523385359246, + 0.1821270651320757, + 0.1798833597536848, + 0.17774334601482075, + 0.17570600028699326, + 0.17377005212265106, + 0.17193399028921888, + 0.17019606971010487, + 0.1685543192851686, + 0.16700655055701966, + 0.16555036718367266, + 0.16418317517256145, + 0.16290219382575144, + 0.16170446734145166, + 0.1605868770126368, + 0.15954615395979044, + 0.15857889233154568, + 0.15768156290423546, + 0.15685052700928287, + 0.1560820507157655, + 0.15537231919456232, + 0.1547174511901024, + 0.1541135135260104, + 0.1535565355717, + 0.15304252359841122, + 0.15256747495506887, + 0.152127391996796, + 0.15171829570185316, + 0.1513362389161369, + 0.1509773191681695, + 0.1506376910016485, + 0.15031357777710655, + 0.1500012828989566, + 0.14969720042915455, + 0.14939782505382698, + 0.14909976137444464, + 0.14879973250040301, + 0.1484945879251691, + 0.1481813106734054, + 0.1478570237116176, + 0.14751899561992554, + 0.1471646455273189, + 0.14679154731742342, + 0.14639743311609618, + 0.14598019607619642, + 0.14553789247862206, + 0.1450687431720064, + 0.14457113437648225, + 0.14404361787948675, + 0.1434849106537791, + 0.1428938939296282, + 0.14226961175449102, + 0.1416112690744998, + 0.14091822937263734, + 0.14019001189869607, + 0.1394262885259524, + 0.1386268802689811, + 0.13779175349622264, + 0.13692101586979413, + 0.13601491204367608, + 0.13507381914979344, + 0.1340982420997283, + 0.13308880872783668, + 0.13204626479946885, + 0.1309714689058282, + 0.12986538726478447, + 0.12872908844472167, + 0.1275637380262944, + 0.12637059321478988, + 0.12515099741371957, + 0.12390637476829076, + 0.1226382246855701, + 0.12134811633648203, + 0.12003768314328703, + 0.11870861725489328, + 0.11736266401127349, + 0.11600161639739369, + 0.11462730948644037, + 0.11324161487172558, + 0.11184643508649715, + 0.11044369801094261, + 0.10903535126596636, + 0.10762335659383256, + 0.10620968422645863, + 0.10479630724305251, + 0.10338519591983214, + 0.10197831207578448, + 0.10057760341974728, + 0.09918499790553612, + 0.09780239810335298, + 0.09643167559727442, + 0.09507466542020761, + 0.09373316053929463, + 0.09240890640629551, + 0.09110359558899699, + 0.08981886250110574, + 0.08855627824941338, + 0.08731734561820581, + 0.08610349421192733, + 0.08491607577798387, + 0.08375635973224327, + 0.08262552891026831, + 0.08152467556757363, + 0.08045479765222008, + 0.07941679537285154, + 0.07841146808481779, + 0.0774395115163254, + 0.07650151535560278, + 0.07559796121886968, + 0.07472922101746232, + 0.07389555574079398, + 0.07309711466994025, + 0.07233393503453608, + 0.07160594212337834, + 0.07091294985665957, + 0.07025466182513257, + 0.06963067279874822, + 0.06904047070444076, + 0.06848343906977976, + 0.06795885992619555, + 0.06746591716243809, + 0.0670037003158801, + 0.06657120878624595, + 0.06616735645337418, + 0.06579097667772493, + 0.06544082765955393, + 0.06511559813002356, + 0.06481391334502515, + 0.06453434135018099, + 0.0642753994833943, + 0.06403556107944633, + 0.06381326233952225, + 0.06360690932719526, + 0.06341488505132957, + 0.0632355565955916, + 0.06306728225378833, + 0.06290841863009797, + 0.06275732766341854, + 0.0626123835355383, + 0.062471979423627505, + 0.06233453405865662, + 0.06219849805275528, + 0.06206235996022993, + 0.061924652038937246, + 0.06178395568095708, + 0.06163890648399504, + 0.061488198937656266, + 0.0613305907016418, + 0.061164906456001554, + 0.060990041306807796, + 0.06080496373395925, + 0.06060871807126065, + 0.06040042651241286, + 0.06017929064006538, + 0.05994459247859218, + 0.0596956950747245, + 0.05943204261357679, + 0.05915316008090597, + 0.058858652485619535, + 0.05854820365956686, + 0.05822157465448408, + 0.05787860175859329, + 0.05751919415775508, + 0.05714333126822771, + 0.05675105976997009, + 0.05634249037103246, + 0.05591779433489383, + 0.05547719980361917, + 0.055020987950421006, + 0.054549488995611545, + 0.05406307812003208, + 0.053562171309838535, + 0.053047221166027385, + 0.0525187127113007, + 0.05197715922581534, + 0.051423098142051725, + 0.05085708702748735, + 0.0502796996819938, + 0.04969152237491282, + 0.049093150244627806, + 0.04848518388116698, + 0.0478682261099627, + 0.04724287899239206, + 0.046609741056152745, + 0.04596940476591475, + 0.04532245424206515, + 0.04466946323274829, + 0.044010993341829195, + 0.043347592512897166, + 0.04267979376700117, + 0.04200811418949374, + 0.04133305415917372, + 0.04065509681087611, + 0.039974707720787477, + 0.039292334802061894, + 0.038608408396809915, + 0.03792334154922032, + 0.03723753044347414, + 0.03655135498921742, + 0.035865179536679045, + 0.03517935370305359, + 0.03449421329151197, + 0.03381008128414885, + 0.03312726889032353, + 0.032446076632184025, + 0.03176679544968117, + 0.031089707808057514, + 0.030415088791631757, + 0.029743207168672313, + 0.029074326413245342, + 0.02840870567113107, + 0.02774660065818456, + 0.02708826448088588 + ], + "yaxis": "y" + }, + { + "legendgroup": "Premium", + "marker": { + "color": "rgb(255, 127, 14)" + }, + "mode": "lines", + "name": "Premium", + "showlegend": true, + "type": "scatter", + "x": [ + 0.23, + 0.23450000000000001, + 0.23900000000000002, + 0.24350000000000002, + 0.248, + 0.2525, + 0.257, + 0.2615, + 0.266, + 0.2705, + 0.275, + 0.2795, + 0.28400000000000003, + 0.28850000000000003, + 0.29300000000000004, + 0.2975, + 0.302, + 0.3065, + 0.311, + 0.3155, + 0.32, + 0.3245, + 0.329, + 0.3335, + 0.338, + 0.3425, + 0.34700000000000003, + 0.35150000000000003, + 0.356, + 0.36050000000000004, + 0.365, + 0.36950000000000005, + 0.374, + 0.3785, + 0.383, + 0.3875, + 0.392, + 0.3965, + 0.401, + 0.40549999999999997, + 0.41000000000000003, + 0.4145, + 0.41900000000000004, + 0.4235, + 0.42800000000000005, + 0.4325, + 0.437, + 0.4415, + 0.446, + 0.4505, + 0.455, + 0.4595, + 0.464, + 0.4685, + 0.473, + 0.47750000000000004, + 0.482, + 0.48650000000000004, + 0.491, + 0.49550000000000005, + 0.5, + 0.5045000000000001, + 0.509, + 0.5135, + 0.518, + 0.5225, + 0.527, + 0.5315, + 0.536, + 0.5405, + 0.545, + 0.5495, + 0.554, + 0.5585, + 0.5630000000000001, + 0.5675, + 0.5720000000000001, + 0.5765, + 0.581, + 0.5855, + 0.59, + 0.5945, + 0.599, + 0.6035, + 0.608, + 0.6125, + 0.617, + 0.6215, + 0.626, + 0.6305000000000001, + 0.635, + 0.6395, + 0.644, + 0.6485, + 0.653, + 0.6575, + 0.662, + 0.6665, + 0.671, + 0.6755, + 0.68, + 0.6845, + 0.6890000000000001, + 0.6935, + 0.6980000000000001, + 0.7025, + 0.707, + 0.7115, + 0.716, + 0.7205, + 0.725, + 0.7295, + 0.734, + 0.7384999999999999, + 0.743, + 0.7474999999999999, + 0.752, + 0.7565, + 0.761, + 0.7655, + 0.77, + 0.7745, + 0.779, + 0.7835, + 0.788, + 0.7925, + 0.7969999999999999, + 0.8015, + 0.8059999999999999, + 0.8105, + 0.815, + 0.8195, + 0.824, + 0.8285, + 0.833, + 0.8375, + 0.842, + 0.8465, + 0.851, + 0.8554999999999999, + 0.86, + 0.8644999999999999, + 0.869, + 0.8734999999999999, + 0.878, + 0.8825, + 0.887, + 0.8915, + 0.896, + 0.9005, + 0.905, + 0.9095, + 0.914, + 0.9185, + 0.9229999999999999, + 0.9275, + 0.9319999999999999, + 0.9365, + 0.941, + 0.9455, + 0.95, + 0.9545, + 0.959, + 0.9635, + 0.968, + 0.9725, + 0.977, + 0.9814999999999999, + 0.986, + 0.9904999999999999, + 0.995, + 0.9994999999999999, + 1.004, + 1.0085, + 1.0130000000000001, + 1.0175, + 1.022, + 1.0265, + 1.0310000000000001, + 1.0355, + 1.04, + 1.0445, + 1.049, + 1.0535, + 1.058, + 1.0625, + 1.067, + 1.0715000000000001, + 1.076, + 1.0805, + 1.085, + 1.0895000000000001, + 1.094, + 1.0985, + 1.103, + 1.1075, + 1.112, + 1.1165, + 1.121, + 1.1255, + 1.1300000000000001, + 1.1345, + 1.139, + 1.1435, + 1.1480000000000001, + 1.1525, + 1.157, + 1.1615, + 1.1660000000000001, + 1.1705, + 1.175, + 1.1795, + 1.184, + 1.1885000000000001, + 1.193, + 1.1975, + 1.202, + 1.2065000000000001, + 1.211, + 1.2155, + 1.22, + 1.2245000000000001, + 1.229, + 1.2335, + 1.238, + 1.2425, + 1.2469999999999999, + 1.2515, + 1.256, + 1.2605, + 1.265, + 1.2695, + 1.274, + 1.2785, + 1.283, + 1.2875, + 1.292, + 1.2965, + 1.301, + 1.3054999999999999, + 1.31, + 1.3145, + 1.319, + 1.3235, + 1.328, + 1.3325, + 1.337, + 1.3415, + 1.346, + 1.3505, + 1.355, + 1.3595, + 1.3639999999999999, + 1.3685, + 1.373, + 1.3775, + 1.382, + 1.3865, + 1.391, + 1.3955, + 1.4, + 1.4045, + 1.409, + 1.4135, + 1.418, + 1.4224999999999999, + 1.427, + 1.4315, + 1.436, + 1.4405, + 1.445, + 1.4495, + 1.454, + 1.4585, + 1.463, + 1.4675, + 1.472, + 1.4765, + 1.4809999999999999, + 1.4855, + 1.49, + 1.4945, + 1.4989999999999999, + 1.5035, + 1.508, + 1.5125, + 1.517, + 1.5215, + 1.526, + 1.5305, + 1.535, + 1.5395, + 1.544, + 1.5485, + 1.553, + 1.5574999999999999, + 1.562, + 1.5665, + 1.571, + 1.5755, + 1.58, + 1.5845, + 1.589, + 1.5935, + 1.598, + 1.6025, + 1.607, + 1.6115, + 1.6159999999999999, + 1.6205, + 1.625, + 1.6295, + 1.634, + 1.6385, + 1.643, + 1.6475, + 1.652, + 1.6565, + 1.661, + 1.6655, + 1.67, + 1.6744999999999999, + 1.679, + 1.6835, + 1.688, + 1.6925, + 1.697, + 1.7015, + 1.706, + 1.7105, + 1.715, + 1.7195, + 1.724, + 1.7285, + 1.7329999999999999, + 1.7375, + 1.742, + 1.7465, + 1.751, + 1.7555, + 1.76, + 1.7645, + 1.769, + 1.7735, + 1.778, + 1.7825, + 1.787, + 1.7915, + 1.796, + 1.8005, + 1.805, + 1.8094999999999999, + 1.814, + 1.8185, + 1.823, + 1.8275, + 1.832, + 1.8365, + 1.841, + 1.8455, + 1.85, + 1.8545, + 1.859, + 1.8635, + 1.8679999999999999, + 1.8725, + 1.877, + 1.8815, + 1.886, + 1.8905, + 1.895, + 1.8995, + 1.904, + 1.9085, + 1.913, + 1.9175, + 1.922, + 1.9264999999999999, + 1.931, + 1.9355, + 1.94, + 1.9445, + 1.949, + 1.9535, + 1.958, + 1.9625, + 1.967, + 1.9715, + 1.976, + 1.9805, + 1.9849999999999999, + 1.9895, + 1.994, + 1.9985, + 2.003, + 2.0075000000000003, + 2.012, + 2.0165, + 2.021, + 2.0255, + 2.0300000000000002, + 2.0345, + 2.039, + 2.0435, + 2.048, + 2.0525, + 2.057, + 2.0615, + 2.0660000000000003, + 2.0705, + 2.075, + 2.0795, + 2.084, + 2.0885000000000002, + 2.093, + 2.0975, + 2.1020000000000003, + 2.1065, + 2.111, + 2.1155, + 2.12, + 2.1245000000000003, + 2.129, + 2.1335, + 2.138, + 2.1425, + 2.1470000000000002, + 2.1515, + 2.156, + 2.1605000000000003, + 2.165, + 2.1695, + 2.174, + 2.1785, + 2.1830000000000003, + 2.1875, + 2.192, + 2.1965, + 2.201, + 2.2055000000000002, + 2.21, + 2.2145, + 2.2190000000000003, + 2.2235, + 2.228, + 2.2325, + 2.237, + 2.2415, + 2.246, + 2.2505, + 2.255, + 2.2595, + 2.264, + 2.2685, + 2.273, + 2.2775, + 2.282, + 2.2865, + 2.291, + 2.2955, + 2.3, + 2.3045, + 2.309, + 2.3135, + 2.318, + 2.3225, + 2.327, + 2.3315, + 2.336, + 2.3405, + 2.345, + 2.3495, + 2.354, + 2.3585, + 2.363, + 2.3675, + 2.372, + 2.3765, + 2.381, + 2.3855, + 2.39, + 2.3945, + 2.399, + 2.4035, + 2.408, + 2.4125, + 2.417, + 2.4215, + 2.426, + 2.4305, + 2.435, + 2.4395, + 2.444, + 2.4485, + 2.453, + 2.4575, + 2.462, + 2.4665, + 2.471, + 2.4755 + ], + "xaxis": "x", + "y": [ + 0.604049368699416, + 0.6153238644823034, + 0.6264748310029326, + 0.6374898090610832, + 0.6483564770567071, + 0.6590626816678661, + 0.6695964682372623, + 0.6799461107498811, + 0.6901001412852155, + 0.7000473788291297, + 0.7097769573326309, + 0.7192783529075938, + 0.7285414100528997, + 0.7375563668084472, + 0.7463138787389776, + 0.7548050416547825, + 0.7630214129819023, + 0.7709550317004912, + 0.7785984367765315, + 0.7859446840189522, + 0.7929873613015311, + 0.7997206020964687, + 0.806139097274474, + 0.8122381051342449, + 0.8180134596325388, + 0.8234615767944231, + 0.8285794592918244, + 0.8333646991869906, + 0.8378154788460099, + 0.8419305700359949, + 0.8457093312277987, + 0.849151703134353, + 0.8522582025225811, + 0.8550299143445711, + 0.8574684822409918, + 0.8595760974767647, + 0.8613554863756319, + 0.8628098963264049, + 0.8639430804394195, + 0.8647592809369566, + 0.8652632113660611, + 0.8654600377263738, + 0.8653553586091707, + 0.8649551844468268, + 0.864265915974327, + 0.8632943220062888, + 0.8620475166341371, + 0.8605329359487317, + 0.8587583143937149, + 0.856731660854302, + 0.8544612345850435, + 0.8519555210783909, + 0.8492232079736165, + 0.8462731611028349, + 0.8431144007676171, + 0.8397560783358874, + 0.8362074532446323, + 0.8324778704893396, + 0.8285767386761093, + 0.8245135087071072, + 0.820297653164412, + 0.8159386464514907, + 0.8114459457454748, + 0.806828972807211, + 0.8020970966896932, + 0.7972596173790865, + 0.7923257503960668, + 0.7873046123787665, + 0.7822052076621562, + 0.7770364158624059, + 0.7718069804685188, + 0.7665254984374755, + 0.7612004107832959, + 0.7558399941447537, + 0.7504523533111238, + 0.7450454146802563, + 0.7396269206184826, + 0.7342044246874515, + 0.7287852876988917, + 0.7233766745546143, + 0.7179855518257887, + 0.7126186860225983, + 0.7072826425029366, + 0.701983784966753, + 0.6967282754810383, + 0.6915220749792478, + 0.6863709441782281, + 0.6812804448553671, + 0.676255941428787, + 0.6713026027839127, + 0.6664254042906536, + 0.661629129956716, + 0.6569183746642503, + 0.6522975464390268, + 0.6477708687037106, + 0.6433423824694487, + 0.6390159484229226, + 0.6347952488692663, + 0.6306837894946458, + 0.6266849009160146, + 0.6228017399893435, + 0.6190372908516469, + 0.6153943656762695, + 0.6118756051250415, + 0.6084834784852731, + 0.6052202834837999, + 0.6020881457746717, + 0.5990890181013073, + 0.5962246791382511, + 0.5934967320217625, + 0.5909066025825718, + 0.588455537298011, + 0.5861446009845234, + 0.5839746742550865, + 0.581946450769476, + 0.5800604343084101, + 0.5783169357055119, + 0.5767160696736109, + 0.5752577515642803, + 0.573941694101491, + 0.5727674041320066, + 0.5717341794365376, + 0.5708411056467181, + 0.570087053313729, + 0.5694706751747274, + 0.5689904036633312, + 0.5686444487100772, + 0.5684307958781141, + 0.5683472048784298, + 0.5683912085075975, + 0.5685601120493556, + 0.5688509931794125, + 0.5692607024106261, + 0.5697858641131281, + 0.5704228781412066, + 0.5711679220956758, + 0.5720169542471922, + 0.5729657171424549, + 0.5740097419115795, + 0.5751443532910633, + 0.5763646753727675, + 0.5776656380852658, + 0.5790419844096979, + 0.580488278328039, + 0.5819989134973848, + 0.5835681226396129, + 0.5851899876314721, + 0.5868584502759568, + 0.5885673237316896, + 0.590310304572994, + 0.5920809854494322, + 0.5938728683098237, + 0.5956793781522057, + 0.5974938772577918, + 0.5993096798638505, + 0.6011200672275003, + 0.602918303029766, + 0.6046976490668438, + 0.6064513811734621, + 0.6081728053213864, + 0.6098552738346636, + 0.6114922016620443, + 0.6130770826461496, + 0.6146035057284773, + 0.6160651710291356, + 0.6174559057403437, + 0.618769679773244, + 0.6200006210983218, + 0.6211430307208728, + 0.6221913972343518, + 0.6231404108961749, + 0.6239849771724982, + 0.624720229700759, + 0.6253415426213218, + 0.6258445422322035, + 0.6262251179239033, + 0.6264794323544535, + 0.6266039308281045, + 0.6265953498445545, + 0.6264507247892249, + 0.6261673967387086, + 0.6257430183593989, + 0.6251755588810417, + 0.6244633081308738, + 0.6236048796178876, + 0.6225992126605925, + 0.6214455735555392, + 0.6201435557876243, + 0.6186930792868967, + 0.6170943887402476, + 0.6153480509698804, + 0.6134549513937886, + 0.6114162895868078, + 0.6092335739638713, + 0.6069086156100055, + 0.6044435212844469, + 0.6018406856287339, + 0.599102782611138, + 0.596232756241891, + 0.5932338105957292, + 0.5901093991800433, + 0.5868632136885235, + 0.5834991721815792, + 0.5800214067360305, + 0.5764342506075016, + 0.5727422249498559, + 0.5689500251365336, + 0.5650625067291789, + 0.5610846711391118, + 0.5570216510274654, + 0.5528786954895887, + 0.5486611550692392, + 0.5443744666477279, + 0.5400241382527231, + 0.5356157338308378, + 0.5311548580274843, + 0.5266471410166474, + 0.5220982234224669, + 0.5175137413734301, + 0.512899311729129, + 0.5082605175183336, + 0.5036028936260947, + 0.4989319127663673, + 0.4942529717754252, + 0.4895713782601401, + 0.48489233763384154, + 0.48022094057122294, + 0.47556215091235915, + 0.47092079404461423, + 0.46630154578977245, + 0.46170892182233075, + 0.4571472676435283, + 0.4526207491341378, + 0.44813334370769514, + 0.443688832084248, + 0.43929079070327703, + 0.4349425847928468, + 0.43064736211050103, + 0.4264080473698186, + 0.422227337364898, + 0.41810769680339793, + 0.41405135485704997, + 0.41006030243678504, + 0.40613629019792014, + 0.4022808272789315, + 0.3984951807755583, + 0.3947803759500566, + 0.39113719717348255, + 0.38756618959691047, + 0.38406766154551963, + 0.3806416876273927, + 0.37728811254686195, + 0.37400655561011714, + 0.37079641590871304, + 0.3676568781645109, + 0.3645869192174566, + 0.3615853151355436, + 0.3586506489241509, + 0.3557813188100002, + 0.3529755470728137, + 0.35023138939590137, + 0.34754674470490743, + 0.3449193654621158, + 0.34234686838194045, + 0.33982674553153347, + 0.3373563757788568, + 0.33493303654910417, + 0.3325539158489838, + 0.3302161245171805, + 0.32791670865823147, + 0.32565266221613115, + 0.3234209396432174, + 0.32121846861932024, + 0.3190421627757155, + 0.3168889343782328, + 0.314755706923792, + 0.31263942760482355, + 0.3105370795963537, + 0.30844569412110534, + 0.3063623622486757, + 0.30428424638582025, + 0.3022085914159782, + 0.30013273544752417, + 0.29805412013171034, + 0.29597030051297146, + 0.2938789543761141, + 0.2917778910569477, + 0.28966505968507245, + 0.2875385568298846, + 0.2853966335232995, + 0.28323770163526224, + 0.2810603395808114, + 0.27886329734022236, + 0.27664550077660277, + 0.2744060552382432, + 0.2721442484359511, + 0.2698595525886436, + 0.26755162583339837, + 0.26522031289927483, + 0.2628656450470946, + 0.26048783928042607, + 0.2580872968358687, + 0.25566460096361593, + 0.2532205140120415, + 0.2507559738327379, + 0.24827208952503782, + 0.24577013654151106, + 0.24325155117827418, + 0.24071792447618603, + 0.23817099556105376, + 0.2356126444529017, + 0.23304488437612753, + 0.23046985360395053, + 0.22788980687200952, + 0.22530710639720777, + 0.22272421253899857, + 0.2201436741412098, + 0.21756811859324485, + 0.21500024165004536, + 0.21244279705059865, + 0.20989858597498756, + 0.20737044638002042, + 0.20486124225338084, + 0.20237385282595452, + 0.19991116178159463, + 0.19747604650298645, + 0.19507136739162004, + 0.19269995729900935, + 0.19036461110537453, + 0.1880680754809301, + 0.1858130388637513, + 0.18360212168694068, + 0.18143786688644561, + 0.17932273071947358, + 0.177259073921926, + 0.17524915323172602, + 0.17329511330329178, + 0.17139897903673945, + 0.1695626483437162, + 0.1677878853700002, + 0.16607631419327482, + 0.16442941301269648, + 0.1628485088450761, + 0.16133477274072258, + 0.15988921553017896, + 0.1585126841112956, + 0.15720585828430733, + 0.15596924814079544, + 0.15480319201066306, + 0.15370785496951664, + 0.15268322790712466, + 0.1517291271559281, + 0.15084519467692026, + 0.1500308987985715, + 0.14928553550286455, + 0.14860823025094375, + 0.14799794033933492, + 0.14745345777619465, + 0.14697341266558808, + 0.14655627708636948, + 0.14620036945085926, + 0.14590385932717986, + 0.145664772707813, + 0.14548099770571465, + 0.1453502906581168, + 0.14527028261701022, + 0.14523848620422672, + 0.14525230280799997, + 0.14530903009692656, + 0.14540586982634368, + 0.14553993591130132, + 0.1457082627395447, + 0.14590781369722056, + 0.14613548987941613, + 0.14638813895708097, + 0.14666256417144508, + 0.14695553342666126, + 0.14726378845112362, + 0.14758405399772, + 0.1479130470531815, + 0.14824748602668966, + 0.14858409988799995, + 0.1489196372255452, + 0.14925087519526903, + 0.1495746283313699, + 0.14988775719061068, + 0.15018717680249513, + 0.1504698648983053, + 0.15073286989282275, + 0.1509733185934769, + 0.15118842361268187, + 0.1513754904602383, + 0.15153192429388912, + 0.1516552363074177, + 0.15174304973705852, + 0.15179310546845626, + 0.15180326722794946, + 0.1517715263435629, + 0.15169600606275338, + 0.15157496541569274, + 0.15140680261462558, + 0.15119005798165677, + 0.15092341639916682, + 0.15060570927890235, + 0.15023591604767791, + 0.14981316514949178, + 0.1493367345657407, + 0.14880605185706877, + 0.14822069373223007, + 0.1475803851511371, + 0.14688499797103638, + 0.1461345491464559, + 0.1453291984952225, + 0.1444692460444325, + 0.14355512897176348, + 0.1425874181589432, + 0.14156681437553098, + 0.1404941441124023, + 0.13937035508547405, + 0.1381965114312369, + 0.13697378861658982, + 0.13570346808627354, + 0.1343869316719025, + 0.13302565578715345, + 0.13162120543413725, + 0.13017522804629206, + 0.12868944719335978, + 0.12716565617408396, + 0.125605711522241, + 0.12401152645146243, + 0.12238506426404458, + 0.1207283317485613, + 0.11904337259061866, + 0.11733226082049737, + 0.1155970943207523, + 0.11383998841605926, + 0.1120630695667365, + 0.11026846918643912, + 0.10845831760349585, + 0.10663473818429581, + 0.10479984163598483, + 0.10295572050455079, + 0.10110444388314654, + 0.09924805234422991, + 0.09738855310780954, + 0.09552791545677351, + 0.093668066408942, + 0.0918108866541622, + 0.0899582067634237, + 0.08811180367565503, + 0.08627339746654764, + 0.08444464840246893, + 0.08262715428126524, + 0.0808224480605221, + 0.07903199577266695, + 0.07725719472513905, + 0.07549937198276008, + 0.0737597831283825, + 0.0720396112968949, + 0.07033996647672552, + 0.06866188507210554, + 0.06700632971853068, + 0.06537418934311105, + 0.06376627946080245, + 0.06218334269689124, + 0.06062604952554796, + 0.05909499921376444, + 0.05759072095957284, + 0.05611367521307408, + 0.054664255168510645, + 0.05324278841538349, + 0.051849538736436405, + 0.05048470804022006, + 0.049148438415891796, + 0.04784081429790141, + 0.046561864728272305, + 0.0453115657042808, + 0.04408984259949208, + 0.04289657264630348, + 0.0417315874683799, + 0.04059467565164537, + 0.03948558534280196, + 0.03840402686469308, + 0.03734967533820533, + 0.03632217330079701, + 0.035321133312174756, + 0.034346140538082895, + 0.03339675530363245, + 0.03247251560808308, + 0.031572939593478955, + 0.030697527960046395, + 0.02984576632177165, + 0.029017127496091293, + 0.028211073722151193, + 0.027427058802604945, + 0.026664530164443315, + 0.02592293083486263, + 0.02520170132868434, + 0.024500281444344133, + 0.023818111965960356 + ], + "yaxis": "y" + }, + { + "legendgroup": "Very Good", + "marker": { + "color": "rgb(44, 160, 44)" + }, + "mode": "lines", + "name": "Very Good", + "showlegend": true, + "type": "scatter", + "x": [ + 0.23, + 0.23378000000000002, + 0.23756000000000002, + 0.24134, + 0.24512, + 0.2489, + 0.25268, + 0.25646, + 0.26024, + 0.26402000000000003, + 0.26780000000000004, + 0.27158000000000004, + 0.27536, + 0.27914, + 0.28292, + 0.2867, + 0.29048, + 0.29426, + 0.29804, + 0.30182000000000003, + 0.30560000000000004, + 0.30938, + 0.31316, + 0.31694, + 0.32072, + 0.3245, + 0.32828, + 0.33206, + 0.33584, + 0.33962000000000003, + 0.34340000000000004, + 0.34718000000000004, + 0.35096000000000005, + 0.35474, + 0.35852000000000006, + 0.3623, + 0.36608, + 0.36986, + 0.37364, + 0.37742000000000003, + 0.38120000000000004, + 0.38498, + 0.38876, + 0.39254, + 0.39632, + 0.4001, + 0.40388, + 0.40766, + 0.41144000000000003, + 0.41522000000000003, + 0.41900000000000004, + 0.42278000000000004, + 0.42656000000000005, + 0.43034, + 0.43412, + 0.4379, + 0.44168, + 0.44546, + 0.44924000000000003, + 0.45302000000000003, + 0.4568, + 0.46058, + 0.46436, + 0.46814, + 0.47192, + 0.4757, + 0.47948, + 0.48326, + 0.48704000000000003, + 0.49082000000000003, + 0.49460000000000004, + 0.49838000000000005, + 0.50216, + 0.5059400000000001, + 0.5097200000000001, + 0.5135, + 0.5172800000000001, + 0.52106, + 0.5248400000000001, + 0.52862, + 0.5324000000000001, + 0.53618, + 0.53996, + 0.54374, + 0.54752, + 0.5513, + 0.55508, + 0.55886, + 0.56264, + 0.56642, + 0.5702, + 0.57398, + 0.57776, + 0.5815400000000001, + 0.5853200000000001, + 0.5891000000000001, + 0.59288, + 0.5966600000000001, + 0.60044, + 0.6042200000000001, + 0.608, + 0.61178, + 0.61556, + 0.61934, + 0.62312, + 0.6269, + 0.63068, + 0.63446, + 0.63824, + 0.64202, + 0.6458, + 0.64958, + 0.65336, + 0.6571400000000001, + 0.6609200000000001, + 0.6647000000000001, + 0.6684800000000001, + 0.6722600000000001, + 0.6760400000000001, + 0.6798200000000001, + 0.6836, + 0.6873800000000001, + 0.69116, + 0.6949400000000001, + 0.69872, + 0.7025, + 0.70628, + 0.71006, + 0.71384, + 0.71762, + 0.7214, + 0.72518, + 0.72896, + 0.73274, + 0.7365200000000001, + 0.7403, + 0.7440800000000001, + 0.74786, + 0.75164, + 0.7554200000000001, + 0.7592, + 0.76298, + 0.76676, + 0.7705400000000001, + 0.77432, + 0.7781, + 0.78188, + 0.78566, + 0.78944, + 0.79322, + 0.7969999999999999, + 0.80078, + 0.80456, + 0.8083400000000001, + 0.81212, + 0.8159000000000001, + 0.8196800000000001, + 0.82346, + 0.82724, + 0.8310200000000001, + 0.8348000000000001, + 0.83858, + 0.84236, + 0.84614, + 0.84992, + 0.8537, + 0.85748, + 0.86126, + 0.86504, + 0.86882, + 0.8726, + 0.8763799999999999, + 0.88016, + 0.8839400000000001, + 0.8877200000000001, + 0.8915, + 0.8952800000000001, + 0.8990600000000001, + 0.90284, + 0.90662, + 0.9104000000000001, + 0.91418, + 0.91796, + 0.92174, + 0.9255200000000001, + 0.9293, + 0.93308, + 0.93686, + 0.94064, + 0.94442, + 0.9482, + 0.95198, + 0.9557599999999999, + 0.9595400000000001, + 0.9633200000000001, + 0.9671, + 0.97088, + 0.9746600000000001, + 0.9784400000000001, + 0.98222, + 0.986, + 0.9897800000000001, + 0.99356, + 0.99734, + 1.00112, + 1.0049000000000001, + 1.00868, + 1.0124600000000001, + 1.01624, + 1.0200200000000001, + 1.0238, + 1.0275800000000002, + 1.03136, + 1.0351400000000002, + 1.03892, + 1.0427000000000002, + 1.04648, + 1.0502600000000002, + 1.05404, + 1.05782, + 1.0616, + 1.0653800000000002, + 1.06916, + 1.07294, + 1.0767200000000001, + 1.0805, + 1.0842800000000001, + 1.08806, + 1.0918400000000001, + 1.09562, + 1.0994000000000002, + 1.10318, + 1.1069600000000002, + 1.11074, + 1.1145200000000002, + 1.1183, + 1.1220800000000002, + 1.12586, + 1.1296400000000002, + 1.13342, + 1.1372, + 1.14098, + 1.1447600000000002, + 1.1485400000000001, + 1.15232, + 1.1561000000000001, + 1.1598800000000002, + 1.1636600000000001, + 1.16744, + 1.1712200000000001, + 1.175, + 1.1787800000000002, + 1.18256, + 1.1863400000000002, + 1.19012, + 1.1939000000000002, + 1.19768, + 1.20146, + 1.20524, + 1.2090200000000002, + 1.2128, + 1.21658, + 1.2203600000000001, + 1.2241400000000002, + 1.2279200000000001, + 1.2317, + 1.23548, + 1.23926, + 1.2430400000000001, + 1.24682, + 1.2506, + 1.25438, + 1.2581600000000002, + 1.26194, + 1.26572, + 1.2695, + 1.27328, + 1.27706, + 1.2808400000000002, + 1.28462, + 1.2884, + 1.29218, + 1.29596, + 1.29974, + 1.30352, + 1.3073000000000001, + 1.3110800000000002, + 1.3148600000000001, + 1.31864, + 1.3224200000000002, + 1.3262, + 1.32998, + 1.33376, + 1.33754, + 1.34132, + 1.3451000000000002, + 1.34888, + 1.35266, + 1.35644, + 1.36022, + 1.3639999999999999, + 1.36778, + 1.3715600000000001, + 1.3753400000000002, + 1.3791200000000001, + 1.3829, + 1.3866800000000001, + 1.39046, + 1.39424, + 1.39802, + 1.4018000000000002, + 1.40558, + 1.4093600000000002, + 1.41314, + 1.41692, + 1.4207, + 1.42448, + 1.4282599999999999, + 1.4320400000000002, + 1.43582, + 1.4396000000000002, + 1.44338, + 1.44716, + 1.4509400000000001, + 1.45472, + 1.4585, + 1.46228, + 1.4660600000000001, + 1.46984, + 1.4736200000000002, + 1.4774, + 1.48118, + 1.48496, + 1.48874, + 1.49252, + 1.4963000000000002, + 1.50008, + 1.5038600000000002, + 1.50764, + 1.51142, + 1.5152, + 1.51898, + 1.52276, + 1.5265400000000002, + 1.5303200000000001, + 1.5341, + 1.5378800000000001, + 1.54166, + 1.5454400000000001, + 1.54922, + 1.553, + 1.5567800000000003, + 1.5605600000000002, + 1.56434, + 1.5681200000000002, + 1.5719, + 1.57568, + 1.57946, + 1.58324, + 1.5870199999999999, + 1.5908000000000002, + 1.59458, + 1.59836, + 1.6021400000000001, + 1.60592, + 1.6097000000000001, + 1.61348, + 1.61726, + 1.6210400000000003, + 1.6248200000000002, + 1.6286, + 1.6323800000000002, + 1.63616, + 1.63994, + 1.64372, + 1.6475, + 1.65128, + 1.6550600000000002, + 1.65884, + 1.6626200000000002, + 1.6664, + 1.67018, + 1.6739600000000001, + 1.67774, + 1.68152, + 1.6853000000000002, + 1.6890800000000001, + 1.69286, + 1.6966400000000001, + 1.70042, + 1.7042, + 1.70798, + 1.71176, + 1.71554, + 1.7193200000000002, + 1.7231, + 1.7268800000000002, + 1.73066, + 1.73444, + 1.73822, + 1.742, + 1.74578, + 1.7495600000000002, + 1.7533400000000001, + 1.75712, + 1.7609000000000001, + 1.76468, + 1.76846, + 1.77224, + 1.7760200000000002, + 1.7798000000000003, + 1.7835800000000002, + 1.78736, + 1.7911400000000002, + 1.79492, + 1.7987, + 1.80248, + 1.80626, + 1.81004, + 1.8138200000000002, + 1.8176, + 1.82138, + 1.8251600000000001, + 1.82894, + 1.83272, + 1.8365, + 1.8402800000000001, + 1.8440600000000003, + 1.8478400000000001, + 1.85162, + 1.8554000000000002, + 1.85918, + 1.86296, + 1.86674, + 1.8705200000000002, + 1.8743, + 1.8780800000000002, + 1.88186, + 1.88564, + 1.88942, + 1.8932, + 1.89698, + 1.9007600000000002, + 1.9045400000000001, + 1.9083200000000002, + 1.9121000000000001, + 1.91588, + 1.9196600000000001, + 1.92344, + 1.92722, + 1.931, + 1.9347800000000002, + 1.93856, + 1.9423400000000002, + 1.94612, + 1.9499, + 1.95368, + 1.95746, + 1.96124, + 1.9650200000000002, + 1.9688, + 1.9725800000000002, + 1.9763600000000001, + 1.98014, + 1.9839200000000001, + 1.9877, + 1.99148, + 1.9952600000000003, + 1.9990400000000002, + 2.0028200000000003, + 2.0066, + 2.01038, + 2.0141600000000004, + 2.0179400000000003, + 2.02172, + 2.0255000000000005, + 2.0292800000000004, + 2.0330600000000003, + 2.03684, + 2.04062, + 2.0444, + 2.0481800000000003, + 2.0519600000000002, + 2.05574, + 2.0595200000000005, + 2.0633000000000004, + 2.0670800000000003, + 2.07086, + 2.07464, + 2.0784200000000004, + 2.0822000000000003, + 2.08598, + 2.0897600000000005, + 2.0935400000000004, + 2.0973200000000003, + 2.1011, + 2.10488, + 2.10866, + 2.1124400000000003, + 2.11622 + ], + "xaxis": "x", + "y": [ + 0.6859777770953573, + 0.6971218688830132, + 0.7081588577402547, + 0.7190802706471098, + 0.7298777832503395, + 0.7405432354586869, + 0.7510686465914302, + 0.7614462300348317, + 0.7716684073630931, + 0.7817278218828311, + 0.7916173515624884, + 0.8013301213108164, + 0.8108595145712444, + 0.8201991842019677, + 0.8293430626144851, + 0.8382853711465181, + 0.8470206286483671, + 0.8555436592649738, + 0.8638495993993054, + 0.8719339038458571, + 0.8797923510864479, + 0.8874210477436708, + 0.894816432190666, + 0.9019752773189936, + 0.9088946924695254, + 0.9155721245342826, + 0.9220053582400828, + 0.928192515627647, + 0.9341320547424757, + 0.9398227675563912, + 0.9452637771409862, + 0.950454534116448, + 0.9553948124013074, + 0.9600847042905079, + 0.9645246148909419, + 0.9687152559450586, + 0.9726576390745129, + 0.9763530684769737, + 0.979803133110138, + 0.9830096983977571, + 0.9859748974931059, + 0.9887011221356806, + 0.9911910131371678, + 0.9934474505327422, + 0.9954735434337426, + 0.9972726196173826, + 0.9988482148888681, + 1.0002040622507018, + 1.0013440809133065, + 1.0022723651803207, + 1.002993173241076, + 1.003510915901736, + 1.0038301452856064, + 1.003955543531976, + 1.00389191152165, + 1.0036441576562112, + 1.0032172867166922, + 1.0026163888261843, + 1.001846628539496, + 1.000913234081844, + 0.999821486757125, + 0.9985767105451765, + 0.9971842619061008, + 0.9956495198085615, + 0.9939778759977558, + 0.9921747255176129, + 0.9902454575006738, + 0.9881954462380576, + 0.9860300425408466, + 0.9837545654033326, + 0.9813742939775849, + 0.9788944598679424, + 0.9763202397531837, + 0.973656748343401, + 0.9709090316777366, + 0.9680820607685577, + 0.9651807255968485, + 0.9622098294630284, + 0.9591740836966949, + 0.956078102728247, + 0.9529263995246682, + 0.9497233813912024, + 0.9464733461400382, + 0.9431804786264885, + 0.9398488476526088, + 0.9364824032374891, + 0.9330849742528944, + 0.9296602664221758, + 0.9262118606798173, + 0.9227432118880812, + 0.919257647906689, + 0.9157583690105063, + 0.9122484476495343, + 0.9087308285446066, + 0.9052083291113443, + 0.9016836402040471, + 0.8981593271702795, + 0.8946378312059564, + 0.8911214709998508, + 0.8876124446553839, + 0.884112831876735, + 0.8806245964052055, + 0.877149588690948, + 0.8736895487841447, + 0.8702461094288908, + 0.8668207993421004, + 0.8634150466589752, + 0.8600301825257678, + 0.8566674448198623, + 0.8533279819765601, + 0.8500128569013612, + 0.8467230509460822, + 0.843459467926719, + 0.8402229381607224, + 0.8370142225011494, + 0.8338340163450825, + 0.8306829535937924, + 0.8275616105422915, + 0.8244705096762289, + 0.821410123354549, + 0.8183808773568908, + 0.8153831542754536, + 0.8124172967318627, + 0.8094836104006123, + 0.8065823668217252, + 0.8037138059865464, + 0.8008781386819545, + 0.7980755485797477, + 0.7953061940595504, + 0.7925702097553037, + 0.7898677078171648, + 0.7871987788825269, + 0.7845634927517936, + 0.7819618987665607, + 0.7793940258898663, + 0.7768598824902853, + 0.7743594558337151, + 0.7718927112887908, + 0.7694595912539941, + 0.7670600138165207, + 0.7646938711550807, + 0.7623610277006841, + 0.760061318071442, + 0.7577945447991848, + 0.7555604758674471, + 0.7533588420819664, + 0.7511893342963357, + 0.7490516005168151, + 0.7469452429114938, + 0.7448698147500891, + 0.7428248173014952, + 0.7408096967169965, + 0.7388238409275156, + 0.736866576583674, + 0.7349371660675997, + 0.7330348046053748, + 0.7311586175088199, + 0.7293076575748803, + 0.7274809026703135, + 0.7256772535285358, + 0.7238955317845698, + 0.7221344782728443, + 0.7203927516112818, + 0.7186689270936376, + 0.7169614959103936, + 0.7152688647167279, + 0.713589355564188, + 0.711921206210604, + 0.7102625708206751, + 0.7086115210674401, + 0.7069660476424293, + 0.7053240621800727, + 0.703683399599313, + 0.7020418208631224, + 0.7003970161539524, + 0.6987466084608308, + 0.6970881575712469, + 0.6954191644585913, + 0.6937370760535714, + 0.692039290385609, + 0.6903231620781288, + 0.6885860081793962, + 0.6868251143085602, + 0.685037741094706, + 0.6832211308848246, + 0.6813725146950914, + 0.6794891193782228, + 0.6775681749785177, + 0.6756069222448439, + 0.6736026202710158, + 0.671552554232102, + 0.669454043184595, + 0.6673044478979884, + 0.6651011786850014, + 0.6628417031976696, + 0.6605235541566208, + 0.6581443369812109, + 0.6557017372885884, + 0.6531935282305678, + 0.6506175776378615, + 0.6479718549423522, + 0.645254437849146, + 0.6424635187314949, + 0.6395974107230568, + 0.6366545534835618, + 0.6336335186155726, + 0.6305330147118174, + 0.627351892014431, + 0.6240891466693134, + 0.6207439245608788, + 0.6173155247144546, + 0.6138034022556781, + 0.6102071709182953, + 0.6065266050939634, + 0.6027616414196092, + 0.5989123799001299, + 0.5949790845661367, + 0.5909621836685347, + 0.5868622694136444, + 0.5826800972444633, + 0.5784165846754915, + 0.5740728096902434, + 0.5696500087122507, + 0.565149574161823, + 0.5605730516123801, + 0.5559221365613133, + 0.5511986708317325, + 0.5464046386223509, + 0.5415421622238048, + 0.536613497420581, + 0.5316210285982398, + 0.526567263576415, + 0.5214548281883546, + 0.5162864606281632, + 0.5110650055870769, + 0.5057934082002106, + 0.5004747078251116, + 0.4951120316734607, + 0.4897085883168172, + 0.4842676610871393, + 0.4787926013922903, + 0.47328682196624705, + 0.4677537900731328, + 0.46219702068359136, + 0.4566200696412112, + 0.45102652683606564, + 0.4454200094015364, + 0.4398041549497988, + 0.4341826148604793, + 0.4285590476361306, + 0.4229371123372488, + 0.4173204621087301, + 0.4117127378087373, + 0.40611756175010544, + 0.4005385315635866, + 0.3949792141913806, + 0.3894431400186544, + 0.3839337971499943, + 0.3784546258370038, + 0.3730090130626388, + 0.3676002872872479, + 0.3622317133606865, + 0.3569064876044362, + 0.35162773306711564, + 0.3463984949564396, + 0.3412217362502851, + 0.3361003334892375, + 0.33103707275274324, + 0.32603464582077085, + 0.3210956465227654, + 0.31622256727550835, + 0.31141779581149326, + 0.30668361209928635, + 0.30202218545745224, + 0.29743557186349356, + 0.29292571145944113, + 0.2884944262556438, + 0.28414341803446247, + 0.27987426645559316, + 0.27568842736482757, + 0.27158723130810575, + 0.26757188225278966, + 0.26364345651811133, + 0.25980290191675937, + 0.2560510371095862, + 0.2523885511753743, + 0.2488160033975282, + 0.2453338232694827, + 0.24194231072048183, + 0.2386416365631921, + 0.23543184316443422, + 0.23231284534003752, + 0.2292844314745404, + 0.22634626486611983, + 0.22349788529674347, + 0.2207387108271352, + 0.21806803981565623, + 0.21548505315973246, + 0.2129888167578913, + 0.2105782841899372, + 0.2082522996121543, + 0.2060096008638434, + 0.20384882278080413, + 0.2017685007107582, + 0.19976707422497528, + 0.19784289101972938, + 0.19599421100047143, + 0.19421921054094643, + 0.1925159869087894, + 0.19088256284844843, + 0.1893168913116581, + 0.18781686032501538, + 0.18638029798363578, + 0.1850049775592795, + 0.18368862271079983, + 0.1824289127842773, + 0.18122348818974818, + 0.18006995584103885, + 0.17896589464486642, + 0.17790886102508013, + 0.17689639446767577, + 0.17592602307205493, + 0.1749952690938829, + 0.17410165446485598, + 0.1732427062747032, + 0.17241596220084535, + 0.17161897587126168, + 0.17084932214634493, + 0.17010460230579819, + 0.169382449126944, + 0.1686805318412523, + 0.16799656095630064, + 0.16732829293092488, + 0.16667353469185267, + 0.16603014798072496, + 0.16539605352106168, + 0.16476923499540774, + 0.1641477428236206, + 0.16352969773400763, + 0.16291329411980876, + 0.16229680317429707, + 0.16167857579860934, + 0.16105704527720158, + 0.16043072971670547, + 0.15979823424473324, + 0.15915825296604652, + 0.15850957067429294, + 0.15785106431833631, + 0.15718170422297, + 0.15650055506458016, + 0.15580677660306003, + 0.15509962417197926, + 0.15437844892969252, + 0.15364269787471865, + 0.1528919136293054, + 0.15212573399569032, + 0.15134389129006576, + 0.15054621145974964, + 0.14973261298949814, + 0.14890310560329764, + 0.1480577887683147, + 0.1471968500079981, + 0.1463205630315826, + 0.14542928568747537, + 0.1445234577481875, + 0.1436035985345972, + 0.142670304387465, + 0.14172424599416547, + 0.14076616557864804, + 0.13979687396264925, + 0.13881724750614183, + 0.13782822493498045, + 0.13683080406361434, + 0.13582603842066865, + 0.13481503378508122, + 0.13379894464036923, + 0.13277897055447552, + 0.13175635249251816, + 0.13073236906961225, + 0.12970833275082258, + 0.1286855860051527, + 0.1276654974203427, + 0.12664945778514058, + 0.12563887614557984, + 0.12463517584169456, + 0.12363979053101395, + 0.12265416020508924, + 0.12167972720524714, + 0.12071793224370235, + 0.11977021043614075, + 0.1188379873518402, + 0.11792267508741691, + 0.1170256683702732, + 0.11614834069785845, + 0.11529204051888783, + 0.11445808746271256, + 0.11364776862309615, + 0.11286233490272068, + 0.1121029974248218, + 0.11137092401843039, + 0.11066723578378869, + 0.10999300374458405, + 0.10934924559373452, + 0.10873692253952459, + 0.10815693625897495, + 0.10761012596537274, + 0.10709726559694557, + 0.10661906113368605, + 0.10617614804934798, + 0.10576908890562363, + 0.10539837109547909, + 0.1050644047425593, + 0.1047675207634961, + 0.1045079690998204, + 0.10428591712603899, + 0.10410144824024403, + 0.10395456064340029, + 0.10384516631320467, + 0.1037730901781056, + 0.10373806949674211, + 0.1037397534476853, + 0.10377770293395089, + 0.10385139060630373, + 0.10396020110888023, + 0.10410343155012956, + 0.1042802922015119, + 0.10448990742579252, + 0.10473131683613571, + 0.10500347668654973, + 0.10530526149353212, + 0.10563546588805747, + 0.10599280669630343, + 0.10637592524675962, + 0.10678338990057983, + 0.10721369880125527, + 0.10766528283889121, + 0.10813650882356066, + 0.10862568286140947, + 0.10913105392639082, + 0.10965081761970497, + 0.1101831201082499, + 0.11072606223261504, + 0.11127770377441323, + 0.11183606787202105, + 0.11239914557311109, + 0.11296490051169952, + 0.11353127369681662, + 0.11409618839932442, + 0.11465755512287565, + 0.11521327664452141, + 0.115761253110042, + 0.11629938716869684, + 0.11682558913176855, + 0.11733778213901663, + 0.11783390731695541, + 0.11831192891274507, + 0.1187698393874035, + 0.11920566445206224, + 0.11961746803104427, + 0.12000335713568511, + 0.12036148663302151, + 0.1206900638937438, + 0.12098735330415009, + 0.12125168062725074, + 0.12148143719863692, + 0.12167508394327431, + 0.12183115519996987, + 0.12194826234092598, + 0.12202509717450327, + 0.12206043512008102, + 0.12205313814472242, + 0.12200215745221069, + 0.12190653591592572, + 0.12176541024797648, + 0.12157801289797351, + 0.12134367367583417, + 0.12106182109403917, + 0.12073198342580521, + 0.120353789476701, + 0.11992696906830275, + 0.11945135323356011, + 0.11892687412461554, + 0.11835356463488826, + 0.11773155773828611, + 0.11706108554945181, + 0.11634247810996469, + 0.11557616190641042, + 0.11476265812719645, + 0.11390258066591168, + 0.11299663387992076, + 0.11204561011372445, + 0.11105038699741503, + 0.11001192453130465, + 0.10893126196849548, + 0.10780951450779863, + 0.10664786980998749, + 0.10544758435088952, + 0.10420997962527193, + 0.10293643821587116, + 0.10162839974223903 + ], + "yaxis": "y" + }, + { + "legendgroup": "Good", + "marker": { + "color": "rgb(214, 39, 40)" + }, + "mode": "lines", + "name": "Good", + "showlegend": true, + "type": "scatter", + "x": [ + 0.23, + 0.23356000000000002, + 0.23712, + 0.24068, + 0.24424, + 0.24780000000000002, + 0.25136000000000003, + 0.25492000000000004, + 0.25848, + 0.26204, + 0.2656, + 0.26916, + 0.27272, + 0.27628, + 0.27984, + 0.2834, + 0.28696, + 0.29052, + 0.29408, + 0.29764, + 0.3012, + 0.30476000000000003, + 0.30832, + 0.31188, + 0.31544, + 0.319, + 0.32256, + 0.32612, + 0.32968, + 0.33324, + 0.3368, + 0.34036, + 0.34392, + 0.34748, + 0.35104, + 0.35459999999999997, + 0.35816000000000003, + 0.36172000000000004, + 0.36528, + 0.36883999999999995, + 0.37239999999999995, + 0.37595999999999996, + 0.37951999999999997, + 0.38308, + 0.38664, + 0.3902, + 0.39376, + 0.39732, + 0.40088, + 0.40443999999999997, + 0.408, + 0.41156, + 0.41512, + 0.41868, + 0.42224, + 0.42579999999999996, + 0.42935999999999996, + 0.43291999999999997, + 0.43648, + 0.44004, + 0.4436, + 0.44716, + 0.45072, + 0.45428, + 0.45784, + 0.4614, + 0.46496, + 0.46852, + 0.47208, + 0.47564, + 0.47919999999999996, + 0.48275999999999997, + 0.48632, + 0.48988, + 0.49344, + 0.497, + 0.50056, + 0.50412, + 0.5076799999999999, + 0.5112399999999999, + 0.5147999999999999, + 0.5183599999999999, + 0.5219199999999999, + 0.52548, + 0.52904, + 0.5326, + 0.53616, + 0.53972, + 0.54328, + 0.54684, + 0.5504, + 0.55396, + 0.55752, + 0.56108, + 0.56464, + 0.5682, + 0.57176, + 0.5753199999999999, + 0.57888, + 0.58244, + 0.586, + 0.58956, + 0.59312, + 0.59668, + 0.60024, + 0.6038, + 0.60736, + 0.61092, + 0.61448, + 0.6180399999999999, + 0.6215999999999999, + 0.6251599999999999, + 0.62872, + 0.63228, + 0.63584, + 0.6394, + 0.64296, + 0.64652, + 0.65008, + 0.6536399999999999, + 0.6571999999999999, + 0.6607599999999999, + 0.6643199999999999, + 0.6678799999999999, + 0.6714399999999999, + 0.6749999999999999, + 0.6785599999999999, + 0.68212, + 0.68568, + 0.68924, + 0.6928, + 0.69636, + 0.69992, + 0.70348, + 0.70704, + 0.7106, + 0.71416, + 0.71772, + 0.72128, + 0.7248399999999999, + 0.7283999999999999, + 0.7319599999999999, + 0.73552, + 0.73908, + 0.74264, + 0.7461999999999999, + 0.74976, + 0.7533199999999999, + 0.75688, + 0.7604399999999999, + 0.7639999999999999, + 0.7675599999999999, + 0.7711199999999999, + 0.7746799999999999, + 0.7782399999999999, + 0.7817999999999999, + 0.7853599999999998, + 0.78892, + 0.7924799999999999, + 0.79604, + 0.7995999999999999, + 0.80316, + 0.8067199999999999, + 0.81028, + 0.8138399999999999, + 0.8173999999999999, + 0.8209599999999999, + 0.8245199999999999, + 0.8280799999999999, + 0.8316399999999999, + 0.8351999999999999, + 0.8387599999999998, + 0.84232, + 0.8458799999999999, + 0.84944, + 0.8529999999999999, + 0.8565599999999999, + 0.8601199999999999, + 0.8636799999999999, + 0.8672399999999999, + 0.8707999999999999, + 0.8743599999999999, + 0.8779199999999999, + 0.8814799999999999, + 0.8850399999999999, + 0.8886, + 0.89216, + 0.8957199999999998, + 0.89928, + 0.9028399999999999, + 0.9064, + 0.9099599999999999, + 0.91352, + 0.9170799999999999, + 0.9206399999999999, + 0.9241999999999999, + 0.9277599999999998, + 0.9313199999999999, + 0.9348799999999998, + 0.9384399999999999, + 0.9419999999999998, + 0.94556, + 0.9491199999999999, + 0.95268, + 0.9562399999999999, + 0.9598, + 0.9633599999999999, + 0.9669199999999999, + 0.9704799999999999, + 0.9740399999999999, + 0.9775999999999999, + 0.9811599999999999, + 0.9847199999999999, + 0.9882799999999999, + 0.9918399999999999, + 0.9953999999999998, + 0.99896, + 1.0025199999999999, + 1.0060799999999999, + 1.0096399999999999, + 1.0131999999999999, + 1.0167599999999999, + 1.02032, + 1.02388, + 1.02744, + 1.031, + 1.03456, + 1.03812, + 1.04168, + 1.04524, + 1.0488, + 1.05236, + 1.05592, + 1.05948, + 1.06304, + 1.0666, + 1.07016, + 1.07372, + 1.0772799999999998, + 1.08084, + 1.0843999999999998, + 1.08796, + 1.0915199999999998, + 1.09508, + 1.0986399999999998, + 1.1022, + 1.1057599999999999, + 1.10932, + 1.1128799999999999, + 1.1164399999999999, + 1.1199999999999999, + 1.12356, + 1.12712, + 1.13068, + 1.13424, + 1.1378, + 1.14136, + 1.14492, + 1.14848, + 1.15204, + 1.1556, + 1.15916, + 1.16272, + 1.16628, + 1.16984, + 1.1734, + 1.17696, + 1.18052, + 1.18408, + 1.1876399999999998, + 1.1912, + 1.1947599999999998, + 1.19832, + 1.2018799999999998, + 1.20544, + 1.2089999999999999, + 1.21256, + 1.2161199999999999, + 1.2196799999999999, + 1.2232399999999999, + 1.2268, + 1.23036, + 1.23392, + 1.23748, + 1.24104, + 1.2446, + 1.24816, + 1.25172, + 1.25528, + 1.25884, + 1.2623999999999997, + 1.2659599999999998, + 1.26952, + 1.27308, + 1.2766399999999998, + 1.2801999999999998, + 1.28376, + 1.28732, + 1.2908799999999998, + 1.2944399999999998, + 1.2979999999999998, + 1.3015599999999998, + 1.3051199999999998, + 1.3086799999999998, + 1.3122399999999999, + 1.3157999999999999, + 1.3193599999999999, + 1.3229199999999999, + 1.3264799999999999, + 1.33004, + 1.3336, + 1.33716, + 1.3407199999999997, + 1.34428, + 1.34784, + 1.3514, + 1.3549599999999997, + 1.35852, + 1.36208, + 1.36564, + 1.3691999999999998, + 1.3727599999999998, + 1.37632, + 1.3798799999999998, + 1.3834399999999998, + 1.3869999999999998, + 1.39056, + 1.3941199999999998, + 1.3976799999999998, + 1.4012399999999998, + 1.4047999999999998, + 1.4083599999999998, + 1.4119199999999998, + 1.4154799999999998, + 1.4190399999999999, + 1.4225999999999999, + 1.4261599999999999, + 1.4297199999999999, + 1.4332799999999999, + 1.43684, + 1.4404, + 1.4439599999999997, + 1.4475199999999997, + 1.45108, + 1.45464, + 1.4581999999999997, + 1.4617599999999997, + 1.46532, + 1.46888, + 1.4724399999999997, + 1.4759999999999998, + 1.47956, + 1.4831199999999998, + 1.4866799999999998, + 1.4902399999999998, + 1.4938, + 1.4973599999999998, + 1.5009199999999998, + 1.5044799999999998, + 1.50804, + 1.5115999999999998, + 1.5151599999999998, + 1.5187199999999998, + 1.5222799999999996, + 1.5258399999999999, + 1.5293999999999999, + 1.5329599999999999, + 1.5365199999999997, + 1.54008, + 1.54364, + 1.5472, + 1.5507599999999997, + 1.55432, + 1.55788, + 1.5614399999999997, + 1.5649999999999997, + 1.56856, + 1.57212, + 1.5756799999999997, + 1.5792399999999998, + 1.5828, + 1.58636, + 1.5899199999999998, + 1.5934799999999998, + 1.59704, + 1.6005999999999998, + 1.6041599999999998, + 1.6077199999999998, + 1.6112799999999998, + 1.6148399999999998, + 1.6183999999999998, + 1.6219599999999998, + 1.6255199999999996, + 1.6290799999999999, + 1.6326399999999999, + 1.6361999999999999, + 1.6397599999999997, + 1.64332, + 1.64688, + 1.65044, + 1.6539999999999997, + 1.65756, + 1.66112, + 1.6646799999999997, + 1.6682399999999997, + 1.6718, + 1.67536, + 1.6789199999999997, + 1.6824799999999998, + 1.6860399999999998, + 1.6896, + 1.6931599999999998, + 1.6967199999999998, + 1.7002799999999998, + 1.7038399999999998, + 1.7073999999999998, + 1.7109599999999998, + 1.7145199999999998, + 1.7180799999999998, + 1.7216399999999998, + 1.7251999999999998, + 1.7287599999999999, + 1.7323199999999999, + 1.7358799999999999, + 1.7394399999999999, + 1.7429999999999997, + 1.74656, + 1.75012, + 1.75368, + 1.7572399999999997, + 1.7607999999999997, + 1.76436, + 1.76792, + 1.7714799999999997, + 1.7750399999999997, + 1.7786, + 1.7821599999999997, + 1.7857199999999998, + 1.7892799999999998, + 1.79284, + 1.7963999999999998, + 1.7999599999999998, + 1.8035199999999998, + 1.80708, + 1.8106399999999998, + 1.8141999999999998, + 1.8177599999999998, + 1.8213199999999998, + 1.8248799999999998, + 1.8284399999999998, + 1.8319999999999999, + 1.8355599999999996, + 1.8391199999999999, + 1.8426799999999999, + 1.8462399999999997, + 1.8497999999999997, + 1.85336, + 1.85692, + 1.8604799999999997, + 1.8640399999999997, + 1.8676, + 1.87116, + 1.8747199999999997, + 1.8782799999999997, + 1.88184, + 1.8853999999999997, + 1.8889599999999998, + 1.8925199999999998, + 1.89608, + 1.8996399999999998, + 1.9031999999999998, + 1.9067599999999998, + 1.9103199999999998, + 1.9138799999999998, + 1.9174399999999998, + 1.9209999999999998, + 1.9245599999999996, + 1.9281199999999998, + 1.9316799999999998, + 1.9352399999999998, + 1.9387999999999996, + 1.9423599999999999, + 1.9459199999999999, + 1.9494799999999999, + 1.9530399999999997, + 1.9566, + 1.96016, + 1.9637199999999997, + 1.9672799999999997, + 1.97084, + 1.9744, + 1.9779599999999997, + 1.9815199999999997, + 1.9850799999999997, + 1.98864, + 1.9921999999999997, + 1.9957599999999998, + 1.9993199999999998, + 2.0028799999999998, + 2.00644 + ], + "xaxis": "x", + "y": [ + 0.5578411628332631, + 0.5696427809702442, + 0.5814611203184505, + 0.5932881301143391, + 0.6051156695195061, + 0.6169355202674213, + 0.6287393995745632, + 0.6405189732851325, + 0.6522658692177783, + 0.6639716906821025, + 0.6756280301321832, + 0.6872264829239221, + 0.6987586611427213, + 0.7102162074677748, + 0.7215908090391715, + 0.732874211294055, + 0.7440582317381594, + 0.755134773619351, + 0.766095839470063, + 0.776933544486039, + 0.7876401297092686, + 0.7982079749836943, + 0.8086296116529604, + 0.8188977349703009, + 0.8290052161915806, + 0.838945114323437, + 0.8487106874995818, + 0.8582954039593687, + 0.8676929526039816, + 0.8768972531067919, + 0.8859024655557375, + 0.8947029996069366, + 0.9032935231301054, + 0.9116689703277849, + 0.9198245493118353, + 0.9277557491221395, + 0.9354583461739602, + 0.9429284101219195, + 0.9501623091301239, + 0.9571567145394847, + 0.9639086049248722, + 0.9704152695362549, + 0.9766743111196041, + 0.9826836481148251, + 0.9884415162295762, + 0.9939464693893529, + 0.9991973800657415, + 1.0041934389862555, + 1.0089341542306496, + 1.013419349720094, + 1.017649163107002, + 1.021624043074755, + 1.0253447460579335, + 1.028812332395032, + 1.032028161926957, + 1.0349938890558972, + 1.0377114572803965, + 1.0401830932236986, + 1.0424113001735607, + 1.0443988511528888, + 1.0461487815416186, + 1.0476643812712672, + 1.0489491866145917, + 1.0500069715936646, + 1.0508417390305818, + 1.0514577112657604, + 1.0518593205695723, + 1.0520511992736685, + 1.0520381696489884, + 1.0518252335579348, + 1.0514175619086368, + 1.0508204839396236, + 1.050039476363441, + 1.0490801523980027, + 1.0479482507145046, + 1.046649624330804, + 1.045190229479026, + 1.0435761144759912, + 1.041813408624818, + 1.0399083111756102, + 1.0378670803726957, + 1.035696022615299, + 1.0334014817578407, + 1.0309898285752461, + 1.028467450417811, + 1.0258407410791186, + 1.0231160908994745, + 1.0202998771261054, + 1.0173984545501165, + 1.014418146438826, + 1.0113652357806753, + 1.0082459568583446, + 1.0050664871641686, + 1.0018329396702192, + 0.99855135546376, + 0.9952276967569528, + 0.9918678402779232, + 0.9884775710484003, + 0.9850625765513095, + 0.9816284412897688, + 0.9781806417370799, + 0.9747245416753642, + 0.9712653879186839, + 0.9678083064145762, + 0.9643582987161571, + 0.9609202388151965, + 0.9574988703248348, + 0.9540988039990125, + 0.9507245155741338, + 0.9473803439170356, + 0.9440704894619776, + 0.9407990129181576, + 0.9375698342281253, + 0.9343867317565072, + 0.9312533416876149, + 0.928173157609809, + 0.9251495302639808, + 0.9221856674330982, + 0.9192846339495935, + 0.9164493517973048, + 0.9136826002847851, + 0.9109870162671466, + 0.9083650943940155, + 0.9058191873618592, + 0.9033515061497093, + 0.9009641202183336, + 0.8986589576539795, + 0.896437805239144, + 0.8943023084342536, + 0.8922539712556963, + 0.8902941560373668, + 0.8884240830647239, + 0.8866448300722659, + 0.8849573315974022, + 0.8833623781857844, + 0.8818606154453752, + 0.8804525429487571, + 0.8791385129854905, + 0.8779187291686318, + 0.8767932449018374, + 0.8757619617158118, + 0.8748246274851359, + 0.8739808345387616, + 0.8732300176796702, + 0.8725714521312967, + 0.8720042514303566, + 0.8715273652876504, + 0.8711395774402263, + 0.8708395035199398, + 0.8706255889649961, + 0.8704961070024047, + 0.870449156730474, + 0.8704826613314758, + 0.8705943664454051, + 0.8707818387363916, + 0.8710424646836712, + 0.8713734496292385, + 0.8717718171142157, + 0.8722344085357145, + 0.8727578831554598, + 0.8733387184907069, + 0.873973211116988, + 0.8746574779110912, + 0.8753874577611986, + 0.8761589137695055, + 0.876967435970805, + 0.8778084445884544, + 0.8786771938469101, + 0.8795687763576177, + 0.8804781280923858, + 0.8814000339557447, + 0.8823291339647567, + 0.883259930041856, + 0.8841867934231065, + 0.8851039726810576, + 0.8860056023581323, + 0.8868857122030748, + 0.8877382369996764, + 0.8885570269735544, + 0.8893358587594125, + 0.8900684469078078, + 0.8907484559072039, + 0.8913695126937471, + 0.8919252196181396, + 0.8924091678358508, + 0.892814951084019, + 0.8931361798055945, + 0.8933664955786527, + 0.893499585806345, + 0.8935291986207416, + 0.8934491579517045, + 0.8932533787102105, + 0.8929358820338449, + 0.8924908105409491, + 0.891912443538794, + 0.8911952121302801, + 0.8903337141632335, + 0.8893227289660232, + 0.8881572318132683, + 0.8868324080657184, + 0.8853436669289716, + 0.8836866547765788, + 0.8818572679841935, + 0.879851665222929, + 0.8776662791617096, + 0.8752978275304188, + 0.8727433234978158, + 0.8700000853206854, + 0.8670657452233624, + 0.8639382574696293, + 0.8606159055921714, + 0.8570973087479721, + 0.8533814271715582, + 0.8494675667015467, + 0.8453553823597356, + 0.841044880965776, + 0.8365364227744447, + 0.8318307221264942, + 0.82692884710814, + 0.821832218218309, + 0.8165426060468637, + 0.8110621279710571, + 0.805393243881532, + 0.7995387509530981, + 0.7935017774794464, + 0.7872857757947102, + 0.7808945143084267, + 0.7743320686840222, + 0.7676028121942339, + 0.7607114052901086, + 0.7536627844231812, + 0.7464621501632391, + 0.7391149546566054, + 0.7316268884722384, + 0.7240038668850003, + 0.7162520156472907, + 0.7083776563018143, + 0.7003872910895552, + 0.6922875875080513, + 0.6840853625758403, + 0.6757875668594351, + 0.6674012683193612, + 0.6589336360317987, + 0.6503919238419481, + 0.6417834540047378, + 0.633115600867565, + 0.6243957746487271, + 0.6156314053637945, + 0.606829926950685, + 0.5979987616423421, + 0.5891453046340012, + 0.5802769090898003, + 0.5714008715311766, + 0.562524417646985, + 0.553654688562589, + 0.5447987276024886, + 0.5359634675780585, + 0.5271557186291438, + 0.5183821566451015, + 0.5096493122878888, + 0.5009635606366473, + 0.49233111147010383, + 0.4837580002000007, + 0.4752500794656761, + 0.4668130113968277, + 0.45845226054852567, + 0.45017308750958374, + 0.4419805431825802, + 0.4338794637310489, + 0.4258744661867746, + 0.41796994470759286, + 0.4101700674737596, + 0.4024787742087454, + 0.39489977430824186, + 0.38743654555929685, + 0.3800923334297888, + 0.37287015090687536, + 0.3657727788617467, + 0.3588027669167986, + 0.35196243479038136, + 0.34525387409344216, + 0.3386789505518, + 0.33223930662730644, + 0.32593636451091823, + 0.3197713294605852, + 0.31374519345694507, + 0.30785873915005435, + 0.3021125440707443, + 0.2965069850807379, + 0.2910422430363171, + 0.28571830764110684, + 0.28053498246442715, + 0.27549189010269287, + 0.27058847746238046, + 0.26582402114427756, + 0.26119763290993553, + 0.2567082652125534, + 0.2523547167758054, + 0.2481356382055085, + 0.24404953762037288, + 0.2400947862894635, + 0.2362696242653511, + 0.23257216600329175, + 0.22900040595808996, + 0.2255522241515737, + 0.22222539170485522, + 0.21901757633071509, + 0.21592634778257055, + 0.2129491832575241, + 0.21008347275194855, + 0.20732652436894652, + 0.2046755695778109, + 0.20212776842631602, + 0.19968021470725308, + 0.1973299410811565, + 0.19507392415754618, + 0.19290908953733937, + 0.19083231681927693, + 0.18884044457334054, + 0.18693027528413766, + 0.18509858026716622, + 0.18334210456071065, + 0.18165757179587377, + 0.18004168904691942, + 0.17849115166372237, + 0.17700264808764207, + 0.17557286465162644, + 0.1741984903647745, + 0.17287622168096495, + 0.17160276725051005, + 0.17037485265309543, + 0.16918922510956505, + 0.16804265816939654, + 0.16693195636996605, + 0.1658539598629984, + 0.16480554900287045, + 0.16378364889074384, + 0.16278523386783383, + 0.16180733195048697, + 0.16084702919912736, + 0.15990147401259877, + 0.15896788133891437, + 0.15804353679298144, + 0.15712580067149812, + 0.1562121118548863, + 0.15529999158589242, + 0.15438704711429654, + 0.15347097519709219, + 0.1525495654434553, + 0.15162070349388948, + 0.1506823740230652, + 0.14973266355607345, + 0.1487697630881252, + 0.14779197049806048, + 0.14679769274650442, + 0.14578544784999034, + 0.14475386662296286, + 0.14370169418021048, + 0.14262779119298666, + 0.14153113489282398, + 0.14041081981787062, + 0.13926605829742, + 0.13809618067120655, + 0.1369006352409667, + 0.1356789879527121, + 0.13443092180915345, + 0.1331562360126918, + 0.13185484484039675, + 0.13052677625340112, + 0.12917217024412853, + 0.12779127692576123, + 0.12638445436932852, + 0.12495216619473758, + 0.12349497892298812, + 0.1220135590976961, + 0.12050867018489553, + 0.11898116926088548, + 0.11743200349864108, + 0.11586220646400205, + 0.11427289423349514, + 0.1126652613462258, + 0.11104057660279086, + 0.10940017872461379, + 0.10774547188749535, + 0.1060779211434828, + 0.10439904774540784, + 0.10271042438862323, + 0.10101367038457706, + 0.09931044678089677, + 0.09760245144262797, + 0.09589141410918206, + 0.09417909144137782, + 0.09246726207274501, + 0.09075772167897192, + 0.08905227807904513, + 0.08735274638122836, + 0.08566094418659322, + 0.08397868686232199, + 0.08230778289647538, + 0.08065002934534736, + 0.07900720738393179, + 0.07738107796939628, + 0.07577337762680379, + 0.07418581436565093, + 0.07262006373510586, + 0.0710777650251288, + 0.06956051761996071, + 0.06806987750975038, + 0.06660735396540149, + 0.06517440638102104, + 0.0637724412876631, + 0.06240280954140267, + 0.06106680368811468, + 0.059765655506713075, + 0.05850053373199152, + 0.05727254195763397, + 0.0560827167194156, + 0.05493202575809257, + 0.05382136646099838, + 0.05275156448091004, + 0.051723372530334995, + 0.05073746934898813, + 0.049794458841883216, + 0.0488948693851594, + 0.0480391532964867, + 0.047227686466663574, + 0.04646076814881195, + 0.045738620901412744, + 0.04506139068128305, + 0.04442914708249383, + 0.04384188371714632, + 0.04329951873387853, + 0.04280189546994248, + 0.04234878323268942, + 0.041939878206318906, + 0.04157480447977621, + 0.041253115191731075, + 0.040974293788631315, + 0.040737755391893185, + 0.04054284827036446, + 0.04038885541428049, + 0.040274996207010784, + 0.04020042819097996, + 0.040164248924222615, + 0.04016549792410785, + 0.04020315869483549, + 0.04027616083536539, + 0.04038338222448858, + 0.04052365127978689, + 0.04069574928724961, + 0.040898412798326594, + 0.041130336091190677, + 0.041390173692962044, + 0.041676542959610194, + 0.041988026710196906, + 0.04232317591205421, + 0.04268051241340946, + 0.04305853171986816, + 0.043455705811055324, + 0.04387048599358798, + 0.04430130578641484, + 0.044746583834410075, + 0.04520472684595205, + 0.045674132550051455, + 0.04615319266842464, + 0.04664029589773387, + 0.04713383089704173, + 0.047632189275350824, + 0.048133768573931285, + 0.048636975237969504, + 0.04914022757191514, + 0.049641958672750855, + 0.050140619335275645, + 0.0506346809233658, + 0.05112263820107185, + 0.05160301211732058, + 0.052074352537921365, + 0.05253524091852978, + 0.052984292912196425, + 0.05342016090513229, + 0.05384153647434741, + 0.05424715276087622, + 0.0546357867523869, + 0.05500626146908403, + 0.05535744804695736, + 0.055688267712601915, + 0.05599769364403843, + 0.05628475271219476, + 0.05654852709797405, + 0.05678815578012367, + 0.05700283588944381, + 0.057191823925217815, + 0.05735443683012209, + 0.057490052920270825, + 0.057598112667472307, + 0.05767811933121384, + 0.05772963943835629, + 0.05775230310899449, + 0.05774580422743445, + 0.057709900457742117 + ], + "yaxis": "y" + }, + { + "legendgroup": "Fair", + "marker": { + "color": "rgb(148, 103, 189)" + }, + "mode": "lines", + "name": "Fair", + "showlegend": true, + "type": "scatter", + "x": [ + 0.33, + 0.33438, + 0.33876, + 0.34314, + 0.34752, + 0.3519, + 0.35628000000000004, + 0.36066000000000004, + 0.36504000000000003, + 0.36942, + 0.3738, + 0.37818, + 0.38256, + 0.38694, + 0.39132, + 0.39570000000000005, + 0.40008, + 0.40446000000000004, + 0.40884000000000004, + 0.41322000000000003, + 0.4176, + 0.42198, + 0.42636, + 0.43074, + 0.43512, + 0.4395, + 0.44388, + 0.44826, + 0.45264000000000004, + 0.45702, + 0.46140000000000003, + 0.46578, + 0.47016, + 0.47454, + 0.47892, + 0.4833, + 0.48768, + 0.49206000000000005, + 0.49644, + 0.50082, + 0.5052, + 0.50958, + 0.51396, + 0.51834, + 0.5227200000000001, + 0.5271, + 0.53148, + 0.53586, + 0.54024, + 0.54462, + 0.549, + 0.55338, + 0.55776, + 0.56214, + 0.56652, + 0.5709, + 0.57528, + 0.5796600000000001, + 0.58404, + 0.58842, + 0.5928, + 0.59718, + 0.6015600000000001, + 0.60594, + 0.61032, + 0.6147, + 0.6190800000000001, + 0.62346, + 0.62784, + 0.63222, + 0.6366, + 0.6409800000000001, + 0.64536, + 0.64974, + 0.65412, + 0.6585000000000001, + 0.66288, + 0.66726, + 0.67164, + 0.6760200000000001, + 0.6804, + 0.6847799999999999, + 0.68916, + 0.69354, + 0.6979200000000001, + 0.7023, + 0.70668, + 0.71106, + 0.7154400000000001, + 0.71982, + 0.7242, + 0.72858, + 0.7329600000000001, + 0.73734, + 0.7417199999999999, + 0.7461, + 0.75048, + 0.7548600000000001, + 0.75924, + 0.76362, + 0.768, + 0.7723800000000001, + 0.77676, + 0.78114, + 0.78552, + 0.7899, + 0.79428, + 0.7986599999999999, + 0.80304, + 0.80742, + 0.8118000000000001, + 0.81618, + 0.82056, + 0.82494, + 0.8293200000000001, + 0.8337000000000001, + 0.8380799999999999, + 0.84246, + 0.84684, + 0.8512200000000001, + 0.8556000000000001, + 0.85998, + 0.86436, + 0.8687400000000001, + 0.8731200000000001, + 0.8775, + 0.88188, + 0.88626, + 0.8906400000000001, + 0.8950199999999999, + 0.8994, + 0.90378, + 0.9081600000000001, + 0.9125399999999999, + 0.91692, + 0.9213, + 0.9256800000000001, + 0.9300599999999999, + 0.9344399999999999, + 0.93882, + 0.9432, + 0.9475800000000001, + 0.9519600000000001, + 0.95634, + 0.96072, + 0.9651000000000001, + 0.9694800000000001, + 0.97386, + 0.97824, + 0.98262, + 0.9870000000000001, + 0.9913799999999999, + 0.99576, + 1.00014, + 1.00452, + 1.0089, + 1.01328, + 1.01766, + 1.02204, + 1.0264199999999999, + 1.0308, + 1.03518, + 1.03956, + 1.0439399999999999, + 1.04832, + 1.0527, + 1.05708, + 1.06146, + 1.0658400000000001, + 1.07022, + 1.0746, + 1.07898, + 1.08336, + 1.08774, + 1.09212, + 1.0965, + 1.10088, + 1.10526, + 1.10964, + 1.11402, + 1.1184, + 1.12278, + 1.12716, + 1.13154, + 1.13592, + 1.1402999999999999, + 1.14468, + 1.14906, + 1.15344, + 1.1578199999999998, + 1.1622, + 1.16658, + 1.17096, + 1.17534, + 1.17972, + 1.1841, + 1.18848, + 1.19286, + 1.19724, + 1.20162, + 1.206, + 1.21038, + 1.21476, + 1.2191400000000001, + 1.22352, + 1.2279, + 1.23228, + 1.23666, + 1.24104, + 1.24542, + 1.2498, + 1.25418, + 1.25856, + 1.26294, + 1.26732, + 1.2717, + 1.2760799999999999, + 1.28046, + 1.28484, + 1.28922, + 1.2936, + 1.2979800000000001, + 1.30236, + 1.30674, + 1.31112, + 1.3155000000000001, + 1.31988, + 1.32426, + 1.32864, + 1.33302, + 1.3374000000000001, + 1.34178, + 1.34616, + 1.35054, + 1.3549200000000001, + 1.3593, + 1.3636800000000002, + 1.36806, + 1.37244, + 1.37682, + 1.3812000000000002, + 1.38558, + 1.38996, + 1.39434, + 1.3987200000000002, + 1.4031, + 1.40748, + 1.41186, + 1.4162400000000002, + 1.42062, + 1.425, + 1.4293799999999999, + 1.4337600000000001, + 1.43814, + 1.44252, + 1.4468999999999999, + 1.4512800000000001, + 1.4556600000000002, + 1.46004, + 1.46442, + 1.4688, + 1.4731800000000002, + 1.47756, + 1.48194, + 1.48632, + 1.4907000000000001, + 1.49508, + 1.49946, + 1.50384, + 1.5082200000000001, + 1.5126, + 1.51698, + 1.52136, + 1.52574, + 1.53012, + 1.5345, + 1.53888, + 1.54326, + 1.54764, + 1.55202, + 1.5564, + 1.56078, + 1.56516, + 1.56954, + 1.5739200000000002, + 1.5783, + 1.58268, + 1.58706, + 1.5914400000000002, + 1.59582, + 1.6002, + 1.60458, + 1.6089600000000002, + 1.61334, + 1.61772, + 1.6220999999999999, + 1.6264800000000001, + 1.63086, + 1.63524, + 1.6396199999999999, + 1.6440000000000001, + 1.64838, + 1.65276, + 1.6571399999999998, + 1.66152, + 1.6659, + 1.67028, + 1.6746599999999998, + 1.67904, + 1.6834200000000001, + 1.6878, + 1.6921800000000002, + 1.69656, + 1.7009400000000001, + 1.70532, + 1.7097000000000002, + 1.71408, + 1.71846, + 1.72284, + 1.7272200000000002, + 1.7316, + 1.73598, + 1.74036, + 1.7447400000000002, + 1.74912, + 1.7535, + 1.7578799999999999, + 1.7622600000000002, + 1.76664, + 1.77102, + 1.7753999999999999, + 1.7797800000000001, + 1.78416, + 1.78854, + 1.79292, + 1.7973000000000001, + 1.8016800000000002, + 1.80606, + 1.81044, + 1.81482, + 1.8192000000000002, + 1.82358, + 1.82796, + 1.83234, + 1.8367200000000001, + 1.8411, + 1.84548, + 1.84986, + 1.85424, + 1.85862, + 1.863, + 1.86738, + 1.87176, + 1.87614, + 1.88052, + 1.8849, + 1.88928, + 1.89366, + 1.89804, + 1.9024200000000002, + 1.9068, + 1.91118, + 1.91556, + 1.9199400000000002, + 1.92432, + 1.9287, + 1.93308, + 1.9374600000000002, + 1.94184, + 1.94622, + 1.9506, + 1.9549800000000002, + 1.95936, + 1.96374, + 1.9681199999999999, + 1.9725000000000001, + 1.97688, + 1.98126, + 1.9856399999999998, + 1.9900200000000001, + 1.9944, + 1.99878, + 2.00316, + 2.00754, + 2.01192, + 2.0162999999999998, + 2.02068, + 2.02506, + 2.02944, + 2.03382, + 2.0382, + 2.04258, + 2.04696, + 2.0513399999999997, + 2.05572, + 2.0601, + 2.06448, + 2.06886, + 2.0732399999999997, + 2.07762, + 2.082, + 2.0863799999999997, + 2.09076, + 2.09514, + 2.09952, + 2.1039, + 2.10828, + 2.11266, + 2.11704, + 2.1214199999999996, + 2.1258, + 2.13018, + 2.13456, + 2.13894, + 2.14332, + 2.1477, + 2.1520799999999998, + 2.15646, + 2.16084, + 2.16522, + 2.1696, + 2.17398, + 2.17836, + 2.18274, + 2.1871199999999997, + 2.1915, + 2.19588, + 2.20026, + 2.20464, + 2.2090199999999998, + 2.2134, + 2.21778, + 2.2221599999999997, + 2.22654, + 2.23092, + 2.2353, + 2.23968, + 2.2440599999999997, + 2.24844, + 2.25282, + 2.2572, + 2.26158, + 2.26596, + 2.27034, + 2.27472, + 2.2790999999999997, + 2.28348, + 2.28786, + 2.29224, + 2.29662, + 2.301, + 2.30538, + 2.30976, + 2.3141399999999996, + 2.31852, + 2.3228999999999997, + 2.32728, + 2.33166, + 2.33604, + 2.34042, + 2.3448, + 2.34918, + 2.35356, + 2.35794, + 2.36232, + 2.3667000000000002, + 2.37108, + 2.37546, + 2.37984, + 2.38422, + 2.3886, + 2.39298, + 2.3973600000000004, + 2.4017399999999998, + 2.40612, + 2.4105, + 2.41488, + 2.41926, + 2.42364, + 2.42802, + 2.4324000000000003, + 2.4367799999999997, + 2.44116, + 2.44554, + 2.44992, + 2.4543, + 2.4586799999999998, + 2.46306, + 2.4674400000000003, + 2.47182, + 2.4762, + 2.48058, + 2.48496, + 2.4893400000000003, + 2.4937199999999997, + 2.4981, + 2.5024800000000003, + 2.50686, + 2.51124, + 2.5156199999999997 + ], + "xaxis": "x", + "y": [ + 0.29054711702186514, + 0.29461870582810196, + 0.29869089459644615, + 0.302763076772498, + 0.30683468944079234, + 0.31090521489941647, + 0.3149741821522402, + 0.3190411683136011, + 0.3231057999203646, + 0.3271677541463898, + 0.3312267599145438, + 0.3352825989015357, + 0.33933510643098735, + 0.34338417225030804, + 0.34742974118711495, + 0.35147181368111896, + 0.35551044618759137, + 0.3595457514487388, + 0.3635778986295268, + 0.3676071133147371, + 0.3716336773642779, + 0.37565792862403463, + 0.379680260489815, + 0.3837011213222215, + 0.3877210137105859, + 0.3917404935843973, + 0.3957601691709735, + 0.3997806997984551, + 0.4038027945435291, + 0.4078272107236409, + 0.41185475223380574, + 0.41588626772848736, + 0.4199226486493919, + 0.4239648271003893, + 0.4280137735711701, + 0.4320704945116236, + 0.43613602975932697, + 0.44021144982292487, + 0.4442978530245866, + 0.44839636250513, + 0.4525081230958044, + 0.4566342980611386, + 0.46077606571765745, + 0.4649346159336831, + 0.469111146515836, + 0.47330685948825, + 0.4775229572709172, + 0.4817606387639579, + 0.48602109534500276, + 0.4903055067872477, + 0.4946150371061046, + 0.4989508303427328, + 0.5033140062930762, + 0.5077056561913651, + 0.5121268383573625, + 0.5165785738169322, + 0.5210618419057952, + 0.5255775758666139, + 0.5301266584497851, + 0.5347099175285598, + 0.5393281217393133, + 0.543981976157975, + 0.5486721180237888, + 0.5533991125217136, + 0.5581634486348849, + 0.5629655350786374, + 0.5678056963276531, + 0.5726841687478184, + 0.577601096844374, + 0.5825565296379082, + 0.587550417179676, + 0.5925826072176305, + 0.5976528420244224, + 0.602760755398459, + 0.6079058698489125, + 0.6130875939753426, + 0.618305220052326, + 0.6235579218291827, + 0.6288447525545603, + 0.6341646432352589, + 0.6395164011382808, + 0.6448987085446461, + 0.6503101217630434, + 0.6557490704108851, + 0.6612138569697903, + 0.6667026566219547, + 0.672213517373271, + 0.6777443604684205, + 0.6832929811025208, + 0.6888570494332079, + 0.6944341118963412, + 0.7000215928277738, + 0.7056167963928764, + 0.7112169088247452, + 0.7168190009712003, + 0.7224200311499006, + 0.728016848310058, + 0.7336061954984129, + 0.7391847136262736, + 0.7447489455335862, + 0.7502953403451306, + 0.7558202581130964, + 0.7613199747394191, + 0.7667906871704215, + 0.7722285188554403, + 0.7776295254603021, + 0.7829897008256619, + 0.7883049831594388, + 0.7935712614517547, + 0.7987843821000445, + 0.8039401557312362, + 0.8090343642071826, + 0.8140627677988388, + 0.8190211125140117, + 0.8239051375628851, + 0.8287105829449307, + 0.8334331971402712, + 0.8380687448880455, + 0.8426130150338634, + 0.8470618284280282, + 0.8514110458558218, + 0.8556565759808317, + 0.8597943832820446, + 0.8638204959651916, + 0.8677310138286943, + 0.8715221160644466, + 0.8751900689736187, + 0.8787312335776901, + 0.8821420731049814, + 0.8854191603330864, + 0.8885591847678039, + 0.8915589596393968, + 0.89441542869734, + 0.8971256727850645, + 0.8996869161766414, + 0.9020965326578324, + 0.9043520513344661, + 0.9064511621517051, + 0.9083917211084087, + 0.9101717551515025, + 0.9117894667360165, + 0.913243238037256, + 0.9145316348024167, + 0.9156534098298412, + 0.9166075060650548, + 0.9173930593036815, + 0.918009400492356, + 0.9184560576197836, + 0.9187327571911623, + 0.9188394252802923, + 0.918776188154806, + 0.9185433724710922, + 0.9181415050366395, + 0.9175713121387067, + 0.9168337184393707, + 0.9159298454382274, + 0.9148610095051518, + 0.9136287194867491, + 0.9122346738912698, + 0.910680757657931, + 0.908969038517744, + 0.9071017629540651, + 0.9050813517722023, + 0.902910395288495, + 0.9005916481503321, + 0.8981280237996101, + 0.8955225885931024, + 0.8927785555941842, + 0.8898992780512399, + 0.8868882425789701, + 0.8837490620596055, + 0.8804854682818314, + 0.8771013043359116, + 0.8736005167841835, + 0.86998714762669, + 0.8662653260822707, + 0.8624392602058905, + 0.858513228363464, + 0.854491570585726, + 0.8503786798230617, + 0.8461789931234037, + 0.8418969827554812, + 0.8375371472998168, + 0.8331040027299033, + 0.8286020735059494, + 0.8240358837035209, + 0.8194099481992034, + 0.8147287639352446, + 0.8099968012847929, + 0.8052184955390461, + 0.8003982385371972, + 0.7955403704595969, + 0.7906491718040363, + 0.7857288555644686, + 0.7807835596308603, + 0.7758173394281721, + 0.7708341608117407, + 0.7658378932355584, + 0.7608323032091135, + 0.7558210480575995, + 0.7508076699993912, + 0.7457955905537653, + 0.7407881052908458, + 0.7357883789347901, + 0.7307994408301747, + 0.7258241807805212, + 0.7208653452668259, + 0.7159255340528656, + 0.7110071971829818, + 0.7061126323769242, + 0.7012439828252333, + 0.6964032353875325, + 0.6915922191949943, + 0.6868126046571239, + 0.6820659028719345, + 0.677353465437476, + 0.67267648466162, + 0.6680359941659476, + 0.6634328698785501, + 0.6588678314095382, + 0.6543414438020756, + 0.6498541196507834, + 0.6454061215784551, + 0.6409975650610981, + 0.6366284215904958, + 0.6322985221626336, + 0.6280075610795691, + 0.6237551000515916, + 0.6195405725858064, + 0.6153632886466499, + 0.6112224395732219, + 0.607117103237788, + 0.6030462494292774, + 0.5990087454451696, + 0.5950033618747448, + 0.5910287785563301, + 0.5870835906908712, + 0.5831663150939168, + 0.5792753965679007, + 0.5754092143764811, + 0.57156608880259, + 0.5677442877718317, + 0.5639420335228599, + 0.5601575093064578, + 0.5563888660951464, + 0.5526342292853188, + 0.5488917053741145, + 0.5451593885935245, + 0.5414353674845025, + 0.5377177313942483, + 0.5340045768801919, + 0.5302940140046867, + 0.5265841725048642, + 0.5228732078226532, + 0.5191593069805022, + 0.5154406942889461, + 0.5117156368727747, + 0.5079824500032171, + 0.5042395022242321, + 0.5004852202617097, + 0.4967180937051088, + 0.4929366794518152, + 0.48913960590526606, + 0.4853255769186784, + 0.4814933754770156, + 0.4776418671106352, + 0.4737700030348816, + 0.4698768230107106, + 0.4659614579222688, + 0.46202313206816403, + 0.4580611651640178, + 0.4540749740546878, + 0.4500640741353953, + 0.4460280804817811, + 0.4419667086897332, + 0.4378797754266045, + 0.4337671986962219, + 0.4296289978208428, + 0.42546529314394843, + 0.4212763054584901, + 0.41706235516589457, + 0.4128238611718039, + 0.40856133952517387, + 0.4042754018079742, + 0.3999667532833106, + 0.39563619081037504, + 0.39128460053510944, + 0.3869129553660257, + 0.38252231224501787, + 0.3781138092234945, + 0.37368866235448506, + 0.3692481624117905, + 0.3647936714475173, + 0.36032661919964903, + 0.3558484993615267, + 0.35136086572534686, + 0.34686532821191884, + 0.34236354879909364, + 0.33785723736132783, + 0.3333481474329497, + 0.32883807190767167, + 0.32432883868690976, + 0.3198223062893979, + 0.31532035943451453, + 0.31082490461160245, + 0.3063378656474259, + 0.3018611792837015, + 0.29739679077644793, + 0.29294664952862437, + 0.2885127047672727, + 0.2840969012760577, + 0.27970117519378024, + 0.2753274498890784, + 0.27097763192114277, + 0.26665360709589164, + 0.26235723662660715, + 0.2580903534076094, + 0.253854758409073, + 0.24965221720063713, + 0.24548445661094626, + 0.24135316152978437, + 0.23725997185893327, + 0.2332064796173867, + 0.2291942262060048, + 0.2252246998361818, + 0.22129933312654276, + 0.21741950087117026, + 0.21358651798230066, + 0.2098016376099124, + 0.20606604944007356, + 0.20238087817340794, + 0.19874718218449078, + 0.19516595236248757, + 0.19163811113282245, + 0.18816451165917888, + 0.1847459372246342, + 0.18138310079026038, + 0.17807664472905993, + 0.1748271407326559, + 0.17163508988772538, + 0.16850092291874727, + 0.1654250005932418, + 0.16240761428529338, + 0.15944898669279464, + 0.15654927270350547, + 0.15370856040469932, + 0.15092687223087403, + 0.14820416624372415, + 0.14554033753831758, + 0.14293521976918364, + 0.1403885867898095, + 0.1379001543988587, + 0.13546958218624772, + 0.13309647547208572, + 0.13078038733135663, + 0.12852082069712364, + 0.12631723053496519, + 0.1241690260812943, + 0.12207557313818473, + 0.12003619641731711, + 0.11805018192566927, + 0.1161167793856048, + 0.1142352046820755, + 0.11240464232971227, + 0.11062424795268078, + 0.10889315077028387, + 0.1072104560814191, + 0.10557524774114582, + 0.10398659062277887, + 0.1024435330590938, + 0.10094510925643291, + 0.09949034167569219, + 0.09807824337439686, + 0.0967078203042975, + 0.09537807355916365, + 0.09408800156769861, + 0.09283660222676679, + 0.09162287497038497, + 0.09044582277021662, + 0.0893044540635826, + 0.08819778460529665, + 0.08712483923992663, + 0.08608465359138039, + 0.08507627566701537, + 0.08409876737377907, + 0.08315120594418275, + 0.0822326852702271, + 0.08134231714369336, + 0.08047923240152169, + 0.07964258197529794, + 0.07883153784416844, + 0.07804529389079493, + 0.07728306666025649, + 0.07654409602208155, + 0.0758276457358826, + 0.07513300392132848, + 0.07445948343346126, + 0.07380642214461942, + 0.07317318313447767, + 0.07255915478995546, + 0.07196375081697713, + 0.0713864101662852, + 0.07082659687572379, + 0.07028379983160424, + 0.06975753245195786, + 0.06924733229465622, + 0.06875276059354762, + 0.06827340172590919, + 0.06780886261466072, + 0.06735877206891065, + 0.06692278006652666, + 0.06650055698252465, + 0.0660917927671626, + 0.06569619607770517, + 0.06531349336789163, + 0.0649434279391909, + 0.06458575895797188, + 0.06424026044274152, + 0.06390672022562288, + 0.06358493889224365, + 0.06327472870420278, + 0.06297591250825563, + 0.06268832263633112, + 0.062411799800445346, + 0.0621461919865247, + 0.06189135335108222, + 0.06164714312461662, + 0.06141342452551386, + 0.06119006368813694, + 0.06097692860868086, + 0.06077388811225704, + 0.06058081084454493, + 0.060397564291219286, + 0.06022401382822075, + 0.06006002180579085, + 0.05990544666904033, + 0.05976014211765983, + 0.059623956307216806, + 0.0594967310943144, + 0.05937830132771196, + 0.05926849418733005, + 0.05916712857288078, + 0.0590740145436803, + 0.058988952811013816, + 0.05891173428423505, + 0.058842139671593484, + 0.05877993913659232, + 0.05872489201049106, + 0.05867674656137646, + 0.05863523982003901, + 0.058600097462704184, + 0.05857103375048487, + 0.05854775152523865, + 0.05852994226133664, + 0.05851728617267472, + 0.058509452374088534, + 0.05850609909616705, + 0.058506873952299795, + 0.05851141425663603, + 0.05851934739148663, + 0.05853029122255473, + 0.05854385456024593, + 0.058559637665178764, + 0.058577232795893844, + 0.058596224796646466, + 0.05861619172305911, + 0.05863670550331337, + 0.058657332632469195, + 0.05867763489741799, + 0.05869717012990278, + 0.05871549298497462, + 0.05873215574219801, + 0.05874670912687234, + 0.058758703148498515, + 0.0587676879536913, + 0.058773214690718265, + 0.058774836382836586, + 0.058772108807595765, + 0.05876459137928349, + 0.0587518480317063, + 0.05873344809852218, + 0.05870896718837483, + 0.0586779880521207, + 0.0586401014394891, + 0.058594906942572605, + 0.058542013823609435, + 0.058481041824591434, + 0.058411621956309556, + 0.058333397264534174, + 0.05824602357111889, + 0.05814917018791398, + 0.0580425206014783, + 0.057925773126686286, + 0.057798641527440284, + 0.057660855602813504, + 0.057512161737072375, + 0.057352323412149, + 0.05718112168126319, + 0.05699835560252283, + 0.056803842631463784 + ], + "yaxis": "y" + } + ], + "layout": { + "barmode": "overlay", + "hovermode": "closest", + "legend": { + "traceorder": "reversed" + }, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "xaxis": { + "anchor": "y2", + "domain": [ + 0, + 1 + ], + "zeroline": false + }, + "yaxis": { + "anchor": "free", + "domain": [ + 0, + 1 + ], + "position": 0 + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import plotly.figure_factory as ff\n", "\n", @@ -1391,9 +45503,115 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 58, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
speciesislandbill_length_mmbill_depth_mmflipper_length_mmbody_mass_gsex
0AdelieTorgersen39.118.7181.03750.0Male
1AdelieTorgersen39.517.4186.03800.0Female
2AdelieTorgersen40.318.0195.03250.0Female
3AdelieTorgersenNaNNaNNaNNaNNaN
4AdelieTorgersen36.719.3193.03450.0Female
\n", + "
" + ], + "text/plain": [ + " species island bill_length_mm bill_depth_mm flipper_length_mm \\\n", + "0 Adelie Torgersen 39.1 18.7 181.0 \n", + "1 Adelie Torgersen 39.5 17.4 186.0 \n", + "2 Adelie Torgersen 40.3 18.0 195.0 \n", + "3 Adelie Torgersen NaN NaN NaN \n", + "4 Adelie Torgersen 36.7 19.3 193.0 \n", + "\n", + " body_mass_g sex \n", + "0 3750.0 Male \n", + "1 3800.0 Female \n", + "2 3250.0 Female \n", + "3 NaN NaN \n", + "4 3450.0 Female " + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "penguins = sns.load_dataset(\"penguins\")\n", "penguins.head()" @@ -1410,9 +45628,1533 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 59, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:25.024362\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots()\n", "ax.hist(penguins[\"flipper_length_mm\"], bins=30, density=True, edgecolor=\"k\")\n", @@ -1431,9 +47173,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 60, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABK4AAAN6CAYAAACnkgHIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAB2HAAAdhwGP5fFlAAB/+klEQVR4nOzde5xdZX0v/s+eS25Dc4MAkkCoINBCgRDgiBwRuUSCBxWVQKnEGAJIqqWCLy5qheMNCq2WYg+Bg8cQAlpA0KgYbIjVQ3+ICjEcohISriYhKCF3kkyS/ftjnM0Mc8meZCZ7Ae/368WLlb2e51nP7PnOmpnPrPWsUrlcLgcAAAAACqau1hMAAAAAgM4IrgAAAAAoJMEVAAAAAIUkuAIAAACgkARXAAAAABSS4AoAAACAQhJcAQAAAFBIgisAAAAACklwBQAAAEAhCa4AAAAAKCTBFQAAAACFJLgCAAAAoJAEVwAAAAAUkuAKAAAAgEISXAEAAABQSIIrAAAAAApJcAUAAABAIQmuAAAAACgkwRUAAAAAhdRQ6wnQvVWr1mfLlnLV7QcPHpCGhvps3rwlq1dv6MOZQfXUJUWkLikidUkRqUuKSF1SROoyqa8vZciQQb06puCq4LZsKWfLlq3b2Xf7+kFfUpcUkbqkiNQlRaQuKSJ1SRG9eeuy92/sc6sgAAAAAIUkuAIAAACgkARXAAAAABSS4AoAAACAQhJcAQAAAFBIgisAAAAACklwBQAAAEAhCa4AAAAAKCTBFQAAAACFJLgCAAAAoJAEVwAAAAAUkuAKAAAAgEISXAEAAABQSIIrAAAAAApJcAUAAABAIQmuAAAAACgkwRUAAAAAhSS4AgAAAKCQBFcAAAAAFJLgCgAAAIBCElwBAAAAUEiCKwAAAAAKSXAFAAAAQCEJrgAAAAAoJMEVAAAAAIUkuAIAAACgkARXAAAAABSS4AoAAACAQhJcAQAAAFBIgisAAAAACklwBQAAAEAhNdR6AgDQ1xob69PU1D+l0quv1dfXVf4/bNignTKPcjlZt25jmpu37JTjAQDA653gCoA3vKam/mlsrO90X6lUSkND5/v6ai4rV67faccDAIDXM8EVAG94rVdabd6yNS+teuVPr716+VW5XO7zOew6ZGAa6uvaXfUFAAB0T3AFwJvGS6teyQ3fnpckaexXn1KplHK5nOZNfX/r3ifPGpM9hjf1+XEAAOCNxOLsAAAAABSS4AoAAACAQhJcAQAAAFBIgisAAAAACklwBQAAAEAhCa4AAAAAKCTBFQAAAACFJLgCAAAAoJAEVwAAAAAUkuAKAAAAgEISXAEAAABQSIIrAAAAAApJcAUAAABAIQmuAAAAACgkwRUAAAAAhSS4AgAAAKCQGvpy8A0bNmTGjBmZPXt2nn766STJqFGjMm7cuEycODFDhgzZofEffPDBzJw5M/Pnz8+aNWsyYsSIjB07NhMnTsyhhx7abd9PfepTue+++7Z5jJEjR2bu3Lk7NE8AAAAAeq7Pgqvly5dn8uTJWbRoUbvXFy5cmIULF+Y73/lOpk2bloMOOmi7xv/KV76SW2+9td1rS5cuzdKlS3Pfffflkksuybnnnttl/9/+9rfbdVwAAAAAdo4+Ca42b96cqVOnZtGiRSmVSpkwYULGjx+f+vr6zJkzJzNnzsyyZcsyderU3HvvvT2+8mr69OmV0Orggw/OlClTstdee+WJJ57ItGnTsnTp0lx77bXZe++9M27cuA79N2zYkGeffTZJcv755+fUU0/t8liNjY09mhsAAAAAvaNPgqs777wzjz/+eJLk8ssvz6RJkyr7jj766IwZMyaf+tSnsmTJktxyyy255JJLqh57xYoVuf7665Mkhx56aG6//fb069cvSXL44Yfn5JNPzoQJE/L888/nmmuuyfHHH1/Z3+qJJ57I1q1bkyT//b//9/zFX/zFjny4AAAAAPSBPlmc/bbbbkuS7Lvvvpk4cWKH/ePHj88JJ5yQJLnjjjuyadOmqse+++67s379+iTJZZdd1iGUGj58eC6//PIkyZIlSzJnzpwOY/zud7+rbG/vrYoAAAAA9K1eD64WL16cp556Kkny3ve+N3V1nR/i9NNPT5KsXbs2Dz30UNXjtwZRe+21V4488shO25xwwgkZPHhwkuT+++/vsL91fauRI0fu8ALxAAAAAPSNXg+u5s2bV9k+6qijumw3duzYyvbDDz9c1dibNm3KggULtjl2XV1dxowZkyT5xS9+0WF/6xVXrrYCAAAAKK5eX+Nq8eLFle3Ro0d32W748OFpamrKunXr2vXpzrPPPpvNmzcnSfbZZ59u2+69995JWtbEWrFiRYYPH54kKZfLWbhwYZLkwAMPzA9+8IPce++9efzxx7Nu3brsvvvuOeaYYzJ58uTst99+Vc0LAAAAgN7X68HViy++mKTlqqc99tij27a77757nn766UqfasdOWm4V7E7bY7/44ouV4Oq5557LunXrkiQzZszI2rVr2/VbsmRJ7r777nz3u9/NFVdckY985CNVzQ0AAACA3tXrwdXq1auTJAMGDEh9fX23bQcNGtSuz7asWrWqQ9+uDBw4sLK9Zs2aynbr+lZJy/paY8aMyVlnnZV99903q1evzty5c3P33Xenubk5X/ziF7PLLrvkAx/4QFXz6wuDBw/oUfv6+rrK/4cN6/49gp1FXVJrrTVYKpXS2K/le1Mppcr/W1/rS6VSqTIXXwd0xfmSIlKXFJG6pIjUZd/o9eCq9QmBr33aX2f69+/frk+1Y7ft25UBA14NfNr2a/tEwcmTJ+fSSy+t/DKRJMcdd1xOOeWUTJkyJc3NzfnCF76Qd7/73TVbxL2hYft+mSqVStvdF/qKuqQI2p7zW154NcTaWcf3dcC2qBOKSF1SROqSIlKXvavXg6vWpwh2+MWgE+VyuV2faseuZvzWsV/b7/zzz88pp5ySF154Ie9617s6Heftb397zj///Pzbv/1b1q1bl3vuuScf+9jHqppjb9u8eUuP2tfX16VUKqVcLmfLlq19NCvoGXVJrbXWYPLq94dSS2KVlJNyyt307l2+DuiO8yVFpC4pInVJEanLFr0d2vV6cNV6C9/GjRu32bYnV2e1Hbua8dvub2xsbDfGQQcdtM0nCp5xxhn5t3/7tyTJz3/+85oFV6tXb+hRwQ8bNigNDfXZsmVrXn55fR/ODKqnLqm11hosl8tp3tTyB4HGfvUppZRyXn2tL7UGZr4O6I7zJUWkLikidUkRqcuW8G748KZeHbO6S516oKmpZYIbN27M1q3dBy7r17d8IgcPHtyjsZPklVde6bZt2/3bc5vfW97ylsq8li5d2uP+AAAAAOyYXg+uWp/2t2XLlvzxj3/stm3rUwJ33333qsYeOXJkZXv58uXdtm27v9rxX6t1nazm5ubt6g8AAADA9uv1WwX322+/yvZzzz3XZWi0YsWKrFu3Lkmy//77VzX2qFGj0r9//2zcuDHPPfdct22ff/75JMmIESMqV1xt3bo1P//5z/PSSy9l8ODBede73tVl/y1btlSeYrjrrrtWNT8AAAAAek+vB1eHHXZYZfvRRx/NkUce2Wm7Rx55pLI9ZsyYqsauq6vLIYcckkceeSSPPvpol+22bt2aefPmdRi7rq4uf/d3f5c1a9Zk33337Ta4WrBgQWWdrIMPPriq+QEAAADQe3r9VsF99tknBx54YJJk1qxZ7Z7u19a9996bpGXdqmOOOabq8ceNG5ckeeqpp/LYY4912mbu3LlZvXp1kuSkk05qt681SHvmmWe67J8kM2bMqGyPHz++6vkBAAAA0Dt6PbhKkrPPPjtJ8uSTT+amm27qsH/27NmZO3dukpan9w0cOLDqsU877bTssssuSZIrr7yycrthqxUrVuSaa65J0rK21SmnnNJu/1lnnVXZvuqqq7J27doOx7j77rvz/e9/P0lyzDHHVH1FGAAAAAC9p9dvFUySCRMm5M4778yCBQvyta99LYsXL87pp5+exsbGPPDAA5kxY0bK5XL23HPPXHjhhR36t16xNXLkyErA1WrXXXfNRRddlC9/+cv5zW9+kzPOOCMXXHBBRo8enSeffDI33nhjlixZkiT5zGc+k/79+7frf/zxx+c973lP7r///ixYsCAf+tCHMmXKlBx44IF5+eWXM2vWrPzgBz9Ikuy222754he/2BdvEQAAAADb0CfBVV1dXaZNm5aPfexjWbRoUWbNmpVZs2a1azNixIjcfPPNGTp0aI/HnzhxYpYsWZLp06dn8eLFufTSSzsc/+KLL+7yFr9rr702SXL//ffnmWeeyec+97kObUaPHp0bbrghe++9d4/nBwAAAMCO65PgKmm5Te+ee+7Jbbfdlvvuuy/PPPNMmpubM2rUqJx44omZPHlyhg8fvt3jX3HFFTnuuONy++23Z/78+Vm5cmWGDh2asWPHZtKkSTniiCO67DtgwID867/+a37605/mrrvuyq9//eusXLkyTU1Neetb35pTTjklZ555ZgYMGLDd8wMAAABgx/RZcJUk/fv3z5QpUzJlypQe9XviiSeqanfsscfm2GOP3Z6pJUne9a53dftkQQAAAABqp08WZwcAAACAHSW4AgAAAKCQBFcAAAAAFJLgCgAAAIBCElwBAAAAUEiCKwAAAAAKSXAFAAAAQCEJrgAAAAAoJMEVAAAAAIUkuAIAAACgkARXAAAAABSS4AoAAACAQhJcAQAAAFBIgisAAAAACklwBQAAAEAhCa4AAAAAKCTBFQAAAACFJLgCAAAAoJAEVwAAAAAUkuAKAAAAgEISXAEAAABQSIIrAAAAAApJcAUAAABAIQmuAAAAACgkwRUAAAAAhSS4AgAAAKCQBFcAAAAAFJLgCgAAAIBCElwBAAAAUEiCKwAAAAAKSXAFAAAAQCEJrgAAAAAoJMEVAAAAAIUkuAIAAACgkARXAAAAABSS4AoAAACAQhJcAQAAAFBIgisAAAAACklwBQAAAEAhCa4AAAAAKCTBFQAAAACFJLgCAAAAoJAEVwAAAAAUkuAKAAAAgEISXAEAAABQSIIrAAAAAApJcAUAAABAIQmuAAAAACgkwRUAAAAAhSS4AgAAAKCQBFcAAAAAFJLgCgAAAIBCElwBAAAAUEiCKwAAAAAKSXAFAAAAQCEJrgAAAAAoJMEVAAAAAIUkuAIAAACgkARXAAAAABSS4AoAAACAQhJcAQAAAFBIgisAAAAACklwBQAAAEAhCa4AAAAAKCTBFQAAAACFJLgCAAAAoJAEVwAAAAAUkuAKAAAAgEISXAEAAABQSIIrAAAAAApJcAUAAABAIQmuAAAAACgkwRUAAAAAhSS4AgAAAKCQBFcAAAAAFJLgCgAAAIBCElwBAAAAUEiCKwAAAAAKSXAFAAAAQCEJrgAAAAAoJMEVAAAAAIUkuAIAAACgkARXAAAAABSS4AoAAACAQhJcAQAAAFBIgisAAAAACklwBQAAAEAhCa4AAAAAKCTBFQAAAACFJLgCAAAAoJAEVwAAAAAUkuAKAAAAgEISXAEAAABQSIIrAAAAAApJcAUAAABAIQmuAAAAACgkwRUAAAAAhSS4AgAAAKCQBFcAAAAAFJLgCgAAAIBCElwBAAAAUEiCKwAAAAAKSXAFAAAAQCEJrgAAAAAoJMEVAAAAAIUkuAIAAACgkARXAAAAABSS4AoAAACAQhJcAQAAAFBIgisAAAAACklwBQAAAEAhCa4AAAAAKCTBFQAAAACFJLgCAAAAoJAEVwAAAAAUkuAKAAAAgEJq6MvBN2zYkBkzZmT27Nl5+umnkySjRo3KuHHjMnHixAwZMmSHxn/wwQczc+bMzJ8/P2vWrMmIESMyduzYTJw4MYceeuh2jfm5z30ud911V0aOHJm5c+fu0PwAAAAA2H59FlwtX748kydPzqJFi9q9vnDhwixcuDDf+c53Mm3atBx00EHbNf5XvvKV3Hrrre1eW7p0aZYuXZr77rsvl1xySc4999wejfnzn/88d99993bNBwAAAIDe1SfB1ebNmzN16tQsWrQopVIpEyZMyPjx41NfX585c+Zk5syZWbZsWaZOnZp77723x1deTZ8+vRJaHXzwwZkyZUr22muvPPHEE5k2bVqWLl2aa6+9NnvvvXfGjRtX1ZgbNmzIP/zDP6RcLvf44wUAAACg9/VJcHXnnXfm8ccfT5JcfvnlmTRpUmXf0UcfnTFjxuRTn/pUlixZkltuuSWXXHJJ1WOvWLEi119/fZLk0EMPze23355+/folSQ4//PCcfPLJmTBhQp5//vlcc801Of744yv7u/Mv//Ivee6553rwUQIAAADQl/pkcfbbbrstSbLvvvtm4sSJHfaPHz8+J5xwQpLkjjvuyKZNm6oe++6778769euTJJdddlmHUGr48OG5/PLLkyRLlizJnDlztjnmY489lhkzZqRUKmXo0KFVzwUAAACAvtPrwdXixYvz1FNPJUne+973pq6u80OcfvrpSZK1a9fmoYceqnr81iBqr732ypFHHtlpmxNOOCGDBw9Oktx///3djtfc3JzPfvaz2bJlS84+++yMHj266rkAAAAA0Hd6PbiaN29eZfuoo47qst3YsWMr2w8//HBVY2/atCkLFizY5th1dXUZM2ZMkuQXv/hFt2PedNNNWbhwYfbYY49cfPHFVc0DAAAAgL7XJ1dcteru6qXhw4enqampQ5/uPPvss9m8eXOSZJ999um27d57752kZU2sFStWdNpm0aJFmTZtWpLk85//fHbZZZeq5gEAAABA3+v14OrFF19sGbiuLnvssUe3bXffffd2faodO2m5VbA7bY/d2fhbt27NZz/72TQ3N+c973lPTjrppKrmAAAAAMDO0etPFVy9enWSZMCAAamvr++27aBBg9r12ZZVq1Z16NuVgQMHVrbXrFnTYf+MGTPy61//OoMHD87nPve5qo5fC4MHD+hR+/r6usr/hw3r/j2CnUVdUmutNVgqldLYr+V7Uymlyv9bX+tLpVKpMhdfB3TF+ZIiUpcUkbqkiNRl3+j14Kr1CYGvfdpfZ/r379+uT7Vjt+3blQEDXg18Xjv+73//+1x//fVJkk9/+tOVK7+KqKFh+36ZKpVK290X+oq6pAhaA6RXX3g1xNpZx/d1wLaoE4pIXVJE6pIiUpe9q9eDq9anCHb4xaAT5XK5XZ9qx65m/NaxOxv/85//fNavX58jjzwyEyZMqOrYtbJ585Yeta+vr0upVEq5XM6WLVv7aFbQM+qSWmutweTV7w+llsQqKSfllLvp3bt8HdAd50uKSF1SROqSIlKXLXo7tOv14Kr1Fr6NGzdus21Prs5qO3Y147fd39jYWNn+zne+k//6r/9Kv3798oUvfKGqgK2WVq/e0KOCHzZsUBoa6rNly9a8/PL6PpwZVE9dUmutNVgul9O8qeUPAo396lNKKeW8+lpfag3MfB3QHedLikhdUkTqkiJSly3h3fDhTb06Zq8vzt76pMCNGzdm69buA5f161s+kYMHD+7R2EnyyiuvdNu27f4hQ4YkSf7whz/kH//xH5MkH//4x7PffvtVdVwAAAAAdr5ev+Kq9Wl/W7ZsyR//+Mdu149qfdpftWtMjRw5srK9fPnybtu23d86/pe//OWsWrUqu+22W975znfmt7/9bYd+rYHXpk2bKvuHDBmyzacYAgAAANC7ej24ansV03PPPddlKLVixYqsW7cuSbL//vtXNfaoUaPSv3//bNy4Mc8991y3bZ9//vkkyYgRIypXXD322GNJkj/+8Y8544wzuu3/hz/8IR/4wAeSJKeffnquueaaquYIAAAAQO/o9VsFDzvssMr2o48+2mW7Rx55pLI9ZsyYqsauq6vLIYccss2xt27dmnnz5vVobAAAAACKpdevuNpnn31y4IEH5oknnsisWbNy3nnndboA+r333pukZd2qY445purxx40bl0ceeSRPPfVUHnvssRx66KEd2sydOzerV69Okpx00kntXt+WCRMmZP78+Rk5cmRV7QEAAADoG71+xVWSnH322UmSJ598MjfddFOH/bNnz66EQmeccUYGDhxY9dinnXZadtlllyTJlVdeWbndsNWKFSsqt/XtvvvuOeWUU7brYwAAAACgtnr9iquk5aqlO++8MwsWLMjXvva1LF68OKeffnoaGxvzwAMPZMaMGSmXy9lzzz1z4YUXduh/4IEHJkmnVz3tuuuuueiii/LlL385v/nNb3LGGWfkggsuyOjRo/Pkk0/mxhtvzJIlS5Ikn/nMZ9K/f/+++BABAAAA6GN9ElzV1dVl2rRp+djHPpZFixZl1qxZmTVrVrs2I0aMyM0335yhQ4f2ePyJEydmyZIlmT59ehYvXpxLL720w/EvvvjijB8/fkc+DAAAAABqqE+Cq6TlNr177rknt912W+67774888wzaW5uzqhRo3LiiSdm8uTJGT58+HaPf8UVV+S4447L7bffnvnz52flypUZOnRoxo4dm0mTJuWII47oxY8GAAAAgJ2tz4KrJOnfv3+mTJmSKVOm9KjfE088UVW7Y489Nscee+z2TK1Ld955Z6+OBwAAAMD26ZPF2QEAAABgRwmuAAAAACgkwRUAAAAAhSS4AgAAAKCQBFcAAAAAFJLgCgAAAIBCElwBAAAAUEiCKwAAAAAKSXAFAAAAQCEJrgAAAAAoJMEVAAAAAIUkuAIAAACgkARXAAAAABSS4AoAAACAQhJcAQAAAFBIgisAAAAACklwBQAAAEAhCa4AAAAAKCTBFQAAAACFJLgCAAAAoJAEVwAAAAAUkuAKAAAAgEISXAEAAABQSIIrAAAAAApJcAUAAABAIQmuAAAAACgkwRUAAAAAhSS4AgAAAKCQBFcAAAAAFJLgCgAAAIBCElwBAAAAUEiCKwAAAAAKSXAFAAAAQCEJrgAAAAAoJMEVAAAAAIUkuAIAAACgkARXAAAAABSS4AoAAACAQhJcAQAAAFBIgisAAAAACklwBQAAAEAhCa4AAAAAKCTBFQAAAACFJLgCAAAAoJAEVwAAAAAUkuAKAAAAgEISXAEAAABQSIIrAAAAAApJcAUAAABAIQmuAAAAACgkwRUAAAAAhSS4AgAAAKCQBFcAAAAAFJLgCgAAAIBCElwBAAAAUEiCKwAAAAAKSXAFAAAAQCEJrgAAAAAoJMEVAAAAAIUkuAIAAACgkARXAAAAABSS4AoAAACAQhJcAQAAAFBIgisAAAAACklwBQAAAEAhCa4AAAAAKKSGWk8AgL7V2Fifpqb+KZVqO49yOVm3bmOam7fUdiIAAMDrhuAK4A2uqal/Ghvraz2NJC1zWblyfa2nAQAAvE4IrgDe4FqvtNq8ZWteWvVKTeaw65CBaaivq/lVXwAAwOuL4ArgTeKlVa/khm/Pq8mxP3nWmOwxvKkmxwYAAF6/LM4OAAAAQCEJrgAAAAAoJMEVAAAAAIUkuAIAAACgkARXAAAAABSS4AoAAACAQhJcAQAAAFBIgisAAAAACklwBQAAAEAhCa4AAAAAKCTBFQAAAACFJLgCAAAAoJAEVwAAAAAUkuAKAAAAgEISXAEAAABQSIIrAAAAAApJcAUAAABAITXUegIA8GbQr7E+SVJfX5dhwwbVZA6lUlIq1aVcLqdcLtdkDuVysm7dxjQ3b6nJ8QEAeH0RXAHATtA0sDFJUiqV0tBQX+PZlGp69Kam/lm5cn1N5wAAwOuD4AoAdoK6P4VFm7dszUurXqnJHHYfNiilUqlmc9h1yMA01NelVNvcDACA1xHBFQDsRCtWb8gN355Xk2Nfdf470tBQqtkcPnnWmOwxvGmnHxcAgNcvi7MDAAAAUEiCKwAAAAAKSXAFAAAAQCEJrgAAAAAoJMEVAAAAAIUkuAIAAACgkARXAAAAABSS4AoAAACAQhJcAQAAAFBIgisAAAAACklwBQAAAEAhCa4AAAAAKCTBFQAAAACFJLgCAAAAoJAEVwAAAAAUkuAKAAAAgEISXAEAAABQSIIrAAAAAApJcAUAAABAIQmuAAAAACgkwRUAAAAAhSS4AgAAAKCQBFcAAAAAFJLgCgAAAIBCElwBAAAAUEgNfTn4hg0bMmPGjMyePTtPP/10kmTUqFEZN25cJk6cmCFDhuzQ+A8++GBmzpyZ+fPnZ82aNRkxYkTGjh2biRMn5tBDD91m/zlz5uSuu+7KY489ljVr1mS33XbLX/3VX+VDH/pQjj/++B2aGwAAAAA7ps+Cq+XLl2fy5MlZtGhRu9cXLlyYhQsX5jvf+U6mTZuWgw46aLvG/8pXvpJbb7213WtLly7N0qVLc9999+WSSy7Jueee22nf5ubmfPrTn87s2bPbvb5s2bIsW7YsP/7xj/Oe97wn1157bQYMGLBd8wMAAABgx/RJcLV58+ZMnTo1ixYtSqlUyoQJEzJ+/PjU19dnzpw5mTlzZpYtW5apU6fm3nvv7fGVV9OnT6+EVgcffHCmTJmSvfbaK0888USmTZuWpUuX5tprr83ee++dcePGdej/xS9+sRJa7b///pk8eXL23XffLFu2LDNnzsy8efNy//33Z8CAAbn22mt3/A0BAAAAoMf6JLi688478/jjjydJLr/88kyaNKmy7+ijj86YMWPyqU99KkuWLMktt9ySSy65pOqxV6xYkeuvvz5Jcuihh+b2229Pv379kiSHH354Tj755EyYMCHPP/98rrnmmhx//PGV/Unyu9/9LnfeeWeS5LDDDsvMmTPb7T/11FPz8Y9/PD/96U/zve99L5MnT97uq8IAAAAA2H59sjj7bbfdliTZd999M3HixA77x48fnxNOOCFJcscdd2TTpk1Vj3333Xdn/fr1SZLLLrusXeiUJMOHD8/ll1+eJFmyZEnmzJnTbv+9996bcrmcJPn85z/foX9dXV3+/u//vvLv+++/v+q5AQAAANB7ej24Wrx4cZ566qkkyXvf+97U1XV+iNNPPz1Jsnbt2jz00ENVj98aRO2111458sgjO21zwgknZPDgwUk6Bk977rlnjjrqqOy///455JBDOu3/1re+tbL9wgsvVD03AAAAAHpPrwdX8+bNq2wfddRRXbYbO3ZsZfvhhx+uauxNmzZlwYIF2xy7rq4uY8aMSZL84he/aLfvYx/7WGbOnJkf/vCHXfZfsmRJZXvEiBFVzQ0AAACA3tUnV1y1Gj16dJfthg8fnqampg59uvPss89m8+bNSZJ99tmn27Z77713kpY1sVasWFHV+EmydevW3HDDDZV/n3LKKVX3BQAAAKD39Hpw9eKLL7YMXFeXPfbYo9u2u+++e7s+1Y6dtNwq2J22x97W+Fu3bs3y5csze/bsnHnmmfnRj36UJJk0aVL+8i//sqq5AQAAANC7ev2pgqtXr06SDBgwIPX19d22HTRoULs+27Jq1aoOfbsycODAyvaaNWu6bfuRj3wkjzzySOXf/fv3z8UXX5yPfvSjVc2rLw0ePKBH7evr6yr/Hzas+/cIdhZ1WVut73+pVEpjv+7Py32lVCpV5lKLGujsPSilVPn/TnlfSq9u1urzUOs51LoOXg+cLykidUkRqUuKSF32jV4PrlqfEPjap/V1pn///u36VDt2275dGTDg1cBnW+O/dgH2jRs35r777st+++2Xd77znVXNra80NGzfLxalUmm7+0JfUZe11xoc1PL4ta6BDu9B6dUQq2ZzqIFazqEIdVB03iOKSF1SROqSIlKXvavXg6vWpwhW8wNxuVxu16fasasZv3Xsasb/+7//++yzzz555ZVX8rOf/Sy333575s+fnwsuuCDXXXdd3vve91Y1v76wefOWHrWvr69LqVRKuVzOli1b+2hW0DPqsrZa3/+k/bmxFmpVA529B6WWxCopJ+Xs3Pel1p+HWs/BuaBrzpcUkbqkiNQlRaQuW/R2aNfrwVXrLXwbN27cZtueXJ3Vduxqxm+7v7Gxsdu273vf+yrbxxxzTE4++eRMmjQpGzduzOc+97m84x3vyLBhw6qaY29bvXpDjwp+2LBBaWioz5YtW/Pyy+v7cGZQPXVZW63vf7lcTvOmnoXhvaU1JKlVDXT2HjT2q08ppZSzk96XNjlRrT4PtZ5DY0OvL625XcrlZN26jWlurtHnoRvOlxSRuqSI1CVFpC5bwrvhw5t6dcxe/wmy9UmBGzduzNat3Qcu69e3fCIHDx7co7GT5JVXXum2bdv9Q4YMqWr8VkcccUQmTZpUmeN//Md/9Kg/ANBR08CWPyS1Xj5fq/8aG+vT1NT9kgMAABRDr19x1fq0vy1btuSPf/xj5cmBnWl92l93bdoaOXJkZXv58uXdtm27v9rx2zrhhBNy0003JUkWLlzY4/4AQHt1f1pLbPOWrXlpVfd/gOoruw4ZmIb6uhRgmTEAAKrQ68HVfvvtV9l+7rnnugyNVqxYkXXr1iVJ9t9//6rGHjVqVPr375+NGzfmueee67bt888/nyQZMWJEuyuuli9fnmeffTaDBg3KIYcc0mX/tn2qXTweANi2Fas35IZvz6vJsT951pjs0cuXrwMA0Hd6/VbBww47rLL96KOPdtnukUceqWyPGTOmqrHr6uoqYVN3Y2/dujXz5s3rMPbWrVvznve8J+ecc06uu+66bo/1+9//vrK9xx57VDU/AAAAAHpPrwdX++yzTw488MAkyaxZs7p8atG9996bpGXdqmOOOabq8ceNG5ckeeqpp/LYY4912mbu3LlZvXp1kuSkk06qvF5XV1cJ1n75y1+2C6e6ml+SvOMd76h6fgAAAAD0jj55vM/ZZ5+dJHnyyScr60S1NXv27MydOzdJcsYZZ2TgwIFVj33aaadll112SZJceeWVldsNW61YsSLXXHNNkpa1rU455ZR2+88888wkLWtwXXXVVZ3eBnjXXXflhz/8YZKWhdqrvSIMAAAAgN7T62tcJcmECRNy5513ZsGCBfna176WxYsX5/TTT09jY2MeeOCBzJgxI+VyOXvuuWcuvPDCDv1br9gaOXJkJeBqteuuu+aiiy7Kl7/85fzmN7/JGWeckQsuuCCjR4/Ok08+mRtvvDFLlixJknzmM59J//7tnxp06qmn5nvf+17+8z//M//3//7fnH766Zk8eXLe+ta35uWXX84PfvCDSmg1dOjQXH311X3xFgEAAACwDX0SXNXV1WXatGn52Mc+lkWLFmXWrFmZNWtWuzYjRozIzTffnKFDh/Z4/IkTJ2bJkiWZPn16Fi9enEsvvbTD8S+++OKMHz++0/5f+9rXcskll2Tu3LlZtGhRPvOZz3Ros/fee+frX/969t133x7PDwAAAIAd1yfBVdJym94999yT2267Lffdd1+eeeaZNDc3Z9SoUTnxxBMzefLkDB8+fLvHv+KKK3Lcccfl9ttvz/z587Ny5coMHTo0Y8eOzaRJk3LEEUd02XfQoEG58cYbM2fOnNx999157LHHsmrVquyyyy454IADMm7cuEyYMKHD1VoAAAAA7Dx9FlwlSf/+/TNlypRMmTKlR/2eeOKJqtode+yxOfbYY7dnaklaFm5vu3g7AAAAAMXRJ4uzAwAAAMCOElwBAAAAUEiCKwAAAAAKSXAFAAAAQCEJrgAAAAAoJMEVAAAAAIUkuAIAAACgkARXAAAAABSS4AoAAACAQhJcAQAAAFBIgisAAAAACklwBQAAAEAhCa4AAAAAKCTBFQAAAACFJLgCAAAAoJAEVwAAAAAUkuAKAAAAgEISXAEAAABQSIIrAAAAAApJcAUAAABAIQmuAAAAACgkwRUAAAAAhSS4AgAAAKCQBFcAAAAAFJLgCgAAAIBCElwBAAAAUEiCKwAAAAAKSXAFAAAAQCEJrgAAAAAoJMEVAAAAAIUkuAIAAACgkBpqPQEAAIBaaGysT1NT/5RKtZ1HuZysW7cxzc1bajsRgAISXAEAAG9KTU3909hYX+tpJGmZy8qV62s9DYDCEVwBAABvSq1XWm3esjUvrXqlJnPYdcjANNTX1fyqL4CiElwBAABvai+teiU3fHteTY79ybPGZI/hTTU5NsDrgcXZAQAAACgkwRUAAAAAhSS4AgAAAKCQBFcAAAAAFJLgCgAAAIBCElwBAAAAUEiCKwAAAAAKSXAFAAAAQCEJrgAAAAAoJMEVAAAAAIUkuAIAAACgkARXAAAAABSS4AoAAACAQhJcAQAAAFBIgisAAAAACklwBQAAAEAhCa4AAAAAKCTBFQAAAACFJLgCAAAAoJAEVwAAAAAUkuAKAAAAgEISXAEAAABQSIIrAAAAAApJcAUAAABAIQmuAAAAACgkwRUAAAAAhSS4AgAAAKCQBFcAAAAAFJLgCgAAAIBCElwBAAAAUEiCKwAAAAAKSXAFAAAAQCEJrgAAAAAoJMEVAAAAAIUkuAIAAACgkARXAAAAABSS4AoAAACAQmqo9QQA3sgaG+vT1NQ/pVLt5lBf3/I3ilJqOAkACqfa71Gt30fq6+sybNigXp1DuZysW7cxzc1benVcAN44BFcAfaipqX8aG+trPY0WcisA2ujp96hSqZSGht7/ntbU1D8rV67v9XEBeGMQXAH0oda/Ym/esjUvrXqlJnPYfdiglGp5yRcAhVTt96i230PK5XKvHX/XIQPTUF9X06uSASg+wRXATvDSqldyw7fn1eTYV53/jjQ0+K0AgM5t63tUY7/6lEqllMvlNG/qvVv6PnnWmOwxvKnXxgPgjcni7AAAAAAUkuAKAAAAgEISXAEAAABQSIIrAAAAAApJcAUAAABAIQmuAAAAACgkwRUAAAAAhSS4AgAAAKCQBFcAAAAAFJLgCgAAAIBCElwBAAAAUEiCKwAAAAAKSXAFAAAAQCEJrgAAAAAoJMEVAAAAAIUkuAIAAACgkARXAAAAABSS4AoAAACAQhJcAQAAAFBIgisAAAAACklwBQAAAEAhCa4AAAAAKCTBFQAAAACFJLgCAAAAoJAEVwAAAAAUkuAKAAAAgEISXAEAAABQSIIrAAAAAApJcAUAAABAIQmuAAAAACgkwRUAAAAAhSS4AgAAAKCQBFcAAAAAFJLgCgAAAIBCElwBAAAAUEiCKwAAAAAKSXAFAAAAQCE11HoCAAA7S7/G+iRJfX1dhg0bVLN5lMvJunUb09y8pWZzAIqhCOcl5ySgyARXAMCbRtPAxiRJqVRKQ0N9befS1D8rV66v6RyA2ivKeck5CSgqwRUA8KZRl1KSZPOWrXlp1Ss1mcOuQwamob4upVJNDg8UTK3PS85JQNH1aXC1YcOGzJgxI7Nnz87TTz+dJBk1alTGjRuXiRMnZsiQITs0/oMPPpiZM2dm/vz5WbNmTUaMGJGxY8dm4sSJOfTQQ7fZ/1e/+lX+/d//PY8++mj++Mc/pq6uLnvuuWeOOeaYfPSjH83o0aN3aH4AQDGtWL0hN3x7Xk2O/cmzxmSP4U01OTZQXLU6LzknAUXXZ8HV8uXLM3ny5CxatKjd6wsXLszChQvzne98J9OmTctBBx20XeN/5Stfya233trutaVLl2bp0qW57777cskll+Tcc8/ttO/WrVtz1VVX5d///d877Hvqqafy1FNP5c4778znP//5TJgwYbvmBwAAAMCO6ZPgavPmzZk6dWoWLVqUUqmUCRMmZPz48amvr8+cOXMyc+bMLFu2LFOnTs29997b4yuvpk+fXgmtDj744EyZMiV77bVXnnjiiUybNi1Lly7Ntddem7333jvjxo3r0P+6666rhFajR4/OpEmT8hd/8Rdpbm7Oww8/nOnTp2ft2rX5/Oc/n+HDh+ekk07a8TcFAAAAgB7pk+DqzjvvzOOPP54kufzyyzNp0qTKvqOPPjpjxozJpz71qSxZsiS33HJLLrnkkqrHXrFiRa6//vokyaGHHprbb789/fr1S5IcfvjhOfnkkzNhwoQ8//zzueaaa3L88cdX9ifJM888Uwm9Dj300Hzzm9/MLrvs0m5+48ePz1lnnZU1a9bkS1/6Uo477rh2YwAAAADQ9+r6YtDbbrstSbLvvvtm4sSJHfaPHz8+J5xwQpLkjjvuyKZNm6oe++6778769S1Pu7jssss6BErDhw/P5ZdfniRZsmRJ5syZ027/97///WzZ0vKY1yuvvLJdaNVq//33z/nnn58kWbZsWR555JGq5wcAAABA7+j14Grx4sV56qmnkiTvfe97U1fX+SFOP/30JMnatWvz0EMPVT1+axC111575cgjj+y0zQknnJDBgwcnSe6///52+1pDqBEjRuSQQw7p8jhvf/vbK9sLFy6sen4AAAAA9I5ev1Vw3rxXn4Rx1FFHddlu7Nixle2HH34473rXu7Y59qZNm7JgwYJtjl1XV5cxY8bkpz/9aX7xi1+02/c//sf/yEEHHZT+/ftv83itNm7cWHVbAAAAAHpHrwdXixcvrmyPHj26y3bDhw9PU1NT1q1b165Pd5599tls3rw5SbLPPvt023bvvfdO0rIm1ooVKzJ8+PAkyYc//OGqjvXLX/6ysr3XXntV1QcAAACA3tPrtwq++OKLLQPX1WWPPfbotu3uu+/erk+1YyfbDpPaHrva8Vtt2LAhM2fOTNLycbS9bRAAAACAnaPXr7havXp1kmTAgAGpr6/vtu2gQYPa9dmWVatWdejblYEDB1a216xZU9X4ra677rosXbo0SXLqqadmt91261H/3jR48IAeta+vr6v8f9iw7t8j2FnezHXZ+rGXSqU09uv+nNhnSq9u1moOpVLLJGpVA519Hkp/emNK2UmfmwJ8Hmo+h1ofvyBz6O7r4c18vmTnq/Z7VF+dL2v9vaH12K1zebOel4rwedgezpcUkbrsG70eXLU+IfC1T/vrTOs6U9U+VbBtu22tUTVgwKuBT0+eWnjHHXdUrrYaPHhwLrnkkqr79oWGhu375lUqlba7L/SVN3tdtv5g+GaeQxFqoMN7UHr1l7KazaEGaj2HWh+/CHPo7uuhCF8rvLlU9fXQR+fLotR7rc8JtZ5DUT4PPfV6nTdvbOqyd/V6cNX6FMFqTrrlcrldn2rHrmb81rF7Mv4999yTL37xi5Xxr7766pqvb7V585Yeta+vr0upVEq5XM6WLVv7aFbQM2/mumz92JP256VaqfUcalUDnX0eSi2/gSXlpJyd+77U+vNQhDnU+vhFmENnXw9v5vMlO1+136P6+nxZy3r3fbr9sV9P5x3nS4pIXbbo7dCu14Or1lv4qnkSX0+uzmo7djXjt93f2Ni4zbFnzpyZL33pS5VvFp/97Gdz0kknVTWvvrR69YYeFfywYYPS0FCfLVu25uWX1/fhzKB6b+a6bP3Yy+Vymjf1LIjuNW1+Bq7VHFrPrbWqgc4+D4396lNKKeXspM9NAT4PNZ9DrY9fkDl09/XwZj5fsvNV+z2qr86Xtf7ekPg+nRTj87A9nC8pInXZEt4NH97Uq2P2enDV1NQywY0bN2br1q3dXu20fn3LJ3Lw4ME9GjtJXnnllW7btt0/ZMiQbtt+9atfzU033VT59xVXXJFzzjmnqjkBAAAA0Dd6PbhqvbVuy5Yt+eMf/1h5cmBnWp/2112btkaOHFnZXr58ebdt2+7vavzm5uZ85jOfyaxZs5K03FL4+c9/Pn/9139d1XwAAAAA6DvVLf7UA/vtt19l+7nnnuuy3YoVK7Ju3bokyf7771/V2KNGjaosyt7d2Eny/PPPJ0lGjBjR6RVXGzduzNSpUyuhVWNjY/75n/9ZaAUAAABQEL0eXB122GGV7UcffbTLdo888khle8yYMVWNXVdXl0MOOWSbY2/dujXz5s3rcuzNmzfn7/7u7/Kzn/0sScstiLfccktOPfXUquYBAAAAQN/r9eBqn332yYEHHpgkmTVrVpdPxrj33nuTtIRGxxxzTNXjjxs3Lkny1FNP5bHHHuu0zdy5c7N69eok6XSB9X/6p3/Kf/7nfyZJhg4dmhkzZuTtb3971XMAAAAAoO/1+hpXSXL22WfnyiuvzJNPPpmbbropH//4x9vtnz17dubOnZskOeOMMzJw4MCqxz7ttNNyww03ZO3atbnyyiszc+bMdou2r1ixItdcc02SlrWtTjnllHb9f/WrX2X69OlJWm4PvOmmmypXcQHQN/o1tjwSt76+LsOGDdpG695XX9/yd5pSSjv92ACdaWysT1NT/5RqeFqq9bmx1t8bWo+d+P5Qa9vz9dD6uevN+imXk3XrNqa5uUZPmAQ61SfB1YQJE3LnnXdmwYIF+drXvpbFixfn9NNPT2NjYx544IHMmDEj5XI5e+65Zy688MIO/Vuv2Bo5cmQl4Gq166675qKLLsqXv/zl/OY3v8kZZ5yRCy64IKNHj86TTz6ZG2+8MUuWLEmSfOYzn6msidXqX/7lXypXgX3gAx9I//7989vf/rbbj2e33XbLiBEjtvv9AHizaxrYmCQplUppaKiv3UT8XgIURFNT/zQ21vB82FaNzo2F+d6Q+P5QYzvy9dDb9dPU1D8rV67vtfGAHdcnwVVdXV2mTZuWj33sY1m0aFFmzZpVWQS91YgRI3LzzTdn6NChPR5/4sSJWbJkSaZPn57Fixfn0ksv7XD8iy++OOPHj2/3+jPPPJNf/vKXlX/fddddueuuu7Z5vE984hP55Cc/2eN5AtCi7k+/EWzesjUvrXplpx9/92GDUqrlZQ0Ar9F6SqrVeTGp/bmx1t8bktq/B7TYnq+Htp+3rpan6YldhwxMQ31dTa+CBDrXJ8FV0nKb3j333JPbbrst9913X5555pk0Nzdn1KhROfHEEzN58uQMHz58u8e/4oorctxxx+X222/P/Pnzs3LlygwdOjRjx47NpEmTcsQRR3To87vf/W5HPiQAdtCK1Rtyw7fn7fTjXnX+O9LQ4CdRoHheWvVKTc6LSXHOjbX63pAU5z2gRU++Hhr71adUKqVcLqd5047f2vfJs8Zkj+FN224I7HR9FlwlSf/+/TNlypRMmTKlR/2eeOKJqtode+yxOfbYY6se95RTTql6bAAAAABqq9efKggAAAAAvUFwBQAAAEAhCa4AAAAAKCTBFQAAAACFJLgCAAAAoJAEVwAAAAAUkuAKAAAAgEISXAEAAABQSIIrAAAAAApJcAUAAABAIQmuAAAAACgkwRUAAAAAhSS4AgAAAKCQBFcAAAAAFJLgCgAAAIBCElwBAAAAUEgNtZ4A8MbV2Fifpqb+qa9vycjr6+sybNignT6PcjlZt25jmpu37PRjAwAAsP0EV0CfaWrqn8bG+sq/S6VSGhrqu+nRt3NZuXJ9TY4NAADA9hFcAX2mVGr5/+YtW7Ni9YYkSblc3qlz2HXIwDTU11XmAgAAwOuH4ArocytWb8i0ex5LuVxO86ade7veJ88akz2GN+3UYwIAANA7LM4OAAAAQCEJrgAAAAAoJMEVAAAAAIUkuAIAAACgkARXAAAAABSS4AoAAACAQhJcAQAAAFBIgisAAAAACklwBQAAAEAhCa4AAAAAKCTBFQAAAACFJLgCAAAAoJAEVwAAAAAUkuAKAAAAgEISXAEAAABQSIIrAAAAAApJcAUAAABAIQmuAAAAACgkwRUAAAAAhSS4AgAAAKCQBFcAAAAAFJLgCgAAAIBCElwBAAAAUEiCKwAAAAAKSXAFAAAAQCEJrgAAAAAoJMEVAAAAAIUkuAIAAACgkARXAAAAABSS4AoAAACAQhJcAQAAAFBIgisAAAAACklwBQAAAEAhCa4AAAAAKCTBFQAAAACFJLgCAAAAoJAEVwAAAAAUUkOtJwAA8GbSr7E+SVJfX5dhwwa121dfX9flvt5ULifr1m1Mc/OWPjsG8PrQ3TlpZ2k995VSqsnxgWITXAEA7ERNAxuTJKVSKQ0N9Z226W5fr82jqX9Wrlzfp8cAiq+ac9JOI7cCOiG4AgDYier+9JvZ5i1b89KqV9rtK5Ve/a2tXC73yfF3HTIwDfV1KfkFEUj356SdZfdhg9qd/wDaElwBANTAitUbcsO357V7rbFffUqlUsrlcpo39c1tfJ88a0z2GN7UJ2MDr1+dnZN2lqvOf0caGgRXQOcszg4AAABAIQmuAAAAACgkwRUAAAAAhSS4AgAAAKCQBFcAAAAAFJLgCgAAAIBCElwBAAAAUEiCKwAAAAAKSXAFAAAAQCEJrgAAAAAoJMEVAAAAAIUkuAIAAACgkARXAAAAABSS4AoAAACAQhJcAQAAAFBIgisAAAAACklwBQAAAEAhNdR6AgAA7Dz9GuuTJPX1dRk2bFDN5lEuJ+vWbUxz85aazQEAKD7BFQDAm0jTwMYkSalUSkNDfW3n0tQ/K1eur+kcAIBiE1wBALyJ1KWUJNm8ZWteWvVKTeaw65CBaaivS6lUk8MDAK8jgisAgDehFas35IZvz6vJsT951pjsMbypJscGAF5fLM4OAAAAQCEJrgAAAAAoJMEVAAAAAIUkuAIAAACgkARXAAAAABSS4AoAAACAQhJcAQAAAFBIgisAAAAACklwBQAAAEAhCa4AAAAAKCTBFQAAAACFJLgCAAAAoJAEVwAAAAAUkuAKAAAAgEISXAEAAABQSIIrAAAAAAqpodYTAAAAgFrq11ifJKmvr8uwYYNqModyOVm3bmOam7fU5PgkjY31aWrqn1Jp+/rX19dV/r8jdaQW2hNcAQAA8KbWNLAxSVIqldLQUF+7eTT1z8qV62t2/De7pqb+aWzc8c9/b9SRWniV4AoAAIA3tbq0XGKzecvWvLTqlZ1+/F2HDExDfd12X+lD72h9/7e3DkptPoHlcnm75qAWOhJcAQAAQJIVqzfkhm/P2+nH/eRZY7LH8Kadflw699KqV7arDhr71adUKqVcLqd50/bd5qcWOrI4OwAAAACFJLgCAAAAoJAEVwAAAAAUkuAKAAAAgEISXAEAAABQSIIrAAAAAApJcAUAAABAIQmuAAAAACgkwRUAAAAAhSS4AgAAAKCQBFcAAAAAFJLgCgAAAIBCElwBAAAAUEiCKwAAAAAKqaEvB9+wYUNmzJiR2bNn5+mnn06SjBo1KuPGjcvEiRMzZMiQHRr/wQcfzMyZMzN//vysWbMmI0aMyNixYzNx4sQceuihPR7voYceyqRJk3LYYYflzjvv3KG5AQAAALBj+iy4Wr58eSZPnpxFixa1e33hwoVZuHBhvvOd72TatGk56KCDtmv8r3zlK7n11lvbvbZ06dIsXbo09913Xy655JKce+65VY/38ssv5/Of//x2zQUAAACA3tcnwdXmzZszderULFq0KKVSKRMmTMj48eNTX1+fOXPmZObMmVm2bFmmTp2ae++9t8dXXk2fPr0SWh188MGZMmVK9tprrzzxxBOZNm1ali5dmmuvvTZ77713xo0bt83x1q5dm/POOy/PPffcdn28AAAAAPS+Pgmu7rzzzjz++ONJkssvvzyTJk2q7Dv66KMzZsyYfOpTn8qSJUtyyy235JJLLql67BUrVuT6669Pkhx66KG5/fbb069fvyTJ4YcfnpNPPjkTJkzI888/n2uuuSbHH398ZX9nnn/++fzt3/5tnnjiie34SAEAAGDH9GusT5LU19dl2LBBNZtHuZysW7cxzc1bajYHeK0+WZz9tttuS5Lsu+++mThxYof948ePzwknnJAkueOOO7Jp06aqx7777ruzfv36JMlll13WIZQaPnx4Lr/88iTJkiVLMmfOnE7HKZfLuffee/PhD3+4ElrV1VmrHgAAgJ2raWBjkqRUKqWhob5m/zU21qepqX+N3w1or9evuFq8eHGeeuqpJMl73/veLsOg008/PQ888EDWrl2bhx56KO9617uqGr81iNprr71y5JFHdtrmhBNOyODBg7N69ercf//9OfXUUzu0mTBhQh577LEkSWNjYz73uc/l5ptvzpIlS6qaBwAAAPSGupSSJJu3bM1Lq16pyRx2HTIwDfV1KZVqcnjoUq8HV/PmzatsH3XUUV22Gzt2bGX74Ycfriq42rRpUxYsWLDNsevq6jJmzJj89Kc/zS9+8YtO27SGVgceeGCuvvrqHHzwwbn55pu3OQcAAADoCytWb8gN35637YZ94JNnjckew5tqcmzoTp9ccdVq9OjRXbYbPnx4mpqasm7dunZ9uvPss89m8+bNSZJ99tmn27Z77713kpY1sVasWJHhw4e327/PPvvk/PPPzwc/+MHU19dXdXwAAAAAdp5eD65efPHFJC1XPe2xxx7dtt19993z9NNPV/pUO3bScqtgd9oe+8UXX+wQXN1///3WtAIAAAAosF4PrlavXp0kGTBgwDavZBo0aFC7PtuyatWqDn27MnDgwMr2mjVrOux/vYRWgwcP6FH7+vq6yv9r+TQKSF6tx1allNLYb+de4Vj60036tfqaaH0PSqWd/7FXtFmn4E07h06OX/rTizutLmv9HhRhDrU+/utgDjulLgvwHtT63FwEr6fvD31WlwWoRXMowPG3cw69Xpe1fh9qffw4Nyc7fm7ujbr0eeio14Or1icEvvZpf53p379/uz7Vjt22b1cGDHg18OnJUwuLpqFh+4t9e/tCnym9ejLf6YcuwNdEqQArXZpDJ8evQV3W+j0owhxqffzCz2En1WWt34MinJuLoNafh6rn0Id1+bp5D97gc6j18bdrDn1Ql7V+H4pwfOfmHfw89EJd+jy8qteDq9Yrmar5JJfL5XZ9qh27mvFbx+7J+EW0efOWHrWvr69LqVRKuVzOli1b+2hWUJ3WeqwoJ+WUu+7Qh2r1NdH2PWh7XqoVc3j1+KWWnyhqUpe1fg+KMIdaH7+oc9jZdVnr9+DN/PPK6+n7w86oy6K/B2+WOdT6+D2ZQ1/WZa3fhyIc37l5+z4PvVmXr+fPQ28Hbr0eXLXewrdx48Zttu3J1Vltx65m/Lb7Gxsbqxq/iFav3tCjYh02bFAaGuqzZcvWvPzy+j6cGWxbaz22Kqec5k09C2N3VOs3nFp9TbS+B+Xyzv/YK9p8z3zTzqGT4zf2q08ppZ1Xl7V+D4owh1of/3Uwh51SlwV4D2p9bi6C19P3hz6rywLUojkU4PjbOYder8tavw+1Pn6cm5MdPzf3Rl2+3j8P9fV1Gd7LT6fs9UuRmppaJrhx48Zs3dp94LJ+fcsnYfDgwT0aO0leeeWVbtu23T9kyJCqxgcAAACgOHo9uGp92t+WLVvyxz/+sdu2rU8J3H333asae+TIkZXt5cuXd9u27f5qxwcAAACgOHo9uNpvv/0q288991yX7VasWJF169YlSfbff/+qxh41alRlUfbuxk6S559/PkkyYsQIV1wBAAAAvA71enB12GGHVbYfffTRLts98sgjle0xY8ZUNXZdXV0OOeSQbY69devWzJs3r0djAwAAAFAsvR5c7bPPPjnwwAOTJLNmzepyJf577703Scu6Vcccc0zV448bNy5J8tRTT+Wxxx7rtM3cuXOzevXqJMlJJ51U9dgAAAAAFEevB1dJcvbZZydJnnzyydx0000d9s+ePTtz585NkpxxxhkZOHBg1WOfdtpp2WWXXZIkV155ZeV2w1YrVqzINddck6RlbatTTjlluz4GAAAAAGqroS8GnTBhQu68884sWLAgX/va17J48eKcfvrpaWxszAMPPJAZM2akXC5nzz33zIUXXtihf+sVWyNHjqwEXK123XXXXHTRRfnyl7+c3/zmNznjjDNywQUXZPTo0XnyySdz4403ZsmSJUmSz3zmM5U1sQAAAAB4femT4Kquri7Tpk3Lxz72sSxatCizZs3KrFmz2rUZMWJEbr755gwdOrTH40+cODFLlizJ9OnTs3jx4lx66aUdjn/xxRdn/PjxO/JhAAAAAFBDfRJcJS236d1zzz257bbbct999+WZZ55Jc3NzRo0alRNPPDGTJ0/O8OHDt3v8K664Iscdd1xuv/32zJ8/PytXrszQoUMzduzYTJo0KUcccUQvfjQAAAAA7Gx9FlwlSf/+/TNlypRMmTKlR/2eeOKJqtode+yxOfbYY7dnap167W2JAAAAANROnyzODgAAAAA7qk+vuAIAAACoRmNjfZqa+qdUqs3x6+tbru0ppUYToFOCKwAAAKDmmpr6p7GxvtbTiNyqWARXAAAAQM21Xmm1ecvWvLTqlZ1+/N2HDUqpVpd70SXBFQAAAFAYL616JTd8e95OP+5V578jDQ2Cq6KxODsAAAAAhSS4AgAAAKCQBFcAAAAAFJLgCgAAAIBCElwBAAAAUEiCKwAAAAAKSXAFAAAAQCEJrgAAAAAoJMEVAAAAAIUkuAIAAACgkARXAAAAABSS4AoAAACAQhJcAQAAAFBIgisAAAAACklwBQAAAEAhCa4AAAAAKKSGWk8A6DuNjfVpauqfUqk2x6+vr3023q+xPknLXIYNG7TTj9/6HpRSo08CAADA65jgCt7Ampr6p/FPwc2bVdPAxiRJqVRKQ0MN3wu5FQAAQI8JruANrPVKq81btualVa/s9OPvPmxQSrW63OtP6v6UGL2Z3wMAAIDXK8EVvAm8tOqV3PDteTv9uFed/440NBQjtFmxesOb/j0AAAB4van9AjQAAAAA0AnBFQAAAACFJLgCAAAAoJAEVwAAAAAUkuAKAAAAgEISXAEAAABQSIIrAAAAAApJcAUAAABAIQmuAAAAACgkwRUAAAAAhSS4AgAAAKCQBFcAAAAAFJLgCgAAAIBCElwBAAAAUEiCKwAAAAAKSXAFAAAAQCEJrgAAAAAoJMEVAAAAAIUkuAIAAACgkARXAAAAABSS4AoAAACAQhJcAQAAAFBIgisAAAAACklwBQAAAEAhCa4AAAAAKCTBFQAAAACFJLgCAAAAoJAEVwAAAAAUkuAKAAAAgEISXAEAAABQSIIrAAAAAApJcAUAAABAIQmuAAAAACgkwRUAAAAAhSS4AgAAAKCQBFcAAAAAFJLgCgAAAIBCElwBAAAAUEgNtZ4AAABvLv0a65Mk9fV1GTZsUE3mUColpVJdyuVyyuXyTj9+fX3L349LKe30YwPA64ngCgCAnappYGOSpFQqpaGhvsazqXFwJLcCgG4JrgAA2Knq/pTWbN6yNS+teqUmc9h92KCUSqWazaH1+ABA9wRXAADUxIrVG3LDt+fV5NhXnf+ONDSUajaH1uMDAN2zODsAAAAAhSS4AgAAAKCQBFcAAAAAFJLgCgAAAIBCElwBAAAAUEiCKwAAAAAKSXAFAAAAQCEJrgAAAAAoJMEVAAAAAIUkuAIAAACgkARXAAAAABSS4AoAAACAQhJcAQAAAFBIgisAAAAACklwBQAAAEAhCa4AAAAAKCTBFQAAAACFJLgCAAAAoJAEVwAAAAAUkuAKAAAAgEISXAEAAABQSIIrAAAAAApJcAUAAABAIQmuAAAAACgkwRUAAAAAhSS4AgAAAKCQBFcAAAAAFJLgCgAAAIBCElwBAAAAUEiCKwAAAAAKSXAFAAAAQCEJrgAAAAAoJMEVAAAAAIUkuAIAAACgkARXAAAAABSS4AoAAACAQhJcAQAAAFBIDbWeAG9MjY31aWrqn1KpNscvlZJSqS7lcjnlcvlNO4f6+pZsupQafSIAAIDXhX6N9UlafocYNmxQTebg9xc6I7iiTzQ19U/jn058tVWEE14B5lCAKQAAAMXVNLAxSVIqldLQUOPf5fz+QhuCK/pE65VWm7dszUurXtnpx9992KCUSqWaHb9ocwAAAOhO3Z/SIr+/UDSCK/rUS6teyQ3fnrfTj3vV+e9IQ0MpK1ZvqMnxizYHAACAavj9haKxODsAAAAAhSS4AgAAAKCQBFcAAAAAFJLgCgAAAIBCElwBAAAAUEiCKwAAAAAKSXAFAAAAQCEJrgAAAAAopIa+HHzDhg2ZMWNGZs+enaeffjpJMmrUqIwbNy4TJ07MkCFDdmj8Bx98MDNnzsz8+fOzZs2ajBgxImPHjs3EiRNz6KGHbrP/Cy+8kFtuuSU/+9nPsmzZsgwaNCj77bdfPvCBD+RDH/pQ6uvrd2h+AAAAAGy/Pguuli9fnsmTJ2fRokXtXl+4cGEWLlyY73znO5k2bVoOOuig7Rr/K1/5Sm699dZ2ry1dujRLly7Nfffdl0suuSTnnntul/1/8YtfZOrUqVmzZk3ltU2bNuWRRx7JI488ku9973u56aabsssuu2zX/AAAAADYMX1yq+DmzZszderULFq0KKVSKWeeeWamT5+e2267LR/96EdTX1+fZcuWZerUqVm1alWPx58+fXoltDr44IPzta99Lf/+7/+eL3zhC9lrr72yZcuWXHvttfnxj3/caf8lS5ZUQqtBgwblkksuybe+9a3cdNNNefe7350k+dWvfpVPf/rT2/8mAAAAALBD+uSKqzvvvDOPP/54kuTyyy/PpEmTKvuOPvrojBkzJp/61KeyZMmS3HLLLbnkkkuqHnvFihW5/vrrkySHHnpobr/99vTr1y9Jcvjhh+fkk0/OhAkT8vzzz+eaa67J8ccfX9nf6rrrrsuaNWvS2NiY6dOn57DDDqvsO/744/PFL34xM2fOzE9+8pP83//7f/POd75ze98KAAAAALZTn1xxddtttyVJ9t1330ycOLHD/vHjx+eEE05Iktxxxx3ZtGlT1WPffffdWb9+fZLksssu6xBKDR8+PJdffnmSliur5syZ027/8uXLK1divf/9728XWrW69NJLM2LEiCTpcDsiAAAAADtHrwdXixcvzlNPPZUkee9735u6us4PcfrppydJ1q5dm4ceeqjq8VuDqL322itHHnlkp21OOOGEDB48OEly//33t9s3d+7cbNmyJUly2mmnddq/f//+OeWUU5IkP//5z7N69eqq5wcAAABA7+j14GrevHmV7aOOOqrLdmPHjq1sP/zww1WNvWnTpixYsGCbY9fV1WXMmDFJWhZh72x+DQ0NOeKII7Y5v+bm5nYfEwAAAAA7R59ccdVq9OjRXbYbPnx4mpqaOvTpzrPPPpvNmzcnSfbZZ59u2+69995JWtbEWrFiRYf57bnnnh1uM+ysf5IOT0YEAAAAoO/1+uLsL774YpKWq5722GOPbtvuvvvuefrppyt9qh07ablVsDttj/3iiy9m+PDh7cboaf9aqa8vZXvzxfr6PlnCrIdzKGX3YYNqd/y62h7fHF49/oihA1Mul7O5eWvN5lDr9+DNXgdFmEPb4zc01qVUKu30uqz1e1CEOdT6+EWew86sy6K+B2+2OdT6+NXMoa/r8vXwHrwZ5lDr4/d0Dn1Vl7V+H2p9fHPYseP3Rl22ZACt27X/nb6n2s6/t5TK5XK5Nwc877zz8rOf/SyDBg3a5i12H/zgB7NgwYKMGjUqDzzwwDbHvu+++/KpT30qSXL99ddX1qHqzG233ZYvfelLSZKZM2dWbi087LDDsmHDhhx//PG56aabuuy/du3ayu2CH/zgB3P11Vdvc34AAAAA9J5ej+9anxDY3W14rfr379+uT7Vjt+3blQEDBnTar3V7e/sDAAAAsHP0enDV+hTBUmnbl4e1XuzV1ZMHuxq7mvHbXkjWtl99fX1Vx+rquAAAAADsHL2eyAwa1HIf6MaNG7fZtidXZ7Udu5rx2+5vbGysbA8cOLDdsbuyYcOGyna18wMAAACg9/R6cNX6pMCNGzdm69buFyNbv359kmTw4ME9GjtJXnnllW7btt0/ZMiQDmO0Hrua/tXODwAAAIDe0+vBVevT+rZs2ZI//vGP3bZtfVrf7rvvXtXYI0eOrGwvX76827Zt97cdv3V+Pem/racjAgAAAND7ej242m+//Srbzz33XJftVqxYkXXr1iVJ9t9//6rGHjVqVGVR9e7GTpLnn38+STJixIh2V1y1zm/ZsmXZvHnzNvu37QMAAADAztPrwdVhhx1W2X700Ue7bPfII49UtseMGVPV2HV1dTnkkEO2OfbWrVszb968Tsc+9NBDk7TcyrhgwYJtzq++vr7dxwQAAADAztHrwdU+++yTAw88MEkya9asdk/3a+vee+9N0rLm1DHHHFP1+OPGjUuSPPXUU3nsscc6bTN37tysXr06SXLSSSe123fiiSdWnizYOofX2rhxY2bPnp0kOfroo61xBQAAAFADvR5cJcnZZ5+dJHnyySdz0003ddg/e/bszJ07N0lyxhlnVJ70V43TTjstu+yyS5LkyiuvrNxu2GrFihW55pprkrSsbXXKKae02z98+PDKa3fffXcefvjhDse47rrr8oc//CFJcs4551Q9NwAAAAB6T6nc1SVRO2Dr1q358Ic/XLkV733ve19OP/30NDY25oEHHsiMGTOyZcuW7Lnnnvne976XoUOHtuvfesXWyJEjKwFXWzNmzMiXv/zlJC3rT11wwQUZPXp0nnzyydx4441ZsmRJkuRf/uVfMn78+A79X3jhhZx66qlZt25d+vXrl/POOy/HHnts1q5dm29961v5yU9+kiQ5/vjjOw3eAAAAAOh7fRJcJS1PDPzYxz6WRYsWdbp/xIgR+cY3vlEJqdraVnCVJFdffXWmT5/e6b66urpcfPHFOe+887qc3y9/+ctceOGFWbNmTaf7jzjiiNx88835sz/7sy7HAAAAAKDv9FlwlbSsFXXbbbflvvvuyzPPPJPm5uaMGjUqJ554YiZPnpzhw4d32q+a4CpJ/uu//iu333575s+fn5UrV2bo0KEZO3ZsJk2alCOOOGKb81u+fHm+8Y1v5Kc//WmWLVuW+vr6vO1tb8v73ve+nHXWWWloaNi+DxwAAACAHdanwRUAAAAAbK8+WZwdAAAAAHaU4AoAAACAQhJcAQAAAFBIgisAAAAACklwBQAAAEAhCa4AAAAAKCTBFQAAAACFJLgCAAAAoJAEVwAAAAAUkuAKAAAAgEISXAEAAABQSIIrAAAAAApJcAUAAABAITXUegJ0tGLFiowfPz4rV67MY489lv79+7fb//DDD2fixIk9Hvfqq6/OBz/4wQ6vv/DCC7nlllvys5/9LMuWLcugQYOy33775QMf+EA+9KEPpb6+frs/Ft44tlWXbZXL5cyePTv33HNPHn/88axZsya77LJLDj744Hzwgx/MqaeemlKp1O3x1CXV6Eldbtq0KXfddVd+8IMfZNGiRXnllVcycuTIvPOd78xHP/rR7L333ts8nrqkK88//3xuu+22PPTQQ1myZEmam5uz6667ZsyYMTnzzDPz9re/vdv+Dz74YGbOnJn58+dnzZo1GTFiRMaOHZuJEyfm0EMP3ebx1Sad2dG6fK1LL7003/ve93LVVVflr//6r7fZXl3SmR2py40bN+Y73/lOfvzjH+d3v/td1q5dm6amprztbW/LySefnDPPPDMDBgzo9vjqks7sSF2uWbMmt912W+bMmZOnn346SfKWt7wl73rXu3L22Wf7GbMXlMrlcrnWk+BVW7duzUUXXZQf//jHSdKrwdU//dM/5bTTTmv32i9+8YtMnTo1a9as6bTPkUcemZtuuim77LJLj4/HG0c1ddlq06ZN+fu///s88MADXY73rne9K9dff30GDhzY6X51STV6UpfLli3L+eefn4ULF3a6v1+/fvnHf/zHnHrqqV0eT13Slbvuuitf+MIXsmnTpi7bfPjDH87//J//Mw0NHf9m+JWvfCW33nprp/3q6+tzySWX5Nxzz+1ybLVJZ3a0Ll9rzpw5+du//dskqSq4Upd0Zkfq8plnnsnUqVOzePHiLvvuu+++mTZtWv78z/+80/3qks7sSF0+8cQTueCCC7Js2bJO+w0cODBXXnllTj/99C7HVpfbJrgqmCuvvDLf/va3K//u7BexdevW5bnnntvmWI8//ng+97nPJUnGjh2bW2+9NY2NjZX9S5Ysyfvf//6sWbMmgwYNyoUXXpgjjzwyq1evzre//e385Cc/SZK8+93vzrRp03rjw+N1qpq6bPUP//APufPOO5Mkf/7nf57zzjsv++67b55//vl84xvfqAQH733ve/PVr361Q391SbWqrcu1a9fmwx/+cOUvYAcffHAmTZqU0aNH5/nnn8//+T//JwsWLEhdXV2uvfbaDgF/oi7p2ty5czN16tSUy+X82Z/9WT760Y/m6KOPTv/+/fPb3/423/zmN/Pss88mSSZNmpQrrriiXf/p06fn6quvTtJSm1OmTMlee+2VJ554ItOmTcvSpUuTJDfccEPGjRvX4fhqk87saF2+1sMPP5zzzjsvGzduTLLt4Epd0pkdqcu1a9fmAx/4QJ5//vkkyXve8568733vy+67754XX3wxs2bNyv33358kGTVqVO69994MHjy43fHVJZ3ZkbpcuXJl/sf/+B/5wx/+kCT5wAc+kJNPPjmDBw/OY489lptvvjmrVq1KfX19brnllrzjHe/ocHx1WaUyhbB+/fryRRddVD7ggAPa/bdhw4btGm/NmjXld7/73eUDDjigfNRRR5VfeOGFDm1aj3fwwQeXf/3rX3fY/4UvfKEyj5/97GfbNQ9e33pal88880z5wAMPLB9wwAHl0047rUO7jRs3lidMmFAZZ/78+R3GUJdsS0/r8tprr620+du//dtyc3Nzu/0bN24sn3vuueUDDjigfPTRR5dffvnlDmOoSzqzefPm8gknnFA+4IADykceeWR50aJFHdqsWbOmfPrpp5cPOOCA8kEHHVR+8sknK/teeuml8uGHH14+4IADyh/+8IfLGzdubNf3pZdeKp944onlAw44oPzud7+7w/5yWW3S0Y7W5Wt997vfLf/VX/1Vu/PtHXfc0e0c1CWvtaN1ef3111dq5qabbur0GDfeeGOlzXXXXddhv7rktXa0Lq+++upKzdx6660d+j7zzDOV7/Pvf//7O52DuqyOxdkL4JFHHsmECRPyox/9KElSV7fjn5Z//Md/zJIlS5K0XAGzxx57tNu/fPnyyu0173//+3PYYYd1GOPSSy/NiBEjkqTLWxh449qeunzwwQdT/tNFnH/3d3/X4eqXfv365ZJLLqn8+6c//Wm7/eqSbelpXW7evDn//u//niTZddddc80113S4xLtfv365+uqr069fv6xcuTLf+MY32u1Xl3TlV7/6VX7/+98nSS688MLst99+Hdrssssu+fznP5+k5fbWH/zgB5V9d999d9avX58kueyyy9KvX792fYcPH57LL788SctfZOfMmdNuv9qkMztal61efvnl/MM//EMuvfTSbNy4seqfT9UlndnRuvzud7+bJDnggANy/vnnd3qMCy64oDLu97///Xb71CWd2ZG6LJfL7erynHPO6dB39OjR+dCHPpQk+e1vf1u5YrCVuqye4KrGrrvuupx99tmV26daF67eEY8++mjuuuuuJMk73vGOTm97mTt3brZs2ZIkne5Pkv79++eUU05Jkvz85z/P6tWrd2hevH5sb12+9NJLle3Ro0d32mb//fevbLdeVttKXdKd7anL3/zmN5X1At73vvd1uTbAiBEjKpdvt95q0Epd0pVHHnmksv3ud7+7y3aHH354Bg0alCR58sknK6+3BlF77bVXjjzyyE77nnDCCZXbXdQm1djRukxafpZ8z3veU7n1f5999slVV11V1fHVJZ3Zkbp84YUXKn+Q765vqVTKf/tv/63Sp+16QeqSzuxIXa5evTr//b//9xxwwAE5+eSTu3zw1Fvf+tbK9gsvvNBun7qsnuCqxh577LEkLX9V/epXv5qrr7663TpUPVUul/OlL30p5XI59fX1+exnP9tpu3nz5iVJGhoacsQRR3Q53tixY5Mkzc3NlT688W1vXe6+++6V7aeeeqrTNm3/0tC2faIu6d721GXbhTIPOeSQbtu2/mDx7LPP5uWXX668ri7pypgxY3L++efn/e9/f97ylrd02a5cLleuRm1dI2jTpk1ZsGBBkuSoo47qsm9dXV3GjBmTpGXx1rbUJp3Zkbps9cwzz2TVqlWpq6vLmWeeme9+97vZd999qzq+uqQzO1KXDQ0Nueiii3LWWWdV6qa7/q3a1rW6pDM7UpdDhgzJP/3TP+X73/9+/u7v/q7Lvq1rVSbJbrvt1m6fuqzeth8hQp8aPHhwLrjggpx//vm98pSA2bNnV34QPv3009td3dJW69M49txzzw63JrTV9tGdixYtyrve9a4dniPFt711+c53vjMNDQ3ZvHlzbrzxxrz73e9uV19bt27N9ddfn6Tlr2KvXWhYXdKd7anL5ubmynZTU1O3bdveQvj8889n2LBhSdQlXTvmmGNyzDHHbLPd448/nldeeSVJy9VVSUtAunnz5iQtV7N0p7W2VqxYkRUrVmT48OFJ1Cad25G6bFVXV5fjjz8+F110Uf7yL/+yR8dXl3RmR+pyt912y9SpU6s6zq9+9askyYABAyrnykRd0rneOF925/e//33lTqiDDjqow9Mu1WX1BFc1dsMNN/TKmlatWp80UF9fn49//ONdtnvxxReTbPsLr+3aWK19eOPb3rocOXJkLrroovzzP/9zfvvb3+b000/Pueeem3333TfLli3LjBkz8utf/zpJ8olPfCIHHHBAu/7qku5sT122hk9JyzoC3Wl7dVbb21jVJTvqlltuqWy33pLatkZ6Wlutv4ypTXZEZ3XZ6n3ve18+8IEPbNe46pId0V1dbstPf/rTym1cb3/729v9zKAu2RE9qcuNGzfm97//fWbPnp0ZM2Zk5cqVGTBgQP7n//yfHdqqy+oJrmqsN0Orhx9+OL/73e+SJCeddFK7ZPa1Wu+Nbb1XtysDBw7s0Ic3vh2py/PPPz+jRo3KV7/61SxatKjDI7b32GOPfO5zn+v0se7qku5sT10efPDBqaury9atWzN37tycddZZnbZrbm7Oz3/+88q/N2zYUNlWl+yI+++/P7Nnz07SEu6feOKJSZJVq1ZV2vSkttqu2aI22V5d1WWrHfk5QF2yvbZVl91ZuXJlu2Bg4sSJ7farS7ZXT+ryueeey8knn9zutbe97W35yle+kkMPPbRDe3VZPWtcvYHMnDmzsj158uRu227atClJOjz17bUGDBjQoQ90Z8WKFXn00UfbLdTe1osvvpgf/OAHHZ6qkahLet/QoUPzzne+M0nLX2Jbn9zyWtOmTWt3lVXbWwzVJdvrscceqzwVMEk++9nPVtZla1sj21tbapPt0V1d9gZ1yfbYkbrcuHFjPvnJT1YWcB83blyOPfbYdm3UJdujp3XZ9ur9Vs8991zuuOOODg+lStRlTwiu3iCWLVuWBx54IEnLInOHH354t+3r6+t7fIzevDqMN6bly5fnIx/5SG677bY0Nzfnk5/8ZObMmZP/9//+Xx544IF86lOfSr9+/XL//ffnb/7mb/Lcc8+1668u6QsXX3xxZd2Aiy++ODfccEOWLFmS5ubmPPXUU7nqqqvy9a9/vd3DAtquM6Au2R6/+c1vct5552X9+vVJkkmTJrX7K23bGunqSUSt2i423Laf2qSntlWXvUFd0lM7UpcbN27MJz7xicrDK/bee+986Utf6tBOXdJT21OXe+yxR2688cbcdddd+Zd/+Ze84x3vyMaNG3PvvffmIx/5SIdlK9Rl9d6cH/Ub0P333195lOb73ve+bbZvvdxwW4lt29tlulswDpLk85//fBYvXpy6urp8/etfzyc+8Ynsvffe6devX0aNGpWPf/zj+T//5/+ksbExy5cvz8UXX9yuv7qkLxx00EG57rrr0tjYmObm5nz961/PCSeckEMOOSTjx4/Pt771rRxwwAG58sorK33a/mVLXdJTjz76aD760Y9m5cqVSZJTTjkll112Wbs2bW8LeO0T3V6r7f62f+lVm/RENXXZG9QlPbEjdbl27dqcf/75+dnPfpYkGTFiRG655ZYMGTKkQ1t1SU9sb13uu+++OeGEE3LooYdm/Pjx+eY3v1m5bfWZZ57JP/7jP7Zrry6rJ7h6g2i92qqurq7TtYNeq/XpWq0Jcldan56QtDzRC7rywgsv5Kc//WmSlku0jz/++E7bHXnkkTnzzDOTJP/v//2/PPbYY5V96pK+csopp+Suu+7Kcccd1+7pgbvttlsuvPDC3HXXXe3+6tX2ccXqkp6YM2dOPvaxj1XWoHjPe96Tf/qnf+rwF9K2T7lsWzudabu/7S9kapNqVVuXvUFdUq0dqcs//OEPOeeccyrrU44YMSLTp0/Pvvvu22l7dUm1evt8eemll1YWX//xj3/crgbVZfUEV28Aq1atyqOPPpokGTt2bLtfuLrS+sWzradstd3f9mkG8FqPP/545XaWrkKrVieddFJlu21wpS7pS3/xF3+R//2//3d+8Ytf5Ec/+lF+9rOf5cEHH8zf//3fZ8CAAXnqqacqbUeOHFnZVpdU6/bbb88nP/nJyl9GP/CBD+RrX/tap+thtK2xntRW21ta1SbV6Eld9gZ1STV2pC4XL16cM888M7/5zW+StJxPZ86cmf3337/LPuqSavTF+bKxsbGy3mpzc3Oefvrpyj51WT3B1RvAgw8+mM2bNydJVVdbJcl+++2XpGVtrNa+nWm7gHZrH+hM278E/Nmf/Vm3bVsf5Z60XObdSl2yMzQ1NeWtb31r9thjj3ZrC82fPz9Jy19td91118rr6pJqfP3rX88XvvCFbN26NUnLWhjXXHNNl+tXjBo1qrIY62vX+3ut1toaMWJEuyuu1Cbb0tO67A3qkm3ZkbpcsGBB/uZv/qayEPsBBxyQb33rW11eadVKXbItPa3LTZs2ZfHixfnJT35SuaWwK0OHDq1st30AkLqsnuDqDeBXv/pVZfvoo4+uqk/r4zg3btyYBQsWdNnukUceSdKycNxhhx22A7PkjW7YsGGV7d///vfdtn3hhRcq221DLHVJX/jGN76RL37xi7nnnnu6bLNx48b813/9V5LkmGOOabdPXbItN998c2644YYkLQutX3rppbniiiu6XXS9rq4uhxxySJJUrpruzNatWzNv3rwkLQ9faUtt0p3tqcveoC7pzo7U5ZNPPpnJkyfn5ZdfTtKy/MTtt99e1RUo6pLubE9d/sd//EdOPfXUfPzjH89//ud/djt+29+N2taruqye4OoNoPVWq0GDBuVtb3tbVX1OPPHESnp87733dtpm48aNmT17dpKWQOzNej8t1TnssMMqawd9//vfb/cUrNf60Y9+VNkeO3ZsZVtd0hd++MMfZubMmfnmN7/ZZZs777yzcvXfaaed1m6fuqQ7c+fOzT//8z8naQmjvvjFL+bcc8+tqm/rVdJPPfVUu9umXzt+6zobbW+zTtQmXduRutxR6pKu7Ehdrl27Nh//+McrV7a8853vzDe+8Y2qa0dd0pXtrcvDDz+8Emx997vf7bLdihUrKsHWvvvum7e85S2VfeqyeoKr17mtW7dm8eLFSZK//Mu/rPrS7+HDh+eUU05Jktx99915+OGHO7S57rrr8oc//CFJcs455/TSjHmj+rM/+7PKL/yPP/54/vVf/7XTdvfdd1/l5P72t7+93eWu6pK+0Pro4oULF+a+++7rsP/xxx/PV7/61STJX/3VX1XWIWilLunKypUr87nPfa7y70svvTRnnHFG1f1PO+207LLLLkmSK6+8MuvWrWu3f8WKFbnmmmuStKxt1VqHrdQmndnRutxR6pLO7GhdXn311ZWrVv7qr/4q//Zv/9buCcDboi7pzI7U5ciRIys/Mz700EOdhlcbNmzIpz/96cr39ylTprTbry6r17DtJhTZ8uXLK2sL7bPPPj3qe+mll+Y///M/s27dukyZMiXnnXdejj322Kxduzbf+ta38pOf/CRJy0Lbrb/4QXc+/elP5+c//3mWLVuW//W//lceeeSRnHHGGdlnn33y8ssv5/777893v/vdlMvlDB48OFdddVWHMdQlve0jH/lIZs6cmRUrVuSyyy7Lb3/727zjHe9Ikvx//9//lxkzZmTDhg0ZNGhQvvzlL3d6Wbi6pDMzZszISy+9lKRl8f+3v/3t+e1vf9ttn0GDBmX06NFJkl133TUXXXRRvvzlL+c3v/lNzjjjjFxwwQUZPXp0nnzyydx4442VdVw+85nPVNbEaktt8lo7Wpe9QV3yWjtSl88991zlapSGhoZccMEF7R6o0pX99tsv/fr1q/xbXfJaO3q+/OxnP5tf//rXWb16dS677LI89NBDOfnkk7Pbbrtl4cKF+eY3v1mp1RNPPDEf+tCHOoynLqtTKnd3Pw81cfnll1dOzo899linP6i2mjdvXs4666wkydSpU3PRRRf16Fi//OUvc+GFF2bNmjWd7j/iiCNy8803b3Oxbd74qq3LZ555Jn/7t3+bRYsWdTnWW97ylnz961+vrO/yWuqSalVbl48++mguvPDCLhfP3HXXXfOv//qvOfLII7s8lrrktY477rhtPgnotY4++ujcdttt7V67+uqrM3369E7b19XV5eKLL855553X5Zhqk7Z6qy5f6+GHH87EiROTJFdddVX++q//utv26pK2dqQuv/a1r2XatGk9PuYDDzyQUaNGtXtNXdJWb5wv58+fn0984hN58cUXu+zzvve9L1/60pe6/DlVXW6bK65e59o+kW3PPffscf+jjjoqP/zhD/ONb3wjP/3pT7Ns2bLU19fnbW97W973vvflrLPOqqxbBNXYd999c++99+a73/1ufvSjH+V3v/tdVq9enV122SX7779/TjrppJx55pkZNGhQl2OoS3rbEUcckVmzZlVqaunSpamrq8uf//mf56STTso555zT7mltnVGXtLVixYoe/7DblSuuuCLHHXdcbr/99syfPz8rV67M0KFDM3bs2EyaNClHHHFEt/3VJq16sy53lLqk1Y7W5e9+97tem4u6pFVvnS8PO+yw/PCHP8wdd9yR//iP/8jTTz+dTZs2ZbfddsvYsWNz5plnbvMBaupy21xxBQAAAEAhWZwdAAAAgEISXAEAAABQSIIrAAAAAApJcAUAAABAIQmuAAAAACgkwRUAAAAAhSS4AgAAAKCQBFcAAAAAFJLgCgAAAIBCElwBAAAAUEiCKwAAAAAKSXAFAAAAQCEJrgAAAAAoJMEVAAAAAIUkuAIAAACgkARXAAAAABSS4AoA3qS+//3v52/+5m/y3/7bf8vBBx+co48+Oueff34OPPDAHHjggTnnnHM69DnhhBMq++l9v//977t9/9/IXnzxxdx6662d7rv88ssr78vDDz+8k2cGANSS4AoA3oRuvPHGfPrTn86vfvWrrFy5Mps3b86qVatSV+dHA3a+22+/PaeeemrmzJlT66kAAAXTUOsJAAA71/r16/Nv//ZvSZJSqZS//uu/zpgxY7J169aMGjUqP/nJT2o8Q95svvCFL9R6CgBAQQmuAOBN5umnn05zc3OS5N3vfneuvPLKyr7f//733fadO3dun84NAADacj8AALzJrF+/vrJ9wAEH1HAmAADQPcEVALzJbN26tbLd0ODiawAAistPqwDwJnHOOefkF7/4RbvXvv7/t3fvQVXWeRzH38pF8bJrskQIeC/bNiE1ddWyXF1MVASxlMzWFSWj3WFV3MS0Fi9p02qry04h1VgECiEKm4wySw7rJRdNYtMMxFtJGhcDFJL7/sHwDEfOOaB7VJw+r78envN7vs/veQ6N8ZnfJSqKqKgoAEaMGMG6deus1vjNb35DQUEBALm5uWbru7u78+mnn3Lx4kViYmLIzMyksLAQJycnBg0axLRp0wgICMDOzs7sPZp2LAwICGD9+vVkZ2fz/vvvk52dTWlpKT169GDo0KEEBQUxatSoVp+7srKSxMREMjIyOHPmDGVlZXTv3p2BAwcyfvx4Zs6ciZOTk9lr//73vxvv58CBA+Tn5/PWW2/x9ddf07lzZ/r27ctLL73EE0880Wo/bG3//v2kpKRw7NgxSkpKsLOzw83NjVGjRhEUFMSAAQPMXnfhwgXGjx8PwJIlSwgJCSEnJ4dt27aRlZVFUVERXbp04YEHHsDX15cZM2bg4OBgtS9ffvklcXFxZGVlUVhYSNeuXXnggQcIDAxk2rRppKWlsXjxYgA+/PBDRo4cadKPJllZWS2+f3Oqq6uJj49nz549nDlzhqqqKlxdXRk9ejTPPfccAwcOvKF3eSOa9/vPf/4zwcHBfP7558TFxRnfRc+ePfH29ub3v/89Q4YMMa7dt28f27Zt48SJE5SVleHi4sKYMWNYuHAhHh4eLe6VnJxMREQEAElJSTz88MN88sknJCUlkZeXR2VlJa6urowdO5Z58+bRq1cvAGpqakhMTCQlJYUzZ85QU1ODp6cnEydOZN68eXTt2vWWvR8RERFbU3AlIiIiNpeTk0NISAilpaXGuerqarKyssjKymLHjh288847/PznP7daZ/v27axatYq6ujrjXFFREXv37mXv3r3Mnj2bFStWWNwN8fDhw4SHh1NUVGRy/vLly0Zf3n33XTZt2sSwYcOs9uXAgQO88sorRl+qqqrIycnB1dXV6nW2Vl5ezuLFi9m/f3+Lz/Lz88nPzyc+Pp6QkBDCwsLo0KGD1XpRUVH84x//MBmJ1/y7SkxM5P333+eee+4xe/2WLVvYuHEjDQ0NxrnS0lLj+tTUVCZNmnSTT9vSN998w6uvvsq5c+dMzp8/f57z58+zY8cOVq9ejb+/v83uac2GDRuIiYkxef5Lly5x6dIlMjIyWL9+Pb6+vqxYsYLk5GSTa7/77js+/vhj0tPT+eijj6xO3a2srCQ4OJiDBw+anD9//jyxsbGkpaWxdetWevbsycKFC/nyyy9N2p06dYpTp06RkZFBfHw8Xbp0scHTi4iI3HoKrkRERH4iwsLCKC0tJS8vj02bNgHg6+vL5MmTAejRo4dN7nP16lVCQ0MpLS2lf//+PP3007i6upKfn8/27du5fPkyx44dY86cOSQlJeHo6Gi2TnZ2NqmpqdTV1TF69GgmTZpE586dOXr0KMnJydTU1BAXF0ddXR2RkZEtrj906BAhISHGQvSPPfYY48aNw9nZmeLiYvbt28fBgwcpKirid7/7HbGxsSajY6732muv0bFjR4KCgnjkkUf45ptvyM3N5cEHH7TJe2uLyspKZs+eTV5eHgDu7u74+/szYMAAampqyMnJYdeuXVRWVvL2229z9epVVqxYYbFeSkoK+fn5dOrUialTpzJ8+HAAPv/8c3bu3ElNTQ1fffUVa9asYcOGDS2u37Jli3Hezs6OqVOnGqPgjhw5wq5duzh48GCLEAXA2dnZ2N3ypZdeAuD+++/nT3/6EwBubm5m+/yXv/yF2tpa+vbtS0BAAB4eHhQUFJCcnMy5c+eorq5m5cqVeHl50b9//7a81puWmJjIuXPn6NKlCwEBAXh5eVFWVkZqairHjx+ntraWVatWcfDgQXbu3Ml9993HM888Q79+/SgsLCQ+Pp7z589TVlbGq6++yvbt2y3eqymsu++++3j66afp06cPBQUFxMbGUlxcTElJCa+99hrV1dUcP36cwYMH4+fnxy9+8Qtyc3OJi4vjypUrnDx5kujoaBYtWnRL342IiIitKLgSERH5iXj00UcB6N69u3Guf//+TJgwwfi5tV0F26KsrAxoDMXeeOMNk2DqueeeY+7cueTl5ZGbm8sHH3zAggULzNZpGlETERHB3LlzjfN+fn4EBgYSHBzMlStXSEhIYPr06Xh7e5v0ITw8nJqaGhwdHfnb3/7WYlranDlzSEtLY+nSpdTU1LBo0SLS09MtBmlVVVWsW7eO6dOn38xrsYnXX3/dCK2mT59OZGSkSX/9/f0JCQlh/vz55OfnExsby+OPP25xKmN+fj4uLi588MEHJlML/f39mTBhAiEhIQDs2bOHlStXmoSb3377rTGNskuXLkRHRzNixAiTGjNnzmTBggUmI++aODk5mfzuAdxzzz0tzl2vtraWwMBAVq1aZbJG2/PPP8/8+fM5evQo1dXVfPzxx7z88stWa/2/zp07Z/b9BQUF4e/vz+nTpykvL2fnzp0MGTKEmJgYk//+AgMDmTJlCpcuXSI7O5uCggLc3d0t3uvRRx8lOjqabt26GecnT57MU089RW1tLceOHQNg9uzZrFy50hht5+vra0yLra+v55///KeCKxERuWtocXYRERGxuQEDBrQIraBxlM3mzZuNNZPee+89k2mA1wsMDDQJrZp4e3uzfPlyABoaGtiyZYvJ5/Hx8ZSUlAAQHh7eIrRq4uvry+zZswG4ePEiu3btstgXd3d3AgICLH5+qxUUFLBz504AfvWrX7F27VqzIZubmxtvvvmmMX3y7bfftlp3+fLlZtfDeuKJJxg6dCjQGBYdP37c5PMtW7ZQVVUFwLJly0xCqyZeXl6sXr26DU/Xdv369SMyMrLFxgJOTk6EhoYaP584ccKm97VkyZIlLd6fo6MjM2bMMH7u0KED69evNwmtoDFE9vX1NX5uCiXNsbe3Z926dSahFYCnpyePP/648bO7uzvLly9vMUXUy8uLX/7yl0Dj71JFRUUbn1BEROTOUnAlIiIiNhccHGxx5FK/fv0YN24cAD/88APZ2dlm23Xo0IEXX3zR4j2apkFB47TAphAFYPfu3UDjH/uBgYFW+zpr1izjOCMjw2K74cOHt7pe1K20Z88eamtrAZgxY4bFdb0AHnroIby8vIDGKZeXL182287BwcHqCKemoAMwGTXV0NDAvn37gMYpptYCPR8fH5tO2Zs0aZLFxeIHDx5sHBcXF9vsnpY4ODiYBE/N9e7d2zgeNGgQffv2Nduu+QirptGK5jzyyCMmNZvz9PQ0jsePH29xt9DmC8CXl5dbvJeIiEh7oqmCIiIiYnPNR4CYM2LECNLT04HGYKVpGmNzffr0MfmD/Hr29vYMHTqU9PR0Kisryc3NxcvLi/LycvLz84HGKWyHDx+22peGhgYcHByMNaIssbRL3+3SNA0MGkOZf/3rX1bbNx+Z89///pcnn3yyRZu+fftaDBjBdFppU2gGjVMMmxa8HzZsmNUaAKNHj+bMmTNW27SVtQXMm/e3eZB5q3h4eNCpU6dW+9KnTx+LNZrvaNl8gfzrWfv9+9nPfmYcWwrIrr+XtZGOIiIi7YmCKxEREbGpbt26ce+991pt06tXL+P4+h3/mrRllE7zOoWFhUDjjm5NO7yVl5cbC3+3RWlpKfX19WZHMzUPB+6EixcvGsdNi5q3VdO0yetdP3Xtes1H7jQPVb7//nvj2NKaTM1ZGil0M6x9D81HxDXf5e9WaeuGBtZ28GvrKD5Luzper3k4dbP3EhERaU80VVBERERs6vo1eMxp/sf11atXb7pO586dW9S5cuVKq9dZ0tDQYHHtn9ZGFd1qlt7T/3OtpSllrfnhhx+M4+bfgSXWgpsbdbN9vhUsTVm8FdrTc4uIiNxO+hdQREREbKotU7QqKyuN4549e9q0TvNQbOzYscTExLRa527QPCA6dOgQzs7Od6wvzd/xjz/+2Gr7trQRERERMUcjrkRERMSmysrKTAIlc7799lvj2NK0wkuXLrV6L3N1mhZsBzh9+nSrNe4WLi4uxrGt1ou6WW5ubsbxhQsXWm3fljYiIiIi5ii4EhEREZuqr6+3uFNgk//85z/G8a9//Wuzbb7++mur0+Oqq6uN+zg7OxuLdt97773G2lcFBQWcOnXKal9KSkoIDQ1lzZo1xMXFWW17J3l7exvHn376aavtN2zYQEREBFFRURQUFNi0L4MGDTKm/2VnZ1NTU2O1/dGjR216fxEREfnpUHAlIiIiNhcbG2vxs1OnTrF//36gcQc0S7vEVVVVkZCQYLFOcnIypaWlAEyYMMFkQXUfHx/jePPmzVb7+u6775KRkUFsbKxJoNbeNH+mxMREqyPScnNziYmJITk5mXfeeYeuXbvatC/29vY89dRTQOOC9p988onFtkeOHOHEiRNW6zUtGm5tVz0RERH5aVJwJSIiIja3b98+oqOjW5z//vvvCQsLo7a2FoCwsDCrdTZv3sxnn33W4vyRI0d44403gMb1lhYsWGDy+dy5c40RQenp6fz1r381G4rs3r2brVu3Ao3hyfz581t/uDvkwQcfZNy4cUDjYusLFy402d2vSWFhIX/84x+NXfVmzpzZ5t3vbkRwcLCxOPnrr79OTk5OizZnz55l6dKlrdZqWjOrvLzctp0UERGRu54WZxcRERGb69ChAxs3buTAgQNMnjyZ7t27c/LkSRISEoxwYuLEifj6+lqtce3aNebNm8fUqVMZPXo0AIcPHyY1NZW6ujoAwsPD8fT0NLnWzc2N1atXEx4eTkNDAzExMWRmZjJ16lQ8PDwoKSlh//79ZGZmGte88MILeHl52fpV2NTatWsJDAzk4sWLnDx5kkmTJhEQEICXlxf19fWcPHmSpKQkY2fE/v37s2jRolvSl4EDBxIaGsqmTZsoLy/n2Wefxc/Pj5EjR9KxY0dycnJISkri2rVr2NnZGd+XnZ1di1pubm6cPn2avLw83nzzTQYPHkyPHj0sTiMVERGRnw4FVyIiImJzERERbNy4kaysLLKyslp8PmvWLFauXGm1Rq9evZgyZQrR0dGkpKSQkpJi8rmDgwORkZEEBgaavX7KlCk4OjryyiuvUF5eTl5eHhs2bGjRzt7enhdffJE//OEPN/CEd4azszMJCQmEhYWRnZ1NRUUFH330kdm2w4YNY/PmzXTr1u2W9Sc0NJSKigree+89amtrSU5OJjk52aTNxIkTcXBwMKYTOjo6tqjj5+fHW2+9BTRO3QR4+OGH2bFjxy3ru4iIiNwdFFyJiIiIzY0fP54nn3yS6OhoDhw4wOXLl+nRowfDhw8nKCiIESNGtKnO4sWLGTVqFFu3buWLL76goqKCXr16MXbsWJ5//nl69+5t9XofHx9GjRpFQkIC//73v8nPz6e8vBwHBwc8PT0ZOXIks2bNYsCAAbZ47NvC1dWV7du3k5GRQVpaGl988QUlJSXU1dXh7OzM4MGDmTJlCj4+PsbaUbfS0qVL8fHxYdu2bWRlZVFcXEznzp156KGHeOaZZ/D19WXJkiVGe3PTFl944QU6depEUlISFy5cwN7e3pjqKCIiIj9tHRr0fwUiIiJiA3PmzDFGV2VkZODh4XFTdQYNGgSAu7t7m3bPk/Zv7ty5fPbZZ9jZ2ZGdnU2nTp3udJdERETkLqERVyIiIiJyw15++WV+/PFHevfuzZIlSyyO7iouLiY7OxuA+++/X6GViIiI3BAFVyIiIiJyw2pra9m7dy/QGEhNmzatRZtr166xbNkyrl27BkBAQMBt7aOIiIjc/RRciYiIiNyk7777jq+++somtYYOHUrPnj1tUut2ePbZZ0lLS6O+vp5ly5axe/duxowZg4uLCxUVFZw9e5bU1FSKiooAGDJkCHPmzLnt/Tx9+jRnz561Sa0xY8bg5ORkk1oiIiLSNgquRERERG7S4cOHiYiIsEmtDz/8kJEjR9qk1u0wbNgw1qxZQ2RkJFVVVWRmZpKZmWm27W9/+1vWrl2LnZ3dbe4lpKWlERUVZZNa/8/abSIiInJzFFyJiIiIyE0JDAzkscceIyEhgUOHDnH27FmuXr1Kt27dcHFxwdvbGz8/v7sqkBMREZH2RbsKioiIiIiIiIhIu9TxTndARERERERERETEHAVXIiIiIiIiIiLSLim4EhERERERERGRdknBlYiIiIiIiIiItEsKrkREREREREREpF1ScCUiIiIiIiIiIu2SgisREREREREREWmXFFyJiIiIiIiIiEi7pOBKRERERERERETaJQVXIiIiIiIiIiLSLim4EhERERERERGRdknBlYiIiIiIiIiItEsKrkREREREREREpF1ScCUiIiIiIiIiIu2SgisREREREREREWmXFFyJiIiIiIiIiEi7pOBKRERERERERETapf8BNDkeYwX/kDEAAAAASUVORK5CYII=", + "text/plain": [ + "" + ] + }, + "execution_count": 60, + "metadata": { + "image/png": { + "height": 378.25, + "width": 509.15 + } + }, + "output_type": "execute_result" + } + ], "source": [ "(\n", " so.Plot(penguins, x=\"flipper_length_mm\").add(\n", @@ -1452,9 +47211,55 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 61, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "(\n", " ggplot(penguins, aes(x=\"flipper_length_mm\"))\n", @@ -1472,9 +47277,77 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "alt.Chart(penguins).mark_bar().encode(\n", " alt.X(\"flipper_length_mm:Q\", bin=True),\n", @@ -1492,9 +47365,1234 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 63, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "alignmentgroup": "True", + "bingroup": "x", + "hovertemplate": "flipper_length_mm=%{x}
count=%{y}", + "legendgroup": "", + "marker": { + "color": "#636efa", + "pattern": { + "shape": "" + } + }, + "name": "", + "nbinsx": 30, + "offsetgroup": "", + "orientation": "v", + "showlegend": false, + "type": "histogram", + "x": [ + 181, + 186, + 195, + null, + 193, + 190, + 181, + 195, + 193, + 190, + 186, + 180, + 182, + 191, + 198, + 185, + 195, + 197, + 184, + 194, + 174, + 180, + 189, + 185, + 180, + 187, + 183, + 187, + 172, + 180, + 178, + 178, + 188, + 184, + 195, + 196, + 190, + 180, + 181, + 184, + 182, + 195, + 186, + 196, + 185, + 190, + 182, + 179, + 190, + 191, + 186, + 188, + 190, + 200, + 187, + 191, + 186, + 193, + 181, + 194, + 185, + 195, + 185, + 192, + 184, + 192, + 195, + 188, + 190, + 198, + 190, + 190, + 196, + 197, + 190, + 195, + 191, + 184, + 187, + 195, + 189, + 196, + 187, + 193, + 191, + 194, + 190, + 189, + 189, + 190, + 202, + 205, + 185, + 186, + 187, + 208, + 190, + 196, + 178, + 192, + 192, + 203, + 183, + 190, + 193, + 184, + 199, + 190, + 181, + 197, + 198, + 191, + 193, + 197, + 191, + 196, + 188, + 199, + 189, + 189, + 187, + 198, + 176, + 202, + 186, + 199, + 191, + 195, + 191, + 210, + 190, + 197, + 193, + 199, + 187, + 190, + 191, + 200, + 185, + 193, + 193, + 187, + 188, + 190, + 192, + 185, + 190, + 184, + 195, + 193, + 187, + 201, + 192, + 196, + 193, + 188, + 197, + 198, + 178, + 197, + 195, + 198, + 193, + 194, + 185, + 201, + 190, + 201, + 197, + 181, + 190, + 195, + 181, + 191, + 187, + 193, + 195, + 197, + 200, + 200, + 191, + 205, + 187, + 201, + 187, + 203, + 195, + 199, + 195, + 210, + 192, + 205, + 210, + 187, + 196, + 196, + 196, + 201, + 190, + 212, + 187, + 198, + 199, + 201, + 193, + 203, + 187, + 197, + 191, + 203, + 202, + 194, + 206, + 189, + 195, + 207, + 202, + 193, + 210, + 198, + 211, + 230, + 210, + 218, + 215, + 210, + 211, + 219, + 209, + 215, + 214, + 216, + 214, + 213, + 210, + 217, + 210, + 221, + 209, + 222, + 218, + 215, + 213, + 215, + 215, + 215, + 216, + 215, + 210, + 220, + 222, + 209, + 207, + 230, + 220, + 220, + 213, + 219, + 208, + 208, + 208, + 225, + 210, + 216, + 222, + 217, + 210, + 225, + 213, + 215, + 210, + 220, + 210, + 225, + 217, + 220, + 208, + 220, + 208, + 224, + 208, + 221, + 214, + 231, + 219, + 230, + 214, + 229, + 220, + 223, + 216, + 221, + 221, + 217, + 216, + 230, + 209, + 220, + 215, + 223, + 212, + 221, + 212, + 224, + 212, + 228, + 218, + 218, + 212, + 230, + 218, + 228, + 212, + 224, + 214, + 226, + 216, + 222, + 203, + 225, + 219, + 228, + 215, + 228, + 216, + 215, + 210, + 219, + 208, + 209, + 216, + 229, + 213, + 230, + 217, + 230, + 217, + 222, + 214, + null, + 215, + 222, + 212, + 213 + ], + "xaxis": "x", + "yaxis": "y" + } + ], + "layout": { + "barmode": "relative", + "legend": { + "tracegroupgap": 0 + }, + "margin": { + "t": 60 + }, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "flipper_length_mm" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "count" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig = px.histogram(penguins, x=\"flipper_length_mm\", nbins=30)\n", "fig.show()" @@ -1519,9 +48617,1313 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 64, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:25.484696\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig = plt.figure(constrained_layout=True)\n", "# Create a layout with 3 panels in the given ratios\n", @@ -1548,9 +49950,1556 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 65, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:25.711406\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sns.jointplot(data=penguins, x=\"bill_length_mm\", y=\"bill_depth_mm\");" ] @@ -1564,9 +51513,102 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from lets_plot.bistro.joint import *\n", "\n", @@ -1587,9 +51629,77 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 67, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.VConcatChart(...)" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "base = alt.Chart(penguins)\n", "\n", @@ -1646,9 +51756,2367 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 68, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "bill_length_mm=%{x}
bill_depth_mm=%{y}", + "legendgroup": "", + "marker": { + "color": "#636efa", + "symbol": "circle" + }, + "mode": "markers", + "name": "", + "orientation": "v", + "showlegend": false, + "type": "scatter", + "x": [ + 39.1, + 39.5, + 40.3, + null, + 36.7, + 39.3, + 38.9, + 39.2, + 34.1, + 42, + 37.8, + 37.8, + 41.1, + 38.6, + 34.6, + 36.6, + 38.7, + 42.5, + 34.4, + 46, + 37.8, + 37.7, + 35.9, + 38.2, + 38.8, + 35.3, + 40.6, + 40.5, + 37.9, + 40.5, + 39.5, + 37.2, + 39.5, + 40.9, + 36.4, + 39.2, + 38.8, + 42.2, + 37.6, + 39.8, + 36.5, + 40.8, + 36, + 44.1, + 37, + 39.6, + 41.1, + 37.5, + 36, + 42.3, + 39.6, + 40.1, + 35, + 42, + 34.5, + 41.4, + 39, + 40.6, + 36.5, + 37.6, + 35.7, + 41.3, + 37.6, + 41.1, + 36.4, + 41.6, + 35.5, + 41.1, + 35.9, + 41.8, + 33.5, + 39.7, + 39.6, + 45.8, + 35.5, + 42.8, + 40.9, + 37.2, + 36.2, + 42.1, + 34.6, + 42.9, + 36.7, + 35.1, + 37.3, + 41.3, + 36.3, + 36.9, + 38.3, + 38.9, + 35.7, + 41.1, + 34, + 39.6, + 36.2, + 40.8, + 38.1, + 40.3, + 33.1, + 43.2, + 35, + 41, + 37.7, + 37.8, + 37.9, + 39.7, + 38.6, + 38.2, + 38.1, + 43.2, + 38.1, + 45.6, + 39.7, + 42.2, + 39.6, + 42.7, + 38.6, + 37.3, + 35.7, + 41.1, + 36.2, + 37.7, + 40.2, + 41.4, + 35.2, + 40.6, + 38.8, + 41.5, + 39, + 44.1, + 38.5, + 43.1, + 36.8, + 37.5, + 38.1, + 41.1, + 35.6, + 40.2, + 37, + 39.7, + 40.2, + 40.6, + 32.1, + 40.7, + 37.3, + 39, + 39.2, + 36.6, + 36, + 37.8, + 36, + 41.5, + 46.5, + 50, + 51.3, + 45.4, + 52.7, + 45.2, + 46.1, + 51.3, + 46, + 51.3, + 46.6, + 51.7, + 47, + 52, + 45.9, + 50.5, + 50.3, + 58, + 46.4, + 49.2, + 42.4, + 48.5, + 43.2, + 50.6, + 46.7, + 52, + 50.5, + 49.5, + 46.4, + 52.8, + 40.9, + 54.2, + 42.5, + 51, + 49.7, + 47.5, + 47.6, + 52, + 46.9, + 53.5, + 49, + 46.2, + 50.9, + 45.5, + 50.9, + 50.8, + 50.1, + 49, + 51.5, + 49.8, + 48.1, + 51.4, + 45.7, + 50.7, + 42.5, + 52.2, + 45.2, + 49.3, + 50.2, + 45.6, + 51.9, + 46.8, + 45.7, + 55.8, + 43.5, + 49.6, + 50.8, + 50.2, + 46.1, + 50, + 48.7, + 50, + 47.6, + 46.5, + 45.4, + 46.7, + 43.3, + 46.8, + 40.9, + 49, + 45.5, + 48.4, + 45.8, + 49.3, + 42, + 49.2, + 46.2, + 48.7, + 50.2, + 45.1, + 46.5, + 46.3, + 42.9, + 46.1, + 44.5, + 47.8, + 48.2, + 50, + 47.3, + 42.8, + 45.1, + 59.6, + 49.1, + 48.4, + 42.6, + 44.4, + 44, + 48.7, + 42.7, + 49.6, + 45.3, + 49.6, + 50.5, + 43.6, + 45.5, + 50.5, + 44.9, + 45.2, + 46.6, + 48.5, + 45.1, + 50.1, + 46.5, + 45, + 43.8, + 45.5, + 43.2, + 50.4, + 45.3, + 46.2, + 45.7, + 54.3, + 45.8, + 49.8, + 46.2, + 49.5, + 43.5, + 50.7, + 47.7, + 46.4, + 48.2, + 46.5, + 46.4, + 48.6, + 47.5, + 51.1, + 45.2, + 45.2, + 49.1, + 52.5, + 47.4, + 50, + 44.9, + 50.8, + 43.4, + 51.3, + 47.5, + 52.1, + 47.5, + 52.2, + 45.5, + 49.5, + 44.5, + 50.8, + 49.4, + 46.9, + 48.4, + 51.1, + 48.5, + 55.9, + 47.2, + 49.1, + 47.3, + 46.8, + 41.7, + 53.4, + 43.3, + 48.1, + 50.5, + 49.8, + 43.5, + 51.5, + 46.2, + 55.1, + 44.5, + 48.8, + 47.2, + null, + 46.8, + 50.4, + 45.2, + 49.9 + ], + "xaxis": "x", + "y": [ + 18.7, + 17.4, + 18, + null, + 19.3, + 20.6, + 17.8, + 19.6, + 18.1, + 20.2, + 17.1, + 17.3, + 17.6, + 21.2, + 21.1, + 17.8, + 19, + 20.7, + 18.4, + 21.5, + 18.3, + 18.7, + 19.2, + 18.1, + 17.2, + 18.9, + 18.6, + 17.9, + 18.6, + 18.9, + 16.7, + 18.1, + 17.8, + 18.9, + 17, + 21.1, + 20, + 18.5, + 19.3, + 19.1, + 18, + 18.4, + 18.5, + 19.7, + 16.9, + 18.8, + 19, + 18.9, + 17.9, + 21.2, + 17.7, + 18.9, + 17.9, + 19.5, + 18.1, + 18.6, + 17.5, + 18.8, + 16.6, + 19.1, + 16.9, + 21.1, + 17, + 18.2, + 17.1, + 18, + 16.2, + 19.1, + 16.6, + 19.4, + 19, + 18.4, + 17.2, + 18.9, + 17.5, + 18.5, + 16.8, + 19.4, + 16.1, + 19.1, + 17.2, + 17.6, + 18.8, + 19.4, + 17.8, + 20.3, + 19.5, + 18.6, + 19.2, + 18.8, + 18, + 18.1, + 17.1, + 18.1, + 17.3, + 18.9, + 18.6, + 18.5, + 16.1, + 18.5, + 17.9, + 20, + 16, + 20, + 18.6, + 18.9, + 17.2, + 20, + 17, + 19, + 16.5, + 20.3, + 17.7, + 19.5, + 20.7, + 18.3, + 17, + 20.5, + 17, + 18.6, + 17.2, + 19.8, + 17, + 18.5, + 15.9, + 19, + 17.6, + 18.3, + 17.1, + 18, + 17.9, + 19.2, + 18.5, + 18.5, + 17.6, + 17.5, + 17.5, + 20.1, + 16.5, + 17.9, + 17.1, + 17.2, + 15.5, + 17, + 16.8, + 18.7, + 18.6, + 18.4, + 17.8, + 18.1, + 17.1, + 18.5, + 17.9, + 19.5, + 19.2, + 18.7, + 19.8, + 17.8, + 18.2, + 18.2, + 18.9, + 19.9, + 17.8, + 20.3, + 17.3, + 18.1, + 17.1, + 19.6, + 20, + 17.8, + 18.6, + 18.2, + 17.3, + 17.5, + 16.6, + 19.4, + 17.9, + 19, + 18.4, + 19, + 17.8, + 20, + 16.6, + 20.8, + 16.7, + 18.8, + 18.6, + 16.8, + 18.3, + 20.7, + 16.6, + 19.9, + 19.5, + 17.5, + 19.1, + 17, + 17.9, + 18.5, + 17.9, + 19.6, + 18.7, + 17.3, + 16.4, + 19, + 17.3, + 19.7, + 17.3, + 18.8, + 16.6, + 19.9, + 18.8, + 19.4, + 19.5, + 16.5, + 17, + 19.8, + 18.1, + 18.2, + 19, + 18.7, + 13.2, + 16.3, + 14.1, + 15.2, + 14.5, + 13.5, + 14.6, + 15.3, + 13.4, + 15.4, + 13.7, + 16.1, + 13.7, + 14.6, + 14.6, + 15.7, + 13.5, + 15.2, + 14.5, + 15.1, + 14.3, + 14.5, + 14.5, + 15.8, + 13.1, + 15.1, + 14.3, + 15, + 14.3, + 15.3, + 15.3, + 14.2, + 14.5, + 17, + 14.8, + 16.3, + 13.7, + 17.3, + 13.6, + 15.7, + 13.7, + 16, + 13.7, + 15, + 15.9, + 13.9, + 13.9, + 15.9, + 13.3, + 15.8, + 14.2, + 14.1, + 14.4, + 15, + 14.4, + 15.4, + 13.9, + 15, + 14.5, + 15.3, + 13.8, + 14.9, + 13.9, + 15.7, + 14.2, + 16.8, + 14.4, + 16.2, + 14.2, + 15, + 15, + 15.6, + 15.6, + 14.8, + 15, + 16, + 14.2, + 16.3, + 13.8, + 16.4, + 14.5, + 15.6, + 14.6, + 15.9, + 13.8, + 17.3, + 14.4, + 14.2, + 14, + 17, + 15, + 17.1, + 14.5, + 16.1, + 14.7, + 15.7, + 15.8, + 14.6, + 14.4, + 16.5, + 15, + 17, + 15.5, + 15, + 13.8, + 16.1, + 14.7, + 15.8, + 14, + 15.1, + 15.2, + 15.9, + 15.2, + 16.3, + 14.1, + 16, + 15.7, + 16.2, + 13.7, + null, + 14.3, + 15.7, + 14.8, + 16.1 + ], + "yaxis": "y" + }, + { + "alignmentgroup": "True", + "bingroup": "x", + "hovertemplate": "bill_length_mm=%{x}
count=%{y}", + "legendgroup": "", + "marker": { + "color": "#636efa" + }, + "name": "", + "offsetgroup": "", + "opacity": 0.5, + "showlegend": false, + "type": "histogram", + "x": [ + 39.1, + 39.5, + 40.3, + null, + 36.7, + 39.3, + 38.9, + 39.2, + 34.1, + 42, + 37.8, + 37.8, + 41.1, + 38.6, + 34.6, + 36.6, + 38.7, + 42.5, + 34.4, + 46, + 37.8, + 37.7, + 35.9, + 38.2, + 38.8, + 35.3, + 40.6, + 40.5, + 37.9, + 40.5, + 39.5, + 37.2, + 39.5, + 40.9, + 36.4, + 39.2, + 38.8, + 42.2, + 37.6, + 39.8, + 36.5, + 40.8, + 36, + 44.1, + 37, + 39.6, + 41.1, + 37.5, + 36, + 42.3, + 39.6, + 40.1, + 35, + 42, + 34.5, + 41.4, + 39, + 40.6, + 36.5, + 37.6, + 35.7, + 41.3, + 37.6, + 41.1, + 36.4, + 41.6, + 35.5, + 41.1, + 35.9, + 41.8, + 33.5, + 39.7, + 39.6, + 45.8, + 35.5, + 42.8, + 40.9, + 37.2, + 36.2, + 42.1, + 34.6, + 42.9, + 36.7, + 35.1, + 37.3, + 41.3, + 36.3, + 36.9, + 38.3, + 38.9, + 35.7, + 41.1, + 34, + 39.6, + 36.2, + 40.8, + 38.1, + 40.3, + 33.1, + 43.2, + 35, + 41, + 37.7, + 37.8, + 37.9, + 39.7, + 38.6, + 38.2, + 38.1, + 43.2, + 38.1, + 45.6, + 39.7, + 42.2, + 39.6, + 42.7, + 38.6, + 37.3, + 35.7, + 41.1, + 36.2, + 37.7, + 40.2, + 41.4, + 35.2, + 40.6, + 38.8, + 41.5, + 39, + 44.1, + 38.5, + 43.1, + 36.8, + 37.5, + 38.1, + 41.1, + 35.6, + 40.2, + 37, + 39.7, + 40.2, + 40.6, + 32.1, + 40.7, + 37.3, + 39, + 39.2, + 36.6, + 36, + 37.8, + 36, + 41.5, + 46.5, + 50, + 51.3, + 45.4, + 52.7, + 45.2, + 46.1, + 51.3, + 46, + 51.3, + 46.6, + 51.7, + 47, + 52, + 45.9, + 50.5, + 50.3, + 58, + 46.4, + 49.2, + 42.4, + 48.5, + 43.2, + 50.6, + 46.7, + 52, + 50.5, + 49.5, + 46.4, + 52.8, + 40.9, + 54.2, + 42.5, + 51, + 49.7, + 47.5, + 47.6, + 52, + 46.9, + 53.5, + 49, + 46.2, + 50.9, + 45.5, + 50.9, + 50.8, + 50.1, + 49, + 51.5, + 49.8, + 48.1, + 51.4, + 45.7, + 50.7, + 42.5, + 52.2, + 45.2, + 49.3, + 50.2, + 45.6, + 51.9, + 46.8, + 45.7, + 55.8, + 43.5, + 49.6, + 50.8, + 50.2, + 46.1, + 50, + 48.7, + 50, + 47.6, + 46.5, + 45.4, + 46.7, + 43.3, + 46.8, + 40.9, + 49, + 45.5, + 48.4, + 45.8, + 49.3, + 42, + 49.2, + 46.2, + 48.7, + 50.2, + 45.1, + 46.5, + 46.3, + 42.9, + 46.1, + 44.5, + 47.8, + 48.2, + 50, + 47.3, + 42.8, + 45.1, + 59.6, + 49.1, + 48.4, + 42.6, + 44.4, + 44, + 48.7, + 42.7, + 49.6, + 45.3, + 49.6, + 50.5, + 43.6, + 45.5, + 50.5, + 44.9, + 45.2, + 46.6, + 48.5, + 45.1, + 50.1, + 46.5, + 45, + 43.8, + 45.5, + 43.2, + 50.4, + 45.3, + 46.2, + 45.7, + 54.3, + 45.8, + 49.8, + 46.2, + 49.5, + 43.5, + 50.7, + 47.7, + 46.4, + 48.2, + 46.5, + 46.4, + 48.6, + 47.5, + 51.1, + 45.2, + 45.2, + 49.1, + 52.5, + 47.4, + 50, + 44.9, + 50.8, + 43.4, + 51.3, + 47.5, + 52.1, + 47.5, + 52.2, + 45.5, + 49.5, + 44.5, + 50.8, + 49.4, + 46.9, + 48.4, + 51.1, + 48.5, + 55.9, + 47.2, + 49.1, + 47.3, + 46.8, + 41.7, + 53.4, + 43.3, + 48.1, + 50.5, + 49.8, + 43.5, + 51.5, + 46.2, + 55.1, + 44.5, + 48.8, + 47.2, + null, + 46.8, + 50.4, + 45.2, + 49.9 + ], + "xaxis": "x3", + "yaxis": "y3" + }, + { + "alignmentgroup": "True", + "bingroup": "y", + "hovertemplate": "bill_depth_mm=%{y}
count=%{x}", + "legendgroup": "", + "marker": { + "color": "#636efa" + }, + "name": "", + "offsetgroup": "", + "opacity": 0.5, + "showlegend": false, + "type": "histogram", + "xaxis": "x2", + "y": [ + 18.7, + 17.4, + 18, + null, + 19.3, + 20.6, + 17.8, + 19.6, + 18.1, + 20.2, + 17.1, + 17.3, + 17.6, + 21.2, + 21.1, + 17.8, + 19, + 20.7, + 18.4, + 21.5, + 18.3, + 18.7, + 19.2, + 18.1, + 17.2, + 18.9, + 18.6, + 17.9, + 18.6, + 18.9, + 16.7, + 18.1, + 17.8, + 18.9, + 17, + 21.1, + 20, + 18.5, + 19.3, + 19.1, + 18, + 18.4, + 18.5, + 19.7, + 16.9, + 18.8, + 19, + 18.9, + 17.9, + 21.2, + 17.7, + 18.9, + 17.9, + 19.5, + 18.1, + 18.6, + 17.5, + 18.8, + 16.6, + 19.1, + 16.9, + 21.1, + 17, + 18.2, + 17.1, + 18, + 16.2, + 19.1, + 16.6, + 19.4, + 19, + 18.4, + 17.2, + 18.9, + 17.5, + 18.5, + 16.8, + 19.4, + 16.1, + 19.1, + 17.2, + 17.6, + 18.8, + 19.4, + 17.8, + 20.3, + 19.5, + 18.6, + 19.2, + 18.8, + 18, + 18.1, + 17.1, + 18.1, + 17.3, + 18.9, + 18.6, + 18.5, + 16.1, + 18.5, + 17.9, + 20, + 16, + 20, + 18.6, + 18.9, + 17.2, + 20, + 17, + 19, + 16.5, + 20.3, + 17.7, + 19.5, + 20.7, + 18.3, + 17, + 20.5, + 17, + 18.6, + 17.2, + 19.8, + 17, + 18.5, + 15.9, + 19, + 17.6, + 18.3, + 17.1, + 18, + 17.9, + 19.2, + 18.5, + 18.5, + 17.6, + 17.5, + 17.5, + 20.1, + 16.5, + 17.9, + 17.1, + 17.2, + 15.5, + 17, + 16.8, + 18.7, + 18.6, + 18.4, + 17.8, + 18.1, + 17.1, + 18.5, + 17.9, + 19.5, + 19.2, + 18.7, + 19.8, + 17.8, + 18.2, + 18.2, + 18.9, + 19.9, + 17.8, + 20.3, + 17.3, + 18.1, + 17.1, + 19.6, + 20, + 17.8, + 18.6, + 18.2, + 17.3, + 17.5, + 16.6, + 19.4, + 17.9, + 19, + 18.4, + 19, + 17.8, + 20, + 16.6, + 20.8, + 16.7, + 18.8, + 18.6, + 16.8, + 18.3, + 20.7, + 16.6, + 19.9, + 19.5, + 17.5, + 19.1, + 17, + 17.9, + 18.5, + 17.9, + 19.6, + 18.7, + 17.3, + 16.4, + 19, + 17.3, + 19.7, + 17.3, + 18.8, + 16.6, + 19.9, + 18.8, + 19.4, + 19.5, + 16.5, + 17, + 19.8, + 18.1, + 18.2, + 19, + 18.7, + 13.2, + 16.3, + 14.1, + 15.2, + 14.5, + 13.5, + 14.6, + 15.3, + 13.4, + 15.4, + 13.7, + 16.1, + 13.7, + 14.6, + 14.6, + 15.7, + 13.5, + 15.2, + 14.5, + 15.1, + 14.3, + 14.5, + 14.5, + 15.8, + 13.1, + 15.1, + 14.3, + 15, + 14.3, + 15.3, + 15.3, + 14.2, + 14.5, + 17, + 14.8, + 16.3, + 13.7, + 17.3, + 13.6, + 15.7, + 13.7, + 16, + 13.7, + 15, + 15.9, + 13.9, + 13.9, + 15.9, + 13.3, + 15.8, + 14.2, + 14.1, + 14.4, + 15, + 14.4, + 15.4, + 13.9, + 15, + 14.5, + 15.3, + 13.8, + 14.9, + 13.9, + 15.7, + 14.2, + 16.8, + 14.4, + 16.2, + 14.2, + 15, + 15, + 15.6, + 15.6, + 14.8, + 15, + 16, + 14.2, + 16.3, + 13.8, + 16.4, + 14.5, + 15.6, + 14.6, + 15.9, + 13.8, + 17.3, + 14.4, + 14.2, + 14, + 17, + 15, + 17.1, + 14.5, + 16.1, + 14.7, + 15.7, + 15.8, + 14.6, + 14.4, + 16.5, + 15, + 17, + 15.5, + 15, + 13.8, + 16.1, + 14.7, + 15.8, + 14, + 15.1, + 15.2, + 15.9, + 15.2, + 16.3, + 14.1, + 16, + 15.7, + 16.2, + 13.7, + null, + 14.3, + 15.7, + 14.8, + 16.1 + ], + "yaxis": "y2" + } + ], + "layout": { + "barmode": "overlay", + "legend": { + "tracegroupgap": 0 + }, + "margin": { + "t": 60 + }, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 0.7363 + ], + "title": { + "text": "bill_length_mm" + } + }, + "xaxis2": { + "anchor": "y2", + "domain": [ + 0.7413, + 1 + ], + "matches": "x2", + "showgrid": true, + "showline": false, + "showticklabels": false, + "ticks": "" + }, + "xaxis3": { + "anchor": "y3", + "domain": [ + 0, + 0.7363 + ], + "matches": "x", + "showgrid": true, + "showticklabels": false + }, + "xaxis4": { + "anchor": "y4", + "domain": [ + 0.7413, + 1 + ], + "matches": "x2", + "showgrid": true, + "showline": false, + "showticklabels": false, + "ticks": "" + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 0.7326 + ], + "title": { + "text": "bill_depth_mm" + } + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0, + 0.7326 + ], + "matches": "y", + "showgrid": true, + "showticklabels": false + }, + "yaxis3": { + "anchor": "x3", + "domain": [ + 0.7426, + 1 + ], + "matches": "y3", + "showgrid": true, + "showline": false, + "showticklabels": false, + "ticks": "" + }, + "yaxis4": { + "anchor": "x4", + "domain": [ + 0.7426, + 1 + ], + "matches": "y3", + "showgrid": true, + "showline": false, + "showticklabels": false, + "ticks": "" + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig = px.scatter(\n", " penguins,\n", @@ -1673,9 +54141,154 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 69, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
monthJanFebMarAprMayJunJulAugSepOctNovDec
year
1949112118132129121135148148136119104118
1950115126141135125149170170158133114140
1951145150178163172178199199184162146166
1952171180193181183218230242209191172194
1953196196236235229243264272237211180201
\n", + "
" + ], + "text/plain": [ + "month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec\n", + "year \n", + "1949 112 118 132 129 121 135 148 148 136 119 104 118\n", + "1950 115 126 141 135 125 149 170 170 158 133 114 140\n", + "1951 145 150 178 163 172 178 199 199 184 162 146 166\n", + "1952 171 180 193 181 183 218 230 242 209 191 172 194\n", + "1953 196 196 236 235 229 243 264 272 237 211 180 201" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "flights = sns.load_dataset(\"flights\")\n", "flights = flights.pivot(index=\"month\", columns=\"year\", values=\"passengers\").T\n", @@ -1692,9 +54305,1534 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 70, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:25.930128\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots()\n", "im = ax.imshow(flights.values, cmap=\"inferno\")\n", @@ -1716,9 +55854,2494 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 71, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:26.157270\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sns.heatmap(flights);" ] @@ -1734,9 +58357,67 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 72, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "flights = sns.load_dataset(\"flights\")\n", "(\n", @@ -1755,9 +58436,77 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 73, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "alt.Chart(flights).mark_rect().encode(\n", " x=alt.X(\"month\", type=\"nominal\", sort=None), y=\"year:O\", color=\"passengers:Q\"\n", @@ -1775,9 +58524,108 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 74, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
total_billtipsexsmokerdaytimesize
016.991.01FemaleNoSunDinner2
110.341.66MaleNoSunDinner3
221.013.50MaleNoSunDinner3
323.683.31MaleNoSunDinner2
424.593.61FemaleNoSunDinner4
\n", + "
" + ], + "text/plain": [ + " total_bill tip sex smoker day time size\n", + "0 16.99 1.01 Female No Sun Dinner 2\n", + "1 10.34 1.66 Male No Sun Dinner 3\n", + "2 21.01 3.50 Male No Sun Dinner 3\n", + "3 23.68 3.31 Male No Sun Dinner 2\n", + "4 24.59 3.61 Female No Sun Dinner 4" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "tips = sns.load_dataset(\"tips\")\n", "tips.head()" @@ -1794,9 +58642,813 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 75, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:26.269815\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "colormap = plt.cm.Set1\n", "colorst = [colormap(i) for i in np.linspace(0, 0.9, len(tips[\"time\"].unique()))]\n", @@ -1826,9 +59478,875 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 76, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:26.345722\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sns.boxplot(data=tips, x=\"time\", y=\"tip\");" ] @@ -1843,9 +60361,80 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 77, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "(ggplot(tips) + geom_boxplot(aes(y=\"tip\", x=\"time\", fill=\"time\")))" ] @@ -1859,9 +60448,77 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 78, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "alt.Chart(tips).mark_boxplot(size=50).encode(\n", " x=\"time:N\", y=\"tip:Q\", color=\"time:N\"\n", @@ -1878,9 +60535,1408 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 79, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "alignmentgroup": "True", + "hovertemplate": "time=%{x}
tip=%{y}", + "legendgroup": "Dinner", + "marker": { + "color": "#636efa" + }, + "name": "Dinner", + "notched": false, + "offsetgroup": "Dinner", + "orientation": "v", + "showlegend": true, + "type": "box", + "x": [ + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner" + ], + "x0": " ", + "xaxis": "x", + "y": [ + 1.01, + 1.66, + 3.5, + 3.31, + 3.61, + 4.71, + 2, + 3.12, + 1.96, + 3.23, + 1.71, + 5, + 1.57, + 3, + 3.02, + 3.92, + 1.67, + 3.71, + 3.5, + 3.35, + 4.08, + 2.75, + 2.23, + 7.58, + 3.18, + 2.34, + 2, + 2, + 4.3, + 3, + 1.45, + 2.5, + 3, + 2.45, + 3.27, + 3.6, + 2, + 3.07, + 2.31, + 5, + 2.24, + 2.54, + 3.06, + 1.32, + 5.6, + 3, + 5, + 6, + 2.05, + 3, + 2.5, + 2.6, + 5.2, + 1.56, + 4.34, + 3.51, + 3, + 1.5, + 1.76, + 6.73, + 3.21, + 2, + 1.98, + 3.76, + 2.64, + 3.15, + 2.47, + 1, + 2.01, + 2.09, + 1.97, + 3, + 3.14, + 5, + 2.2, + 1.25, + 3.08, + 3, + 3.5, + 1, + 4.3, + 3.25, + 4.73, + 4, + 1.5, + 3, + 1.5, + 2.5, + 3, + 2.5, + 3.48, + 4.08, + 1.64, + 4.06, + 4.29, + 3.76, + 4, + 3, + 1, + 4, + 2.55, + 4, + 3.5, + 5.07, + 2.5, + 2, + 2.74, + 2, + 2, + 5.14, + 5, + 3.75, + 2.61, + 2, + 3.5, + 2.5, + 2, + 2, + 3, + 3.48, + 2.24, + 4.5, + 1.61, + 2, + 10, + 3.16, + 5.15, + 3.18, + 4, + 3.11, + 2, + 2, + 4, + 3.55, + 3.68, + 5.65, + 3.5, + 6.5, + 3, + 5, + 3.5, + 2, + 3.5, + 4, + 1.5, + 3.41, + 3, + 2.03, + 2.23, + 2, + 5.16, + 9, + 2.5, + 6.5, + 1.1, + 3, + 1.5, + 1.44, + 3.09, + 3, + 2.72, + 2.88, + 2, + 3, + 3.39, + 1.47, + 3, + 1.25, + 1, + 1.17, + 4.67, + 5.92, + 2, + 2, + 1.75, + 3 + ], + "y0": " ", + "yaxis": "y" + }, + { + "alignmentgroup": "True", + "hovertemplate": "time=%{x}
tip=%{y}", + "legendgroup": "Lunch", + "marker": { + "color": "#EF553B" + }, + "name": "Lunch", + "notched": false, + "offsetgroup": "Lunch", + "orientation": "v", + "showlegend": true, + "type": "box", + "x": [ + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch" + ], + "x0": " ", + "xaxis": "x", + "y": [ + 4, + 3, + 2.71, + 3, + 3.4, + 1.83, + 5, + 2.03, + 5.17, + 2, + 4, + 5.85, + 3, + 1.5, + 1.8, + 2.92, + 2.31, + 1.68, + 2.5, + 2, + 2.52, + 4.2, + 1.48, + 2, + 2, + 2.18, + 1.5, + 2.83, + 1.5, + 2, + 3.25, + 1.25, + 2, + 2, + 2, + 2.75, + 3.5, + 6.7, + 5, + 5, + 2.3, + 1.5, + 1.36, + 1.63, + 1.73, + 2, + 4.19, + 2.56, + 2.02, + 4, + 1.44, + 2, + 5, + 2, + 2, + 4, + 2.01, + 2, + 2.5, + 4, + 3.23, + 2.2, + 3.48, + 1.92, + 3, + 1.58, + 2.5, + 2 + ], + "y0": " ", + "yaxis": "y" + } + ], + "layout": { + "boxmode": "overlay", + "legend": { + "title": { + "text": "time" + }, + "tracegroupgap": 0 + }, + "margin": { + "t": 60 + }, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "xaxis": { + "anchor": "y", + "categoryarray": [ + "Dinner", + "Lunch" + ], + "categoryorder": "array", + "domain": [ + 0, + 1 + ], + "title": { + "text": "time" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "tip" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig = px.box(tips, x=\"time\", y=\"tip\", color=\"time\")\n", "fig.show()" @@ -1904,9 +61960,1168 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 80, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:26.480250\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "colormap = plt.cm.Set1\n", "colorst = [colormap(i) for i in np.linspace(0, 0.9, len(tips[\"time\"].unique()))]\n", @@ -1932,9 +63147,1226 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 81, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:26.566825\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sns.violinplot(data=tips, x=\"time\", y=\"tip\");" ] @@ -1948,9 +64380,58 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 82, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "(ggplot(tips, aes(x=\"time\", y=\"tip\", fill=\"time\")) + geom_violin())" ] @@ -1964,9 +64445,77 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 83, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "alt.Chart(tips).transform_density(\n", " \"tip\", as_=[\"tip\", \"density\"], groupby=[\"time\"]\n", @@ -2006,9 +64555,3128 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 84, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "alignmentgroup": "True", + "box": { + "visible": true + }, + "customdata": [ + [ + 16.99, + "Female", + "No", + "Sun", + 2 + ], + [ + 10.34, + "Male", + "No", + "Sun", + 3 + ], + [ + 21.01, + "Male", + "No", + "Sun", + 3 + ], + [ + 23.68, + "Male", + "No", + "Sun", + 2 + ], + [ + 24.59, + "Female", + "No", + "Sun", + 4 + ], + [ + 25.29, + "Male", + "No", + "Sun", + 4 + ], + [ + 8.77, + "Male", + "No", + "Sun", + 2 + ], + [ + 26.88, + "Male", + "No", + "Sun", + 4 + ], + [ + 15.04, + "Male", + "No", + "Sun", + 2 + ], + [ + 14.78, + "Male", + "No", + "Sun", + 2 + ], + [ + 10.27, + "Male", + "No", + "Sun", + 2 + ], + [ + 35.26, + "Female", + "No", + "Sun", + 4 + ], + [ + 15.42, + "Male", + "No", + "Sun", + 2 + ], + [ + 18.43, + "Male", + "No", + "Sun", + 4 + ], + [ + 14.83, + "Female", + "No", + "Sun", + 2 + ], + [ + 21.58, + "Male", + "No", + "Sun", + 2 + ], + [ + 10.33, + "Female", + "No", + "Sun", + 3 + ], + [ + 16.29, + "Male", + "No", + "Sun", + 3 + ], + [ + 16.97, + "Female", + "No", + "Sun", + 3 + ], + [ + 20.65, + "Male", + "No", + "Sat", + 3 + ], + [ + 17.92, + "Male", + "No", + "Sat", + 2 + ], + [ + 20.29, + "Female", + "No", + "Sat", + 2 + ], + [ + 15.77, + "Female", + "No", + "Sat", + 2 + ], + [ + 39.42, + "Male", + "No", + "Sat", + 4 + ], + [ + 19.82, + "Male", + "No", + "Sat", + 2 + ], + [ + 17.81, + "Male", + "No", + "Sat", + 4 + ], + [ + 13.37, + "Male", + "No", + "Sat", + 2 + ], + [ + 12.69, + "Male", + "No", + "Sat", + 2 + ], + [ + 21.7, + "Male", + "No", + "Sat", + 2 + ], + [ + 19.65, + "Female", + "No", + "Sat", + 2 + ], + [ + 9.55, + "Male", + "No", + "Sat", + 2 + ], + [ + 18.35, + "Male", + "No", + "Sat", + 4 + ], + [ + 15.06, + "Female", + "No", + "Sat", + 2 + ], + [ + 20.69, + "Female", + "No", + "Sat", + 4 + ], + [ + 17.78, + "Male", + "No", + "Sat", + 2 + ], + [ + 24.06, + "Male", + "No", + "Sat", + 3 + ], + [ + 16.31, + "Male", + "No", + "Sat", + 3 + ], + [ + 16.93, + "Female", + "No", + "Sat", + 3 + ], + [ + 18.69, + "Male", + "No", + "Sat", + 3 + ], + [ + 31.27, + "Male", + "No", + "Sat", + 3 + ], + [ + 16.04, + "Male", + "No", + "Sat", + 3 + ], + [ + 17.46, + "Male", + "No", + "Sun", + 2 + ], + [ + 13.94, + "Male", + "No", + "Sun", + 2 + ], + [ + 9.68, + "Male", + "No", + "Sun", + 2 + ], + [ + 30.4, + "Male", + "No", + "Sun", + 4 + ], + [ + 18.29, + "Male", + "No", + "Sun", + 2 + ], + [ + 22.23, + "Male", + "No", + "Sun", + 2 + ], + [ + 32.4, + "Male", + "No", + "Sun", + 4 + ], + [ + 28.55, + "Male", + "No", + "Sun", + 3 + ], + [ + 18.04, + "Male", + "No", + "Sun", + 2 + ], + [ + 12.54, + "Male", + "No", + "Sun", + 2 + ], + [ + 10.29, + "Female", + "No", + "Sun", + 2 + ], + [ + 34.81, + "Female", + "No", + "Sun", + 4 + ], + [ + 9.94, + "Male", + "No", + "Sun", + 2 + ], + [ + 25.56, + "Male", + "No", + "Sun", + 4 + ], + [ + 19.49, + "Male", + "No", + "Sun", + 2 + ], + [ + 38.01, + "Male", + "Yes", + "Sat", + 4 + ], + [ + 26.41, + "Female", + "No", + "Sat", + 2 + ], + [ + 11.24, + "Male", + "Yes", + "Sat", + 2 + ], + [ + 48.27, + "Male", + "No", + "Sat", + 4 + ], + [ + 20.29, + "Male", + "Yes", + "Sat", + 2 + ], + [ + 13.81, + "Male", + "Yes", + "Sat", + 2 + ], + [ + 11.02, + "Male", + "Yes", + "Sat", + 2 + ], + [ + 18.29, + "Male", + "Yes", + "Sat", + 4 + ], + [ + 17.59, + "Male", + "No", + "Sat", + 3 + ], + [ + 20.08, + "Male", + "No", + "Sat", + 3 + ], + [ + 16.45, + "Female", + "No", + "Sat", + 2 + ], + [ + 3.07, + "Female", + "Yes", + "Sat", + 1 + ], + [ + 20.23, + "Male", + "No", + "Sat", + 2 + ], + [ + 15.01, + "Male", + "Yes", + "Sat", + 2 + ], + [ + 12.02, + "Male", + "No", + "Sat", + 2 + ], + [ + 17.07, + "Female", + "No", + "Sat", + 3 + ], + [ + 26.86, + "Female", + "Yes", + "Sat", + 2 + ], + [ + 25.28, + "Female", + "Yes", + "Sat", + 2 + ], + [ + 14.73, + "Female", + "No", + "Sat", + 2 + ], + [ + 10.51, + "Male", + "No", + "Sat", + 2 + ], + [ + 17.92, + "Male", + "Yes", + "Sat", + 2 + ], + [ + 28.97, + "Male", + "Yes", + "Fri", + 2 + ], + [ + 22.49, + "Male", + "No", + "Fri", + 2 + ], + [ + 5.75, + "Female", + "Yes", + "Fri", + 2 + ], + [ + 16.32, + "Female", + "Yes", + "Fri", + 2 + ], + [ + 22.75, + "Female", + "No", + "Fri", + 2 + ], + [ + 40.17, + "Male", + "Yes", + "Fri", + 4 + ], + [ + 27.28, + "Male", + "Yes", + "Fri", + 2 + ], + [ + 12.03, + "Male", + "Yes", + "Fri", + 2 + ], + [ + 21.01, + "Male", + "Yes", + "Fri", + 2 + ], + [ + 12.46, + "Male", + "No", + "Fri", + 2 + ], + [ + 11.35, + "Female", + "Yes", + "Fri", + 2 + ], + [ + 15.38, + "Female", + "Yes", + "Fri", + 2 + ], + [ + 44.3, + "Female", + "Yes", + "Sat", + 3 + ], + [ + 22.42, + "Female", + "Yes", + "Sat", + 2 + ], + [ + 20.92, + "Female", + "No", + "Sat", + 2 + ], + [ + 15.36, + "Male", + "Yes", + "Sat", + 2 + ], + [ + 20.49, + "Male", + "Yes", + "Sat", + 2 + ], + [ + 25.21, + "Male", + "Yes", + "Sat", + 2 + ], + [ + 18.24, + "Male", + "No", + "Sat", + 2 + ], + [ + 14.31, + "Female", + "Yes", + "Sat", + 2 + ], + [ + 14, + "Male", + "No", + "Sat", + 2 + ], + [ + 7.25, + "Female", + "No", + "Sat", + 1 + ], + [ + 38.07, + "Male", + "No", + "Sun", + 3 + ], + [ + 23.95, + "Male", + "No", + "Sun", + 2 + ], + [ + 25.71, + "Female", + "No", + "Sun", + 3 + ], + [ + 17.31, + "Female", + "No", + "Sun", + 2 + ], + [ + 29.93, + "Male", + "No", + "Sun", + 4 + ], + [ + 14.07, + "Male", + "No", + "Sun", + 2 + ], + [ + 13.13, + "Male", + "No", + "Sun", + 2 + ], + [ + 17.26, + "Male", + "No", + "Sun", + 3 + ], + [ + 24.55, + "Male", + "No", + "Sun", + 4 + ], + [ + 19.77, + "Male", + "No", + "Sun", + 4 + ], + [ + 29.85, + "Female", + "No", + "Sun", + 5 + ], + [ + 48.17, + "Male", + "No", + "Sun", + 6 + ], + [ + 25, + "Female", + "No", + "Sun", + 4 + ], + [ + 13.39, + "Female", + "No", + "Sun", + 2 + ], + [ + 16.49, + "Male", + "No", + "Sun", + 4 + ], + [ + 21.5, + "Male", + "No", + "Sun", + 4 + ], + [ + 12.66, + "Male", + "No", + "Sun", + 2 + ], + [ + 16.21, + "Female", + "No", + "Sun", + 3 + ], + [ + 13.81, + "Male", + "No", + "Sun", + 2 + ], + [ + 17.51, + "Female", + "Yes", + "Sun", + 2 + ], + [ + 24.52, + "Male", + "No", + "Sun", + 3 + ], + [ + 20.76, + "Male", + "No", + "Sun", + 2 + ], + [ + 31.71, + "Male", + "No", + "Sun", + 4 + ], + [ + 10.59, + "Female", + "Yes", + "Sat", + 2 + ], + [ + 10.63, + "Female", + "Yes", + "Sat", + 2 + ], + [ + 50.81, + "Male", + "Yes", + "Sat", + 3 + ], + [ + 15.81, + "Male", + "Yes", + "Sat", + 2 + ], + [ + 7.25, + "Male", + "Yes", + "Sun", + 2 + ], + [ + 31.85, + "Male", + "Yes", + "Sun", + 2 + ], + [ + 16.82, + "Male", + "Yes", + "Sun", + 2 + ], + [ + 32.9, + "Male", + "Yes", + "Sun", + 2 + ], + [ + 17.89, + "Male", + "Yes", + "Sun", + 2 + ], + [ + 14.48, + "Male", + "Yes", + "Sun", + 2 + ], + [ + 9.6, + "Female", + "Yes", + "Sun", + 2 + ], + [ + 34.63, + "Male", + "Yes", + "Sun", + 2 + ], + [ + 34.65, + "Male", + "Yes", + "Sun", + 4 + ], + [ + 23.33, + "Male", + "Yes", + "Sun", + 2 + ], + [ + 45.35, + "Male", + "Yes", + "Sun", + 3 + ], + [ + 23.17, + "Male", + "Yes", + "Sun", + 4 + ], + [ + 40.55, + "Male", + "Yes", + "Sun", + 2 + ], + [ + 20.69, + "Male", + "No", + "Sun", + 5 + ], + [ + 20.9, + "Female", + "Yes", + "Sun", + 3 + ], + [ + 30.46, + "Male", + "Yes", + "Sun", + 5 + ], + [ + 18.15, + "Female", + "Yes", + "Sun", + 3 + ], + [ + 23.1, + "Male", + "Yes", + "Sun", + 3 + ], + [ + 15.69, + "Male", + "Yes", + "Sun", + 2 + ], + [ + 26.59, + "Male", + "Yes", + "Sat", + 3 + ], + [ + 38.73, + "Male", + "Yes", + "Sat", + 4 + ], + [ + 24.27, + "Male", + "Yes", + "Sat", + 2 + ], + [ + 12.76, + "Female", + "Yes", + "Sat", + 2 + ], + [ + 30.06, + "Male", + "Yes", + "Sat", + 3 + ], + [ + 25.89, + "Male", + "Yes", + "Sat", + 4 + ], + [ + 48.33, + "Male", + "No", + "Sat", + 4 + ], + [ + 13.27, + "Female", + "Yes", + "Sat", + 2 + ], + [ + 28.17, + "Female", + "Yes", + "Sat", + 3 + ], + [ + 12.9, + "Female", + "Yes", + "Sat", + 2 + ], + [ + 28.15, + "Male", + "Yes", + "Sat", + 5 + ], + [ + 11.59, + "Male", + "Yes", + "Sat", + 2 + ], + [ + 7.74, + "Male", + "Yes", + "Sat", + 2 + ], + [ + 30.14, + "Female", + "Yes", + "Sat", + 4 + ], + [ + 20.45, + "Male", + "No", + "Sat", + 4 + ], + [ + 13.28, + "Male", + "No", + "Sat", + 2 + ], + [ + 22.12, + "Female", + "Yes", + "Sat", + 2 + ], + [ + 24.01, + "Male", + "Yes", + "Sat", + 4 + ], + [ + 15.69, + "Male", + "Yes", + "Sat", + 3 + ], + [ + 11.61, + "Male", + "No", + "Sat", + 2 + ], + [ + 10.77, + "Male", + "No", + "Sat", + 2 + ], + [ + 15.53, + "Male", + "Yes", + "Sat", + 2 + ], + [ + 10.07, + "Male", + "No", + "Sat", + 2 + ], + [ + 12.6, + "Male", + "Yes", + "Sat", + 2 + ], + [ + 32.83, + "Male", + "Yes", + "Sat", + 2 + ], + [ + 35.83, + "Female", + "No", + "Sat", + 3 + ], + [ + 29.03, + "Male", + "No", + "Sat", + 3 + ], + [ + 27.18, + "Female", + "Yes", + "Sat", + 2 + ], + [ + 22.67, + "Male", + "Yes", + "Sat", + 2 + ], + [ + 17.82, + "Male", + "No", + "Sat", + 2 + ], + [ + 18.78, + "Female", + "No", + "Thur", + 2 + ] + ], + "hovertemplate": "time=%{x}
tip=%{y}
total_bill=%{customdata[0]}
sex=%{customdata[1]}
smoker=%{customdata[2]}
day=%{customdata[3]}
size=%{customdata[4]}", + "legendgroup": "Dinner", + "marker": { + "color": "#636efa" + }, + "name": "Dinner", + "offsetgroup": "Dinner", + "orientation": "v", + "points": "all", + "scalegroup": "True", + "showlegend": true, + "type": "violin", + "x": [ + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner", + "Dinner" + ], + "x0": " ", + "xaxis": "x", + "y": [ + 1.01, + 1.66, + 3.5, + 3.31, + 3.61, + 4.71, + 2, + 3.12, + 1.96, + 3.23, + 1.71, + 5, + 1.57, + 3, + 3.02, + 3.92, + 1.67, + 3.71, + 3.5, + 3.35, + 4.08, + 2.75, + 2.23, + 7.58, + 3.18, + 2.34, + 2, + 2, + 4.3, + 3, + 1.45, + 2.5, + 3, + 2.45, + 3.27, + 3.6, + 2, + 3.07, + 2.31, + 5, + 2.24, + 2.54, + 3.06, + 1.32, + 5.6, + 3, + 5, + 6, + 2.05, + 3, + 2.5, + 2.6, + 5.2, + 1.56, + 4.34, + 3.51, + 3, + 1.5, + 1.76, + 6.73, + 3.21, + 2, + 1.98, + 3.76, + 2.64, + 3.15, + 2.47, + 1, + 2.01, + 2.09, + 1.97, + 3, + 3.14, + 5, + 2.2, + 1.25, + 3.08, + 3, + 3.5, + 1, + 4.3, + 3.25, + 4.73, + 4, + 1.5, + 3, + 1.5, + 2.5, + 3, + 2.5, + 3.48, + 4.08, + 1.64, + 4.06, + 4.29, + 3.76, + 4, + 3, + 1, + 4, + 2.55, + 4, + 3.5, + 5.07, + 2.5, + 2, + 2.74, + 2, + 2, + 5.14, + 5, + 3.75, + 2.61, + 2, + 3.5, + 2.5, + 2, + 2, + 3, + 3.48, + 2.24, + 4.5, + 1.61, + 2, + 10, + 3.16, + 5.15, + 3.18, + 4, + 3.11, + 2, + 2, + 4, + 3.55, + 3.68, + 5.65, + 3.5, + 6.5, + 3, + 5, + 3.5, + 2, + 3.5, + 4, + 1.5, + 3.41, + 3, + 2.03, + 2.23, + 2, + 5.16, + 9, + 2.5, + 6.5, + 1.1, + 3, + 1.5, + 1.44, + 3.09, + 3, + 2.72, + 2.88, + 2, + 3, + 3.39, + 1.47, + 3, + 1.25, + 1, + 1.17, + 4.67, + 5.92, + 2, + 2, + 1.75, + 3 + ], + "y0": " ", + "yaxis": "y" + }, + { + "alignmentgroup": "True", + "box": { + "visible": true + }, + "customdata": [ + [ + 27.2, + "Male", + "No", + "Thur", + 4 + ], + [ + 22.76, + "Male", + "No", + "Thur", + 2 + ], + [ + 17.29, + "Male", + "No", + "Thur", + 2 + ], + [ + 19.44, + "Male", + "Yes", + "Thur", + 2 + ], + [ + 16.66, + "Male", + "No", + "Thur", + 2 + ], + [ + 10.07, + "Female", + "No", + "Thur", + 1 + ], + [ + 32.68, + "Male", + "Yes", + "Thur", + 2 + ], + [ + 15.98, + "Male", + "No", + "Thur", + 2 + ], + [ + 34.83, + "Female", + "No", + "Thur", + 4 + ], + [ + 13.03, + "Male", + "No", + "Thur", + 2 + ], + [ + 18.28, + "Male", + "No", + "Thur", + 2 + ], + [ + 24.71, + "Male", + "No", + "Thur", + 2 + ], + [ + 21.16, + "Male", + "No", + "Thur", + 2 + ], + [ + 10.65, + "Female", + "No", + "Thur", + 2 + ], + [ + 12.43, + "Female", + "No", + "Thur", + 2 + ], + [ + 24.08, + "Female", + "No", + "Thur", + 4 + ], + [ + 11.69, + "Male", + "No", + "Thur", + 2 + ], + [ + 13.42, + "Female", + "No", + "Thur", + 2 + ], + [ + 14.26, + "Male", + "No", + "Thur", + 2 + ], + [ + 15.95, + "Male", + "No", + "Thur", + 2 + ], + [ + 12.48, + "Female", + "No", + "Thur", + 2 + ], + [ + 29.8, + "Female", + "No", + "Thur", + 6 + ], + [ + 8.52, + "Male", + "No", + "Thur", + 2 + ], + [ + 14.52, + "Female", + "No", + "Thur", + 2 + ], + [ + 11.38, + "Female", + "No", + "Thur", + 2 + ], + [ + 22.82, + "Male", + "No", + "Thur", + 3 + ], + [ + 19.08, + "Male", + "No", + "Thur", + 2 + ], + [ + 20.27, + "Female", + "No", + "Thur", + 2 + ], + [ + 11.17, + "Female", + "No", + "Thur", + 2 + ], + [ + 12.26, + "Female", + "No", + "Thur", + 2 + ], + [ + 18.26, + "Female", + "No", + "Thur", + 2 + ], + [ + 8.51, + "Female", + "No", + "Thur", + 2 + ], + [ + 10.33, + "Female", + "No", + "Thur", + 2 + ], + [ + 14.15, + "Female", + "No", + "Thur", + 2 + ], + [ + 16, + "Male", + "Yes", + "Thur", + 2 + ], + [ + 13.16, + "Female", + "No", + "Thur", + 2 + ], + [ + 17.47, + "Female", + "No", + "Thur", + 2 + ], + [ + 34.3, + "Male", + "No", + "Thur", + 6 + ], + [ + 41.19, + "Male", + "No", + "Thur", + 5 + ], + [ + 27.05, + "Female", + "No", + "Thur", + 6 + ], + [ + 16.43, + "Female", + "No", + "Thur", + 2 + ], + [ + 8.35, + "Female", + "No", + "Thur", + 2 + ], + [ + 18.64, + "Female", + "No", + "Thur", + 3 + ], + [ + 11.87, + "Female", + "No", + "Thur", + 2 + ], + [ + 9.78, + "Male", + "No", + "Thur", + 2 + ], + [ + 7.51, + "Male", + "No", + "Thur", + 2 + ], + [ + 19.81, + "Female", + "Yes", + "Thur", + 2 + ], + [ + 28.44, + "Male", + "Yes", + "Thur", + 2 + ], + [ + 15.48, + "Male", + "Yes", + "Thur", + 2 + ], + [ + 16.58, + "Male", + "Yes", + "Thur", + 2 + ], + [ + 7.56, + "Male", + "No", + "Thur", + 2 + ], + [ + 10.34, + "Male", + "Yes", + "Thur", + 2 + ], + [ + 43.11, + "Female", + "Yes", + "Thur", + 4 + ], + [ + 13, + "Female", + "Yes", + "Thur", + 2 + ], + [ + 13.51, + "Male", + "Yes", + "Thur", + 2 + ], + [ + 18.71, + "Male", + "Yes", + "Thur", + 3 + ], + [ + 12.74, + "Female", + "Yes", + "Thur", + 2 + ], + [ + 13, + "Female", + "Yes", + "Thur", + 2 + ], + [ + 16.4, + "Female", + "Yes", + "Thur", + 2 + ], + [ + 20.53, + "Male", + "Yes", + "Thur", + 4 + ], + [ + 16.47, + "Female", + "Yes", + "Thur", + 3 + ], + [ + 12.16, + "Male", + "Yes", + "Fri", + 2 + ], + [ + 13.42, + "Female", + "Yes", + "Fri", + 2 + ], + [ + 8.58, + "Male", + "Yes", + "Fri", + 1 + ], + [ + 15.98, + "Female", + "No", + "Fri", + 3 + ], + [ + 13.42, + "Male", + "Yes", + "Fri", + 2 + ], + [ + 16.27, + "Female", + "Yes", + "Fri", + 2 + ], + [ + 10.09, + "Female", + "Yes", + "Fri", + 2 + ] + ], + "hovertemplate": "time=%{x}
tip=%{y}
total_bill=%{customdata[0]}
sex=%{customdata[1]}
smoker=%{customdata[2]}
day=%{customdata[3]}
size=%{customdata[4]}", + "legendgroup": "Lunch", + "marker": { + "color": "#EF553B" + }, + "name": "Lunch", + "offsetgroup": "Lunch", + "orientation": "v", + "points": "all", + "scalegroup": "True", + "showlegend": true, + "type": "violin", + "x": [ + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch", + "Lunch" + ], + "x0": " ", + "xaxis": "x", + "y": [ + 4, + 3, + 2.71, + 3, + 3.4, + 1.83, + 5, + 2.03, + 5.17, + 2, + 4, + 5.85, + 3, + 1.5, + 1.8, + 2.92, + 2.31, + 1.68, + 2.5, + 2, + 2.52, + 4.2, + 1.48, + 2, + 2, + 2.18, + 1.5, + 2.83, + 1.5, + 2, + 3.25, + 1.25, + 2, + 2, + 2, + 2.75, + 3.5, + 6.7, + 5, + 5, + 2.3, + 1.5, + 1.36, + 1.63, + 1.73, + 2, + 4.19, + 2.56, + 2.02, + 4, + 1.44, + 2, + 5, + 2, + 2, + 4, + 2.01, + 2, + 2.5, + 4, + 3.23, + 2.2, + 3.48, + 1.92, + 3, + 1.58, + 2.5, + 2 + ], + "y0": " ", + "yaxis": "y" + } + ], + "layout": { + "legend": { + "title": { + "text": "time" + }, + "tracegroupgap": 0 + }, + "margin": { + "t": 60 + }, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "violinmode": "overlay", + "xaxis": { + "anchor": "y", + "categoryarray": [ + "Dinner", + "Lunch" + ], + "categoryorder": "array", + "domain": [ + 0, + 1 + ], + "title": { + "text": "time" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "tip" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig = px.violin(\n", " tips,\n", @@ -2031,9 +67699,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 85, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "year\n", + "1989 1\n", + "1992 2\n", + "1994 1\n", + "1995 1\n", + "1996 6\n", + "Name: number, dtype: int64" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "planets = sns.load_dataset(\"planets\").groupby(\"year\")[\"number\"].count()\n", "planets.head()" @@ -2048,9 +67733,1246 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 86, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:26.754087\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots()\n", "ax.stem(planets.index, planets)\n", @@ -2071,9 +68993,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 87, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "" + ] + }, + "execution_count": 87, + "metadata": { + "image/png": { + "height": 378.25, + "width": 508.72499999999997 + } + }, + "output_type": "execute_result" + } + ], "source": [ "(\n", " so.Plot(planets.reset_index(), x=\"year\", y=\"number\")\n", @@ -2091,9 +69030,61 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 88, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "(\n", " ggplot(planets.reset_index(), aes(x=\"year\", y=\"number\"))\n", @@ -2112,9 +69103,1062 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 89, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "marker": { + "color": "darkblue", + "size": 10 + }, + "mode": "markers", + "type": "scatter", + "x": [ + 1989, + 1992, + 1994, + 1995, + 1996, + 1997, + 1998, + 1999, + 2000, + 2001, + 2002, + 2003, + 2004, + 2005, + 2006, + 2007, + 2008, + 2009, + 2010, + 2011, + 2012, + 2013, + 2014 + ], + "y": [ + 1, + 2, + 1, + 1, + 6, + 1, + 5, + 15, + 16, + 12, + 32, + 25, + 26, + 39, + 31, + 53, + 74, + 98, + 102, + 185, + 140, + 118, + 52 + ] + } + ], + "layout": { + "shapes": [ + { + "type": "line", + "x0": 1989, + "x1": 1989, + "y0": 0, + "y1": 1 + }, + { + "type": "line", + "x0": 1992, + "x1": 1992, + "y0": 0, + "y1": 2 + }, + { + "type": "line", + "x0": 1994, + "x1": 1994, + "y0": 0, + "y1": 1 + }, + { + "type": "line", + "x0": 1995, + "x1": 1995, + "y0": 0, + "y1": 1 + }, + { + "type": "line", + "x0": 1996, + "x1": 1996, + "y0": 0, + "y1": 6 + }, + { + "type": "line", + "x0": 1997, + "x1": 1997, + "y0": 0, + "y1": 1 + }, + { + "type": "line", + "x0": 1998, + "x1": 1998, + "y0": 0, + "y1": 5 + }, + { + "type": "line", + "x0": 1999, + "x1": 1999, + "y0": 0, + "y1": 15 + }, + { + "type": "line", + "x0": 2000, + "x1": 2000, + "y0": 0, + "y1": 16 + }, + { + "type": "line", + "x0": 2001, + "x1": 2001, + "y0": 0, + "y1": 12 + }, + { + "type": "line", + "x0": 2002, + "x1": 2002, + "y0": 0, + "y1": 32 + }, + { + "type": "line", + "x0": 2003, + "x1": 2003, + "y0": 0, + "y1": 25 + }, + { + "type": "line", + "x0": 2004, + "x1": 2004, + "y0": 0, + "y1": 26 + }, + { + "type": "line", + "x0": 2005, + "x1": 2005, + "y0": 0, + "y1": 39 + }, + { + "type": "line", + "x0": 2006, + "x1": 2006, + "y0": 0, + "y1": 31 + }, + { + "type": "line", + "x0": 2007, + "x1": 2007, + "y0": 0, + "y1": 53 + }, + { + "type": "line", + "x0": 2008, + "x1": 2008, + "y0": 0, + "y1": 74 + }, + { + "type": "line", + "x0": 2009, + "x1": 2009, + "y0": 0, + "y1": 98 + }, + { + "type": "line", + "x0": 2010, + "x1": 2010, + "y0": 0, + "y1": 102 + }, + { + "type": "line", + "x0": 2011, + "x1": 2011, + "y0": 0, + "y1": 185 + }, + { + "type": "line", + "x0": 2012, + "x1": 2012, + "y0": 0, + "y1": 140 + }, + { + "type": "line", + "x0": 2013, + "x1": 2013, + "y0": 0, + "y1": 118 + }, + { + "type": "line", + "x0": 2014, + "x1": 2014, + "y0": 0, + "y1": 52 + } + ], + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import plotly.graph_objects as go\n", "\n", @@ -2148,9 +70192,102 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 90, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
methodnumberorbital_periodmassdistanceyear
0Radial Velocity1269.3007.1077.402006
1Radial Velocity1874.7742.2156.952008
2Radial Velocity1763.0002.6019.842011
3Radial Velocity1326.03019.40110.622007
4Radial Velocity1516.22010.50119.472009
\n", + "
" + ], + "text/plain": [ + " method number orbital_period mass distance year\n", + "0 Radial Velocity 1 269.300 7.10 77.40 2006\n", + "1 Radial Velocity 1 874.774 2.21 56.95 2008\n", + "2 Radial Velocity 1 763.000 2.60 19.84 2011\n", + "3 Radial Velocity 1 326.030 19.40 110.62 2007\n", + "4 Radial Velocity 1 516.220 10.50 119.47 2009" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "planets = sns.load_dataset(\"planets\")\n", "most_pop_methods = (\n", @@ -2175,9 +70312,1621 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 91, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:27.136496\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "(\n", " planets.groupby([\"year\", \"method\"])[\"number\"]\n", @@ -2198,9 +71947,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 92, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "" + ] + }, + "execution_count": 92, + "metadata": { + "image/png": { + "height": 378.25, + "width": 646.425 + } + }, + "output_type": "execute_result" + } + ], "source": [ "(\n", " so.Plot(\n", @@ -2222,9 +71988,64 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 93, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "(\n", " ggplot(\n", @@ -2245,9 +72066,77 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 94, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "alt.Chart(\n", " planets.groupby([\"year\", \"method\"])[\"number\"]\n", @@ -2271,9 +72160,84 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 95, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ContinentYearGDP per capita
0Africa19521252.572466
1Americas19524079.062552
2Asia19525195.484004
3Europe19525661.057435
4Oceania195210298.085650
\n", + "
" + ], + "text/plain": [ + " Continent Year GDP per capita\n", + "0 Africa 1952 1252.572466\n", + "1 Americas 1952 4079.062552\n", + "2 Asia 1952 5195.484004\n", + "3 Europe 1952 5661.057435\n", + "4 Oceania 1952 10298.085650" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df = pd.read_csv(\n", " \"https://raw.githubusercontent.com/selva86/datasets/master/gdppercap.csv\"\n", @@ -2299,9 +72263,1462 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 96, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:28.013012\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from matplotlib import lines as mlines\n", "\n", @@ -2425,9 +73842,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 97, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "" + ] + }, + "execution_count": 97, + "metadata": { + "image/png": { + "height": 378.25, + "width": 616.25 + } + }, + "output_type": "execute_result" + } + ], "source": [ "(\n", " so.Plot(df, x=\"Year\", y=\"GDP per capita\", color=\"Continent\")\n", @@ -2445,9 +73879,69 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 98, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "(\n", " ggplot(df, aes(x=\"Year\", y=\"GDP per capita\", group=\"Continent\"))\n", @@ -2465,9 +73959,77 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 99, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "alt.Chart(df).mark_line().encode(x=\"Year:O\", y=\"GDP per capita\", color=\"Continent\")" ] @@ -2481,9 +74043,966 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 100, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "mode": "text", + "text": "Africa", + "type": "scatter", + "x": [ + 1952 + ], + "y": [ + 1252.57246582115 + ] + }, + { + "mode": "text", + "text": "Americas", + "type": "scatter", + "x": [ + 1952 + ], + "y": [ + 4079.0625522 + ] + }, + { + "mode": "text", + "text": "Asia", + "type": "scatter", + "x": [ + 1952 + ], + "y": [ + 5195.48400403939 + ] + }, + { + "mode": "text", + "text": "Europe", + "type": "scatter", + "x": [ + 1952 + ], + "y": [ + 5661.05743476 + ] + }, + { + "mode": "text", + "text": "Oceania", + "type": "scatter", + "x": [ + 1952 + ], + "y": [ + 10298.08565 + ] + } + ], + "layout": { + "shapes": [ + { + "line": { + "color": "#636EFA" + }, + "name": "Africa", + "type": "line", + "x0": 1952, + "x1": 1957, + "y0": 1252.57246582115, + "y1": 1385.23606225577 + }, + { + "line": { + "color": "#EF553B" + }, + "name": "Americas", + "type": "line", + "x0": 1952, + "x1": 1957, + "y0": 4079.0625522, + "y1": 4616.04373316 + }, + { + "line": { + "color": "#00CC96" + }, + "name": "Asia", + "type": "line", + "x0": 1952, + "x1": 1957, + "y0": 5195.48400403939, + "y1": 4003.13293994242 + }, + { + "line": { + "color": "#AB63FA" + }, + "name": "Europe", + "type": "line", + "x0": 1952, + "x1": 1957, + "y0": 5661.05743476, + "y1": 6963.01281593333 + }, + { + "line": { + "color": "#FFA15A" + }, + "name": "Oceania", + "type": "line", + "x0": 1952, + "x1": 1957, + "y0": 10298.08565, + "y1": 11598.522455 + } + ], + "showlegend": false, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "xaxis": { + "range": [ + 1947, + 1962 + ] + }, + "yaxis": { + "range": [ + 1002.05797265692, + 13918.226946 + ] + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import plotly.graph_objects as go\n", "\n", @@ -2538,9 +75057,84 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 101, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ContinentYearGDP per capita
0Africa19521252.572466
1Americas19524079.062552
2Asia19525195.484004
3Europe19525661.057435
4Oceania195210298.085650
\n", + "
" + ], + "text/plain": [ + " Continent Year GDP per capita\n", + "0 Africa 1952 1252.572466\n", + "1 Americas 1952 4079.062552\n", + "2 Asia 1952 5195.484004\n", + "3 Europe 1952 5661.057435\n", + "4 Oceania 1952 10298.085650" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df = pd.read_csv(\n", " \"https://raw.githubusercontent.com/selva86/datasets/master/gdppercap.csv\"\n", @@ -2566,9 +75160,1292 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 102, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:28.480172\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from matplotlib import lines as mlines\n", "\n", @@ -2637,9 +76514,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 103, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "" + ] + }, + "execution_count": 103, + "metadata": { + "image/png": { + "height": 378.25, + "width": 592.025 + } + }, + "output_type": "execute_result" + } + ], "source": [ "(\n", " so.Plot(df, y=\"Continent\", x=\"GDP per capita\", color=\"Year\").add(\n", @@ -2657,9 +76551,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 104, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "(\n", " ggplot(df, aes(y=\"Continent\", x=\"GDP per capita\", group=\"Continent\"))\n", @@ -2678,9 +76636,944 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 105, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "marker": { + "color": "#636EFA", + "size": 10 + }, + "mode": "markers", + "name": "1952", + "type": "scatter", + "x": [ + 1252.57246582115, + 4079.0625522, + 5195.48400403939, + 5661.05743476, + 10298.08565 + ], + "y": [ + "Africa", + "Americas", + "Asia", + "Europe", + "Oceania" + ] + }, + { + "marker": { + "color": "#EF553B", + "size": 10 + }, + "mode": "markers", + "name": "1957", + "type": "scatter", + "x": [ + 1385.23606225577, + 4616.04373316, + 4003.13293994242, + 6963.01281593333, + 11598.522455 + ], + "y": [ + "Africa", + "Americas", + "Asia", + "Europe", + "Oceania" + ] + } + ], + "layout": { + "shapes": [ + { + "line": { + "color": "#636EFA", + "width": 2 + }, + "type": "line", + "x0": 1252.57246582115, + "x1": 1385.23606225577, + "y0": "Africa", + "y1": "Africa" + }, + { + "line": { + "color": "#636EFA", + "width": 2 + }, + "type": "line", + "x0": 4079.0625522, + "x1": 4616.04373316, + "y0": "Americas", + "y1": "Americas" + }, + { + "line": { + "color": "#636EFA", + "width": 2 + }, + "type": "line", + "x0": 5195.48400403939, + "x1": 4003.13293994242, + "y0": "Asia", + "y1": "Asia" + }, + { + "line": { + "color": "#636EFA", + "width": 2 + }, + "type": "line", + "x0": 5661.05743476, + "x1": 6963.01281593333, + "y0": "Europe", + "y1": "Europe" + }, + { + "line": { + "color": "#636EFA", + "width": 2 + }, + "type": "line", + "x0": 10298.08565, + "x1": 11598.522455, + "y0": "Oceania", + "y1": "Oceania" + } + ], + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import plotly.graph_objects as go\n", "\n", @@ -2730,9 +77623,78 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 106, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
rtheta
00.000.000000
10.010.062832
20.020.125664
30.030.188496
40.040.251327
\n", + "
" + ], + "text/plain": [ + " r theta\n", + "0 0.00 0.000000\n", + "1 0.01 0.062832\n", + "2 0.02 0.125664\n", + "3 0.03 0.188496\n", + "4 0.04 0.251327" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "r = np.arange(0, 2, 0.01)\n", "theta = 2 * np.pi * r\n", @@ -2749,9 +77711,858 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 107, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:28.762009\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "ax = plt.subplot(111, projection=\"polar\")\n", "ax.plot(polar_data[\"theta\"], polar_data[\"r\"])\n", @@ -2771,9 +78582,1250 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 108, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "mode": "lines", + "r": [ + 0, + 0.01, + 0.02, + 0.03, + 0.04, + 0.05, + 0.06, + 0.07, + 0.08, + 0.09, + 0.1, + 0.11, + 0.12, + 0.13, + 0.14, + 0.15, + 0.16, + 0.17, + 0.18, + 0.19, + 0.2, + 0.21, + 0.22, + 0.23, + 0.24, + 0.25, + 0.26, + 0.27, + 0.28, + 0.29, + 0.3, + 0.31, + 0.32, + 0.33, + 0.34, + 0.35000000000000003, + 0.36, + 0.37, + 0.38, + 0.39, + 0.4, + 0.41000000000000003, + 0.42, + 0.43, + 0.44, + 0.45, + 0.46, + 0.47000000000000003, + 0.48, + 0.49, + 0.5, + 0.51, + 0.52, + 0.53, + 0.54, + 0.55, + 0.56, + 0.5700000000000001, + 0.58, + 0.59, + 0.6, + 0.61, + 0.62, + 0.63, + 0.64, + 0.65, + 0.66, + 0.67, + 0.68, + 0.6900000000000001, + 0.7000000000000001, + 0.71, + 0.72, + 0.73, + 0.74, + 0.75, + 0.76, + 0.77, + 0.78, + 0.79, + 0.8, + 0.81, + 0.8200000000000001, + 0.8300000000000001, + 0.84, + 0.85, + 0.86, + 0.87, + 0.88, + 0.89, + 0.9, + 0.91, + 0.92, + 0.93, + 0.9400000000000001, + 0.9500000000000001, + 0.96, + 0.97, + 0.98, + 0.99, + 1, + 1.01, + 1.02, + 1.03, + 1.04, + 1.05, + 1.06, + 1.07, + 1.08, + 1.09, + 1.1, + 1.11, + 1.12, + 1.1300000000000001, + 1.1400000000000001, + 1.1500000000000001, + 1.16, + 1.17, + 1.18, + 1.19, + 1.2, + 1.21, + 1.22, + 1.23, + 1.24, + 1.25, + 1.26, + 1.27, + 1.28, + 1.29, + 1.3, + 1.31, + 1.32, + 1.33, + 1.34, + 1.35, + 1.36, + 1.37, + 1.3800000000000001, + 1.3900000000000001, + 1.4000000000000001, + 1.41, + 1.42, + 1.43, + 1.44, + 1.45, + 1.46, + 1.47, + 1.48, + 1.49, + 1.5, + 1.51, + 1.52, + 1.53, + 1.54, + 1.55, + 1.56, + 1.57, + 1.58, + 1.59, + 1.6, + 1.61, + 1.62, + 1.6300000000000001, + 1.6400000000000001, + 1.6500000000000001, + 1.6600000000000001, + 1.67, + 1.68, + 1.69, + 1.7, + 1.71, + 1.72, + 1.73, + 1.74, + 1.75, + 1.76, + 1.77, + 1.78, + 1.79, + 1.8, + 1.81, + 1.82, + 1.83, + 1.84, + 1.85, + 1.86, + 1.87, + 1.8800000000000001, + 1.8900000000000001, + 1.9000000000000001, + 1.9100000000000001, + 1.92, + 1.93, + 1.94, + 1.95, + 1.96, + 1.97, + 1.98, + 1.99 + ], + "theta": [ + 0, + 3.6000000000000005, + 7.200000000000001, + 10.8, + 14.400000000000002, + 18, + 21.6, + 25.200000000000003, + 28.800000000000004, + 32.400000000000006, + 36, + 39.599999999999994, + 43.2, + 46.800000000000004, + 50.400000000000006, + 54, + 57.60000000000001, + 61.20000000000001, + 64.80000000000001, + 68.39999999999999, + 72, + 75.6, + 79.19999999999999, + 82.8, + 86.4, + 90, + 93.60000000000001, + 97.2, + 100.80000000000001, + 104.39999999999999, + 108, + 111.6, + 115.20000000000002, + 118.80000000000003, + 122.40000000000002, + 126, + 129.60000000000002, + 133.20000000000002, + 136.79999999999998, + 140.4, + 144, + 147.6, + 151.2, + 154.79999999999998, + 158.39999999999998, + 162, + 165.6, + 169.2, + 172.8, + 176.4, + 180, + 183.6, + 187.20000000000002, + 190.8, + 194.4, + 198.00000000000003, + 201.60000000000002, + 205.20000000000002, + 208.79999999999998, + 212.39999999999998, + 216, + 219.6, + 223.2, + 226.8, + 230.40000000000003, + 234, + 237.60000000000005, + 241.20000000000002, + 244.80000000000004, + 248.4, + 252, + 255.59999999999997, + 259.20000000000005, + 262.8, + 266.40000000000003, + 270, + 273.59999999999997, + 277.2, + 280.8, + 284.40000000000003, + 288, + 291.6, + 295.2, + 298.80000000000007, + 302.4, + 306, + 309.59999999999997, + 313.2, + 316.79999999999995, + 320.40000000000003, + 324, + 327.6000000000001, + 331.2, + 334.80000000000007, + 338.4, + 342, + 345.6, + 349.2, + 352.8, + 356.40000000000003, + 360, + 363.6, + 367.2, + 370.8, + 374.40000000000003, + 378, + 381.6, + 385.20000000000005, + 388.8, + 392.40000000000003, + 396.00000000000006, + 399.6, + 403.20000000000005, + 406.8, + 410.40000000000003, + 414.00000000000006, + 417.59999999999997, + 421.2, + 424.79999999999995, + 428.4, + 432, + 435.59999999999997, + 439.2, + 442.8, + 446.4, + 450, + 453.6, + 457.2, + 460.80000000000007, + 464.4, + 468, + 471.6, + 475.2000000000001, + 478.8, + 482.40000000000003, + 486, + 489.6000000000001, + 493.2000000000001, + 496.8, + 500.40000000000003, + 504, + 507.59999999999997, + 511.19999999999993, + 514.8, + 518.4000000000001, + 522, + 525.6, + 529.1999999999999, + 532.8000000000001, + 536.4000000000001, + 540, + 543.6, + 547.1999999999999, + 550.8000000000001, + 554.4, + 558, + 561.6, + 565.2, + 568.8000000000001, + 572.4, + 576, + 579.6, + 583.2, + 586.8, + 590.4, + 594, + 597.6000000000001, + 601.1999999999999, + 604.8, + 608.4, + 612, + 615.5999999999999, + 619.1999999999999, + 622.8000000000001, + 626.4, + 630, + 633.5999999999999, + 637.1999999999999, + 640.8000000000001, + 644.4000000000001, + 648, + 651.6, + 655.2000000000002, + 658.8, + 662.4, + 666, + 669.6000000000001, + 673.2, + 676.8, + 680.4, + 684, + 687.6000000000001, + 691.2, + 694.8, + 698.4, + 702, + 705.6, + 709.2, + 712.8000000000001, + 716.4 + ], + "type": "scatterpolar" + } + ], + "layout": { + "showlegend": false, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig = go.Figure(\n", " data=go.Scatterpolar(\n", @@ -2798,9 +79850,87 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 109, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
var1var2var3var4var5
01952238
16227108
22914281916
3101929175
\n", + "
" + ], + "text/plain": [ + " var1 var2 var3 var4 var5\n", + "0 19 5 2 23 8\n", + "1 6 2 27 10 8\n", + "2 29 14 28 19 16\n", + "3 10 19 29 17 5" + ] + }, + "execution_count": 109, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df = pd.DataFrame(\n", " dict(\n", @@ -2815,9 +79945,652 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 110, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:28.909970\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from math import pi\n", "\n", @@ -2856,9 +80629,1319 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 111, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " direction strength frequency\n", + "0 N 0-1 0.5\n", + "1 NNE 0-1 0.6\n", + "2 NE 0-1 0.5\n", + "3 ENE 0-1 0.4\n", + "4 E 0-1 0.4\n" + ] + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "strength=0-1
frequency=%{r}
direction=%{theta}", + "legendgroup": "0-1", + "line": { + "color": "#f0f921", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines", + "name": "0-1", + "r": [ + 0.5, + 0.6, + 0.5, + 0.4, + 0.4, + 0.3, + 0.4, + 0.4, + 0.6, + 0.4, + 0.5, + 0.6, + 0.6, + 0.5, + 0.4, + 0.1, + 0.5 + ], + "showlegend": true, + "subplot": "polar", + "theta": [ + "N", + "NNE", + "NE", + "ENE", + "E", + "ESE", + "SE", + "SSE", + "S", + "SSW", + "SW", + "WSW", + "W", + "WNW", + "NW", + "NNW", + "N" + ], + "type": "scatterpolar" + }, + { + "hovertemplate": "strength=1-2
frequency=%{r}
direction=%{theta}", + "legendgroup": "1-2", + "line": { + "color": "#fdca26", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines", + "name": "1-2", + "r": [ + 1.6, + 1.8, + 1.5, + 1.6, + 1.6, + 1.2, + 1.5, + 1.7, + 2.2, + 2, + 2.3, + 2.4, + 2.3, + 2.6, + 2.3, + 0.8, + 1.6 + ], + "showlegend": true, + "subplot": "polar", + "theta": [ + "N", + "NNE", + "NE", + "ENE", + "E", + "ESE", + "SE", + "SSE", + "S", + "SSW", + "SW", + "WSW", + "W", + "WNW", + "NW", + "NNW", + "N" + ], + "type": "scatterpolar" + }, + { + "hovertemplate": "strength=2-3
frequency=%{r}
direction=%{theta}", + "legendgroup": "2-3", + "line": { + "color": "#fb9f3a", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines", + "name": "2-3", + "r": [ + 0.9, + 1.3, + 1.6, + 0.9, + 1, + 0.6, + 0.6, + 0.9, + 1.4, + 1.7, + 1.9, + 2.2, + 1.8, + 1.7, + 1.8, + 0.8, + 0.9 + ], + "showlegend": true, + "subplot": "polar", + "theta": [ + "N", + "NNE", + "NE", + "ENE", + "E", + "ESE", + "SE", + "SSE", + "S", + "SSW", + "SW", + "WSW", + "W", + "WNW", + "NW", + "NNW", + "N" + ], + "type": "scatterpolar" + }, + { + "hovertemplate": "strength=3-4
frequency=%{r}
direction=%{theta}", + "legendgroup": "3-4", + "line": { + "color": "#ed7953", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines", + "name": "3-4", + "r": [ + 0.9, + 0.8, + 1.2, + 1, + 0.8, + 0.4, + 0.5, + 0.5, + 0.8, + 0.9, + 1.3, + 1.1, + 1.2, + 1.2, + 1.3, + 1, + 0.9 + ], + "showlegend": true, + "subplot": "polar", + "theta": [ + "N", + "NNE", + "NE", + "ENE", + "E", + "ESE", + "SE", + "SSE", + "S", + "SSW", + "SW", + "WSW", + "W", + "WNW", + "NW", + "NNW", + "N" + ], + "type": "scatterpolar" + }, + { + "hovertemplate": "strength=4-4
frequency=%{r}
direction=%{theta}", + "legendgroup": "4-4", + "line": { + "color": "#d8576b", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines", + "name": "4-4", + "r": [ + 0.4, + 0.5, + 1.2, + 0.5, + 0.4, + 0.2, + 0.4, + 0.4, + 0.7, + 0.6, + 0.7, + 0.8, + 0.9, + 1, + 1, + 0.7, + 0.4 + ], + "showlegend": true, + "subplot": "polar", + "theta": [ + "N", + "NNE", + "NE", + "ENE", + "E", + "ESE", + "SE", + "SSE", + "S", + "SSW", + "SW", + "WSW", + "W", + "WNW", + "NW", + "NNW", + "N" + ], + "type": "scatterpolar" + }, + { + "hovertemplate": "strength=4-5
frequency=%{r}
direction=%{theta}", + "legendgroup": "4-5", + "line": { + "color": "#bd3786", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines", + "name": "4-5", + "r": [ + 0.3, + 0.3, + 0.6, + 0.2, + 0.1, + 0.1, + 0.05, + 0.1, + 0.1, + 0.2, + 0.3, + 0.4, + 0.9, + 0.9, + 0.9, + 0.3, + 0.3 + ], + "showlegend": true, + "subplot": "polar", + "theta": [ + "N", + "NNE", + "NE", + "ENE", + "E", + "ESE", + "SE", + "SSE", + "S", + "SSW", + "SW", + "WSW", + "W", + "WNW", + "NW", + "NNW", + "N" + ], + "type": "scatterpolar" + }, + { + "hovertemplate": "strength=5-6
frequency=%{r}
direction=%{theta}", + "legendgroup": "5-6", + "line": { + "color": "#9c179e", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines", + "name": "5-6", + "r": [ + 0.2, + 0.1, + 0.1, + 0.1, + 0.1, + 0.1, + 0.05, + 0.05, + 0.1, + 0.05, + 0.2, + 0.2, + 0.4, + 0.7, + 0.7, + 0.4, + 0.2 + ], + "showlegend": true, + "subplot": "polar", + "theta": [ + "N", + "NNE", + "NE", + "ENE", + "E", + "ESE", + "SE", + "SSE", + "S", + "SSW", + "SW", + "WSW", + "W", + "WNW", + "NW", + "NNW", + "N" + ], + "type": "scatterpolar" + }, + { + "hovertemplate": "strength=6+
frequency=%{r}
direction=%{theta}", + "legendgroup": "6+", + "line": { + "color": "#7201a8", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines", + "name": "6+", + "r": [ + 0.1, + 0.1, + 0.1, + 0.1, + 0.1, + 0.05, + 0.05, + 0.05, + 0.05, + 0.1, + 0.1, + 0.1, + 0.9, + 2.2, + 1.5, + 0.2, + 0.1 + ], + "showlegend": true, + "subplot": "polar", + "theta": [ + "N", + "NNE", + "NE", + "ENE", + "E", + "ESE", + "SE", + "SSE", + "S", + "SSW", + "SW", + "WSW", + "W", + "WNW", + "NW", + "NNW", + "N" + ], + "type": "scatterpolar" + } + ], + "layout": { + "legend": { + "title": { + "text": "strength" + }, + "tracegroupgap": 0 + }, + "margin": { + "t": 60 + }, + "polar": { + "angularaxis": { + "direction": "clockwise", + "rotation": 90 + }, + "domain": { + "x": [ + 0, + 1 + ], + "y": [ + 0, + 1 + ] + } + }, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#f2f5fa" + }, + "error_y": { + "color": "#f2f5fa" + }, + "marker": { + "line": { + "color": "rgb(17,17,17)", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "rgb(17,17,17)", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#A2B1C6", + "gridcolor": "#506784", + "linecolor": "#506784", + "minorgridcolor": "#506784", + "startlinecolor": "#A2B1C6" + }, + "baxis": { + "endlinecolor": "#A2B1C6", + "gridcolor": "#506784", + "linecolor": "#506784", + "minorgridcolor": "#506784", + "startlinecolor": "#A2B1C6" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "marker": { + "line": { + "color": "#283442" + } + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "line": { + "color": "#283442" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#506784" + }, + "line": { + "color": "rgb(17,17,17)" + } + }, + "header": { + "fill": { + "color": "#2a3f5f" + }, + "line": { + "color": "rgb(17,17,17)" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#f2f5fa", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#f2f5fa" + }, + "geo": { + "bgcolor": "rgb(17,17,17)", + "lakecolor": "rgb(17,17,17)", + "landcolor": "rgb(17,17,17)", + "showlakes": true, + "showland": true, + "subunitcolor": "#506784" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "dark" + }, + "paper_bgcolor": "rgb(17,17,17)", + "plot_bgcolor": "rgb(17,17,17)", + "polar": { + "angularaxis": { + "gridcolor": "#506784", + "linecolor": "#506784", + "ticks": "" + }, + "bgcolor": "rgb(17,17,17)", + "radialaxis": { + "gridcolor": "#506784", + "linecolor": "#506784", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "rgb(17,17,17)", + "gridcolor": "#506784", + "gridwidth": 2, + "linecolor": "#506784", + "showbackground": true, + "ticks": "", + "zerolinecolor": "#C8D4E3" + }, + "yaxis": { + "backgroundcolor": "rgb(17,17,17)", + "gridcolor": "#506784", + "gridwidth": 2, + "linecolor": "#506784", + "showbackground": true, + "ticks": "", + "zerolinecolor": "#C8D4E3" + }, + "zaxis": { + "backgroundcolor": "rgb(17,17,17)", + "gridcolor": "#506784", + "gridwidth": 2, + "linecolor": "#506784", + "showbackground": true, + "ticks": "", + "zerolinecolor": "#C8D4E3" + } + }, + "shapedefaults": { + "line": { + "color": "#f2f5fa" + } + }, + "sliderdefaults": { + "bgcolor": "#C8D4E3", + "bordercolor": "rgb(17,17,17)", + "borderwidth": 1, + "tickwidth": 0 + }, + "ternary": { + "aaxis": { + "gridcolor": "#506784", + "linecolor": "#506784", + "ticks": "" + }, + "baxis": { + "gridcolor": "#506784", + "linecolor": "#506784", + "ticks": "" + }, + "bgcolor": "rgb(17,17,17)", + "caxis": { + "gridcolor": "#506784", + "linecolor": "#506784", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "updatemenudefaults": { + "bgcolor": "#506784", + "borderwidth": 0 + }, + "xaxis": { + "automargin": true, + "gridcolor": "#283442", + "linecolor": "#506784", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#283442", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "#283442", + "linecolor": "#506784", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#283442", + "zerolinewidth": 2 + } + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "df = px.data.wind()\n", "print(df.head())\n", @@ -2885,11 +81968,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 112, "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " anywhere directed, or applied, seem to have been the effects of the\n", + " division of labour. The effects of the division of labour, in the general\n", + " business of society, will be more easily understood, by considering in\n", + " what manner it operates in some particular manufactures. It is commonly\n", + " supposed to be carried furthest in some very trifling ones; not perhaps\n", + " that it really is carried further in them than in others of more\n", + " importance: but in those trifling manufactures which are destined to\n", + " supply the small wants of but a small number of people, the whole number\n", + " of workmen must necessarily be small; and those employed in every\n", + " different branch of the work can often be collected into the same\n" + ] + } + ], "source": [ "# To run this example, download smith_won.txt from\n", "# https://github.com/aeturrell/coding-for-economists/blob/main/data/smith_won.txt\n", @@ -2902,9 +82002,64 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 113, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:29.657435\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from wordcloud import WordCloud\n", "\n", @@ -2924,9 +82079,64 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 114, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:32.469559\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# To run this example, download book_mask.png from\n", "# https://github.com/aeturrell/coding-for-economists/raw/main/data/book_mask.png\n", @@ -2957,9 +82167,333 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 115, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:32.845574\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import networkx as nx\n", "\n", @@ -2986,9 +82520,102 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 116, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Date NumberYearMonthDayDay of YearAnomaly
01880.0011880111-0.786
11880.0041880122-0.695
21880.0071880133-0.783
31880.011880144-0.725
41880.0121880155-0.802
\n", + "
" + ], + "text/plain": [ + " Date Number Year Month Day Day of Year Anomaly\n", + "0 1880.001 1880 1 1 1 -0.786\n", + "1 1880.004 1880 1 2 2 -0.695\n", + "2 1880.007 1880 1 3 3 -0.783\n", + "3 1880.01 1880 1 4 4 -0.725\n", + "4 1880.012 1880 1 5 5 -0.802" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# To run this example, download the pickle file from\n", "# https://github.com/aeturrell/coding-for-economists/blob/main/data/berkeley_data.pkl\n", @@ -3006,9 +82633,74 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 117, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "final_year = df[\"Year\"].max()\n", "first_year = df[\"Year\"].min()\n", @@ -3054,9 +82746,9588 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 118, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:35.330825\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "def f(x, y):\n", " return np.sin(x) ** 10 + np.cos(10 + y * x) * np.cos(x)\n", @@ -3083,9 +92354,66 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 119, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 119, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "contour_data = {\"x\": X.flatten(), \"y\": Y.flatten(), \"z\": Z.flatten()}\n", "(\n", @@ -3105,9 +92433,11250 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 120, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "type": "contour", + "x": [ + 0, + 0.050505050505050504, + 0.10101010101010101, + 0.15151515151515152, + 0.20202020202020202, + 0.25252525252525254, + 0.30303030303030304, + 0.35353535353535354, + 0.40404040404040403, + 0.45454545454545453, + 0.5050505050505051, + 0.5555555555555556, + 0.6060606060606061, + 0.6565656565656566, + 0.7070707070707071, + 0.7575757575757576, + 0.8080808080808081, + 0.8585858585858586, + 0.9090909090909091, + 0.9595959595959596, + 1.0101010101010102, + 1.0606060606060606, + 1.1111111111111112, + 1.1616161616161615, + 1.2121212121212122, + 1.2626262626262625, + 1.3131313131313131, + 1.3636363636363635, + 1.4141414141414141, + 1.4646464646464645, + 1.5151515151515151, + 1.5656565656565655, + 1.6161616161616161, + 1.6666666666666667, + 1.7171717171717171, + 1.7676767676767677, + 1.8181818181818181, + 1.8686868686868687, + 1.9191919191919191, + 1.9696969696969697, + 2.0202020202020203, + 2.0707070707070705, + 2.121212121212121, + 2.1717171717171717, + 2.2222222222222223, + 2.2727272727272725, + 2.323232323232323, + 2.3737373737373737, + 2.4242424242424243, + 2.474747474747475, + 2.525252525252525, + 2.5757575757575757, + 2.6262626262626263, + 2.676767676767677, + 2.727272727272727, + 2.7777777777777777, + 2.8282828282828283, + 2.878787878787879, + 2.929292929292929, + 2.9797979797979797, + 3.0303030303030303, + 3.080808080808081, + 3.131313131313131, + 3.1818181818181817, + 3.2323232323232323, + 3.282828282828283, + 3.3333333333333335, + 3.3838383838383836, + 3.4343434343434343, + 3.484848484848485, + 3.5353535353535355, + 3.5858585858585856, + 3.6363636363636362, + 3.686868686868687, + 3.7373737373737375, + 3.7878787878787876, + 3.8383838383838382, + 3.888888888888889, + 3.9393939393939394, + 3.9898989898989896, + 4.040404040404041, + 4.090909090909091, + 4.141414141414141, + 4.191919191919192, + 4.242424242424242, + 4.292929292929293, + 4.343434343434343, + 4.393939393939394, + 4.444444444444445, + 4.494949494949495, + 4.545454545454545, + 4.595959595959596, + 4.646464646464646, + 4.696969696969697, + 4.747474747474747, + 4.797979797979798, + 4.848484848484849, + 4.898989898989899, + 4.94949494949495, + 5 + ], + "y": [ + 0, + 0.050505050505050504, + 0.10101010101010101, + 0.15151515151515152, + 0.20202020202020202, + 0.25252525252525254, + 0.30303030303030304, + 0.35353535353535354, + 0.40404040404040403, + 0.45454545454545453, + 0.5050505050505051, + 0.5555555555555556, + 0.6060606060606061, + 0.6565656565656566, + 0.7070707070707071, + 0.7575757575757576, + 0.8080808080808081, + 0.8585858585858586, + 0.9090909090909091, + 0.9595959595959596, + 1.0101010101010102, + 1.0606060606060606, + 1.1111111111111112, + 1.1616161616161615, + 1.2121212121212122, + 1.2626262626262625, + 1.3131313131313131, + 1.3636363636363635, + 1.4141414141414141, + 1.4646464646464645, + 1.5151515151515151, + 1.5656565656565655, + 1.6161616161616161, + 1.6666666666666667, + 1.7171717171717171, + 1.7676767676767677, + 1.8181818181818181, + 1.8686868686868687, + 1.9191919191919191, + 1.9696969696969697, + 2.0202020202020203, + 2.0707070707070705, + 2.121212121212121, + 2.1717171717171717, + 2.2222222222222223, + 2.2727272727272725, + 2.323232323232323, + 2.3737373737373737, + 2.4242424242424243, + 2.474747474747475, + 2.525252525252525, + 2.5757575757575757, + 2.6262626262626263, + 2.676767676767677, + 2.727272727272727, + 2.7777777777777777, + 2.8282828282828283, + 2.878787878787879, + 2.929292929292929, + 2.9797979797979797, + 3.0303030303030303, + 3.080808080808081, + 3.131313131313131, + 3.1818181818181817, + 3.2323232323232323, + 3.282828282828283, + 3.3333333333333335, + 3.3838383838383836, + 3.4343434343434343, + 3.484848484848485, + 3.5353535353535355, + 3.5858585858585856, + 3.6363636363636362, + 3.686868686868687, + 3.7373737373737375, + 3.7878787878787876, + 3.8383838383838382, + 3.888888888888889, + 3.9393939393939394, + 3.9898989898989896, + 4.040404040404041, + 4.090909090909091, + 4.141414141414141, + 4.191919191919192, + 4.242424242424242, + 4.292929292929293, + 4.343434343434343, + 4.393939393939394, + 4.444444444444445, + 4.494949494949495, + 4.545454545454545, + 4.595959595959596, + 4.646464646464646, + 4.696969696969697, + 4.747474747474747, + 4.797979797979798, + 4.848484848484849, + 4.898989898989899, + 4.94949494949495, + 5 + ], + "z": [ + [ + -0.8390715290764524, + -0.838001621428668, + -0.834794626874751, + -0.8294587181387331, + -0.82200741524847, + -0.8124590714438839, + -0.8008349676384359, + -0.7871540533232603, + -0.7714213574387078, + -0.7536066035567922, + -0.7336102109118704, + -0.7112160808720948, + -0.6860343886705959, + -0.6574425111556884, + -0.6245371754188971, + -0.5861145021064914, + -0.5406953915042625, + -0.48661056434900574, + -0.42215215277952267, + -0.3457876409264766, + -0.25641878771589444, + -0.15365533266826975, + -0.03806355908518355, + 0.08865427544965221, + 0.2235902904676868, + 0.36270057085986207, + 0.5010254893655156, + 0.633050096362205, + 0.7531711673194201, + 0.8562182256079333, + 0.937963741128272, + 0.995555314169088, + 1.0278108865306774, + 1.0353358627183185, + 1.0204456832881517, + 0.9869048037625678, + 0.9395186106986907, + 0.8836343120310879, + 0.8246171715461283, + 0.7673682169134457, + 0.7159392527344117, + 0.6732829211313072, + 0.6411531879223834, + 0.620149031831076, + 0.609875050648763, + 0.609179988233837, + 0.6164292349330983, + 0.6297700162884312, + 0.6473567481177935, + 0.6675164624208095, + 0.6888475217635457, + 0.7102565182424427, + 0.7309465010895148, + 0.7503736835347636, + 0.7681897310892636, + 0.7841836054803764, + 0.7982321413872314, + 0.8102635131951746, + 0.8202336467632538, + 0.8281130732362413, + 0.8338807801586612, + 0.8375219205246919, + 0.8390271977190658, + 0.8383927726734055, + 0.835620263346437, + 0.8307167431844263, + 0.8236947713557897, + 0.8145727061550632, + 0.8033761123862428, + 0.790142007320367, + 0.7749287379144029, + 0.7578349289683535, + 0.7390305706353172, + 0.7188014338982626, + 0.6976044857289166, + 0.6761272178079004, + 0.655338751123548, + 0.6365165903251832, + 0.6212314483792966, + 0.6112748440844431, + 0.6085207354665372, + 0.6147228982294421, + 0.6312626780300084, + 0.6588748659339155, + 0.6973900280962853, + 0.7455370135716985, + 0.8008476144094145, + 0.8596957191607736, + 0.9174865566559351, + 0.9689900514058231, + 1.0087893614999186, + 1.0317953687418706, + 1.033764062711223, + 1.011749256796377, + 0.9644291642401011, + 0.8922614210011071, + 0.7974447115962238, + 0.6836923725325582, + 0.555849666663237, + 0.4194074617586595 + ], + [ + -0.8390715290764524, + -0.8366129988251555, + -0.8320225875745116, + -0.8253191642198389, + -0.8165269369177034, + -0.8056748928359072, + -0.7927948464215894, + -0.7779161342455376, + -0.7610539801164373, + -0.7421880649772751, + -0.7212284832891548, + -0.697968485661567, + -0.6720272277126383, + -0.6427906560806768, + -0.6093636166047565, + -0.5705498586353354, + -0.5248773837157624, + -0.47068345073454965, + -0.4062661331024048, + -0.33009822758880675, + -0.24108614752214536, + -0.13884360125791106, + -0.02394013042226073, + 0.10191948203431112, + 0.23582558308609014, + 0.373733255041246, + 0.5106826524256546, + 0.641159403324028, + 0.7595616654768931, + 0.8607211547703595, + 0.9404133465371969, + 0.9957896590451014, + 1.025672661182748, + 1.030673187134032, + 1.0131129001194559, + 0.9767632586284509, + 0.9264374167436606, + 0.8674910957064055, + 0.8052987966194479, + 0.7447714837617692, + 0.6899715700349838, + 0.6438629471351341, + 0.6082114385752826, + 0.5836284530973711, + 0.5697315524926907, + 0.5653829378231671, + 0.5689619067480857, + 0.5786299969467127, + 0.5925562938618169, + 0.6090828073584902, + 0.6268231351046373, + 0.644699308932161, + 0.6619299684067853, + 0.6779870123945781, + 0.692537830966517, + 0.7053870920556615, + 0.7164272602028414, + 0.7256020048014113, + 0.7328825530304865, + 0.7382544847063455, + 0.7417115245099422, + 0.7432531924264333, + 0.7428841305548645, + 0.7406139528157643, + 0.7364571878680433, + 0.7304332220934744, + 0.7225662756429488, + 0.7128856631375752, + 0.701427150029508, + 0.6882371494085653, + 0.6733825521285792, + 0.6569696301122757, + 0.6391750814257469, + 0.6202904059063521, + 0.6007772832227284, + 0.5813268674019171, + 0.5629108602889545, + 0.5468082379540973, + 0.5345900507264979, + 0.5280469995309881, + 0.5290510516604938, + 0.5393528052593818, + 0.5603292313764494, + 0.5927095430222244, + 0.636317522341296, + 0.6898740298591455, + 0.7509016700019486, + 0.8157639540465887, + 0.8798545573756544, + 0.9379306923981408, + 0.9845616679984102, + 1.0146434052342923, + 1.023915850938475, + 1.0094157272210897, + 0.9698031448545543, + 0.9055166672709811, + 0.818734981250624, + 0.7131505488120083, + 0.5935869335260945, + 0.4655125344229048 + ], + [ + -0.8390715290764524, + -0.8352189329048343, + -0.8292288945099806, + -0.8211312819961111, + -0.8109614571971007, + -0.7987596652308137, + -0.7845690315137486, + -0.7684302044930735, + -0.7503696679938684, + -0.7303782579023009, + -0.7083770651020914, + -0.6841701246382893, + -0.6573871113088718, + -0.627424172823741, + -0.5933959857674984, + -0.5541157085452365, + -0.5081202797691875, + -0.45375537440102476, + -0.3893269134157976, + -0.31331492683056383, + -0.22463240300543746, + -0.12289892531990215, + -0.008689170923901923, + 0.11628771821627654, + 0.24911854091264607, + 0.38575567367869656, + 0.5212374361357949, + 0.6500485177351382, + 0.7665870559539968, + 0.8656856834744412, + 0.9431217446459659, + 0.9960494905029643, + 1.0232952942554006, + 1.0254747699639688, + 1.0049153364131325, + 0.9653951795541742, + 0.9117351450818779, + 0.8492995982711334, + 0.7834726209613986, + 0.7191756751704216, + 0.6604825648785496, + 0.6103694386141301, + 0.5706152110172654, + 0.5418451809872465, + 0.5236915605148105, + 0.5150319182879772, + 0.5142615908872238, + 0.5195587766858558, + 0.5291097908199922, + 0.5412743859755516, + 0.5546843569648219, + 0.5682803255305385, + 0.5812998497965195, + 0.5932340093556507, + 0.603769568550305, + 0.612730692583836, + 0.6200293940328104, + 0.6256288674131613, + 0.6295197672404399, + 0.6317069270719533, + 0.6322030752532453, + 0.631026408474979, + 0.6281998415731435, + 0.6237507803789191, + 0.6177109882109306, + 0.6101164531513595, + 0.6010072893841354, + 0.590427924472166, + 0.5784283838612012, + 0.5650684176769885, + 0.5504272629670347, + 0.5346224815190219, + 0.5178409421149578, + 0.5003831362667975, + 0.4827184995669154, + 0.46554465293507247, + 0.44983842646476146, + 0.436882540111686, + 0.4282503621198879, + 0.4257334480671571, + 0.43120312385462634, + 0.4464078220949035, + 0.4727208009223459, + 0.5108659948102251, + 0.5606603286400471, + 0.620816219100945, + 0.688846237719308, + 0.7611022767603981, + 0.832964814943502, + 0.8991763043800199, + 0.9542897496272635, + 0.9931832485902934, + 1.0115774369184134, + 1.0064882738675245, + 0.9765536975634252, + 0.9221887363310881, + 0.8455472320838007, + 0.7502955501790344, + 0.641229968840557, + 0.5237904811735441 + ], + [ + -0.8390715290764524, + -0.8338194327380183, + -0.8264136203882839, + -0.816895316697826, + -0.805311555459405, + -0.7917145134403428, + -0.7761594496064788, + -0.7586992882183468, + -0.7393728699511793, + -0.718183406022369, + -0.6950643175147834, + -0.669831860147966, + -0.6421277549816533, + -0.6113599564797692, + -0.5766546434634506, + -0.5368361074267883, + -0.490451986968704, + -0.4358581609511756, + -0.3713701964438847, + -0.29547715158977444, + -0.20710036658685865, + -0.10586704417164483, + 0.007641305420067412, + 0.13170954452626688, + 0.2634193618409064, + 0.3987189543429255, + 0.5326434342874315, + 0.6596752938640198, + 0.7742115174666286, + 0.8710846588828084, + 0.9460730835184559, + 0.9963331847594358, + 1.020694616235579, + 1.0197774225629093, + 0.9959146104601794, + 0.9528911135133058, + 0.8955356820720832, + 0.8292217353460588, + 0.7593435455482881, + 0.6908338835497402, + 0.627778957587836, + 0.5731683876827842, + 0.5287955939831934, + 0.4953013767116258, + 0.47233440189755294, + 0.4587895970936861, + 0.45308051001923805, + 0.4534043453941155, + 0.4579671584964528, + 0.4651491103783235, + 0.4736030029184472, + 0.48229099037432466, + 0.4904726120947864, + 0.49766129764241535, + 0.5035664471553858, + 0.5080350618767833, + 0.5110021027243213, + 0.5124537348698018, + 0.5124035140963495, + 0.5108790127596704, + 0.5079154414235628, + 0.5035531276316129, + 0.4978366706106771, + 0.4908146190805391, + 0.4825392431346816, + 0.47306630770845637, + 0.46245488128899004, + 0.45076743211966397, + 0.43807102356111227, + 0.4244413540465582, + 0.4099724351495422, + 0.3947953482567867, + 0.37910914905864634, + 0.3632251092473675, + 0.3476219678908915, + 0.3330051070865453, + 0.32035751490739417, + 0.3109664101796311, + 0.3064079502667413, + 0.30847472500560397, + 0.31903730036523487, + 0.33984152150935, + 0.37225620449333924, + 0.4169989663664474, + 0.47387852508875083, + 0.5415971934825361, + 0.6176555416366437, + 0.6983915690998943, + 0.7791699838749896, + 0.8547156096670476, + 0.9195619922988635, + 0.9685659750187822, + 0.9974251843153699, + 1.0031308641515744, + 0.9842945864688014, + 0.9413034245152413, + 0.8762817448557376, + 0.7928649923078397, + 0.6958171607160755, + 0.5905446823788862 + ], + [ + -0.8390715290764524, + -0.8324145074303784, + -0.8235768384782035, + -0.8126115163708685, + -0.799577819865785, + -0.7845405834093379, + -0.7675680704341684, + -0.7487264876765265, + -0.7280681649857174, + -0.7056099359440557, + -0.6812989018333669, + -0.6549649795591065, + -0.6262634543852714, + -0.5946156692851297, + -0.5591609368195887, + -0.5187363484048495, + -0.47190193011365805, + -0.4170254580124245, + -0.35243382948572133, + -0.27662679107168314, + -0.18853565638846975, + -0.08779681591604721, + 0.024999886019830664, + 0.1481318962109981, + 0.27867446780228355, + 0.41257039989896227, + 0.5448504981382534, + 0.6699940885224629, + 0.7823961741682621, + 0.8768885520089318, + 0.9492500893147785, + 0.9966389689037874, + 1.0178879446012747, + 1.013621489341397, + 0.9861783776483236, + 0.9393506556379603, + 0.8779755299182903, + 0.8074362123759181, + 0.7331380906458289, + 0.6600263537034368, + 0.5922009041270533, + 0.5326662997175935, + 0.4832321027587877, + 0.4445564146788674, + 0.4163063104198857, + 0.39739617685742656, + 0.3862600083887071, + 0.3811163751546271, + 0.3801935426562072, + 0.38189464795397615, + 0.38489615090765106, + 0.38818445742082014, + 0.3910438596766506, + 0.39301294496052763, + 0.3938265762658034, + 0.39335741999610885, + 0.3915661990397946, + 0.3884648296041532, + 0.3840924980098596, + 0.37850217571650935, + 0.3717541291046806, + 0.3639132859403415, + 0.3550482777878463, + 0.3452310060214296, + 0.33453630287855196, + 0.3230415955507048, + 0.3108266074441915, + 0.29797334933887576, + 0.28456721257676015, + 0.2707009101810512, + 0.25648405987747425, + 0.2420618499531816, + 0.22764585759526468, + 0.2135582095137425, + 0.20028675771466403, + 0.18854418540701257, + 0.1793189064152144, + 0.1739016329728249, + 0.1738700333867608, + 0.18101618300593583, + 0.1972080723273268, + 0.22418688215532573, + 0.26331465525077224, + 0.315300114416404, + 0.37994095857331134, + 0.45592636116131374, + 0.5407416453626893, + 0.6307074775332097, + 0.7211691758661913, + 0.80683015572427, + 0.8822005824974911, + 0.9421120016260467, + 0.9822348872803248, + 0.9995315474556654, + 0.9925829131435538, + 0.9617437979120054, + 0.9091047834985562, + 0.8382661053751449, + 0.7539552308809534, + 0.6615408623921005 + ], + [ + -0.8390715290764524, + -0.8310041661228839, + -0.8207186226082739, + -0.808280131862201, + -0.7937608473046082, + -0.7772390420293576, + -0.7587969063126758, + -0.7385149822364535, + -0.7164602603053304, + -0.6926644738032328, + -0.6670897738709505, + -0.6395811863773837, + -0.6098090719131326, + -0.5772097211984483, + -0.5409371723135055, + -0.49984292511556705, + -0.4525010024944761, + -0.39729267197770624, + -0.3325577246446425, + -0.25680811237854717, + -0.168986577535562, + -0.06874007728588116, + 0.043331921545790686, + 0.16549826582297605, + 0.2948267054964453, + 0.4272537027256957, + 0.5578049569010751, + 0.6809559774724467, + 0.7910992938713491, + 0.8830656192220342, + 0.9526341673930371, + 0.9969649319773811, + 1.0148939685073473, + 1.0070505620763917, + 0.9757798219195413, + 0.924881655941612, + 0.8592026564556279, + 0.7841369340399955, + 0.7051022692696876, + 0.6270577117564309, + 0.5541184580799559, + 0.4893057513690834, + 0.4344471809258543, + 0.39022015980130953, + 0.3563122948139628, + 0.33165965357524635, + 0.31471898189725833, + 0.30373258734058806, + 0.2969533679885842, + 0.29280989199494145, + 0.29000474675128435, + 0.2875510550580837, + 0.284760303731175, + 0.28119863671171885, + 0.27662871637138686, + 0.27095112884337264, + 0.264154512191164, + 0.2562785663561065, + 0.2473900016519001, + 0.23756892465666649, + 0.2269022186170547, + 0.2154807808858688, + 0.20339843725919926, + 0.1907513814638369, + 0.1776377118377166, + 0.16415697379407004, + 0.15040974278981362, + 0.13649750110437026, + 0.122523621126403, + 0.10859720159960991, + 0.09484255133187922, + 0.08141776181533256, + 0.06854543888856653, + 0.056556783709926235, + 0.045946696343853954, + 0.03743281735061977, + 0.03200637112601751, + 0.030958686207618206, + 0.03586581434236958, + 0.048515952017685236, + 0.07077092851856244, + 0.10436347061612543, + 0.15064487347240874, + 0.21031082781431493, + 0.28314373499470846, + 0.36781523429338236, + 0.46179091693319974, + 0.5613695681271603, + 0.661872534069688, + 0.7579772351504892, + 0.8441658960496058, + 0.9152402621090123, + 0.9668392431881762, + 0.9958919224344405, + 1.0009444572632924, + 0.982315458475789, + 0.9420580026737984, + 0.8837336514539871, + 0.8120301694645475, + 0.7322756732497648 + ], + [ + -0.8390715290764524, + -0.8295884179917421, + -0.8178390471648553, + -0.8039014168051797, + -0.7878612433293076, + -0.7698110769488653, + -0.7498480116679863, + -0.7280680273670521, + -0.7045539893683124, + -0.6793538417731378, + -0.6524461781209361, + -0.6236925910323969, + -0.5927800227739985, + -0.5591612496592826, + -0.5220065873301041, + -0.48018349292615436, + -0.4322815144429353, + -0.37669690143973766, + -0.31178377470567975, + -0.23606765655532563, + -0.1485039964673775, + -0.04875149494356368, + 0.0625796979788848, + 0.18374889765620495, + 0.3118155605163261, + 0.44270917361243034, + 0.571449853718142, + 0.6925089873893833, + 0.8002765008327222, + 0.8895820758647072, + 0.9562055111415823, + 0.9973090369159582, + 1.0117326243375844, + 1.0001111712264679, + 0.964797105671322, + 0.9095993602916149, + 0.8393752483228605, + 0.7595312783800976, + 0.6754992776509198, + 0.5922539529991664, + 0.5139277216575154, + 0.4435605544138096, + 0.3830002098797938, + 0.3329456379950241, + 0.293107267548938, + 0.2624451826237616, + 0.23944124171716447, + 0.22236385572930167, + 0.20949290436904391, + 0.19928469703398113, + 0.19047019755561403, + 0.18209141077223623, + 0.17348907697267496, + 0.1642588269183888, + 0.15419290180119619, + 0.14322141465510074, + 0.13136233274136272, + 0.11868434057062426, + 0.10528264106527566, + 0.09126519445619061, + 0.07674595277665938, + 0.06184195322443122, + 0.04667209100449374, + 0.03135642064369396, + 0.016015558549824126, + 0.0007700961219078697, + -0.014259941169531627, + -0.028955334749028304, + -0.043196800599953324, + -0.05686125655052266, + -0.06981243408554874, + -0.08188239267890912, + -0.0928408659959102, + -0.10235124921768535, + -0.10991555375010684, + -0.1148154135371012, + -0.1160612788901828, + -0.11236592232610658, + -0.10215983437830722, + -0.0836638054939384, + -0.05502742982278673, + -0.014531820717270688, + 0.039158091659919486, + 0.10671943063507916, + 0.18791374275169787, + 0.2813895911272088, + 0.3845873467033818, + 0.49377851843950754, + 0.6042552190396059, + 0.7106637871155673, + 0.8074536360155519, + 0.8893920982324746, + 0.9520822060702908, + 0.9924158454222193, + 1.0089008093347056, + 1.0018163366213153, + 0.97317528998519, + 0.926498346393188, + 0.8664318910153983, + 0.7982623458558566 + ], + [ + -0.8390715290764524, + -0.8281672722483386, + -0.8149381870902017, + -0.7994756276046996, + -0.7818796220953372, + -0.7622578963800528, + -0.7407234825550227, + -0.7173889535994655, + -0.6923543098707885, + -0.6656850544691308, + -0.6373776417425313, + -0.607311701344105, + -0.5751922605503235, + -0.5404900985469473, + -0.5023933205312677, + -0.45978682845417135, + -0.41127713952249517, + -0.35527686744386305, + -0.2901557648383152, + -0.21445412929536112, + -0.12714120858331945, + -0.027888408662932074, + 0.08268261830933676, + 0.20282099335878823, + 0.3295773840655355, + 0.45887398440197, + 0.5857251960832962, + 0.7045983422819154, + 0.809881002018043, + 0.896402281033656, + 0.9599432179047843, + 0.9976691332800108, + 1.0084249629516708, + 0.9928524564374409, + 0.9533127822365801, + 0.8936254924736005, + 0.8186603780277848, + 0.733838251007237, + 0.6446070243878685, + 0.5559592184327462, + 0.4720467257692429, + 0.3959305782921711, + 0.32948109480793863, + 0.2734211879604724, + 0.22748654566957693, + 0.1906636924898092, + 0.16146198520922528, + 0.1381782594855761, + 0.11912160773683617, + 0.10277819504877841, + 0.08790933324125633, + 0.07358771184442535, + 0.059184932850963164, + 0.044327502804614866, + 0.028838387460539582, + 0.012678106434822466, + -0.004105451722915355, + -0.021414333444312955, + -0.03912488460964108, + -0.05710167504115739, + -0.07520442423150374, + -0.09329106411742943, + -0.11121911738201615, + -0.1288465414096295, + -0.1460324642608662, + -0.1626379025300774, + -0.17852642487016152, + -0.19356450644371553, + -0.20762076023628523, + -0.22056229748960257, + -0.23224542154234848, + -0.2424972125127213, + -0.25108493166959855, + -0.2576720543039254, + -0.2617632569866225, + -0.2626454431239427, + -0.25933694248219685, + -0.2505610090049878, + -0.23476119479230265, + -0.21017389633053457, + -0.17496680883121485, + -0.12744157862372563, + -0.06628602419904647, + 0.009151823835513356, + 0.09860619506515866, + 0.20069628762026093, + 0.3128311863485334, + 0.4312493312983906, + 0.5512081324816483, + 0.6673177511315516, + 0.7739901169398841, + 0.8659539493862205, + 0.9387727229828707, + 0.9892980123872384, + 1.0159967431090788, + 1.0191069320913129, + 1.0006000716031462, + 0.9639555281395158, + 0.913778646567516, + 0.8553152895541134 + ], + [ + -0.8390715290764524, + -0.8267407381391808, + -0.8120161178805088, + -0.7950030234221843, + -0.7758166062962405, + -0.7545807289023194, + -0.7314254561666884, + -0.7064811654652777, + -0.6798663016823542, + -0.6516653152520435, + -0.6218939683623571, + -0.5904514126764168, + -0.5570622622522163, + -0.5212167963626246, + -0.48212238107630717, + -0.43868278744556966, + -0.3895228584482933, + -0.33307284069062104, + -0.2677192803106523, + -0.19201828656273695, + -0.10495379956903983, + -0.006210666842856594, + 0.1035773933118174, + 0.2226489280149269, + 0.3480456314187641, + 0.475682423393858, + 0.6005682196109787, + 0.7171667231999279, + 0.8198638256916132, + 0.9034889325129368, + 0.9638254113238224, + 0.9980429706936766, + 1.0049930095110509, + 0.9853258185716512, + 0.9414131753584022, + 0.8770872846591299, + 0.7972325961372575, + 0.7072865358157321, + 0.6127155214804882, + 0.5185323918885278, + 0.42891108201027, + 0.3469362879074739, + 0.27450350067365903, + 0.21236218856578032, + 0.16027584321887658, + 0.11725989608228651, + 0.08185356019287506, + 0.05238431493996809, + 0.02719251495245056, + 0.004796030835473307, + -0.016011857091042303, + -0.036126412250343125, + -0.05614409451266327, + -0.07640675747236778, + -0.09706028392950361, + -0.11811368101022472, + -0.13948945333625962, + -0.16106109249680084, + -0.18267762323683506, + -0.20417770469683444, + -0.2253967267938909, + -0.24617003646209418, + -0.2663344711757129, + -0.28572934885943274, + -0.3041973395787625, + -0.321585307951011, + -0.3377450891971417, + -0.3525339432925504, + -0.36581387241999747, + -0.37744805332344217, + -0.3872915882690493, + -0.3951731315481243, + -0.4008643199548134, + -0.4040358157246352, + -0.4042022875858168, + -0.40066341369324837, + -0.39245304207070897, + -0.37831263293412093, + -0.35670656091116504, + -0.32589457348868894, + -0.2840701418836985, + -0.22956299471589392, + -0.16109120560425458, + -0.0780350855637397, + 0.01930545026850755, + 0.12951376456929364, + 0.2499616013343898, + 0.376848750498616, + 0.5053928686220145, + 0.6301634747746011, + 0.7455311899591882, + 0.8461829869863462, + 0.927640389377708, + 0.9867130539603526, + 1.0218262615560845, + 1.0331768986931948, + 1.022696082408395, + 0.993823798449529, + 0.951127241731698, + 0.8998155876963017 + ], + [ + -0.8390715290764524, + -0.8253088249458314, + -0.80907291558395, + -0.7904838661604049, + -0.7696728270988304, + -0.7467808232624262, + -0.7219561103332918, + -0.6953481404111266, + -0.6670951647308558, + -0.6373020124320539, + -0.6060052316961795, + -0.5731249977857031, + -0.5384070128810345, + -0.5013625336587507, + -0.4612196167324204, + -0.4169022610731596, + -0.3670549008302161, + -0.31012656582487297, + -0.24452161040953888, + -0.16881281539988513, + -0.0819995007632413, + 0.016219545175452266, + 0.12519824079639438, + 0.24316447595280336, + 0.3671511112317993, + 0.49306616246953067, + 0.6159136639923494, + 0.730154539999425, + 0.8301740711150152, + 0.9108032707935233, + 0.9678293693764438, + 0.9984282129081731, + 1.0014596168170784, + 0.9775845557249836, + 0.9291877303239071, + 0.8601164639989425, + 0.7752724604545469, + 0.6801124595545762, + 0.5801241617396873, + 0.48034355137000684, + 0.3849694517886233, + 0.29711305637312213, + 0.2186978157643012, + 0.1505024612549217, + 0.09232088117130274, + 0.04319985740876969, + 0.0017107183341070129, + -0.03378637312389175, + -0.06491801383742982, + -0.09313312828686676, + -0.11960528733175431, + -0.1451968767780675, + -0.17047196639579285, + -0.1957407232984982, + -0.22111826188855116, + -0.24658395037253134, + -0.27203199083371626, + -0.29730910970760394, + -0.3222392976919304, + -0.34663809607763346, + -0.3703198682851711, + -0.3931011900698439, + -0.41480253462544664, + -0.4352494005643203, + -0.4542733071163114, + -0.4717127445195505, + -0.48741404164551727, + -0.5012318945347958, + -0.5130287403668852, + -0.5226712278898771, + -0.5300209875549013, + -0.534916257944009, + -0.5371412966967452, + -0.5363823843148398, + -0.5321727465130697, + -0.5238334790257272, + -0.5104226107578944, + -0.4907084300410741, + -0.4631846525172838, + -0.42614272761170086, + -0.3778100205353564, + -0.31655216105500267, + -0.2411249311209865, + -0.15094794330167086, + -0.04636178072471547, + 0.07117512320356284, + 0.19899183685908606, + 0.33324485236148726, + 0.4691081708917366, + 0.6011075689419387, + 0.7235701122282809, + 0.8311396825300191, + 0.9192954543465826, + 0.9848057543284496, + 1.026055826165784, + 1.0432040821402158, + 1.0381449916933545, + 1.0142839756271398, + 0.976155993122252, + 0.9289405485971365 + ], + [ + -0.8390715290764524, + -0.8238715419848529, + -0.8061086567986956, + -0.7859184204481466, + -0.763448924077478, + -0.738859448171388, + -0.7123176630124352, + -0.6839934276900872, + -0.6540462168371718, + -0.6226027153750904, + -0.5897217689949321, + -0.5553460963722189, + -0.5192439895170666, + -0.48094913974048953, + -0.43971168091583196, + -0.3944771307194561, + -0.34391068483605947, + -0.2864811829538529, + -0.22061164876912445, + -0.1448922102003729, + -0.05833803894150699, + 0.03933788349495429, + 0.14747709270798956, + 0.264297045501371, + 0.3868222447671011, + 0.5109545348534945, + 0.6316940599252302, + 0.7435002138757592, + 0.8407591680814137, + 0.91830529106335, + 0.9719316573662926, + 0.998822452401883, + 0.9978483131380527, + 0.9696834858090777, + 0.9167283416345237, + 0.8428482034136089, + 0.752965014577661, + 0.6525578883726555, + 0.5471389081309752, + 0.4417703097062647, + 0.34067887971910926, + 0.2470053148539943, + 0.16270392348488066, + 0.08858545080866362, + 0.02447674561623278, + -0.030541722748718622, + -0.07786443955772349, + -0.11909679162553281, + -0.15583090203064984, + -0.18948144195733507, + -0.22118819547566707, + -0.2517804743588425, + -0.2817902318404958, + -0.3114967174659806, + -0.34098556348935133, + -0.3702083210865689, + -0.3990332616676399, + -0.42728327847143777, + -0.4547608258660129, + -0.48126239179722285, + -0.5065859407026763, + -0.5305344589739911, + -0.5529177779130168, + -0.5735538209829248, + -0.5922696986349627, + -0.6089027377456994, + -0.623301406900289, + -0.635325880528838, + -0.6448474258934854, + -0.6517448635744794, + -0.6558953044747963, + -0.6571557198617258, + -0.6553322727373218, + -0.65013621916169, + -0.641128705599241, + -0.6276615467559008, + -0.6088261205091109, + -0.5834265055536416, + -0.549994441297258, + -0.5068614081174078, + -0.4522965662063544, + -0.3847088456553666, + -0.30289856140173255, + -0.20633080663053863, + -0.09539229707552976, + 0.028412053675580085, + 0.16236479751664334, + 0.3025761899396712, + 0.4441745943532067, + 0.581641068101764, + 0.7092591948118555, + 0.8216309262068267, + 0.9141953681604194, + 0.9836829418348731, + 1.0284434405039213, + 1.048602561222453, + 1.0460250597065135, + 1.0240898951159854, + 0.9873090500836811, + 0.940842751263923 + ], + [ + -0.8390715290764524, + -0.8224288986077454, + -0.8031234186709238, + -0.781306953624712, + -0.7571455451475348, + -0.7308178920981001, + -0.7025123717695226, + -0.672420647230153, + -0.6407248915009006, + -0.6075751705138338, + -0.5730541743192716, + -0.5371287043426565, + -0.4995911449462143, + -0.4599990586649133, + -0.4176259987056377, + -0.37144022131010884, + -0.3201287548752543, + -0.2621811465416808, + -0.19603979031794969, + -0.12031264473739717, + -0.03403098090804252, + 0.06307803024137318, + 0.17034380942233152, + 0.28597392188828175, + 0.40698533406375953, + 0.5292748223813947, + 0.647840025756329, + 0.7571404693256495, + 0.851565144962196, + 0.9259539620083744, + 0.9761082650834358, + 0.9992232254258517, + 0.9941831455384322, + 0.9616785583713254, + 0.9041286622663582, + 0.8254200449385575, + 0.7304982286588365, + 0.6248680750598694, + 0.5140694214384538, + 0.4031940807007506, + 0.29650003982198936, + 0.1971606034281712, + 0.10716386527813165, + 0.027355290449997793, + -0.042402871901066036, + -0.10299433482611788, + -0.15577761929634332, + -0.2023222769820353, + -0.2441850040710078, + -0.2827457333524967, + -0.3191104782507257, + -0.35407602376099007, + -0.38814331104645683, + -0.42156235572260586, + -0.45439158818745046, + -0.486557632223475, + -0.5179063351739132, + -0.5482408829765048, + -0.5773469356711028, + -0.6050072771256076, + -0.6310094147204091, + -0.6551492596315133, + -0.6772330613609842, + -0.6970787421750124, + -0.7145170537560527, + -0.7293926423042421, + -0.741564982706185, + -0.7509089243491096, + -0.7573140323420452, + -0.7606809747531508, + -0.7609121590423886, + -0.7578931745672709, + -0.7514619700118744, + -0.7413645751182598, + -0.7271996937390655, + -0.70835925381642, + -0.6839770522085187, + -0.6529016214810688, + -0.6137108998577127, + -0.5647840042563737, + -0.5044388465991566, + -0.431133888138629, + -0.3437194076350609, + -0.24171053929153816, + -0.12554375529520778, + 0.0032269244530711005, + 0.14183596253275832, + 0.28634690768917775, + 0.4318431608373958, + 0.5727629162861478, + 0.7033493402242111, + 0.8181667470608947, + 0.9126197058906715, + 0.983407505496413, + 1.0288524961463374, + 1.0490568851044528, + 1.0458661320959783, + 1.0226443094336282, + 0.9838931103128276, + 0.9347672288783098 + ], + [ + -0.8390715290764524, + -0.8209809042008858, + -0.8001172788928065, + -0.7766497357242697, + -0.7507633464978885, + -0.722657463059763, + -0.6925425332489727, + -0.6606334884801844, + -0.6271367356378935, + -0.5922272972653937, + -0.5560132916470564, + -0.5184871627922901, + -0.47946689084101135, + -0.43853532456428257, + -0.3949907318736895, + -0.3478252532662089, + -0.29574871740695724, + -0.23727214183281342, + -0.1708578250617638, + -0.09513184024853383, + -0.009141573299680128, + 0.0873718838043997, + 0.19372640056374718, + 0.308120517443157, + 0.42756483804712614, + 0.5479525511072016, + 0.6642805725314954, + 0.771010634153711, + 0.862536903899227, + 0.9337074502279562, + 0.9803347473333027, + 0.9996280274006678, + 0.9904885197534733, + 0.9536264584014571, + 0.8914833997127114, + 0.8079708041990552, + 0.7080614155038849, + 0.5972894761450426, + 0.48122615321421525, + 0.3649963076958718, + 0.25289244397157495, + 0.14812358797678404, + 0.052714478799891856, + -0.032452141324304795, + -0.107476416490728, + -0.17320443330932178, + -0.23095738173063046, + -0.28226809562067534, + -0.32865748448650245, + -0.3714709409778828, + -0.4117814952414651, + -0.45035480848544757, + -0.4876628499518728, + -0.523929094816387, + -0.5591881289406821, + -0.5933456743644903, + -0.626229846613648, + -0.6576294750126129, + -0.6873194196719079, + -0.7150753771144703, + -0.7406816086812706, + -0.7639347209514448, + -0.7846456706281566, + -0.8026411385718836, + -0.8177646945373828, + -0.829877838335798, + -0.8388608777689259, + -0.8446133858897209, + -0.8470534215765656, + -0.8461137645067824, + -0.8417323684344006, + -0.8338335906219837, + -0.8222971274903261, + -0.8069134673133321, + -0.7873281886511468, + -0.7629821920165986, + -0.7330600047897994, + -0.69646228878147, + -0.6518201340970119, + -0.5975664418440704, + -0.5320731386268416, + -0.4538525204461589, + -0.36180810481642156, + -0.25550725006179864, + -0.13543721975250395, + -0.0032009780103152696, + 0.13838924827055893, + 0.28535297070904997, + 0.4327325899456257, + 0.5749287052239188, + 0.7061506421190161, + 0.8209329561403171, + 0.91465484174271, + 0.9839948725732868, + 1.027259588791169, + 1.0445405061519721, + 1.0376776910778422, + 1.0100352646231159, + 0.9661205172881451, + 0.9110993569824295 + ], + [ + -0.8390715290764524, + -0.8195275681854667, + -0.7970903157004915, + -0.7719470394600361, + -0.7443029925226448, + -0.714379488409127, + -0.6824104826362962, + -0.6486357092337057, + -0.6132874072705503, + -0.5765671838578297, + -0.5386102078182298, + -0.49943614671537706, + -0.45889008051173785, + -0.4165815363205408, + -0.37183474297511876, + -0.32366679314580976, + -0.2708111749793971, + -0.21180099896157617, + -0.14511882892593475, + -0.06940892988649683, + 0.016265421981018907, + 0.11214975419576736, + 0.2175512516496041, + 0.33066062824529574, + 0.44848365554367725, + 0.5669117940478394, + 0.6809434161127937, + 0.7850449461010858, + 0.8736185017395572, + 0.9415233490371463, + 0.9845863670115387, + 1.000034328568511, + 0.9867890376755388, + 0.9455842049302702, + 0.8788876041001445, + 0.7906394647410204, + 0.6858436353578795, + 0.5700675582798141, + 0.4489174313041322, + 0.3275546918455045, + 0.2103096624324996, + 0.10043010848094591, + -0.000019904237446188855, + -0.09011806470487826, + -0.16992505931246904, + -0.24024798630308453, + -0.3023698768609557, + -0.3577865948348687, + -0.40798362334991584, + -0.45427281978886186, + -0.49769590710335265, + -0.538989790751631, + -0.5786005424791864, + -0.6167288855878272, + -0.6533900648752687, + -0.6884741125442645, + -0.7217973187480903, + -0.7531407357510208, + -0.782275647384457, + -0.808978494011628, + -0.833038685645438, + -0.8542624312292585, + -0.8724747555893153, + -0.8875208480914114, + -0.8992671642110773, + -0.9076023656991765, + -0.9124380599617334, + -0.9137090817919805, + -0.9113725023367033, + -0.905403617583531, + -0.8957861217753622, + -0.8824930253940348, + -0.8654552498488818, + -0.8445167120352662, + -0.8193782300437104, + -0.7895373403339763, + -0.7542361694197088, + -0.712433492684495, + -0.662818567367355, + -0.60388204578688, + -0.5340527153875451, + -0.4518983669535755, + -0.35637617358508633, + -0.24710484321048828, + -0.1246202264054721, + 0.009429330974976058, + 0.15218985059061263, + 0.2996431266354815, + 0.44679825529909445, + 0.5880272951703497, + 0.7175161144872739, + 0.8297811799959615, + 0.9201892142192414, + 0.9854121444843239, + 1.0237558573464651, + 1.035317331197303, + 1.0219482896916823, + 0.9870307376475047, + 0.9350960604606076, + 0.871340408781137 + ], + [ + -0.8390715290764524, + -0.8180689000174354, + -0.7940426078720654, + -0.7671991402083103, + -0.7377651557519684, + -0.7059853146185815, + -0.6721185931111275, + -0.636431134430896, + -0.5991826731718508, + -0.5606030830677039, + -0.5208562453216895, + -0.4799906534527115, + -0.4378799912437869, + -0.3941618316188906, + -0.34818755854526917, + -0.2990002030469148, + -0.24535765861033842, + -0.18581560490924764, + -0.11887705188754522, + -0.043204319854251355, + 0.042123896275355316, + 0.13734056296378394, + 0.24174335584785672, + 0.3535166963327122, + 0.46966341414079227, + 0.586075479834566, + 0.6977552949901114, + 0.7991768646423643, + 0.8847534352801488, + 0.949358910404477, + 0.9888382398873723, + 1.0004395898025462, + 0.983109333534231, + 0.9376087472629231, + 0.8664359537290917, + 0.7735640710243442, + 0.6640321028190074, + 0.5434446134330474, + 0.41744656531069063, + 0.29123945739041224, + 0.16919460619572452, + 0.05460132376153734, + -0.05043461682488018, + -0.14494943703490623, + -0.22896300100494177, + -0.3032426366725283, + -0.3690330610405492, + -0.42779367777631877, + -0.48097575163488304, + -0.5298595374697058, + -0.5754581281545026, + -0.6184831071907706, + -0.6593588438419848, + -0.6982682622424013, + -0.7352129646728679, + -0.7700737175492054, + -0.8026621062993373, + -0.8327591859043961, + -0.8601410568906712, + -0.8845938554206269, + -0.9059215889092647, + -0.9239499443176782, + -0.9385282401462962, + -0.9495306660541101, + -0.9568572315913109, + -0.9604345103920566, + -0.9602161414955904, + -0.9561828323629663, + -0.9483410502723063, + -0.9367186565401765, + -0.92135469148166, + -0.9022798725853491, + -0.8794847411800394, + -0.8528742741829343, + -0.8222112958921425, + -0.7870557840489325, + -0.7467122173246524, + -0.7002011011279927, + -0.6462722631765038, + -0.5834752294027664, + -0.5102954313382028, + -0.42535455089981183, + -0.3276603897365623, + -0.21687853041494112, + -0.09358747562450603, + 0.040526440812630404, + 0.18257632726035294, + 0.3285165148141461, + 0.4733344236309709, + 0.6113865181604348, + 0.7368494041023402, + 0.8442368191111381, + 0.9289194416666828, + 0.9875799394649651, + 1.0185417693767278, + 1.0219263005976569, + 0.9996164028976179, + 0.9550318613466547, + 0.8927482987398604, + 0.8180123281082662 + ], + [ + -0.8390715290764524, + -0.816604909187432, + -0.7909742347255034, + -0.7624063159923462, + -0.7311505167820703, + -0.6974763070611499, + -0.6616692752913766, + -0.624023654939193, + -0.5848284064641138, + -0.5443434078709296, + -0.5027629549288564, + -0.460165990885412, + -0.4164563062378342, + -0.37130086040896365, + -0.3240793314499905, + -0.27386158884597717, + -0.21943055862126076, + -0.1593648134742109, + -0.09218780363292986, + -0.016579547550554075, + 0.06836656618233175, + 0.16287204709085226, + 0.2662265501180786, + 0.3766100765702768, + 0.4910247638092331, + 0.6053657060164052, + 0.7146422923899773, + 0.813339386472482, + 0.8958849293682007, + 0.957171278756702, + 0.9930654802480965, + 1.000841278474872, + 0.9794739098611946, + 0.9297565617059768, + 0.8542220434093591, + 0.7568806288960495, + 0.642810609304469, + 0.5176576023418883, + 0.38710899916636554, + 0.25640969086569665, + 0.12997492018351822, + 0.011138016643784499, + -0.09795158993582839, + -0.19628728196009174, + -0.28384735950088286, + -0.36135931470505134, + -0.43003020175113765, + -0.49128436607860043, + -0.5465410329677368, + -0.5970518289367944, + -0.6438049961391448, + -0.6874913815905422, + -0.7285190353581019, + -0.7670592457219322, + -0.8031068883719685, + -0.8365410953952316, + -0.8671770478083733, + -0.8948047163847339, + -0.9192144789551163, + -0.9402121016604413, + -0.957626514865648, + -0.9713135106343743, + -0.9811575330065698, + -0.9870727057001187, + -0.9890035198925706, + -0.9869252700668535, + -0.980844201684988, + -0.9707971177975002, + -0.9568496342383431, + -0.9390913412240396, + -0.9176250830189345, + -0.8925469222405239, + -0.8639137284947128, + -0.8316972126228737, + -0.7957267464554177, + -0.755628067427438, + -0.7107700204957331, + -0.6602354796459111, + -0.6028340458880378, + -0.5371718377791923, + -0.46178713104857383, + -0.3753501585989198, + -0.27691246325663066, + -0.1661780755849929, + -0.04375820774868533, + 0.08863423825204025, + 0.22809229989120433, + 0.37055704000887657, + 0.5110096643764421, + 0.6438076710172992, + 0.7631360815353256, + 0.8635245043892429, + 0.9403669529642079, + 0.9903768387603296, + 1.0119156512613865, + 1.0051498961828038, + 0.9720144346475816, + 0.9159875858759535, + 0.8417096763818688, + 0.754497760388496 + ], + [ + -0.8390715290764524, + -0.815135605220728, + -0.7878852761166039, + -0.7575688474660774, + -0.7244597642043602, + -0.6888538497883944, + -0.6510649766685919, + -0.6114172263128587, + -0.570230584173463, + -0.527796727009183, + -0.4843421081787094, + -0.4399777653842592, + -0.3946390961697217, + -0.34802375780276745, + -0.299540802437283, + -0.24828774734770015, + -0.19307305404046143, + -0.13249835342445138, + -0.065107336981683, + 0.01040286294092635, + 0.09492514862915659, + 0.188670966289069, + 0.29092375499253453, + 0.3998613072586966, + 0.5124876741882671, + 0.6247040557420181, + 0.7315301612653772, + 0.8274653631877817, + 0.9069562263881024, + 0.9649177253717242, + 0.9972433465522227, + 1.0012368842838504, + 0.9759069743318761, + 0.9220832516438616, + 0.8423376809398583, + 0.7407220223010443, + 0.6223579743612733, + 0.49293604541409464, + 0.3581895375487681, + 0.22340979043833706, + 0.09305853523949859, + -0.029484878224463384, + -0.14202598082533077, + -0.24351460916177617, + -0.33388751780839043, + -0.4138331494588925, + -0.48452248423913435, + -0.5473472267047639, + -0.6036978255471787, + -0.6548013946257766, + -0.7016262909173284, + -0.7448484269249794, + -0.7848661477412774, + -0.8218464972317335, + -0.8557857472890227, + -0.8865701932678772, + -0.9140280172086832, + -0.9379680419769929, + -0.9582053034225883, + -0.9745759275237651, + -0.9869447432879735, + -0.9952087589502827, + -0.9992986739341624, + -0.9991795729931479, + -0.9948512275512483, + -0.9863480951355985, + -0.9737389841010954, + -0.9571261346894628, + -0.9366429104742607, + -0.9124483628423559, + -0.884715885394327, + -0.8536125304017175, + -0.8192659328691327, + -0.7817176695829801, + -0.7408653993280072, + -0.6964008900425739, + -0.6477560918369123, + -0.5940734046569183, + -0.5342177439333265, + -0.4668457265820972, + -0.3905407400841153, + -0.30401221183537436, + -0.2063444767639561, + -0.09726752080227488, + 0.02258870392663359, + 0.15150008947370364, + 0.28655625597696033, + 0.4237028247882523, + 0.5579336531134649, + 0.6836270280706178, + 0.7949968688823313, + 0.8866096862686464, + 0.953904221699096, + 0.9936461873209302, + 1.0042566193173301, + 0.9859684182758446, + 0.9407892216785055, + 0.8722759743283105, + 0.7851528835784937, + 0.6848254885667011 + ], + [ + -0.8390715290764524, + -0.8136609976771643, + -0.7847758124369144, + -0.752687017897676, + -0.7176935945337575, + -0.6801193453052927, + -0.6403081810347069, + -0.5986158675319411, + -0.5553952847410264, + -0.510971760474236, + -0.4656056897191942, + -0.4194418695240437, + -0.3724488003873493, + -0.32435611643920803, + -0.27460326093932685, + -0.22231611242348218, + -0.16632904069264562, + -0.10526673500507001, + -0.03769272932281245, + 0.03767954767957833, + 0.121730538542871, + 0.21466331309923836, + 0.3157572172424099, + 0.4231903835531995, + 0.5339717344196543, + 0.6440119165326665, + 0.7483446507376793, + 0.8414878196550262, + 0.917910875658966, + 0.972555882077725, + 1.0013473862388182, + 1.0016239349419154, + 0.9724322785706795, + 0.9146431537967356, + 0.8308721970206863, + 0.7252169548588847, + 0.602846538871704, + 0.4694999798539172, + 0.330959672175576, + 0.19256606048392028, + 0.058829425168272775, + -0.06682346410482215, + -0.18215242034753495, + -0.28606382949199527, + -0.37845381413398005, + -0.4599735351946201, + -0.5317605466496192, + -0.5951774559160761, + -0.6515903792615101, + -0.7022072555220746, + -0.7479827687771806, + -0.7895849530227447, + -0.8274103050323206, + -0.8616302264098173, + -0.892251665905512, + -0.9191779628478111, + -0.9422606916923983, + -0.9613383298848003, + -0.976261675901403, + -0.986908504898928, + -0.993190893914165, + -0.995058346028596, + -0.992498888527082, + -0.9855392947072389, + -0.9742448582436835, + -0.9587188155032713, + -0.9391013880915003, + -0.915568202286037, + -0.8883272855049245, + -0.8576129089826277, + -0.8236735004248446, + -0.7867502059691588, + -0.7470430544206171, + -0.7046635587252438, + -0.6595761085297696, + -0.6115352672757611, + -0.5600311403630637, + -0.5042589706322801, + -0.44313057175361853, + -0.3753429288408018, + -0.2995127356036374, + -0.21437519137863809, + -0.11903246167858558, + -0.013224084928432162, + 0.10241897429770322, + 0.22618031902184674, + 0.3551661055260991, + 0.48534733429800675, + 0.6117520170461613, + 0.7288012169949855, + 0.8307600113123937, + 0.9122541257150065, + 0.9687891657308325, + 0.9972048680923203, + 0.996002888488969, + 0.9655027035307346, + 0.9078037770686663, + 0.8265593624469274, + 0.7265936339897117, + 0.6134148839859217 + ], + [ + -0.8390715290764524, + -0.8121810961510882, + -0.781645924611633, + -0.7477611131529692, + -0.7108527121361861, + -0.6712742143421148, + -0.6294014079002637, + -0.5856236597210044, + -0.5403286854919205, + -0.4938773749125723, + -0.446565889509984, + -0.39857446957261483, + -0.34990620776216524, + -0.3003239583455722, + -0.24929850517487556, + -0.19598470021735023, + -0.1392430580947519, + -0.07772115497656994, + -0.010001762312967843, + 0.06518645166807273, + 0.1487129886608232, + 0.2407745251906219, + 0.34064875466520633, + 0.4465170327511237, + 0.5553964544071692, + 0.6632107998504234, + 0.7650118325554125, + 0.8553402715588918, + 0.9286930212671515, + 0.9800439729803132, + 1.005353578845475, + 1.0020000116258145, + 0.9690729599928904, + 0.9074889534479244, + 0.8199117737847151, + 0.7104889247369108, + 0.5844407128502278, + 0.4475580011943784, + 0.3056750330796043, + 0.1641834850755256, + 0.027643612956442243, + -0.10046973274131116, + -0.21787080765797803, + -0.3234235763936122, + -0.41698546499849587, + -0.49917322040568685, + -0.571094785030353, + -0.6340884325365224, + -0.6895016479585417, + -0.7385298097719608, + -0.7821214194220368, + -0.820944946829234, + -0.8554041142109808, + -0.8856844360962595, + -0.9118138842430351, + -0.9337236767494584, + -0.9512999906255036, + -0.9644224201304419, + -0.9729891087036473, + -0.976931043734075, + -0.9762189487689381, + -0.9708659060597569, + -0.9609278887556861, + -0.9465033574422556, + -0.927732355687028, + -0.9047952064084518, + -0.8779107883703693, + -0.8473341570149029, + -0.8133527177984359, + -0.77627922960166, + -0.7364388706784329, + -0.6941469553717007, + -0.6496742635823224, + -0.6031988269920635, + -0.5547465348535691, + -0.5041276815908063, + -0.45088163127611025, + -0.3942457634076878, + -0.33316631830157495, + -0.26636647860928625, + -0.192480462851096, + -0.11025196018801922, + -0.018782315194118437, + 0.0821992504086273, + 0.1920816753243389, + 0.3091780465635019, + 0.43063348104498617, + 0.5524672137550964, + 0.6697644637362249, + 0.7770120766170026, + 0.86854899505714, + 0.939082310674652, + 0.9842058264384543, + 1.000853558448449, + 0.9876266996838406, + 0.9449486307711485, + 0.8750261359043819, + 0.7816222041917588, + 0.6696721190672024, + 0.5447955819116843 + ], + [ + -0.8390715290764524, + -0.8106959102712915, + -0.7784956940975063, + -0.7427914216786984, + -0.7039378291552532, + -0.662319895623322, + -0.6183472119042905, + -0.5724447448480456, + -0.5250370600630463, + -0.47652257895273087, + -0.42723509489165645, + -0.37739199276447083, + -0.327032437213214, + -0.27595370632690697, + -0.22365880160288923, + -0.16933205349959515, + -0.11186021527978357, + -0.049913400362478044, + 0.017907199909728486, + 0.09285897927544157, + 0.17580229101209763, + 0.26692969925240684, + 0.36552000222266695, + 0.46976099050116926, + 0.5766815663729883, + 0.6822226601625503, + 0.7814584261345434, + 0.8689570406223567, + 0.9392476868661735, + 0.9873410429502896, + 1.0092384765972546, + 1.0023627640927342, + 0.9658513877365147, + 0.9006713113656185, + 0.8095387969958885, + 0.6966552409849149, + 0.5672955900686015, + 0.42730540666752886, + 0.2825729887910766, + 0.13854271228863901, + -0.00017453228501806972, + -0.13005602277067874, + -0.2487715858570165, + -0.355144851624116, + -0.4489976216482594, + -0.530916299827732, + -0.6019842864944287, + -0.6635215746615729, + -0.7168640250431867, + -0.76320237157019, + -0.8034876977993693, + -0.8383984484486293, + -0.868355794997131, + -0.8935701707613145, + -0.9141018426659396, + -0.9299215185137479, + -0.940961789554451, + -0.9471552322201382, + -0.9484590994387607, + -0.9448690943607314, + -0.9364256656660329, + -0.9232159628542417, + -0.9053736372182956, + -0.8830776504164499, + -0.8565505332744835, + -0.8260562052787773, + -0.7918973432920823, + -0.7544120734227002, + -0.713969204315555, + -0.670960289817434, + -0.6257857640273383, + -0.5788317475557242, + -0.5304344975057103, + -0.48083135551856676, + -0.43010056673372404, + -0.3780971015253835, + -0.32439666416966284, + -0.2682640613157478, + -0.2086635562849336, + -0.14432655258433516, + -0.07388538847995528, + 0.003928423191391459, + 0.09003609731879969, + 0.1847413311916994, + 0.28747620622170933, + 0.396606927307315, + 0.5093413440746147, + 0.6217705688739188, + 0.7290602660697496, + 0.8257856158020641, + 0.9063810092260413, + 0.9656552355546816, + 0.9993090977077556, + 1.0043878943032556, + 0.9796073002974929, + 0.9255072418692898, + 0.8444119349406237, + 0.7402014794723704, + 0.6179267245227467, + 0.4833201629221271 + ], + [ + -0.8390715290764524, + -0.8092054497009502, + -0.7753252028807089, + -0.7377782344856324, + -0.6969496654381065, + -0.6532578456335582, + -0.6071481822159318, + -0.5590833244040276, + -0.5095267757907828, + -0.4589165184578148, + -0.40762588252646703, + -0.355911114368912, + -0.30384891792197855, + -0.2512721549147533, + -0.19771684377910556, + -0.14239718524954162, + -0.08422611567216029, + -0.021895751087226842, + 0.04597533373953401, + 0.12063214592973491, + 0.2029279595977103, + 0.29305380586331153, + 0.39029265875431174, + 0.4928422769839198, + 0.5977473255811611, + 0.7009702122050113, + 0.7976121207511592, + 0.8822735660054409, + 0.9495210559918417, + 0.9944071816223445, + 1.0129793416437622, + 1.0027099253678393, + 0.9627890137099469, + 0.8942385050606917, + 0.7998312367835266, + 0.6838260891662501, + 0.551555641182819, + 0.40892245693783635, + 0.2618704179562381, + 0.11589727914141718, + -0.024335669140862715, + -0.1552590372519398, + -0.27450043808242164, + -0.38084642142230674, + -0.47408747096974246, + -0.5547850042441758, + -0.6240042676953275, + -0.6830543583143844, + -0.7332678416696301, + -0.7758400123004021, + -0.8117345320788251, + -0.8416505070605658, + -0.8660378191839276, + -0.8851435268270876, + -0.8990722012510655, + -0.9078461987675429, + -0.9114566706977247, + -0.9099011384730757, + -0.903207568965195, + -0.8914474487065257, + -0.8747413030920497, + -0.8532598069146765, + -0.8272226808315741, + -0.7968965454248227, + -0.7625921859853203, + -0.72466134961072, + -0.6834930747892443, + -0.6395093394193248, + -0.5931592577952907, + -0.5449101268744786, + -0.4952325771574351, + -0.44457643913821454, + -0.3933343096391065, + -0.3417916836991112, + -0.29006603558066846, + -0.23804199072534643, + -0.18531478190955203, + -0.13115817058213713, + -0.0745344664202986, + -0.014161994311402477, + 0.05135120484859784, + 0.1233091021589518, + 0.20267942306514034, + 0.2898231139623042, + 0.38423982881901214, + 0.4843731289839379, + 0.5875173444352237, + 0.6898584168116271, + 0.7866643056119815, + 0.8726189689200338, + 0.9422709850961222, + 0.9905475863586544, + 1.01327105271747, + 1.0076099165957333, + 0.9724035238590134, + 0.9083145609308456, + 0.8177877319924797, + 0.7048199933747639, + 0.5745740752995818, + 0.4328880650272332 + ], + [ + -0.8390715290764524, + -0.8077097241375569, + -0.7721345334747093, + -0.73272184513152, + -0.6898889484605019, + -0.6440895383807328, + -0.5958069419280093, + -0.5455436580634169, + -0.4938042910596762, + -0.44106847170567715, + -0.3877510102159373, + -0.33414874456293475, + -0.2803773692565554, + -0.226306440907175, + -0.17150571066802114, + -0.11521952155005913, + -0.056386781139716284, + 0.006279118312630366, + 0.07414348008535643, + 0.1484407307240544, + 0.23001941379422927, + 0.3190719047229623, + 0.4148887334898691, + 0.5156814721133196, + 0.618514809101502, + 0.7193772451554125, + 0.8134018934699643, + 0.8952267104058714, + 0.9594607464633589, + 1.0012037416795554, + 1.0165542791411157, + 1.00303932591144, + 0.9599062297473163, + 0.8882360869209915, + 0.790862061567453, + 0.6721036537276492, + 0.5373534963740577, + 0.3925727716703669, + 0.24376167331203158, + 0.09647110461019903, + -0.044588493382462735, + -0.17580337641454719, + -0.29476235020443353, + -0.4002193982654835, + -0.4919393041391452, + -0.5704651987277706, + -0.6368519163226709, + -0.6924063829385798, + -0.738467500293348, + -0.7762455659933666, + -0.8067279614546223, + -0.8306461653632717, + -0.8484909077317614, + -0.8605582787056395, + -0.8670096607593699, + -0.8679314871948456, + -0.8633856335273589, + -0.8534462750341979, + -0.8382231528271039, + -0.8178737556601737, + -0.7926078732988165, + -0.762687678617342, + -0.7284255447959158, + -0.6901807818783346, + -0.6483557590964598, + -0.6033915483518583, + -0.55576310318516, + -0.5059737735300288, + -0.45454840114248385, + -0.4020233102181003, + -0.3489304623853218, + -0.29577240037302405, + -0.2429849773214931, + -0.1908867492102539, + -0.1396174247646845, + -0.08907252431720551, + -0.03884644923341931, + 0.01179985016059243, + 0.06392896966800887, + 0.1188595562705779, + 0.17803243456113094, + 0.2428120167058263, + 0.3142375827515047, + 0.39275214420492355, + 0.477947191412335, + 0.5683670243680753, + 0.6614146233294653, + 0.7533913892714035, + 0.8396863467439699, + 0.9151088330361342, + 0.9743357538715061, + 1.0124241914347707, + 1.0253263289958692, + 1.0103391590278958, + 0.9664275376650773, + 0.8943752126812429, + 0.796742026659303, + 0.6776327200980162, + 0.542309082009063, + 0.39669823900581747 + ], + [ + -0.8390715290764524, + -0.8062087433128612, + -0.768923768918121, + -0.7276225497039057, + -0.6827564132510752, + -0.6348164651562674, + -0.5843261474426192, + -0.5318300623261923, + -0.4778761526132057, + -0.4229878444993378, + -0.36762340860059856, + -0.31212201511920257, + -0.2566397804239693, + -0.20108401353247313, + -0.14505882446341892, + -0.08783884387738254, + -0.028388575348810875, + 0.0345582377190939, + 0.10235226906013609, + 0.17621942957758208, + 0.25700616200359827, + 0.3449093596276157, + 0.43923079158452244, + 0.5381999888121719, + 0.6389062114945886, + 0.7373689324382198, + 0.8287583214107599, + 0.9077550594103623, + 0.9690160774712484, + 1.0076935502298099, + 1.0199423653998956, + 1.0033489071772523, + 0.9572222318226898, + 0.8827065616429384, + 0.7826986895792635, + 0.6615813040980966, + 0.5248088277615075, + 0.37840187321418256, + 0.22841675713074877, + 0.0804562755094404, + -0.06072235182723046, + -0.19146454702196342, + -0.3093249935393022, + -0.4130309531524833, + -0.5023284892274218, + -0.5777505169560139, + -0.6403505552881236, + -0.6914433966551392, + -0.7323851517515859, + -0.7644127054090033, + -0.7885493121923673, + -0.805571388309911, + -0.8160233154041854, + -0.8202630726320256, + -0.8185215696580719, + -0.8109616891658629, + -0.7977278528129853, + -0.778981952823813, + -0.7549256019796012, + -0.7258112209795318, + -0.6919454319877218, + -0.6536879295970264, + -0.6114480505481054, + -0.5656802421543514, + -0.5168789120543215, + -0.4655728112210228, + -0.41231898120986726, + -0.3576960824916136, + -0.3022963649929702, + -0.24671461105758555, + -0.19153133497419378, + -0.13728687738389173, + -0.08444340441154206, + -0.03333370055126731, + 0.015900840261278873, + 0.06337586547882354, + 0.10952114839511845, + 0.15511291349105683, + 0.20126376136282512, + 0.24935483937198882, + 0.3009014707042264, + 0.35735390732513483, + 0.4198477990561465, + 0.4889320991316771, + 0.5643127161616741, + 0.6446556233948142, + 0.7274913946817161, + 0.8092535118105166, + 0.8854660518733035, + 0.951074795937059, + 1.0008928576919978, + 1.0301116371843997, + 1.0348140838426734, + 1.0124227560285792, + 0.9620212604433901, + 0.88450371894858, + 0.7825304844930339, + 0.6602955503046339, + 0.5231374184531844, + 0.37704623624219025 + ], + [ + -0.8390715290764524, + -0.8047025169928048, + -0.7656929927725467, + -0.7224806468027873, + -0.6755528023148177, + -0.6254401342925292, + -0.5727084878489452, + -0.5179469091417174, + -0.46174899282775606, + -0.40468416521018197, + -0.34725617274728765, + -0.289848265919573, + -0.23265838986969103, + -0.17563260426939134, + -0.11840990797125214, + -0.06029523087073123, + -0.0002781265502176389, + 0.0628884410191099, + 0.13054224511450582, + 0.20390300859192012, + 0.28381798507132594, + 0.37049205257353346, + 0.4632421979039505, + 0.5603203434213476, + 0.6588451363100697, + 0.7548721359028644, + 0.8436138869799619, + 0.9197992126772088, + 0.9781383279902736, + 1.0138411121180526, + 1.0231237703490423, + 1.0036367344770154, + 0.9547548922273054, + 0.8776890852446271, + 0.775402482102471, + 0.6523428509994879, + 0.5140273410042508, + 0.36653589136309644, + 0.2159797252633167, + 0.0680111471293749, + -0.0725694333793776, + -0.20207141546654134, + -0.3180213887941782, + -0.41912711379748535, + -0.5051242977729368, + -0.57654507718888, + -0.634452072336791, + -0.680179223499308, + -0.7151118608191344, + -0.7405260407507267, + -0.7574938765727919, + -0.7668499204431917, + -0.7692054155129558, + -0.7649932395008701, + -0.7545264193504488, + -0.7380562345041635, + -0.7158207334888426, + -0.688079518486286, + -0.6551347646940461, + -0.6173410088881618, + -0.5751071926412848, + -0.5288941490543393, + -0.47920977272879417, + -0.4266030918394969, + -0.37165774410920444, + -0.31498502780635457, + -0.25721657769212525, + -0.19899650119879417, + -0.14097225363217972, + -0.08378259926558455, + -0.02803995787200035, + 0.025696211073757348, + 0.07695796444699657, + 0.12542047239543092, + 0.17096424338839677, + 0.213740811354885, + 0.25422967280081055, + 0.2932702797273486, + 0.332051448024761, + 0.37204283092431134, + 0.4148596760842701, + 0.46206254009157893, + 0.514906563224214, + 0.5740680376795302, + 0.6393865941217903, + 0.7096667342915746, + 0.7825806976484997, + 0.854705026797462, + 0.9217064616991877, + 0.9786712262845135, + 1.0205488292171725, + 1.0426612074198414, + 1.0412142202159105, + 1.0137440047934336, + 0.9594367993194816, + 0.8792769034822329, + 0.7760010192915529, + 0.6538644360675936, + 0.5182508438549664, + 0.3751785999532726 + ], + [ + -0.8390715290764524, + -0.8031910549774596, + -0.7624422891203962, + -0.7172964375231373, + -0.6682788655557846, + -0.6159620709174816, + -0.5609566842933974, + -0.5038986245149539, + -0.44542952695094795, + -0.38616707975657405, + -0.32666255362945823, + -0.2673450313047908, + -0.20845566444365698, + -0.14998019635699247, + -0.09159294160920689, + -0.03262899966694093, + 0.02789775007562545, + 0.09121646605943243, + 0.1586539923510184, + 0.2314264572426333, + 0.31038511899578053, + 0.395746596373843, + 0.48684735829167813, + 0.5819664223122617, + 0.6782568823061992, + 0.7718157031382181, + 0.8579032747251467, + 0.931302065569611, + 0.9867809851997505, + 1.0196128040623726, + 1.0260798735989347, + 1.0039010090710796, + 0.9525206405609875, + 0.8732191877917983, + 0.7690282822406604, + 0.6444618788925804, + 0.5050998845893032, + 0.3570804407222003, + 0.2065673347638311, + 0.059258777412377006, + -0.08000651446259915, + -0.20750807779012398, + -0.3207518206978285, + -0.4184346151052286, + -0.5002915497546325, + -0.5668647441640164, + -0.6192375816728102, + -0.6587755649713009, + -0.6869062427887306, + -0.704958239472025, + -0.7140661161915254, + -0.7151361249368813, + -0.708859679904492, + -0.6957573761107059, + -0.6762364455437396, + -0.6506476812154443, + -0.6193326686127153, + -0.5826571958222315, + -0.5410308273130636, + -0.4949151953093498, + -0.4448245148065494, + -0.3913215324892192, + -0.3350111711822098, + -0.2765331107994136, + -0.2165538295823852, + -0.15575829831396532, + -0.09484139825621792, + -0.03449891787155229, + 0.024582574445091217, + 0.08173861794239272, + 0.13634519292022518, + 0.18784583492766938, + 0.23579049719840478, + 0.2798872539762739, + 0.3200644266863518, + 0.3565359643878611, + 0.38985786672570677, + 0.4209594584183317, + 0.45113188105964763, + 0.4819584607663344, + 0.5151781819585205, + 0.5524839554094401, + 0.5952703002457819, + 0.6443581922276967, + 0.699735424342693, + 0.7603562304334801, + 0.8240421843160213, + 0.8875167648167455, + 0.9465892432686496, + 0.9964819841348913, + 1.032272307702658, + 1.0493997704408158, + 1.0441758972633783, + 1.0142289018390354, + 0.9588220253945582, + 0.8790001862496593, + 0.7775432033878512, + 0.6587310761757446, + 0.527953120083035, + 0.39121379589421573 + ], + [ + -0.8390715290764524, + -0.8016743671009618, + -0.7591717425627029, + -0.7120702254372638, + -0.6609353601990328, + -0.6063838167066212, + -0.5490734893422614, + -0.4896896870954311, + -0.42892455030544, + -0.3674463465205165, + -0.3058559495060739, + -0.24463002627111227, + -0.1840542783523093, + -0.12415499402773106, + -0.06464212007778156, + -0.004880646886006243, + 0.05609213038468977, + 0.11948905478204574, + 0.1866282597547822, + 0.2587251410462166, + 0.33663843645317393, + 0.4206005451789572, + 0.5099719575591581, + 0.6030637437852225, + 0.6970687233182625, + 0.788130756714824, + 0.8715636585074449, + 0.9422090799044335, + 0.9948999816445945, + 1.024977058552125, + 1.0287933734243422, + 1.0041400794093962, + 0.9505343543302028, + 0.8693285218001283, + 0.7636240026805811, + 0.6380011598753157, + 0.498101684311257, + 0.3501196806723149, + 0.2002679477912404, + 0.0542857101509559, + -0.08295624069013663, + -0.2077151261961735, + -0.3174849813634939, + -0.4109617796881919, + -0.4878910562667895, + -0.5488369203016571, + -0.5949163085006518, + -0.627539678749616, + -0.6481905914891707, + -0.6582642121105702, + -0.6589714675320519, + -0.6513039253589494, + -0.6360462302139954, + -0.6138189396895165, + -0.5851346653308205, + -0.550453566394302, + -0.510229055318901, + -0.46493960741656076, + -0.4151066824977908, + -0.36130133627601374, + -0.30414305235427813, + -0.24429403028267274, + -0.18245121665909256, + -0.11933734531217893, + -0.055691534790030035, + 0.007740340745054561, + 0.07021541443010149, + 0.13100384805375762, + 0.18939979636072574, + 0.24473493472120492, + 0.2963972224037031, + 0.3438582259823147, + 0.3867119629503171, + 0.42472635402485837, + 0.4579048652465041, + 0.48655117383746793, + 0.5113246519560822, + 0.5332704866645319, + 0.5538068144414178, + 0.5746535444663587, + 0.597694119622919, + 0.6247719247512962, + 0.657435985698627, + 0.696663737525887, + 0.7425992357579969, + 0.7943505906996235, + 0.8498886676274501, + 0.9060794742761398, + 0.9588659233467884, + 1.0035930916548172, + 1.0354481551237618, + 1.0499658843573896, + 1.0435367626165197, + 1.013850287957042, + 0.9602121132341919, + 0.8836897367461691, + 0.787065024306518, + 0.6745990590018218, + 0.5516411290435743, + 0.42413469797499204 + ], + [ + -0.8390715290764524, + -0.8001524632314498, + -0.7558814382169207, + -0.7068023165770391, + -0.6535230507117844, + -0.5967069296322043, + -0.5370616863369684, + -0.47532462674942866, + -0.4122409354594093, + -0.3485318312050552, + -0.28484989720466225, + -0.2217211325247005, + -0.1594770919163752, + -0.09818539149754842, + -0.03759180875813119, + 0.02290921064610302, + 0.08425805941688702, + 0.14765305335189882, + 0.21440608607659228, + 0.2857349533440061, + 0.3625096266659321, + 0.444982602296642, + 0.5325431934493219, + 0.6235397143525068, + 0.7152101807273703, + 0.8037509741790808, + 0.8845349777291724, + 0.9524685425327016, + 1.00245391992786, + 1.029904536502282, + 1.031248388029394, + 1.0043524514526678, + 0.9488092598812434, + 0.8660446380954675, + 0.7592302655488179, + 0.633012153701823, + 0.4930917093940611, + 0.34571556629925054, + 0.197140702078194, + 0.053141119239168144, + -0.08138793144134271, + -0.20269029821392215, + -0.3082583292737696, + -0.39679841784347547, + -0.46807885432515095, + -0.5226988689671948, + -0.5618227118215929, + -0.5869199678899146, + -0.5995445567123487, + -0.6011724548594818, + -0.5931048829213605, + -0.5764320370320738, + -0.5520442142150778, + -0.5206731915755259, + -0.4829467849634344, + -0.4394426574255157, + -0.39073226099744257, + -0.33741083063723787, + -0.2801134656049436, + -0.21951990460216914, + -0.15635155439358112, + -0.09136403632077256, + -0.025337566155919262, + 0.040933539088421955, + 0.10665165293167764, + 0.17102668920809022, + 0.23328689477542816, + 0.29268969003153833, + 0.34853322489770777, + 0.4001702570254122, + 0.447027014265798, + 0.48863035940102356, + 0.5246462145010651, + 0.554930334606739, + 0.579589017025092, + 0.5990425893322183, + 0.6140794832235474, + 0.6258847292648797, + 0.6360252711502261, + 0.6463767972544014, + 0.6589833649689991, + 0.675851556523219, + 0.6986938412369902, + 0.7286489557574659, + 0.7660177106802154, + 0.8100580389189111, + 0.8588813642870274, + 0.9094827471461777, + 0.9579205295412818, + 0.999639634858118, + 1.0299097328406153, + 1.044329184048203, + 1.0393318521763062, + 1.0126293694084436, + 0.9635275283397907, + 0.8930715291582898, + 0.8039983745724161, + 0.7005019159953733, + 0.5878423640304544, + 0.47185310543167464 + ], + [ + -0.8390715290764524, + -0.7986253532709989, + -0.7525714617147087, + -0.7014930194159774, + -0.6460427087238503, + -0.5869329837098348, + -0.5249240887421774, + -0.4608080231158476, + -0.3953856293648526, + -0.32943350163511353, + -0.2636580633142233, + -0.19863638440479037, + -0.13474713015428574, + -0.07209994174708538, + -0.010476499892274359, + 0.05069989535057637, + 0.11234862959492115, + 0.17565551208867847, + 0.24192892410530584, + 0.31239246584656094, + 0.38793137314640874, + 0.468822824715589, + 0.5544900058370353, + 0.6433238785243067, + 0.7326132875088167, + 0.8186128576611743, + 0.8967602014022089, + 0.9620318105258371, + 1.0094042837891024, + 1.034368287719704, + 1.0334305485018624, + 1.0045367980091544, + 0.9473568443282094, + 0.8633907907192202, + 0.7558800970691194, + 0.6295345979044655, + 0.4901121755962155, + 0.3439072969550822, + 0.19721495575457215, + 0.05583632243912623, + -0.07531789897620633, + -0.19248850142096863, + -0.29317765977129273, + -0.3761147481914867, + -0.44110424343294297, + -0.48879459185888247, + -0.5204118850518225, + -0.5374995438282012, + -0.5416964657106402, + -0.5345736839444999, + -0.517536293007934, + -0.491785737570218, + -0.45832933443523727, + -0.41801991079867873, + -0.3716085104573218, + -0.3197962666279079, + -0.26327635516169284, + -0.20276197861742076, + -0.13900044909101708, + -0.07277600912159997, + -0.004904983282550189, + 0.06377344290180538, + 0.13240847022694635, + 0.20014963632317156, + 0.2661588678405584, + 0.32962236930669797, + 0.38976221227039454, + 0.44584771182647176, + 0.4972072442503479, + 0.5432421027095435, + 0.5834450476650143, + 0.617426869053581, + 0.6449539213118421, + 0.6659977292576579, + 0.680794263990221, + 0.6899057492061046, + 0.6942728265971446, + 0.6952409408594115, + 0.6945433720057833, + 0.6942256450661438, + 0.6965026277122498, + 0.7035500918029902, + 0.7172454527886057, + 0.7388855386299455, + 0.7689198372443689, + 0.8067430783328102, + 0.8505892677910252, + 0.8975596699192088, + 0.9438004962138921, + 0.984824489552086, + 1.0159476452286516, + 1.0327920098796508, + 1.0317916695252, + 1.0106345301554254, + 0.9685785777505725, + 0.906597354590632, + 0.8273329471290124, + 0.7348619862195609, + 0.6343064644761323, + 0.5313421991734635 + ], + [ + -0.8390715290764524, + -0.797093047155557, + -0.7492418991997032, + -0.6961426448511719, + -0.6384951129473053, + -0.5770635687424438, + -0.5126635394867805, + -0.4461445041461918, + -0.37836565046491555, + -0.31016142250451717, + -0.2422942352937132, + -0.17539395468669566, + -0.1098875612112985, + -0.04592732512833679, + 0.016669231398463297, + 0.07845072843782584, + 0.14031705884424017, + 0.2034437850147789, + 0.26913876406756776, + 0.3386350775850166, + 0.4128375288534845, + 0.49205282374577886, + 0.5757433004448673, + 0.6623481612380716, + 0.7492128428707414, + 0.8326559920007717, + 0.9081855788960942, + 0.9708535418051346, + 1.015715634492311, + 1.0383438983036781, + 1.0353270829127217, + 1.004691967028787, + 0.9461867790622462, + 0.861385772260381, + 0.7535986793155347, + 0.627596191283613, + 0.489188189486382, + 0.34471096735957957, + 0.2004900117376141, + 0.06234466947050055, + -0.0648092787686711, + -0.17722121345416575, + -0.27241589198010674, + -0.34915935194844405, + -0.4073066486129352, + -0.4475703016172112, + -0.4712532977115196, + -0.4799878555938419, + -0.4755124186989489, + -0.4595069391276586, + -0.4334932269082263, + -0.3987954846552595, + -0.3565479238024456, + -0.3077323755015845, + -0.2532288802519552, + -0.19386538948987855, + -0.1304575290856785, + -0.06383441236743417, + 0.005149391895779427, + 0.0756130599524688, + 0.14665625297382856, + 0.217370064902057, + 0.28684979903023283, + 0.3542082203708338, + 0.418588655513229, + 0.4791776520242818, + 0.5352170422793264, + 0.586015484697389, + 0.6309601269032941, + 0.6695299837260199, + 0.7013136878875014, + 0.7260349370785929, + 0.7435886104291527, + 0.7540886695645364, + 0.7579254656157116, + 0.7558253395312644, + 0.7489003756851911, + 0.7386722045702202, + 0.7270523209465486, + 0.7162636890453288, + 0.7086949890836756, + 0.7066893263324006, + 0.7122821627331913, + 0.7269163688556584, + 0.7511728906682416, + 0.7845609310201522, + 0.8254098059249685, + 0.8708950098818807, + 0.917214284500121, + 0.9599079105513497, + 0.9942944914908152, + 1.0159731908250615, + 1.0213295502474458, + 1.0079775016580435, + 0.9750762634113097, + 0.923476854709333, + 0.855676514038839, + 0.7755865066794296, + 0.6881451040710997, + 0.5988285354869382 + ], + [ + -0.8390715290764524, + -0.7955555548548804, + -0.7458928373252738, + -0.6907515061850927, + -0.6308810490954158, + -0.5671002900616869, + -0.5002829102980294, + -0.4313387446291695, + -0.36118808577146555, + -0.29072575007196066, + -0.22077231250189128, + -0.1520121402758374, + -0.08492167465453271, + -0.0196963178312642, + 0.04381077120067943, + 0.10612108945131506, + 0.1681167685039629, + 0.23096562883228383, + 0.29597825589476967, + 0.3644011619195918, + 0.4371632883062688, + 0.5146059612000813, + 0.596236166369857, + 0.6805471020970681, + 0.7649466565290924, + 0.8458232903411683, + 0.9187608762633797, + 0.978891910121003, + 1.0213557915220617, + 1.041809624174541, + 1.036926891068747, + 1.0048169888027565, + 0.9453068553513757, + 0.8600437807802856, + 0.7524031609273505, + 0.6272123732861514, + 0.49032753688686803, + 0.3481194243472092, + 0.20693512427603, + 0.07260180552958928, + -0.049971372823479854, + -0.15705526386503604, + -0.2462110860671987, + -0.31625618541874234, + -0.3671113493784134, + -0.3995685492407595, + -0.4150229642473669, + -0.4152105053982657, + -0.4019833216233034, + -0.3771433731434545, + -0.34234087233834565, + -0.2990327423968869, + -0.24848802374805212, + -0.19182317824545, + -0.13005031450034882, + -0.06412450871022747, + 0.005018786870010021, + 0.07644021764500568, + 0.1491867350012177, + 0.22229282163821584, + 0.2947890658120291, + 0.36571471650232396, + 0.43413180710854904, + 0.4991394654879895, + 0.5598877573083746, + 0.615590755065022, + 0.6655386633961334, + 0.7091090652052557, + 0.7457779306803113, + 0.775131985858817, + 0.7968851085442994, + 0.8109020913624779, + 0.8172327670206558, + 0.8161576387019736, + 0.8082426700007238, + 0.7943961596055945, + 0.7759156028336032, + 0.754508481513169, + 0.7322694971734532, + 0.7115990698242372, + 0.695054508537817, + 0.6851357274371512, + 0.6840203189774409, + 0.6932759328438891, + 0.7135885032384497, + 0.7445502696678055, + 0.784549792644166, + 0.8307965352053294, + 0.8794958358505801, + 0.9261685183998248, + 0.9660864257331735, + 0.9947748518221422, + 1.0085190038167786, + 1.0048071047663856, + 0.9826488173255493, + 0.9427237049712446, + 0.8873379920131985, + 0.820195075884248, + 0.7460115364516329, + 0.6700313987738816 + ], + [ + -0.8390715290764524, + -0.7940128863724685, + -0.7425243632522734, + -0.6853199191072349, + -0.62320130980085, + -0.5570447682668364, + -0.48778510102889466, + -0.4163954647003408, + -0.3438600879141007, + -0.27113672680873513, + -0.19910629715435724, + -0.12850934780405365, + -0.05987285965425064, + 0.006564239754963088, + 0.07091351094560323, + 0.1336704757260158, + 0.19570146089904808, + 0.25816930114289166, + 0.32239083009955266, + 0.38963021126005165, + 0.46084535620708894, + 0.5364175405543103, + 0.6159040867364536, + 0.6978580806121724, + 0.7797557817037122, + 0.8580612261935214, + 0.928439597103182, + 0.9861088033626969, + 1.0262959966665504, + 1.0447465100000646, + 1.0382206094806434, + 1.0049110820235838, + 0.9447229324597513, + 0.8593743192724165, + 0.7523025282086172, + 0.6283862010299847, + 0.49352061726763075, + 0.35410233053474915, + 0.2164897875866622, + 0.08650630763227057, + -0.030958512806823768, + -0.1322110111306899, + -0.21486371357883913, + -0.2778006866117394, + -0.3210241283859794, + -0.34542108359730805, + -0.3524941476805382, + -0.34409739680045137, + -0.32221005034107675, + -0.2887679799763825, + -0.24555989963230324, + -0.19418342479176567, + -0.13604797285042122, + -0.07240749902359195, + -0.004406137759954168, + 0.06687702822683941, + 0.14039303105921197, + 0.21510183520330822, + 0.2899647156753197, + 0.36394743552997694, + 0.43603051386251745, + 0.5052231796109729, + 0.5705785667240587, + 0.6312087418164805, + 0.6862988905182997, + 0.7351203405048842, + 0.7770422442486912, + 0.8115419854323315, + 0.8382149576942062, + 0.8567853260915025, + 0.8671204598472755, + 0.8692524037036328, + 0.8634094168697353, + 0.850058762313483, + 0.829958445899612, + 0.8042108802411272, + 0.7743064287826766, + 0.7421408278994986, + 0.709989060173068, + 0.6804205602519071, + 0.6561472185238006, + 0.639806114066028, + 0.6336918444328876, + 0.6394664530703649, + 0.657885545419828, + 0.6885845820199632, + 0.7299675872854311, + 0.7792308754591653, + 0.832537642608596, + 0.8853376856082765, + 0.9328035422700578, + 0.9703340257499826, + 0.9940622751174963, + 1.0013009142264495, + 0.990862972428105, + 0.9632132488013432, + 0.9204283387707073, + 0.8659707272422535, + 0.8043086374405948, + 0.7404343309753796 + ], + [ + -0.8390715290764524, + -0.792465051745499, + -0.7391365646467627, + -0.6798482016756361, + -0.6154566945331683, + -0.5468986389611694, + -0.4751730389788525, + -0.40131942833733064, + -0.32638887216183515, + -0.25140467600100874, + -0.17731028521368444, + -0.10490407913953272, + -0.03476458307182641, + 0.03282547475131257, + 0.09794289153897502, + 0.16105856167443364, + 0.22302519644450172, + 0.28500365772613523, + 0.3483208170070917, + 0.41426297915824434, + 0.48382211213500204, + 0.5574249925373793, + 0.6346851418124286, + 0.7142215316718651, + 0.793584735962626, + 0.8693200510278043, + 0.9371791869918681, + 0.9924700042582957, + 1.030511060650877, + 1.0471384928691558, + 1.0392006661664481, + 1.0049736586678004, + 0.9444388986318027, + 0.8593821283692145, + 0.7532975375811655, + 0.6311083249540304, + 0.49874052464361973, + 0.362606434342833, + 0.22906430387401383, + 0.10392068748864947, + -0.007968454815571124, + -0.10295993474051707, + -0.17873321215079443, + -0.23425502277457408, + -0.26962490710687315, + -0.2858405370058872, + -0.28452672592087935, + -0.26766938558680103, + -0.23738696833435935, + -0.1957595470458565, + -0.14472240987140148, + -0.08601940506721367, + -0.02120305783724071, + 0.0483354938015307, + 0.12132362028386498, + 0.1965651025346243, + 0.27290772096915245, + 0.34922440189089105, + 0.424407678485615, + 0.49737465963477767, + 0.5670787570793596, + 0.6325247310369961, + 0.6927845809328068, + 0.7470128503958297, + 0.7944606582279794, + 0.8344881267046323, + 0.8665750317534459, + 0.8903297487005672, + 0.905497160208453, + 0.9119671627509441, + 0.9097864938132988, + 0.899177287924435, + 0.8805654378227851, + 0.8546199965770874, + 0.822301378110766, + 0.7849113928263391, + 0.7441331384847927, + 0.7020448104218547, + 0.6610900710298065, + 0.623989925930579, + 0.5935876361095139, + 0.5726286583392336, + 0.5634905379694813, + 0.567890805939382, + 0.5866115156697401, + 0.619284445351777, + 0.6642792333324911, + 0.7187270682701667, + 0.7786957937531368, + 0.8395106894469465, + 0.8961922141779354, + 0.943961665454102, + 0.9787518489844726, + 0.9976553126729107, + 0.9992487521543233, + 0.9837482149643778, + 0.9529732603702797, + 0.9101254108860573, + 0.859412511408039, + 0.8055716143402822 + ], + [ + -0.8390715290764524, + -0.7909120610447612, + -0.7357295296777333, + -0.6743366742982505, + -0.6076480095155894, + -0.5366635524859298, + -0.4624496782082414, + -0.3861154418410424, + -0.30878171341871574, + -0.23153999630951883, + -0.15539845721854606, + -0.08121491682176635, + -0.009620367474930207, + 0.059058513533956275, + 0.12486444742784712, + 0.18824525781344276, + 0.2500424701532329, + 0.3114182486929875, + 0.37371356408983425, + 0.43824161943905926, + 0.506033770881294, + 0.5775680546191628, + 0.6525202039483107, + 0.7295811504990136, + 0.8063817090871415, + 0.8795539965059497, + 0.9449412205820543, + 0.9979453526081816, + 1.0339794915729208, + 1.0489724901458373, + 1.039861324969453, + 1.0050043276707257, + 0.9444566452010694, + 0.8600671527724179, + 0.7553807098988418, + 0.6353570632876578, + 0.5059432742937895, + 0.37355604396806613, + 0.24454062540016586, + 0.12467275099293529, + 0.018759677518632445, + -0.06962166867052738, + -0.13823386409706598, + -0.18614253595727073, + -0.21356044859998402, + -0.22161104631541412, + -0.21205536697858063, + -0.18702362499712238, + -0.14878404472888507, + -0.09956914407420925, + -0.0414663978211035, + 0.023631427620343638, + 0.09402918719615186, + 0.16820241025912294, + 0.24475730881177793, + 0.32239140435133423, + 0.3998636212888536, + 0.47597766237416794, + 0.5495783726849045, + 0.6195582402048865, + 0.6848702447600065, + 0.7445435839637292, + 0.7976997794429647, + 0.843567717769356, + 0.8814969321797919, + 0.910968800196211, + 0.9316054949073038, + 0.9431767861906489, + 0.9456053901865744, + 0.9389725436976757, + 0.9235265740401156, + 0.8996979280066125, + 0.8681237980618414, + 0.8296836485944478, + 0.7855434706611005, + 0.7372018755949133, + 0.6865261226318277, + 0.6357622195455697, + 0.5875018096057408, + 0.5445908627224302, + 0.5099717661447315, + 0.4864608674712664, + 0.47647644756566676, + 0.48174522070900205, + 0.5030260340864505, + 0.539894818689928, + 0.5906330753636898, + 0.6522525226794933, + 0.7206717600593219, + 0.7910391905814552, + 0.858173460041167, + 0.9170723270821052, + 0.9634270079952713, + 0.9940744911872024, + 1.0073263603598093, + 1.0031286780492354, + 0.9830310049772195, + 0.9499698049551178, + 0.907897761813209, + 0.8613115356929038 + ], + [ + -0.8390715290764524, + -0.7893539243745954, + -0.7323033470148116, + -0.6687856597141918, + -0.5997760676410653, + -0.5263411736518913, + -0.44961799884633824, + -0.3707883523033999, + -0.29104594319459326, + -0.21155315628955465, + -0.13338506905782363, + -0.05746050943301212, + 0.015536230899475586, + 0.08523451348014904, + 0.15164385054810733, + 0.21519076944551432, + 0.2767082874201177, + 0.3373634133340921, + 0.3985155511584171, + 0.4615098220430495, + 0.5274225380097877, + 0.5967889438811733, + 0.6693531237266145, + 0.7438840863892197, + 0.8180987571789873, + 0.8887214605350839, + 0.9516915705472984, + 1.002508888282826, + 1.0366836044869339, + 1.050238471023005, + 1.0401987191312556, + 1.0050028973703744, + 0.9447760539961283, + 0.8614245416446441, + 0.7585363866656989, + 0.6410985747464181, + 0.5150681733913716, + 0.38685370108706657, + 0.26277346269984225, + 0.14855730088585778, + 0.04894788019626417, + -0.03256050866133137, + -0.09383004613439314, + -0.13404145336465287, + -0.1535362192085052, + -0.15357793291548638, + -0.13607667568438156, + -0.10331781567156818, + -0.05772784034709337, + -0.0016974842433886764, + 0.0625308555029483, + 0.1329160581132516, + 0.2076244237476064, + 0.2850058475411798, + 0.3635567705729542, + 0.44188350583420966, + 0.5186747255211308, + 0.5926868684702497, + 0.6627421239742906, + 0.7277360971229995, + 0.7866513330098213, + 0.8385732033396323, + 0.8827056435817023, + 0.9183852904441547, + 0.9450933318652499, + 0.962464761571563, + 0.9702949035845977, + 0.968543340884185, + 0.9573359941989754, + 0.9369670841991562, + 0.9079038121818237, + 0.8707972942818651, + 0.8265029642015898, + 0.7761118282156874, + 0.7209904843356814, + 0.662823100353469, + 0.6036435295020859, + 0.5458417838741156, + 0.49212765597759045, + 0.44543657767874084, + 0.40876937609863123, + 0.3849680348466203, + 0.376442483992629, + 0.38487655103690355, + 0.4109517681499312, + 0.4541330985367495, + 0.5125588636042043, + 0.5830674825752165, + 0.6613768500095001, + 0.7424105551242184, + 0.8207421469420734, + 0.8911082963631578, + 0.9489278249169351, + 0.9907590125024832, + 1.0146336330718064, + 1.0202221740455046, + 1.0088082150454407, + 0.9830771136681649, + 0.9467504225397895, + 0.9041184645069525 + ], + [ + -0.8390715290764524, + -0.7877906518728197, + -0.7288581058259516, + -0.6631954829748259, + -0.5918416883876624, + -0.5159331814685556, + -0.43668100639334856, + -0.35534304606207073, + -0.2731889465523234, + -0.1914546888740922, + -0.11128444269569238, + -0.03365955691775061, + 0.04068164421368329, + 0.11132469468004423, + 0.17824695409668848, + 0.24185565490846267, + 0.30297823895607917, + 0.3627903734843057, + 0.42267450316594335, + 0.48401294526174365, + 0.547932760237188, + 0.6150325227741658, + 0.6851309067348227, + 0.757081124564091, + 0.8286919822822725, + 0.8967851763845243, + 0.9574005576296851, + 1.006138974306889, + 1.038609611576103, + 1.050929511384611, + 1.040210873923442, + 1.0049693767052248, + 0.9453949981274772, + 0.8634446829594404, + 0.7627408477355715, + 0.64828712807854, + 0.5260383324223442, + 0.40238104829604726, + 0.28359164853687263, + 0.17533816575362177, + 0.08228216041835673, + 0.007818568534375958, + -0.04603090471048785, + -0.07857793808553099, + -0.09030751158556746, + -0.08263657750022792, + -0.057635488672810854, + -0.017753586416565766, + 0.03441835352831591, + 0.09632848916006426, + 0.1655800284310926, + 0.23998765979068956, + 0.3175870714051877, + 0.3966143070303465, + 0.4754716327054565, + 0.5526934432075521, + 0.6269209311996263, + 0.6968892217016628, + 0.7614265804650383, + 0.8194627634423239, + 0.870042657430168, + 0.9123416996204482, + 0.9456805604110654, + 0.9695376477665352, + 0.9835587661891206, + 0.9875636553535866, + 0.9815493184555274, + 0.9656903300912694, + 0.9403369352258615, + 0.9060127468987049, + 0.8634149595291006, + 0.8134207004569824, + 0.7571028264317969, + 0.6957566428171432, + 0.6309355517949584, + 0.5644889171358151, + 0.49859041362782497, + 0.4357411652671548, + 0.3787305393424834, + 0.33053975350625076, + 0.2941800127908879, + 0.27246733053831407, + 0.26774908929096375, + 0.2816104927233467, + 0.3145996082620892, + 0.36601505284501434, + 0.4337985782759426, + 0.5145651280406509, + 0.6037861372688235, + 0.6961202128176732, + 0.7858623185755298, + 0.8674622275329088, + 0.9360491118363929, + 0.9878945774333263, + 1.020752481407978, + 1.0340298739291762, + 1.028766925741008, + 1.0074308757640573, + 0.9735553136726505, + 0.9312771209510462 + ], + [ + -0.8390715290764524, + -0.7862222537106692, + -0.7253938957751129, + -0.657566471424743, + -0.5838456977332451, + -0.5054412688710594, + -0.42364173101643154, + -0.33978444714267747, + -0.25521815903268413, + -0.1712551858230108, + -0.08911095685371, + -0.009830795861716684, + 0.06579231510865484, + 0.13730037157952293, + 0.2046398360726495, + 0.2682008833084217, + 0.3288085747473745, + 0.38765132522800677, + 0.4461395003878932, + 0.505698144055084, + 0.5675110702421676, + 0.6322464572871651, + 0.6998038804055859, + 0.7691268555137067, + 0.8381216968473107, + 0.9037123641790612, + 0.962043081130596, + 1.008818399446059, + 1.0397476924543085, + 1.0510418316762058, + 1.0398977182054405, + 1.0049039751583573, + 0.9463093561501352, + 0.8661132715665638, + 0.7679624896083247, + 0.6568654663152516, + 0.5387613130827222, + 0.41999988256518817, + 0.30679974479741645, + 0.20475053530058632, + 0.11841580248075542, + 0.05107433043613774, + 0.0046154820098389115, + -0.0204185637171376, + -0.024669940748947378, + -0.009720636003929473, + 0.022189493904160543, + 0.06844075563483558, + 0.12627492631839513, + 0.1929794253286351, + 0.26600719982306853, + 0.3430368045252124, + 0.4219853651374405, + 0.5009910911548628, + 0.5783819346904676, + 0.6526438531501773, + 0.7223973357520641, + 0.7863858432869406, + 0.84347572753574, + 0.8926646675742334, + 0.9330947561721702, + 0.9640667208567801, + 0.9850527748566779, + 0.9957066811339162, + 0.9958704012523004, + 0.9855771060045596, + 0.9650505220160762, + 0.934700879443926, + 0.8951183573360187, + 0.8470659273569088, + 0.7914746119886426, + 0.7294448832536619, + 0.6622576141040014, + 0.5913961654706539, + 0.5185777180219586, + 0.4457872342609919, + 0.3753024102402286, + 0.3096940032398852, + 0.2517844739361705, + 0.2045501589891655, + 0.17095873582067694, + 0.15374416314390035, + 0.1551341667773345, + 0.17655841863294403, + 0.21837608844356143, + 0.27966678383512766, + 0.3581270820810486, + 0.45010515684740715, + 0.5507891887948595, + 0.6545436014238137, + 0.7553641406853984, + 0.8474024443839668, + 0.9254968503779334, + 0.9856416237252933, + 1.025332812719139, + 1.0437449485178742, + 1.0417163256805626, + 1.021547780767301, + 0.9866461752058964, + 0.941064808393282 + ], + [ + -0.8390715290764524, + -0.7846487400927287, + -0.7219108070199317, + -0.6518989546825863, + -0.5757889280694947, + -0.49486714244479757, + -0.410503226839971, + -0.3241175156890094, + -0.2371410635582505, + -0.15096529214129914, + -0.06687903765599462, + 0.014007015257915433, + 0.0908447187736199, + 0.1631329845193165, + 0.2307888425316077, + 0.29418789165156234, + 0.3541562769169357, + 0.41189952877276675, + 0.4688610857454114, + 0.526514494149682, + 0.5861065255264237, + 0.6483813670741858, + 0.7133258503988955, + 0.7799798312455841, + 0.8463525724201438, + 0.9094748641526884, + 0.9655987292709276, + 1.0105344598102404, + 1.0400920442386214, + 1.0505748175767138, + 1.0392610848408999, + 1.0048071014483184, + 0.947513039507652, + 0.8694114104904991, + 0.7741620629825893, + 0.6667652628236623, + 0.5531299071952522, + 0.43955338533140015, + 0.3321798772346732, + 0.23650357880437622, + 0.15697297402506458, + 0.09673411029564069, + 0.057528388560373955, + 0.0397376966734518, + 0.04255056724574764, + 0.06421024820201317, + 0.10230054234460634, + 0.15402785784015455, + 0.2164666036440551, + 0.2867474260783592, + 0.362181040086674, + 0.44032204063335967, + 0.518985291509152, + 0.5962314702576035, + 0.6703382859552759, + 0.7397707569639147, + 0.8031591488860752, + 0.8592881748959271, + 0.907096991491921, + 0.9456870084152753, + 0.9743336430406948, + 0.9924985168560457, + 0.9998396182472652, + 0.9962180593950399, + 0.9817008585156567, + 0.9565595976777774, + 0.9212650161574981, + 0.8764779009386733, + 0.8230372761940192, + 0.7619479043164955, + 0.6943702306903458, + 0.6216166160401718, + 0.545157385416692, + 0.46663839023960807, + 0.3879082998084939, + 0.31104910672469405, + 0.2383982934296841, + 0.17254712111345658, + 0.11629803952377671, + 0.07256647712363849, + 0.04421879914949912, + 0.03384862430033918, + 0.04350655765913722, + 0.07441145663232013, + 0.1266818595376639, + 0.1991315244418801, + 0.2891711966773006, + 0.3928490080883048, + 0.5050450831764077, + 0.6198142686288206, + 0.7308478711582171, + 0.8320049101314037, + 0.917849491403671, + 0.9841263398955072, + 1.0281125612970432, + 1.0487997141457344, + 1.0468838051955294, + 1.0245680127287053, + 0.9852092459902115, + 0.932860685510909 + ], + [ + -0.8390715290764524, + -0.7830701212568654, + -0.7184089302093679, + -0.6461932646217555, + -0.5676722181152595, + -0.48421252214783755, + -0.39726857123021114, + -0.3083472463816963, + -0.21896518731756434, + -0.13059570046921737, + -0.04460314924354699, + 0.03783511083958051, + 0.11581538498518117, + 0.1887941311356877, + 0.25666063049837023, + 0.31977864129091876, + 0.37897913136639777, + 0.4354893963224292, + 0.49079136904579856, + 0.5464131116264532, + 0.6036707409663646, + 0.663390967107994, + 0.7256542460338875, + 0.7896027079025156, + 0.853353772000701, + 0.914049251121092, + 0.9680518689355448, + 1.011279019086182, + 1.039640911137227, + 1.0495310233583832, + 1.0383046999701515, + 1.0046793609748859, + 0.9489980330747821, + 0.8733157447448971, + 0.7812929677793751, + 0.6779076655297395, + 0.5690230400821038, + 0.46086751828089306, + 0.35949378084076034, + 0.2702833208620572, + 0.197552635137025, + 0.14429897204689032, + 0.11210110093217789, + 0.1011678709723583, + 0.11050816803961838, + 0.13818307413161512, + 0.18159599305291668, + 0.237779084716417, + 0.3036430379007057, + 0.3761695713005486, + 0.4525393103432955, + 0.5301993220763914, + 0.6068828076493102, + 0.6805974412361151, + 0.7495987924348644, + 0.8123621518343143, + 0.8675613066624257, + 0.9140578310207751, + 0.950900402928065, + 0.9773311637551156, + 0.992795265273668, + 0.9969501349273399, + 0.9896720341822272, + 0.9710586052083239, + 0.9414269201188071, + 0.9013069799063406, + 0.851430831869966, + 0.7927177855946267, + 0.7262568548357949, + 0.6532885676882616, + 0.5751894084156869, + 0.49346286434768005, + 0.4097407318432867, + 0.3257964950994291, + 0.24356910116215497, + 0.16519070948115258, + 0.09300694167761177, + 0.02957415374027055, + -0.022383229067180897, + -0.06007003389296461, + -0.08078053113484841, + -0.08211932554395734, + -0.06226793346782955, + -0.020268993952702735, + 0.04371043147339726, + 0.12818031509172245, + 0.23023587453939953, + 0.34560480940660404, + 0.46884899353788967, + 0.5937143867921837, + 0.7135998935215775, + 0.8220955150819859, + 0.9135262456589961, + 0.9834335973813257, + 1.0289326825813025, + 1.0488988042815464, + 1.0439610531167374, + 1.0163076188902058, + 0.9693338491894741, + 0.9071851467824343 + ], + [ + -0.8390715290764524, + -0.7814864074741638, + -0.7148883564813497, + -0.6404497353509654, + -0.5594964128292367, + -0.4734791410311613, + -0.38394086407447264, + -0.29247866684589696, + -0.20069809863085555, + -0.11015714544734131, + -0.02229778436384649, + 0.06163473292997905, + 0.14068092009527985, + 0.214255597588178, + 0.282222210483037, + 0.3449356736056925, + 0.40323579807951404, + 0.4583765777843648, + 0.5118841279204671, + 0.5653472677168001, + 0.6201580147105512, + 0.6772322004545578, + 0.7367502543124261, + 0.7979623742585402, + 0.859099065573712, + 0.9174169297075517, + 0.9693917144073836, + 1.011048547113972, + 1.0383965934019106, + 1.0479161579165286, + 1.0370341612015503, + 1.004521552035701, + 0.9507544485288582, + 0.8777986267153562, + 0.7893016034189744, + 0.6902039249766293, + 0.5863067907814261, + 0.483752572398214, + 0.3884850366441023, + 0.30575574599185906, + 0.2397327096331099, + 0.1932491623079194, + 0.16770787332408155, + 0.16313367686813857, + 0.1783477423396032, + 0.21122428880578448, + 0.25898539829380646, + 0.31849215547094073, + 0.38649902562982563, + 0.4598507426887281, + 0.5356142392903392, + 0.6111497915801685, + 0.6841337769656395, + 0.7525494436572747, + 0.8146620526063587, + 0.8689916508833454, + 0.9142919803435536, + 0.9495390619976171, + 0.9739289641681221, + 0.9868817863115319, + 0.9880480404677431, + 0.9773140176369697, + 0.9548037896001006, + 0.9208766345993779, + 0.8761195098707975, + 0.8213346402128746, + 0.7575225240347324, + 0.6858609772207033, + 0.6076814868521132, + 0.5244451628933909, + 0.43772169453026183, + 0.34917542200346047, + 0.26056230612103315, + 0.17373972494494624, + 0.09068752128536631, + 0.013533961518759855, + -0.05542480679988314, + -0.1137272367235976, + -0.15878774677069443, + -0.18799169646804229, + -0.19885221804916564, + -0.1892267926116729, + -0.15757863717231915, + -0.10325495281797323, + -0.026743611165648662, + 0.07013542581620186, + 0.18414579752889498, + 0.31068965238420543, + 0.4440170291030891, + 0.577583298891489, + 0.7045252196204267, + 0.8182057776712629, + 0.9127641035973756, + 0.9836021968692877, + 1.0277462529651722, + 1.0440364287637387, + 1.0331224517727473, + 0.9972697136644385, + 0.9400068397263799, + 0.8656668134241154 + ], + [ + -0.8390715290764524, + -0.7798976090488572, + -0.7113491774604012, + -0.6346687031946873, + -0.5512623633220137, + -0.4626687449567611, + -0.37052322705506885, + -0.276516836048459, + -0.1823474037986365, + -0.08966039805947291, + 0.000022545058164274295, + 0.08538714599071176, + 0.1654180289474219, + 0.23948938958007615, + 0.3074409885463828, + 0.3696221648315513, + 0.4268858799698827, + 0.4805180441497987, + 0.5320949052476448, + 0.5832724985377615, + 0.635525447095298, + 0.6898653617872998, + 0.7465789421125245, + 0.8050300656509128, + 0.8635669283795387, + 0.9195642099351546, + 0.9696123747890046, + 1.0098441366244968, + 1.0363654355994634, + 1.0457390535454785, + 1.0354569048493278, + 1.00433466083741, + 0.9527705901944687, + 0.8828283119394266, + 0.7981277717182413, + 0.703556101215828, + 0.6048355205110788, + 0.50800485649426, + 0.41888147893360245, + 0.34257010139202665, + 0.2830744751490532, + 0.24304978985862846, + 0.22371110311696388, + 0.22489039470259037, + 0.24521565599530049, + 0.2823726001385606, + 0.33340452170912627, + 0.39500840320049396, + 0.46379404899348015, + 0.5364853896567325, + 0.6100563655411805, + 0.6818054481607196, + 0.7493810957110938, + 0.8107744545720569, + 0.8642955979650337, + 0.9085465110028612, + 0.9423992974646659, + 0.9649831426439994, + 0.9756795573300142, + 0.9741229748599262, + 0.9602029457984417, + 0.9340646015492623, + 0.8961051411541008, + 0.8469652509678579, + 0.7875152163280461, + 0.7188359422897139, + 0.6421953410737318, + 0.5590208673799996, + 0.47086963514240804, + 0.3793985621166309, + 0.28633810005750443, + 0.19347380132909162, + 0.10263962941239195, + 0.01572505001974304, + -0.06530558737099813, + -0.13838765440147432, + -0.20133621052576148, + -0.25184675944665863, + -0.2875337563416385, + -0.3060216385883931, + -0.30509669768939024, + -0.2829177788144354, + -0.23827099649680503, + -0.1708406602482058, + -0.08145814926827005, + 0.027714791910444347, + 0.15310999411642723, + 0.28981595079199873, + 0.4317951133986941, + 0.5722487887242206, + 0.7041000030968084, + 0.8205443349686957, + 0.9156048441051964, + 0.9846226950586733, + 1.0246211545507344, + 1.034496712138506, + 1.0150146726874352, + 0.9686138355301666, + 0.8990512588779355, + 0.8109392285637512 + ], + [ + -0.8390715290764524, + -0.7783037363182621, + -0.7077914852552577, + -0.6288505066734512, + -0.542970926767472, + -0.4517830923136746, + -0.35701880291814264, + -0.26046684268511155, + -0.1639207439344959, + -0.06911625995641228, + 0.022343317361579528, + 0.10907365164727742, + 0.1900035367006329, + 0.2644677631375033, + 0.33228480786085424, + 0.3938019799616411, + 0.44988999015830744, + 0.5018721683904261, + 0.5513811028544668, + 0.6001467095084403, + 0.6497330522690161, + 0.7012542112868441, + 0.7551093661669028, + 0.8107814629560435, + 0.8667406215567514, + 0.9204823628780292, + 0.968712879903097, + 1.007671498058501, + 1.033557794261801, + 1.043011617631502, + 1.0335821624097197, + 1.0041198553324935, + 0.9550330329229976, + 0.8883691838974717, + 0.8077051293798081, + 0.7178578439007987, + 0.6244530998707144, + 0.5334085101843159, + 0.4503977503109251, + 0.3803623651989819, + 0.3271271263682242, + 0.29315667053363903, + 0.2794686418015809, + 0.28569581768710844, + 0.3102705013880488, + 0.35069162845828694, + 0.40382997338350035, + 0.46622940799644325, + 0.534370848777284, + 0.6048778978673115, + 0.6746564581498896, + 0.740972265293951, + 0.8014785338572633, + 0.8542099493395096, + 0.8975592392048009, + 0.9302494977350111, + 0.9513107308371035, + 0.960064171862878, + 0.9561139362239423, + 0.9393431548930399, + 0.909910923676304, + 0.8682468541633654, + 0.8150411149720758, + 0.7512290240284989, + 0.6779701151890248, + 0.5966220696460368, + 0.5087101480544173, + 0.41589308327540886, + 0.31992704195980387, + 0.22263026737544714, + 0.12585211422703077, + 0.03145086214193011, + -0.05871566981771843, + -0.14278458025746152, + -0.2188688407033163, + -0.285030991311034, + -0.3392609484265736, + -0.3794733791539174, + -0.40354166231313016, + -0.40938329706851373, + -0.3951051940732707, + -0.35920697887220243, + -0.3008276624991344, + -0.22000805878706978, + -0.11793088839858162, + 0.0029047473923643485, + 0.1386159639503839, + 0.28400745526163956, + 0.43279647105931995, + 0.5779846023086979, + 0.712346555319548, + 0.8289857518382369, + 0.9218927442786751, + 0.986437933583207, + 1.019736191244013, + 1.020837091330874, + 0.990718093736459, + 0.932085323217959, + 0.8490130094067274, + 0.7464738093646035 + ], + [ + -0.8390715290764524, + -0.7767047996527101, + -0.7042153724564675, + -0.6229954864840294, + -0.5346229663135453, + -0.4408239537319598, + -0.34343075473754375, + -0.24433380355816348, + -0.1454257917833827, + -0.048535557763552506, + 0.044650010597349105, + 0.13267560340879078, + 0.2144144105406959, + 0.2891632551132745, + 0.3567219897142053, + 0.41743972563943105, + 0.47220981756775776, + 0.522398803719238, + 0.5697020713009682, + 0.6159302742024957, + 0.6627438622348624, + 0.7113660785995164, + 0.7623146704804484, + 0.8151967762688392, + 0.868608254854323, + 0.9201676561453702, + 0.9666971845580556, + 1.0045409324918233, + 1.0299879850797518, + 1.039748767526925, + 1.0314209065301012, + 1.0038784779202965, + 0.9575267114884312, + 0.894382006222581, + 0.8179616866720258, + 0.7329952393695748, + 0.6449942244413909, + 0.5597374251794891, + 0.4827379805844267, + 0.4187588459505111, + 0.3714324639269197, + 0.3430222736615302, + 0.33434115803083514, + 0.34481917191877753, + 0.37269368497197897, + 0.4152822301576557, + 0.4692932832016991, + 0.531132765181322, + 0.5971727508472864, + 0.6639612425919342, + 0.7283651592457716, + 0.7876503690501266, + 0.8395108714509797, + 0.8820632912085631, + 0.9138228758653936, + 0.9336741575649661, + 0.9408447605429873, + 0.9348859498098335, + 0.9156595619383264, + 0.883328558478763, + 0.8383476644877274, + 0.7814510260099617, + 0.7136349419039579, + 0.6361349123128867, + 0.550397118913856, + 0.45804492460187457, + 0.36084122604186125, + 0.26064781155444533, + 0.15938351502844428, + 0.05898394563890051, + -0.03863334829599801, + -0.13159377110387907, + -0.2180765090361303, + -0.2963091046645192, + -0.3645471709958038, + -0.4210454902159494, + -0.4640319061981242, + -0.49169953849265025, + -0.5022344433447788, + -0.49389372066892656, + -0.4651426679931321, + -0.41484930204441334, + -0.3425218141416009, + -0.24856156538289778, + -0.13449379780366033, + -0.003132984518114723, + 0.14135838422307706, + 0.293549043438901, + 0.44697085978249873, + 0.5944964003182831, + 0.7288321746932017, + 0.8430772490066875, + 0.9312831157976298, + 0.9889462404658665, + 1.0133708584093746, + 1.003855742777225, + 0.9616823397594593, + 0.8899090126588535, + 0.7930025957891826, + 0.676359652088018 + ], + [ + -0.8390715290764524, + -0.7751008094554808, + -0.7006209321339864, + -0.6171039854794773, + -0.5262193509923692, + -0.4297931117946933, + -0.3297622651739706, + -0.22812286194524173, + -0.1268702485267186, + -0.02792913737532264, + 0.06692811197630025, + 0.156174421346894, + 0.23862778125833073, + 0.31354871338199686, + 0.38072137390348015, + 0.4405008019659627, + 0.4938081907266957, + 0.5420593590684285, + 0.5870191955567473, + 0.6305861274045634, + 0.6745240230409653, + 0.7201719565563822, + 0.7681721708798787, + 0.818260812997521, + 0.8691628311784916, + 0.918621369054011, + 0.963574151159879, + 1.0004672827951662, + 1.0256742099097553, + 1.0359683489616307, + 1.0289857867873713, + 1.0036120370578778, + 0.9602350209041688, + 0.9008242005435698, + 0.8288203485512315, + 0.7488477179693208, + 0.666285807697024, + 0.586757257790852, + 0.5155985643556683, + 0.4573798776623531, + 0.4155296602264308, + 0.3921017050722137, + 0.3876994683696775, + 0.4015498989933496, + 0.4316997277426327, + 0.4752943312815957, + 0.5288942189650583, + 0.5887867623177837, + 0.6512594866494638, + 0.7128136358816722, + 0.7703100297046155, + 0.8210509351980861, + 0.86280997670938, + 0.8938261958250597, + 0.912778432084895, + 0.9187531975325639, + 0.9112145713741366, + 0.8899797875014671, + 0.8552002638578662, + 0.8073454507102624, + 0.7471861221585504, + 0.6757742279332232, + 0.5944175608555445, + 0.5046486938750587, + 0.40818851950428214, + 0.3069051972419775, + 0.20276955466265828, + 0.09780829529859161, + -0.005943012601641838, + -0.10648422585696309, + -0.20188820293709708, + -0.29032705526147357, + -0.3700828880995706, + -0.43954080914407395, + -0.4971656087214201, + -0.5414684053429413, + -0.5709747534014952, + -0.5842098656799711, + -0.5797182358146171, + -0.5561328502850627, + -0.512302808138299, + -0.4474779075780822, + -0.361536019372383, + -0.25522611572457377, + -0.13038939551466172, + 0.009884311244309774, + 0.16120581483169538, + 0.3179727481603976, + 0.47360709119515576, + 0.6209368624145223, + 0.7526918507731385, + 0.8620629891608542, + 0.9432611999516082, + 0.9920071247924874, + 1.0058893514170244, + 0.9845449423587425, + 0.9296397925163411, + 0.8446537292779998, + 0.734501767369553, + 0.6050441572465236 + ], + [ + -0.8390715290764524, + -0.7734917761627333, + -0.6970082578347488, + -0.6111763486490631, + -0.5177609556298183, + -0.41869236074802546, + -0.316016535729499, + -0.21183918595960483, + -0.10826184057568432, + -0.0073078582394664506, + 0.08916312731119216, + 0.17955160672223117, + 0.2626209646741094, + 0.33759732669320736, + 0.4042523584678373, + 0.4629514531456655, + 0.5146491396744922, + 0.5608168716424672, + 0.6032959763897032, + 0.6440798521520722, + 0.6850428828679518, + 0.7276465843839734, + 0.7726634264294208, + 0.819963030139607, + 0.8684022728073917, + 0.9158497874278617, + 0.9593575107468357, + 0.9954698632599724, + 1.0206384639656616, + 1.0316910384381863, + 1.0262910556514637, + 1.003322197833101, + 0.9631399269928375, + 0.9076501479935879, + 0.8401994941588998, + 0.765289014395988, + 0.6881484394905244, + 0.6142275147337586, + 0.5486710112229304, + 0.4958435739810912, + 0.458960052581848, + 0.4398586612945654, + 0.4389317516862449, + 0.45520619566360365, + 0.4865461490210134, + 0.5299381153117235, + 0.5818131661201146, + 0.6383637542958513, + 0.6958212708876979, + 0.7506729078285652, + 0.7998097209683341, + 0.840609519731072, + 0.8709665434020187, + 0.8892840067070529, + 0.8944456923632061, + 0.8857798075074278, + 0.8630237104009164, + 0.826293295086638, + 0.7760569301477023, + 0.7131115045370384, + 0.638557405051552, + 0.5537697619055995, + 0.4603644505261436, + 0.36015854327266295, + 0.2551257841080769, + 0.14734812567907518, + 0.0389645953058772, + -0.06788095567789093, + -0.17109106937976323, + -0.2686617802193479, + -0.35872149431411376, + -0.4395569675306929, + -0.5096221694393764, + -0.5675278273134541, + -0.6120131143413688, + -0.6419058792881036, + -0.6560830592547544, + -0.6534471111590645, + -0.6329359638389023, + -0.593581925593218, + -0.5346286328952748, + -0.4557048823005115, + -0.35704145629044315, + -0.23970410257209593, + -0.10580539074684281, + 0.04134710353845755, + 0.1972069981058841, + 0.3560807089017034, + 0.5113687141491567, + 0.6559491683844204, + 0.7826736518880719, + 0.8849246184199845, + 0.9571703855362139, + 0.9954491456440292, + 0.9977197280417177, + 0.9640330834678038, + 0.8965022303073628, + 0.7990758289782736, + 0.6771470839914963, + 0.5370509272141524 + ], + [ + -0.8390715290764524, + -0.771877710243439, + -0.6933774435802383, + -0.6052129230980631, + -0.5092486607544273, + -0.40752350620932204, + -0.3021967859977225, + -0.19548796690251485, + -0.08960831635398589, + 0.013317412365805447, + 0.11134059044666968, + 0.20278875654703346, + 0.2863714828900045, + 0.3612826541497257, + 0.4272849387095523, + 0.4847588168966215, + 0.5346979558658512, + 0.5786360764099419, + 0.6184981072953223, + 0.6563797605580621, + 0.6942730717845691, + 0.7337685201680116, + 0.7757742974876783, + 0.8202975705591873, + 0.8663294291752464, + 0.9118641780453576, + 0.9540658026182214, + 0.9895723680240671, + 1.0149064236688339, + 1.0269402301444281, + 1.0233524850673146, + 1.0030107715588827, + 0.9662220864728501, + 0.9148115122492751, + 0.852013590337804, + 0.7821881733999478, + 0.7103978978249332, + 0.6419036936673808, + 0.5816448418444263, + 0.5337696042921632, + 0.5012719138532172, + 0.48577128988131457, + 0.4874505644620397, + 0.5051432079114416, + 0.5365428091841388, + 0.5784944179054196, + 0.6273223988570846, + 0.6791520444940264, + 0.7301909256095809, + 0.7769483975484246, + 0.8163850428073085, + 0.8459955975384799, + 0.8638372813240022, + 0.8685196124240071, + 0.8591719267918149, + 0.8354018991454771, + 0.797253793123418, + 0.7451703853705203, + 0.6799586495694689, + 0.6027569710769984, + 0.5150009564621264, + 0.41838542961512, + 0.31482136613075934, + 0.20638772411400272, + 0.09527900207180827, + -0.01625019343938297, + -0.12594208247023297, + -0.23159240138071469, + -0.33110453968973785, + -0.422537920689346, + -0.5041464511621844, + -0.574402331848949, + -0.632001038363925, + -0.6758453384599984, + -0.7050099297979718, + -0.7186932611994046, + -0.7161683867068415, + -0.6967489325916918, + -0.659787954340925, + -0.6047254169639241, + -0.5311936982708679, + -0.43918027786417085, + -0.32923404117377975, + -0.2026886654857851, + -0.061866099275781905, + 0.08978215613756041, + 0.24763645116542932, + 0.40600392084315884, + 0.5583610700657232, + 0.6977366248014183, + 0.8172044147539631, + 0.9104358886269179, + 0.9722482027790289, + 0.9990795145465815, + 0.9893294169453808, + 0.9435187412818402, + 0.8642467641241667, + 0.7559513172367497, + 0.6245038582164391, + 0.47669283028798426 + ], + [ + -0.8390715290764524, + -0.7702586221993158, + -0.6897285838640383, + -0.5992140580274413, + -0.5006833525057326, + -0.3962883648734803, + -0.28830625290963097, + -0.17907441760824264, + -0.070917443071455, + 0.033935805058804364, + 0.1334460726709778, + 0.22586757807231758, + 0.30985708534771933, + 0.38457865427881266, + 0.4497897454534347, + 0.505890972552933, + 0.5539212499744662, + 0.595483472403528, + 0.6325935468043534, + 0.6674569682251952, + 0.7021905729638729, + 0.738520202362245, + 0.7774949902229025, + 0.8192632831406628, + 0.8629520661969532, + 0.9066807428387877, + 0.9477222928226355, + 0.9828027587312469, + 1.0085073157283968, + 1.0217419080020262, + 1.0201872741435192, + 1.0026797044536253, + 0.969460975762067, + 0.9222575818129385, + 0.8641738345555928, + 0.7994105928471318, + 0.7328467011708355, + 0.6695394594173327, + 0.6142105026734074, + 0.5707829544446513, + 0.5420251509388697, + 0.529337891823902, + 0.5326995765816498, + 0.5507607809554441, + 0.5810605937813103, + 0.6203241917848544, + 0.6647960875813436, + 0.7105661014556287, + 0.753853869178531, + 0.791230168368084, + 0.8197667472417196, + 0.8371181480272798, + 0.841547433541733, + 0.8319119339968644, + 0.8076253128490003, + 0.7686093747286327, + 0.7152445086224218, + 0.6483229145572645, + 0.5690049351101834, + 0.47877652319290753, + 0.37940518935627715, + 0.2728923093776184, + 0.16142083460164414, + 0.04729864693436745, + -0.06710134275575082, + -0.1794028260727327, + -0.28728775812447993, + -0.388556127364654, + -0.48118139755274003, + -0.5633583391324458, + -0.6335390488903753, + -0.6904524777651565, + -0.7331033597035668, + -0.760748544293064, + -0.7728525048299553, + -0.7690288184815056, + -0.7489797417760045, + -0.7124502684676025, + -0.6592147783746117, + -0.5891123575519638, + -0.5021405420922109, + -0.39860699639422764, + -0.2793258885641343, + -0.14583273849778153, + -0.0005810243958031536, + 0.15292151159311013, + 0.31007716573451594, + 0.46529390020561595, + 0.6122263558816018, + 0.7441548642444821, + 0.8544722893054276, + 0.9372284306368545, + 0.9876681201903591, + 1.002694893603414, + 0.9811984735367805, + 0.9242006360853544, + 0.8347978758079084, + 0.7179067714078786, + 0.5798445264536748, + 0.4277984317910819 + ], + [ + -0.8390715290764524, + -0.7686345225647546, + -0.686061773649374, + -0.5931801047133938, + -0.4920659225420216, + -0.3849887642174192, + -0.27434818997545274, + -0.16260377078218233, + -0.05219700348984164, + 0.05453645408246083, + 0.155465192103323, + 0.24876990318830333, + 0.3330557696740001, + 0.40745971366417044, + 0.4717380824958995, + 0.526316987788799, + 0.572287007499655, + 0.6113273857034268, + 0.6455525860164862, + 0.6772854620762113, + 0.70877478517469, + 0.7418880001659649, + 0.7778200874465229, + 0.8168637267496041, + 0.858282837173061, + 0.9003205530316924, + 0.9403548718641676, + 0.9751931319574493, + 1.0014737681191628, + 1.0161245035508395, + 1.0168139484879701, + 1.002331065478576, + 0.9728350276408813, + 0.9299356291139621, + 0.8765888224031434, + 0.8168190958276801, + 0.7553056882497179, + 0.6968888365116528, + 0.6460622720020252, + 0.6065176349066637, + 0.5807958822610886, + 0.5700824037455683, + 0.5741599503675904, + 0.5915106720524688, + 0.6195393297627337, + 0.6548769172089853, + 0.6937189051380912, + 0.7321549644160957, + 0.7664558205775706, + 0.7932954034464819, + 0.809899902180586, + 0.8141271933003815, + 0.8044885761896485, + 0.7801290101218104, + 0.7407822783688732, + 0.6867146760490449, + 0.6186663309918045, + 0.5377945586484786, + 0.4456198547454963, + 0.3439728609565441, + 0.2349399631489924, + 0.12080572222515207, + 0.003991492417921561, + -0.11300923533418447, + -0.22769739658827193, + -0.3376350618136104, + -0.4405103981100792, + -0.5341988214783523, + -0.6168178160791701, + -0.6867721098049759, + -0.7427850380139596, + -0.7839115094988239, + -0.8095286213906985, + -0.8193021356764982, + -0.8131308498546435, + -0.7910759632988524, + -0.7532879031876671, + -0.6999473638138755, + -0.6312390502489412, + -0.5473745935097646, + -0.44867476918322036, + -0.3357108913524089, + -0.20949247565979343, + -0.07167523791017186, + 0.07524704495997006, + 0.2278086876920109, + 0.3815364517341312, + 0.5310427701300071, + 0.6702619248913348, + 0.7928218857751014, + 0.8925218075672848, + 0.9638651506673293, + 1.0025848531309423, + 1.0060927845029137, + 0.9737921133862164, + 0.9072075881128567, + 0.8099125962594895, + 0.6872593642200926, + 0.5459452260147645, + 0.39346914492416685 + ], + [ + -0.8390715290764524, + -0.7670054219067546, + -0.6823771083666389, + -0.5871114164867842, + -0.48339726794751503, + -0.37362654220283176, + -0.2603258665225885, + -0.14608127733265686, + -0.033454792682116194, + 0.07510850303049739, + 0.17738362305075073, + 0.2714777027267132, + 0.3559458022934605, + 0.4299006751073047, + 0.49310196319589394, + 0.546006963895903, + 0.5897646420833507, + 0.6261380289858657, + 0.6573479112176766, + 0.6858421614415424, + 0.7140085763857418, + 0.7438622526256784, + 0.7767485656678674, + 0.8131071579870986, + 0.8523392353841406, + 0.8928094634820654, + 0.9319959320762398, + 0.9667795670320785, + 0.9938416437170796, + 1.0101187404463432, + 1.0132522517796498, + 1.0019670334081197, + 0.9763217748647112, + 0.9377912838865573, + 0.8891652346503356, + 0.834275023272725, + 0.7775856119714556, + 0.7237083985152093, + 0.6769011300324551, + 0.6406202996849117, + 0.6171808466328484, + 0.6075595999673535, + 0.6113562897164997, + 0.6269031394081377, + 0.6514948342095601, + 0.6816978473386899, + 0.7136931134399465, + 0.7436087170166907, + 0.7678081036961153, + 0.7831118820483862, + 0.7869447837335184, + 0.7774112628979054, + 0.753311739469944, + 0.7141158064026876, + 0.6599090054416052, + 0.5913269955431663, + 0.5094864929022087, + 0.4159176877780551, + 0.3124990714682452, + 0.20139335385458956, + 0.0849824810318396, + -0.03419970325750647, + -0.15353747115123964, + -0.27040506324078195, + -0.382238751699716, + -0.4866071396626354, + -0.5812776469368176, + -0.664277022383395, + -0.7339433279253831, + -0.7889661198225857, + -0.8284107634525577, + -0.851722466273152, + -0.8587063085133578, + -0.8494817733635562, + -0.8244141466598569, + -0.7840302639501502, + -0.7289314730461806, + -0.6597209860582182, + -0.576964535294143, + -0.4812012137548305, + -0.37301502296209305, + -0.25316735542777374, + -0.12277781410030222, + 0.01647231367705415, + 0.16215021593138396, + 0.31093711388840206, + 0.45858937244245723, + 0.6000258774891288, + 0.7295558898375213, + 0.8412402898289769, + 0.9293564882449169, + 0.9889173128885407, + 1.0161807003844674, + 1.009082870501144, + 0.9675340947082208, + 0.8935325568809378, + 0.7910756743996361, + 0.6658757321277252, + 0.5249132233783992, + 0.37588250549365976 + ], + [ + -0.8390715290764524, + -0.7653713308248532, + -0.6786746839109166, + -0.5810083487124504, + -0.474678291138972, + -0.3622035469772326, + -0.24624256692986596, + -0.1295122046968944, + -0.014698614786645418, + 0.09564111056868958, + 0.19918710532848258, + 0.2939731006536926, + 0.3785057387895754, + 0.4518768652872856, + 0.5138541461610799, + 0.5649320795478309, + 0.6063250464486661, + 0.6398875575247119, + 0.6679546614491495, + 0.6931069722606296, + 0.7178783283429586, + 0.7444372963487749, + 0.7742837983163773, + 0.8080065027797241, + 0.845143528552157, + 0.8841780075795643, + 0.9226822252022636, + 0.9576019549759978, + 0.9856498574404571, + 1.0037574664206348, + 1.0095230302111935, + 1.0015898832138093, + 0.9798979997696059, + 0.945768918179868, + 0.901808538694884, + 0.8516393383766081, + 0.799498734094884, + 0.7497594346760977, + 0.7064375660314527, + 0.6727537402244523, + 0.6508015976482651, + 0.6413599576027056, + 0.643862091122232, + 0.6565128280091341, + 0.6765250068702671, + 0.7004339931391218, + 0.7244440330442391, + 0.744762936271991, + 0.757890472184125, + 0.760838482235731, + 0.7512742727005647, + 0.7275908279452931, + 0.6889159707018826, + 0.6350769710564359, + 0.5665374456093433, + 0.4843206564147585, + 0.3899289143916176, + 0.28526414852141974, + 0.17255094962845932, + 0.0542611520015041, + -0.06696165983177213, + -0.1883788151938131, + -0.3072343804432554, + -0.4208330159858738, + -0.5266160007845317, + -0.6222332726092127, + -0.7056093241081739, + -0.7750007575043016, + -0.8290429805715743, + -0.866782882835281, + -0.887693615080473, + -0.8916673127495454, + -0.8789823609441225, + -0.8502440742998598, + -0.8063015757344557, + -0.7481487960776584, + -0.6768229233308208, + -0.5933179383392023, + -0.49853260147223005, + -0.3932701940949863, + -0.27830091946434093, + -0.1544875181631576, + -0.022961762668557734, + 0.11467366427452835, + 0.2561540910598606, + 0.398414325463899, + 0.5375428965904903, + 0.6688599459905407, + 0.7871332244748129, + 0.8869254349099761, + 0.9630435933083709, + 1.0110411737112446, + 1.0277103686698752, + 1.0114976761116619, + 0.9627824728454433, + 0.8839746193076052, + 0.7794109914910359, + 0.6550582843996942, + 0.5180559211623752, + 0.37615404896707555 + ], + [ + -0.8390715290764524, + -0.7637322599510579, + -0.6749545966394775, + -0.574871258768402, + -0.4659098997717505, + -0.3507216365733316, + -0.23210158985824314, + -0.1129018351617594, + 0.0040637202423917836, + 0.11612345614817601, + 0.2208614535376258, + 0.31623838814218674, + 0.40071444399477857, + 0.47336412188849675, + 0.5339681699837201, + 0.5830646329874755, + 0.6219406408751903, + 0.6525501215409019, + 0.6773504809067421, + 0.6990628342696598, + 0.7203739720039941, + 0.7436114817496827, + 0.770433545121173, + 0.8015793119029023, + 0.8367226754143826, + 0.8744612731239886, + 0.9124547008092807, + 0.9477038093672513, + 0.9769401778293472, + 0.9970754736260775, + 1.0056481104785742, + 1.0012019718472236, + 0.9835398888747703, + 0.9538120402740743, + 0.914423699131672, + 0.8687737340277699, + 0.8208604071742911, + 0.7748100745995279, + 0.7343942962151974, + 0.7026002197407846, + 0.6813084399701433, + 0.671114131518561, + 0.6713046340829226, + 0.6799838816399913, + 0.6943148898402077, + 0.7108387690522537, + 0.7258238204639849, + 0.7356010529245304, + 0.7368514125799647, + 0.726822702133828, + 0.7034677975313215, + 0.6655078156661275, + 0.6124325403389296, + 0.5444548517757075, + 0.4624363006928544, + 0.3677982830626386, + 0.26242890310652617, + 0.14859099277122076, + 0.028833014267617495, + -0.09409767634167956, + -0.21734041961235262, + -0.33800642661952873, + -0.45326320185524827, + -0.560416822973905, + -0.6569900101576507, + -0.7407937065874042, + -0.8099899633441571, + -0.863143968080694, + -0.8992628205243237, + -0.9178180958220563, + -0.91874859757304, + -0.9024394889664, + -0.8696748065970829, + -0.8215626838986668, + -0.759436554684937, + -0.6847407627770508, + -0.598914411694396, + -0.5032915811220937, + -0.3990377330070459, + -0.2871400220108231, + -0.16846276420884215, + -0.043868894094625965, + 0.08560473530136652, + 0.21854360686642793, + 0.3529595319108513, + 0.4861442270718224, + 0.614612899828413, + 0.7341690089521302, + 0.840105033081047, + 0.9275329395903514, + 0.9918155399287091, + 1.0290500572753958, + 1.0365418279160914, + 1.0132019474506233, + 0.9598091139717896, + 0.8790922770276326, + 0.7756145061756425, + 0.6554658774980899, + 0.5257995866615033, + 0.3942665511113955 + ], + [ + -0.8390715290764524, + -0.7620882199497762, + -0.6712169433692761, + -0.5687005060248838, + -0.45709300664532504, + -0.3391826786068245, + -0.2179062474781925, + -0.09625546417973382, + 0.022824399887024935, + 0.13654474570781466, + 0.24239256629422679, + 0.33825603751267497, + 0.4225511117908428, + 0.49433882016654523, + 0.5534183869820017, + 0.6003780825756533, + 0.6365854191302978, + 0.6641019148012793, + 0.6855155660601404, + 0.7036957610649064, + 0.7214890137377357, + 0.7413871777819142, + 0.7652099276814077, + 0.7938477005906748, + 0.8271082247224066, + 0.8636987596895849, + 0.901358326245036, + 0.9371320600312452, + 0.9677570140747848, + 0.990109308344196, + 1.0016501720320115, + 1.000805723510502, + 0.9872231914525317, + 0.9618636947130704, + 0.9269158921014853, + 0.8855417344276172, + 0.8414906304618127, + 0.7986373520382867, + 0.7605088668492916, + 0.729864615014263, + 0.7083840665726013, + 0.6964969902638654, + 0.6933692548172641, + 0.6970342196469528, + 0.7046406307177195, + 0.7127752382981307, + 0.7178135012680714, + 0.7162545893028092, + 0.7050059211735562, + 0.6815952384443885, + 0.6443019221433173, + 0.592211381456374, + 0.5252050684139618, + 0.4439031749100268, + 0.3495775189479937, + 0.24404948549695904, + 0.12958354871936606, + 0.008782298409156584, + -0.11551484834428881, + -0.2403293338146293, + -0.36263835282307766, + -0.4794673213061284, + -0.587979285221484, + -0.68555964866488, + -0.7698940061119018, + -0.8390367404186261, + -0.8914682120541337, + -0.9261385039653867, + -0.9424955408486001, + -0.9404949247948198, + -0.9205882669190731, + -0.8836866467390114, + -0.8310966991238047, + -0.7644291871826654, + -0.6854838819430832, + -0.5961197258508449, + -0.4981246472171106, + -0.3931036492104447, + -0.2824054393921993, + -0.16710568794071176, + -0.048058458203397256, + 0.07398316566092678, + 0.1981893078725863, + 0.3234438019776183, + 0.4481392742799039, + 0.5700188912271814, + 0.6861055321471188, + 0.7927499835778555, + 0.8858134984397473, + 0.9609789869175441, + 1.0141626466208236, + 1.0419780061668162, + 1.0421909577063866, + 1.0141002278416342, + 0.9587841401474687, + 0.8791708213106025, + 0.7799127309771998, + 0.667073686218896, + 0.5476628539916137, + 0.42907112054078844 + ], + [ + -0.8390715290764524, + -0.7604392215177467, + -0.6674618213744297, + -0.5624964518233385, + -0.4482285296082534, + -0.32758854997260045, + -0.20365986469389483, + -0.07957839868070028, + 0.041575612318580084, + 0.15689421736258846, + 0.26376643540367245, + 0.3600087160313163, + 0.443995284600991, + 0.5147778989231251, + 0.5721799959037661, + 0.616847085641598, + 0.6502349917799619, + 0.674521219375465, + 0.6924327073926997, + 0.7069948729475964, + 0.7212205522206467, + 0.7377707651424226, + 0.7586293913043174, + 0.7848382724396917, + 0.8163361970446411, + 0.8519342180550149, + 0.8894418889300792, + 0.925936830533563, + 0.9581471895837939, + 0.9828970711005919, + 0.997552614335814, + 1.0004036145066046, + 0.9909233810098615, + 0.9698668656209803, + 0.9391912180496279, + 0.9018097821229458, + 0.861215566655168, + 0.8210291894307624, + 0.784536118126421, + 0.754277334580665, + 0.7317468591071907, + 0.7172311688639079, + 0.7098029542863818, + 0.7074589270497464, + 0.7073722993678895, + 0.7062179150935675, + 0.7005232310120572, + 0.6870012712681213, + 0.662830787889564, + 0.625861706788594, + 0.5747377314902673, + 0.5089401789610973, + 0.4287659205405429, + 0.3352568672834539, + 0.23009894120252802, + 0.1155058695758853, + -0.00590117804537742, + -0.13121169052551712, + -0.2573389895636209, + -0.3811281098485387, + -0.49945878985252157, + -0.6093436025345522, + -0.7080203285606117, + -0.7930367768633191, + -0.8623257598557422, + -0.9142679086882575, + -0.9477402805490334, + -0.9621489495257297, + -0.9574437217351192, + -0.9341127242076105, + -0.8931541278198306, + -0.836022174651436, + -0.7645455885535045, + -0.6808188272919112, + -0.5870705976947146, + -0.48551919113826414, + -0.3782295459650191, + -0.26699113953070824, + -0.15323737680260138, + -0.03802487096856911, + 0.0779156390773803, + 0.19405562350999395, + 0.3098844367800426, + 0.424689903937052, + 0.5373404030552822, + 0.6461109108331613, + 0.7485942585855834, + 0.8417297661438273, + 0.9219652356474073, + 0.9855472576799962, + 1.0289123471463573, + 1.0491315932756415, + 1.0443480856534484, + 1.014142204368767, + 0.959766195709092, + 0.8842056625564989, + 0.7920492177162636, + 0.6891747157164992, + 0.5822866470135878, + 0.4783600738724516 + ], + [ + -0.8390715290764524, + -0.7587852753839694, + -0.6636893283836872, + -0.5562594594552444, + -0.43931739146263216, + -0.31594113653945655, + -0.1893657783644766, + -0.06287595538008327, + 0.06030954965046618, + 0.17716114707503805, + 0.2849691549744463, + 0.38147929955462245, + 0.4650268725554783, + 0.5346588858612736, + 0.5902290735509418, + 0.6324475355784506, + 0.6628666268069503, + 0.6837884464666156, + 0.6980873256738703, + 0.7089524224731688, + 0.7195692859859701, + 0.7327726179677675, + 0.7507126532311152, + 0.7745820278692583, + 0.8044469498151559, + 0.839215472351719, + 0.8767577818541537, + 0.9141712005303823, + 0.9481597032347134, + 0.9754782082791263, + 0.9933794199140614, + 0.9999981577639778, + 0.9946158186061711, + 0.9777648804465211, + 0.9511574075368521, + 0.9174483017936809, + 0.8798690067100933, + 0.8417862855228997, + 0.8062504856911146, + 0.7755969844626946, + 0.7511538170648067, + 0.7330900996581737, + 0.7204172991507256, + 0.7111327172581039, + 0.7024755228502705, + 0.6912531000693757, + 0.674190780411345, + 0.6482610413539334, + 0.6109574578002174, + 0.5604916330551876, + 0.49590521978785007, + 0.41710142777578985, + 0.32480928813269777, + 0.22049857135819523, + 0.1062638045532818, + -0.015306742909556517, + -0.14126554421899876, + -0.2684368199061765, + -0.3935408979022279, + -0.5133111095076938, + -0.6246032418411983, + -0.724497274348542, + -0.8103902958554162, + -0.8800787061293116, + -0.9318274197427562, + -0.9644238815794209, + -0.9772150814744482, + -0.9701261005126224, + -0.9436587662450451, + -0.8988686847884725, + -0.8373184936428095, + -0.7610051346682146, + -0.6722598790257609, + -0.5736222165520142, + -0.4676926628423173, + -0.35697462795020724, + -0.24372077275556017, + -0.12980338818196302, + -0.016629792550179448, + 0.09487864698020934, + 0.20423204121389182, + 0.31124099345367084, + 0.41582137426625587, + 0.5177607415598475, + 0.6164834268266647, + 0.7108572988921871, + 0.7990840879276879, + 0.8787061433617727, + 0.9467463608272226, + 0.9999770051385802, + 1.035290715775218, + 1.0501271157209502, + 1.0428949629320765, + 1.0133255259159313, + 0.9626990919076786, + 0.893902598481086, + 0.8112998581684451, + 0.7204228623193641, + 0.627518662952571, + 0.5390069709575762 + ], + [ + -0.8390715290764524, + -0.7571263923096361, + -0.6598995625778844, + -0.5499898941408478, + -0.4303605198680371, + -0.30424233284335217, + -0.17502733652242083, + -0.0461534590838457, + 0.07901841118933473, + 0.19733485430676384, + 0.30598693046534203, + 0.4026508860099262, + 0.4856261723126897, + 0.5539599222931473, + 0.6075426052843611, + 0.6471565971294622, + 0.6744592874687619, + 0.6918861732393116, + 0.7024675026877858, + 0.7095638126449269, + 0.7165395116061546, + 0.7264070740745929, + 0.7414846374137223, + 0.7631142574524109, + 0.791485026134428, + 0.8255942256536597, + 0.8633617732199846, + 0.9018909541032544, + 0.9378454795401588, + 0.9678932963751055, + 0.9891550139837074, + 0.999591887132329, + 0.998275916919878, + 0.9855018112761973, + 0.9627245147972723, + 0.9323327323228559, + 0.8972937703744795, + 0.8607238892730951, + 0.8254481182052537, + 0.7936127550835841, + 0.7664030852666674, + 0.7439004880629895, + 0.7250905823988171, + 0.7080114377955309, + 0.6900119179380434, + 0.668077744471709, + 0.6391782655881775, + 0.6005900303307086, + 0.5501625771450063, + 0.4865048875048806, + 0.40908493499320986, + 0.31824713251213754, + 0.21516142575551556, + 0.10172246479533581, + -0.019582129270364845, + -0.14581794544957596, + -0.2737522684972801, + -0.39999736409974695, + -0.5211448939442849, + -0.6338902059627847, + -0.7351461726323568, + -0.8221460600068589, + -0.892534193409036, + -0.9444425150853005, + -0.9765508679750796, + -0.9881290548480112, + -0.9790592176534101, + -0.9498375337893641, + -0.901554362765063, + -0.835851740941345, + -0.7548567497900756, + -0.6610892666212906, + -0.5573435414955242, + -0.4465454010160221, + -0.33159077106917006, + -0.21517622735287756, + -0.09963746862077906, + 0.013184398461298666, + 0.12202754425282034, + 0.22622638278809748, + 0.3256490573268587, + 0.42055459668229994, + 0.5113823699255948, + 0.598500212230011, + 0.6819488478355078, + 0.7612263243503455, + 0.8351551181526777, + 0.9018656089225237, + 0.9589135012705859, + 1.0035277515758894, + 1.0329630756379105, + 1.044911175840216, + 1.0379112464095919, + 1.0116959348534929, + 0.9674150217731031, + 0.9076950053201233, + 0.8365160870713346, + 0.7589149004390467, + 0.6805471649963679, + 0.6071649276338688 + ], + [ + -0.8390715290764524, + -0.7554625830880597, + -0.6560926225873935, + -0.5436881230077686, + -0.421358847244943, + -0.29249404177923777, + -0.1606478975893876, + -0.0294162409909326, + 0.09769440668324421, + 0.21740470764700384, + 0.32680608766022234, + 0.42350680870103086, + 0.50577388551812, + 0.5726597871731397, + 0.6240985143700547, + 0.6609527398132412, + 0.6849936673322445, + 0.6987991755753301, + 0.7055640063532346, + 0.7088276077093624, + 0.7121391125132315, + 0.7186923938297891, + 0.7309743960476816, + 0.7504744204850626, + 0.7774989878884834, + 0.8111258497987461, + 0.8493127612472686, + 0.88915431527146, + 0.927257108991501, + 0.9601838200674733, + 0.9849041214966522, + 0.9991873415476608, + 1.0018793039712495, + 0.993022870874517, + 0.9738055938300134, + 0.9463445189283561, + 0.9133430306428152, + 0.8776734442511621, + 0.8419487910749115, + 0.8081465057608758, + 0.7773360533954903, + 0.7495442062056591, + 0.7237692188734768, + 0.6981326009341741, + 0.6701383157861791, + 0.6369968580957205, + 0.5959671683571413, + 0.5446725737366671, + 0.4813563654020283, + 0.40505577327921927, + 0.31568717770354365, + 0.21404785510783056, + 0.10174856846085994, + -0.018903955345069584, + -0.14505500877562802, + -0.2734632540985046, + -0.4006626852103897, + -0.5231171315890935, + -0.6373631416819655, + -0.7401395900393949, + -0.8285033898326227, + -0.8999306257525584, + -0.9524018384771473, + -0.9844696586569124, + -0.9953068638814218, + -0.9847332778723619, + -0.9532205463118458, + -0.9018743792373795, + -0.8323940701627007, + -0.7470089259608588, + -0.6483909024127837, + -0.5395427285894846, + -0.4236617136506346, + -0.3039817334905437, + -0.1835997055656408, + -0.06529777169219343, + 0.04862253198901452, + 0.15647398840562513, + 0.25726399275452166, + 0.3507028144096926, + 0.4371283039982673, + 0.5173465933576684, + 0.592401956670379, + 0.6633028737506437, + 0.7307426947459688, + 0.7948594720345954, + 0.8550785187204311, + 0.9100723069074808, + 0.9578561806468505, + 0.9960172866916716, + 1.0220515594238098, + 1.0337635453239917, + 1.0296701320383803, + 1.0093447049963342, + 0.9736441612650806, + 0.9247769475139819, + 0.8661934098270112, + 0.8023064020097227, + 0.7380757669858188, + 0.6785106268533958 + ], + [ + -0.8390715290764524, + -0.753793858544605, + -0.6522686074895492, + -0.5373545150695074, + -0.4123133106776625, + -0.2806981742915485, + -0.14623082958957587, + -0.012669636993650195, + 0.11632975956546038, + 0.23736013041530785, + 0.34741308156450357, + 0.4440306494285669, + 0.5254511368839444, + 0.5907379204299453, + 0.6398756901296188, + 0.6738157694391318, + 0.6944522224265561, + 0.7045144566957235, + 0.7073703101820824, + 0.7067455365277948, + 0.7063795384862299, + 0.7096507077683225, + 0.7192150181082841, + 0.7367060092110458, + 0.7625412338116446, + 0.7958691602963373, + 0.8346725152149218, + 0.8760216719359523, + 0.9164485799087976, + 0.9523919453238099, + 0.9806516224275025, + 0.9987870491655165, + 1.0054019854113203, + 1.0002748006467699, + 0.9843173519426515, + 0.9593720574540959, + 0.9278815509706497, + 0.892484088895204, + 0.8555975983727429, + 0.8190545261688329, + 0.783839005729261, + 0.7499595837378723, + 0.7164683596940434, + 0.6816149328634925, + 0.6431047885047027, + 0.5984194950655931, + 0.5451517150287468, + 0.4813113879802264, + 0.4055689875040472, + 0.31741501755680224, + 0.21722909236492224, + 0.10626448360825147, + -0.01343690728065623, + -0.1391794263799332, + -0.26777804909528996, + -0.395734498382093, + -0.5194117143316896, + -0.6351980481857162, + -0.7396565559309652, + -0.8296573898177173, + -0.9024924561859079, + -0.9559715856772963, + -0.9884990286111517, + -0.9991287059377283, + -0.9875966670013737, + -0.9543296850281103, + -0.9004296534105292, + -0.827634096531857, + -0.7382533982130864, + -0.635085214582308, + -0.5213062074328623, + -0.4003411983904072, + -0.27571069847189916, + -0.15085998470530654, + -0.028976591758113304, + 0.08719214062268738, + 0.19550492183677728, + 0.2945555445785253, + 0.38374388116939756, + 0.4632704951593099, + 0.5340437806405266, + 0.597499771450627, + 0.6553485218409169, + 0.7092749463089896, + 0.760633447821915, + 0.8101818794494096, + 0.857899390834054, + 0.9029237398387158, + 0.9436274491544343, + 0.9778310179166595, + 1.0031287010048935, + 1.0172821588698562, + 1.0186233788780115, + 1.006403529333988, + 0.9810301073765293, + 0.9441502711652148, + 0.8985611662809132, + 0.847954528172503, + 0.7965283451375844, + 0.7485185503527918 + ], + [ + -0.8390715290764524, + -0.7521202295366174, + -0.6484276168060741, + -0.530989441203835, + -0.4032248518167991, + -0.2688566490633617, + -0.1317795093608531, + 0.004081014023426278, + 0.13491671019258267, + 0.2571906062353829, + 0.3677945052175534, + 0.46420625141472666, + 0.5446394918722411, + 0.6081744455718694, + 0.6548540148587413, + 0.6857268576665884, + 0.7028192004609172, + 0.7090212715953944, + 0.7078826070351226, + 0.7033224885164144, + 0.6992757758590078, + 0.699307953109001, + 0.7062435251778628, + 0.72185639917024, + 0.7466678031745145, + 0.7798861772358483, + 0.8195054038801445, + 0.86255528956374, + 0.9054750031634319, + 0.9445602887791137, + 0.9764224061540013, + 0.9983935115615982, + 1.008820504296231, + 1.0072062477770976, + 0.9941807758339799, + 0.9713115832993153, + 0.9407868248238682, + 0.9050239992731695, + 0.8662664070723946, + 0.8262289573520645, + 0.7858443039184351, + 0.7451420817233387, + 0.703271718529454, + 0.658656946810936, + 0.609251502468939, + 0.5528533702919248, + 0.48743070478383416, + 0.41141604703991547, + 0.32393513023411497, + 0.22494994632156642, + 0.11531002291960951, + -0.0032815254938611212, + -0.12837148458480968, + -0.2569090899542502, + -0.3854265545162975, + -0.5102291056714756, + -0.6275805178231765, + -0.7338749816093041, + -0.8257902751473539, + -0.9004199673117123, + -0.9553837090073648, + -0.9889149103619561, + -0.9999248346058918, + -0.9880419183964669, + -0.9536252990869666, + -0.8977521413355488, + -0.822179196396923, + -0.7292797586050237, + -0.6219575212686699, + -0.5035387115503599, + -0.37764353078869584, + -0.2480378327387622, + -0.11846673395132715, + 0.00752605620953975, + 0.12678585293603692, + 0.23672962725640778, + 0.33550700351543716, + 0.42211949161221435, + 0.4964770224618642, + 0.5593739145445615, + 0.6123738324140121, + 0.6576046793499533, + 0.6974782488445574, + 0.734363534971441, + 0.7702540944246563, + 0.8064760792615071, + 0.8434825343113113, + 0.8807705091665625, + 0.9169412217704572, + 0.9499021928235718, + 0.9771873942363232, + 0.9963510422634496, + 1.005376544695826, + 1.0030371438723946, + 0.9891502698913117, + 0.9646829294439286, + 0.9316881749340444, + 0.8930789962155304, + 0.8522713389670101, + 0.8127480361181857 + ], + [ + -0.8390715290764524, + -0.7504417069533516, + -0.6445697505004889, + -0.5245932741310798, + -0.3940944167812112, + -0.2569713922043231, + -0.11729732176381867, + 0.02083037188551544, + 0.15344751907559936, + 0.2768856845771591, + 0.38793709841529794, + 0.48401773202220155, + 0.5633209739652985, + 0.6249501915405893, + 0.6690143894795787, + 0.6966685695652747, + 0.7100806670584994, + 0.7123111472442234, + 0.7070998171463698, + 0.6985665021642576, + 0.6908463085260128, + 0.687693799350284, + 0.6921007548922165, + 0.7059766861847189, + 0.7299381658326817, + 0.7632418731683936, + 0.803878112468361, + 0.8488190159702189, + 0.8943923311770522, + 0.9367316846497162, + 0.972241225782423, + 0.9980091880984907, + 1.0121120972830775, + 1.0137681288712466, + 1.0033217255100506, + 0.9820679979056686, + 0.9519501079605601, + 0.9151815623976904, + 0.8738550599428043, + 0.8295988582393212, + 0.7833310905042923, + 0.7351443422359021, + 0.6843306117141802, + 0.6295345572631953, + 0.5690044379619563, + 0.5008981774590049, + 0.42359789999259895, + 0.3359899251831308, + 0.23767701372258654, + 0.1291031520462076, + 0.011585533212442729, + -0.11273892847922747, + -0.24103605421704793, + -0.3699445451222105, + -0.4957719543033749, + -0.6146973106090098, + -0.7229657697431854, + -0.817065650525542, + -0.8938824877000948, + -0.9508276647978718, + -0.9859406949820743, + -0.9979646421561624, + -0.9863940862784564, + -0.9514949835517128, + -0.8942960923717392, + -0.8165523724364728, + -0.7206816994586105, + -0.6096770281184812, + -0.4869964943931689, + -0.35643380662370133, + -0.22197086248695228, + -0.0876143375899362, + 0.042781379497540864, + 0.16570060081498764, + 0.2781544383496143, + 0.37785853069847164, + 0.46338383859068066, + 0.5342606820723705, + 0.5910155991392799, + 0.6351238555855028, + 0.6688680349002836, + 0.6951046526793183, + 0.7169547190668247, + 0.7374483022441624, + 0.7591646474630391, + 0.783915594501996, + 0.8125189275271931, + 0.8446991200225438, + 0.8791364581500665, + 0.9136640085431381, + 0.9455887952138201, + 0.9720928951546962, + 0.9906557906737787, + 0.9994341007278038, + 0.9975400500727984, + 0.9851751317962594, + 0.9635979544594049, + 0.9349314177836203, + 0.9018396226220098, + 0.8671249533462111 + ], + [ + -0.8390715290764524, + -0.7487583017159047, + -0.6406951089755097, + -0.5181663883922947, + -0.3849229560595242, + -0.24504433693733893, + -0.10278765888898161, + 0.0375730968301039, + 0.171914470102567, + 0.29643498626414067, + 0.40782775633734186, + 0.5034494952573172, + 0.581478081506829, + 0.641046713789277, + 0.6823387578942649, + 0.7066248891355567, + 0.7162245289627551, + 0.7143778985167768, + 0.7050235903988766, + 0.692488746156082, + 0.6811130698474386, + 0.6748415631595783, + 0.6768312323731036, + 0.6891215105438253, + 0.7124149994227267, + 0.7460039089863546, + 0.7878593494783469, + 0.8348779785991501, + 0.8832570726295867, + 0.928948950457165, + 0.9681325532705475, + 0.9976364805561928, + 1.015254846207185, + 1.0199139775289487, + 1.0116714915692262, + 0.9915556262191136, + 0.9612773347471135, + 0.9228663696509578, + 0.8782923158097795, + 0.8291309071117323, + 0.7763255058610291, + 0.720075613126167, + 0.6598622232133217, + 0.594597763960875, + 0.5228700290094868, + 0.44323769648366385, + 0.35453111062773557, + 0.2561157931459824, + 0.1480860923969832, + 0.03136998710656383, + -0.09225948583363335, + -0.22025797900412852, + -0.3494513852245364, + -0.47622305364589657, + -0.5967240176639022, + -0.7070863619428408, + -0.8036245365173039, + -0.8830145648612803, + -0.9424455448642644, + -0.9797409666527718, + -0.99344907507311, + -0.9829021266489124, + -0.948244489868322, + -0.8904296532940625, + -0.8111866690742345, + -0.7129574061459016, + -0.5988069946135346, + -0.47231066320498816, + -0.33742051633504594, + -0.19831559720328099, + -0.05923806988165302, + 0.07568197897627754, + 0.20261016446689994, + 0.31819517261085606, + 0.4197520585307908, + 0.5054294964706089, + 0.5743447402448342, + 0.6266670114384228, + 0.6636296491846749, + 0.6874547878482495, + 0.701182075398094, + 0.7084045663603316, + 0.7129289606532988, + 0.7183914970371305, + 0.7278722676002309, + 0.7435568134001991, + 0.7664926098536498, + 0.7964786935784702, + 0.8321099814197294, + 0.870976065740996, + 0.9099909015879825, + 0.9458088718861425, + 0.9752680749116612, + 0.9957962072742086, + 1.0057194228887536, + 1.0044294516407841, + 0.9923866479757129, + 0.9709626945037052, + 0.9421519058930526, + 0.9082001280191889 + ], + [ + -0.8390715290764524, + -0.7470700247771397, + -0.6368037930704352, + -0.5117091603273302, + -0.3757114244111889, + -0.2330774232841286, + -0.08825391926224475, + 0.054303851209272364, + 0.1903098737515553, + 0.3158282089431697, + 0.427453538072996, + 0.5224862440475033, + 0.5990938040983118, + 0.6564463145619684, + 0.6948101300085022, + 0.7155812427520202, + 0.7212405541775441, + 0.7152176398206417, + 0.7016583028472831, + 0.6851034931444688, + 0.670101385578917, + 0.6607881128001682, + 0.6604830300515688, + 0.6713488689931435, + 0.6941639545402688, + 0.7282423588745494, + 0.7715195445916301, + 0.8207982757355726, + 0.8721260043309914, + 0.9212546528434149, + 0.9641204361961657, + 0.9972777181225013, + 1.0182278240314857, + 1.025600273384685, + 1.0191673116673237, + 0.9996988990934024, + 0.968689910787348, + 0.9280100214775492, + 0.879536518357825, + 0.8248297310893349, + 0.7649004163051343, + 0.7001005542423553, + 0.6301471143312443, + 0.5542664455191354, + 0.47142879085498035, + 0.3806307943190516, + 0.2811801228796694, + 0.17294027454656394, + 0.05650371935763853, + -0.06672476593686724, + -0.1945381854283501, + -0.32402168739802395, + -0.4517128950078629, + -0.5738051820698922, + -0.6863704481920281, + -0.7855808581266646, + -0.8679138531914998, + -0.9303300701682591, + -0.9704184623164, + -0.9865062592254927, + -0.9777333238210066, + -0.9440912956706042, + -0.8864281994197013, + -0.8064194765535225, + -0.7065069908646642, + -0.5898084928091654, + -0.4600010774841587, + -0.32118298517736577, + -0.17771838251291144, + -0.034069458545875286, + 0.1053804914621913, + 0.23650984147822074, + 0.3556438817567155, + 0.45973766549215983, + 0.5465487040878723, + 0.6147878570463834, + 0.6642327481612071, + 0.6957852291255099, + 0.7114542294384749, + 0.7142489266417438, + 0.7079750337380535, + 0.6969386859678874, + 0.685576454866589, + 0.6780441059751185, + 0.6778080689556538, + 0.6872895243077497, + 0.7076095538716414, + 0.7384742005039027, + 0.7782213066266503, + 0.8240289412472419, + 0.8722615564222258, + 0.9189087903990425, + 0.9600569168279041, + 0.9923272228603799, + 1.0132204018586362, + 1.0213207959302733, + 1.0163366142748682, + 0.9989782754889917, + 0.9707022750262679, + 0.9333681269409071 + ], + [ + -0.8390715290764524, + -0.7453768871216169, + -0.6328959040585211, + -0.5052219680527953, + -0.36646078076708, + -0.221072597749671, + -0.07369950704888827, + 0.07101730119137245, + 0.20862607029250618, + 0.33505513251369545, + 0.446801675040665, + 0.5411129922834605, + 0.6161516385351021, + 0.6711320623518262, + 0.7064126033958761, + 0.7235245204957079, + 0.7251203890075127, + 0.7148287924015273, + 0.6970110474944234, + 0.6764280862327484, + 0.6578399079742457, + 0.6455737623699445, + 0.6431076163234211, + 0.6527199151742806, + 0.6752534087798122, + 0.7100294254511089, + 0.7549305390143708, + 0.8066466631156897, + 0.8610558817258479, + 0.9136908747581751, + 0.9602283570094299, + 0.9969351428370526, + 1.0210112341961408, + 1.0307867502868013, + 1.0257528422807518, + 1.006432955199819, + 0.9741253751857211, + 0.9305667361829351, + 0.8775759871935995, + 0.8167378603779207, + 0.7491746562000325, + 0.67543743815013, + 0.5955260067181322, + 0.5090253132275423, + 0.4153280152611122, + 0.31390143754138267, + 0.204553637975976, + 0.08765738566574502, + -0.035698936328374724, + -0.16365068321201193, + -0.29358915992831275, + -0.42227652639845903, + -0.5460241080103624, + -0.6609101936568723, + -0.7630131077696236, + -0.8486384191090444, + -0.9145241895257419, + -0.9580137143249325, + -0.9771901000535562, + -0.97097060641762, + -0.939160832650537, + -0.8824698741816129, + -0.8024880525111608, + -0.7016292516397423, + -0.5830405938008492, + -0.45048317966695445, + -0.3081886721008514, + -0.16069726646047422, + -0.012682775655311389, + 0.13122989958066308, + 0.2666505048751737, + 0.389608715344066, + 0.4967353399326175, + 0.585434613205982, + 0.6540401445318578, + 0.7019434661833684, + 0.7296803619294359, + 0.7389575697066756, + 0.7326024501875738, + 0.714421937816117, + 0.6889650258008257, + 0.6611947322051217, + 0.6360894869255128, + 0.6182078523672142, + 0.6112616690614948, + 0.6177484269009532, + 0.6386919364040211, + 0.6735304709867964, + 0.7201742539991082, + 0.7752317762948573, + 0.8343804413346783, + 0.8928355126599309, + 0.9458561573524296, + 0.9892214461962924, + 1.0196138151097718, + 1.0348621480278397, + 1.0340189087253708, + 1.0172718204792404, + 0.9857159666769942, + 0.9410325225758769 + ], + [ + -0.8390715290764524, + -0.7436788997655225, + -0.6289715436443497, + -0.4987051914399156, + -0.3571719881296753, + -0.20903181300557985, + -0.059127831256234206, + 0.08770811846146039, + 0.22685543297670183, + 0.35410562451370586, + 0.46585957929512195, + 0.5593150766165244, + 0.6326356042673789, + 0.6850878105169512, + 0.7171313835752658, + 0.7304430953438453, + 0.7278575719703517, + 0.7132120873114, + 0.6910916193414337, + 0.6664828982474529, + 0.6443605412326081, + 0.6292421561553193, + 0.6247596935133262, + 0.6332987492021294, + 0.6557542105578826, + 0.6914391462560419, + 0.7381652696131935, + 0.7924902374195983, + 0.850103149506901, + 0.9062989852917442, + 0.9564790955928301, + 0.9966108955799843, + 1.023586542440509, + 1.0354366814326847, + 1.0313785822215658, + 1.0117041576489392, + 0.977537926864149, + 0.9305137574189823, + 0.8724291274997573, + 0.8049353077286074, + 0.7293117919389933, + 0.6463557650125702, + 0.5563958755613836, + 0.45941808567556275, + 0.3552736255536868, + 0.24392784816350183, + 0.12570540081853235, + 0.0014913948882449915, + -0.12714141873724782, + -0.2578955764211451, + -0.3878034345550964, + -0.5133620644198418, + -0.6307282152406745, + -0.7359485443310603, + -0.8252001837485241, + -0.8950199933783477, + -0.9425061242462409, + -0.9654813168800701, + -0.9626125143114231, + -0.9334852069542987, + -0.8786333212606698, + -0.7995267235015132, + -0.6985190634514189, + -0.5787592430987889, + -0.44407057135454653, + -0.2988026764580515, + -0.14766225970534047, + 0.004470563631905013, + 0.15273356325896112, + 0.2924752261069503, + 0.41944412625155025, + 0.5299708742610333, + 0.6211390178254814, + 0.6909403683130926, + 0.738407933237245, + 0.7637162869418005, + 0.76823569868627, + 0.7545239505287911, + 0.7262399212508088, + 0.6879668197682891, + 0.6449409007266186, + 0.6026931348746662, + 0.5666251747504687, + 0.5415547397808373, + 0.5312764763418704, + 0.5381897631239807, + 0.5630428758005371, + 0.6048326700455094, + 0.6608812869756464, + 0.7270886488951566, + 0.7983352014032881, + 0.8689875526774247, + 0.9334442496721308, + 0.986652832632364, + 1.024533860638343, + 1.0442622423256565, + 1.0443785394574183, + 1.0247291276188573, + 0.9862596916998118, + 0.9307071558030419 + ], + [ + -0.8390715290764524, + -0.7419760737565969, + -0.6250308139611751, + -0.4921592120922941, + -0.3478460134728109, + -0.19695702757250078, + -0.04454230493515984, + 0.10437098191999851, + 0.2449903712124994, + 0.3729696454594595, + 0.4846148517172496, + 0.5770781680019456, + 0.6485302583714456, + 0.6982982150332397, + 0.7269528028755047, + 0.7363268401889675, + 0.7294475445577568, + 0.7103705640340697, + 0.6839124947428663, + 0.6552912838959417, + 0.6296983584842802, + 0.6118401434320807, + 0.6054970256576524, + 0.6131521971036527, + 0.6357394136792216, + 0.6725470927806438, + 0.721297448233725, + 0.7783961181475118, + 0.839323653813066, + 0.899119413412208, + 0.9528945959332171, + 0.9963070026927803, + 1.025936600219672, + 1.0395171394408818, + 1.0360022447210162, + 1.0154705212095099, + 0.9788988104975245, + 0.9278515567295806, + 0.8641442572497591, + 0.7895387772684599, + 0.705518420662345, + 0.6131733176931142, + 0.5132053977584176, + 0.40604095421442454, + 0.29202129389699505, + 0.17163094539513807, + 0.04571970919329647, + -0.0843207521368021, + -0.21645465324103313, + -0.3479890852247647, + -0.47565060143126886, + -0.595739025576527, + -0.7043371802077404, + -0.7975508896242436, + -0.871753690083411, + -0.9238142047440724, + -0.951289684119702, + -0.9525752964326992, + -0.9270041897746467, + -0.8748974551913294, + -0.7975657578760069, + -0.6972658682987952, + -0.5771161350106523, + -0.44097560052248236, + -0.2932922737126502, + -0.13892705289967885, + 0.017039290745869174, + 0.1695081572809496, + 0.31356646743038713, + 0.44468452729948166, + 0.558903032247823, + 0.653005198394085, + 0.724670677108001, + 0.77260734172844, + 0.7966550511227575, + 0.7978524213358686, + 0.7784543487116594, + 0.7418858059725998, + 0.6926176723358424, + 0.6359541867904085, + 0.5777295064224834, + 0.5239223588137587, + 0.48021144102201363, + 0.4515077330230281, + 0.44151050399832015, + 0.4523388445936041, + 0.48428811841462055, + 0.5357500811200777, + 0.6033173817316562, + 0.6820700744322863, + 0.7660171522233743, + 0.8486440632472027, + 0.9235015862058668, + 0.9847652508755007, + 1.0276990358349412, + 1.0489718004891384, + 1.046797412126577, + 1.0208959959611628, + 0.9722996507684993, + 0.9030469734015023 + ], + [ + -0.8390715290764524, + -0.7402684201740626, + -0.6210738175682702, + -0.4855844133235572, + -0.3384838276410084, + -0.1848502055015321, + -0.029946344380689372, + 0.12100057937922978, + 0.26302333372599446, + 0.39163725413623307, + 0.5030552900809417, + 0.5943882829790015, + 0.6638207100173591, + 0.7107487513647859, + 0.7358643378632221, + 0.7411671426625387, + 0.7298876588271743, + 0.7063095647708136, + 0.6754888051103309, + 0.6428795249215508, + 0.6138915105308184, + 0.5934176440723207, + 0.5853802566482492, + 0.5923495808771087, + 0.615284003640488, + 0.6534300632612293, + 0.7044012376117875, + 0.7644311293877829, + 0.8287723574793605, + 0.8921914268445109, + 0.9494958376862234, + 0.9960253633149111, + 1.0280457588937142, + 1.0429992295185175, + 1.0395890752847714, + 1.0177020467216438, + 0.9781965588158986, + 0.9226038293536079, + 0.8527991536057331, + 0.7707005105252673, + 0.6780420214043266, + 0.5762526892611255, + 0.4664498072633655, + 0.3495354177889515, + 0.22636693257309223, + 0.0979622254673013, + -0.03430349717420789, + -0.1685471893222347, + -0.3023014442597559, + -0.4325256170471814, + -0.5557036793830115, + -0.6680153022672898, + -0.7655578799506819, + -0.8445930732883403, + -0.9017917814718652, + -0.9344552604517402, + -0.9406959539256128, + -0.9195679959159194, + -0.8711430814634523, + -0.7965317849315717, + -0.697853281146747, + -0.5781580765002419, + -0.4413092937205029, + -0.29182877229836773, + -0.1347150447650197, + 0.02475885758517457, + 0.18125905866708722, + 0.32960696125098116, + 0.46498931955864403, + 0.5831549953246568, + 0.680592898627444, + 0.7546873457818618, + 0.8038480964809501, + 0.8276121086963163, + 0.8267123705460779, + 0.8031063480568609, + 0.7599534877674045, + 0.701529103772605, + 0.6330622488147897, + 0.5604889422942648, + 0.49011988173491194, + 0.42823305281447904, + 0.3806150271945037, + 0.35208790475098817, + 0.34606907578786056, + 0.36421561595102914, + 0.40620226200240245, + 0.46967085957605936, + 0.5503707601804617, + 0.6424862285593462, + 0.7391220414315345, + 0.8328962249732902, + 0.9165732010466995, + 0.9836644248657896, + 1.0289282437824943, + 1.048715627591222, + 1.0411312077358306, + 1.0060058894087527, + 0.9447036354278618, + 0.8598064842203625 + ], + [ + -0.8390715290764524, + -0.7385559501285518, + -0.6171006574482566, + -0.4789811801349148, + -0.3290864052484135, + -0.17271331605476284, + -0.015343368331798606, + 0.137591609256738, + 0.2809468117053365, + 0.41009861283727317, + 0.5211688969919007, + 0.6112317946790559, + 0.6784926344193314, + 0.7224257304332748, + 0.7438546253116541, + 0.7449569177413589, + 0.7291771818116896, + 0.7010367243967791, + 0.6658383050200776, + 0.62927676838506, + 0.5969811265771869, + 0.57402750534298, + 0.5644727193096936, + 0.5709624799629306, + 0.5944646166968689, + 0.634165770486012, + 0.6875509253021618, + 0.7506614829855593, + 0.8185030597906577, + 0.8855529173007943, + 0.9463027133848023, + 0.9957677375154056, + 1.0298999739293884, + 1.0458582940728998, + 1.0421121129305981, + 1.0183809600407168, + 0.9754370892316112, + 0.9148172833225956, + 0.8385003227577419, + 0.7486067810570471, + 0.6471683810201756, + 0.535997320844894, + 0.41666521659770617, + 0.29058057325207975, + 0.1591366789144632, + 0.023891239033900634, + -0.11326376266673865, + -0.2499788140502043, + -0.3833964957211931, + -0.510186276408776, + -0.6266622936890265, + -0.7289694807284194, + -0.813314821931952, + -0.8762166415674469, + -0.9147454578133579, + -0.9267340687452701, + -0.9109407208392233, + -0.8671559356144387, + -0.7962496183143689, + -0.7001597292950521, + -0.581826896972989, + -0.4450811620183452, + -0.29448806516797427, + -0.13516201663821242, + 0.02744439031040141, + 0.18776571862870256, + 0.3403537458275704, + 0.4801023194182644, + 0.6024578992834072, + 0.7036083108113905, + 0.7806443968597211, + 0.8316913849386682, + 0.8560081939646833, + 0.8540530214116901, + 0.8275121574676395, + 0.7792863676695136, + 0.7134262189521935, + 0.6350056583627197, + 0.5499233736837623, + 0.46462509530646295, + 0.38574752104858767, + 0.31969552408458046, + 0.27217730268322393, + 0.24773487342004777, + 0.2493170746656543, + 0.2779464221068521, + 0.3325278448965847, + 0.4098358606348486, + 0.5046979757986326, + 0.6103683980970032, + 0.7190610717989323, + 0.8225887177095934, + 0.9130388924382721, + 0.9834120121513997, + 1.0281511547975204, + 1.043508692654374, + 1.027717993247111, + 0.9809657171670072, + 0.9051870839707192, + 0.8037284691931179 + ], + [ + -0.8390715290764524, + -0.7368386747620346, + -0.6131114370044238, + -0.4723498991926127, + -0.31965472457734284, + -0.160548333384963, + -0.000736797170674354, + 0.1541387822656017, + 0.29875334192733977, + 0.42834399254820893, + 0.5389438876931703, + 0.6275954435529018, + 0.692532286255836, + 0.7333163136688092, + 0.7509134766900587, + 0.7476906181183121, + 0.7273172967407097, + 0.6945619561070493, + 0.6549813347917497, + 0.6145149582174103, + 0.579011207214125, + 0.5537253503067836, + 0.5428402360110713, + 0.5490644849469967, + 0.5733592527441196, + 0.6148325258842343, + 0.6708205970585577, + 0.737152464614249, + 0.8085681221681826, + 0.8792401932366567, + 0.9433339120105682, + 0.995535735293527, + 1.0314868984203005, + 1.0480740873183965, + 1.0435523928448727, + 1.0175018536087803, + 0.970643653977155, + 0.9045612237302489, + 0.8213820000590939, + 0.7234760525714196, + 0.6132186216668518, + 0.4928470931274733, + 0.3644224696359344, + 0.22988495381882806, + 0.09117649991095345, + -0.049607169040071813, + -0.19007524891759825, + -0.32744664430123555, + -0.4585256544824814, + -0.5797594416547659, + -0.6873737992460556, + -0.7775714819915891, + -0.8467690377155056, + -0.8918445087727248, + -0.9103693425609829, + -0.900802347421965, + -0.8626300789915898, + -0.7964451151886857, + -0.7039600397853575, + -0.5879598734688728, + -0.4521989859408686, + -0.30125045266789624, + -0.14031687675721916, + 0.024987632601024164, + 0.18887405193584358, + 0.3456228178322743, + 0.4898249651648837, + 0.6166093826980015, + 0.7218467558546072, + 0.8023228300019214, + 0.8558762257552793, + 0.8814985830787042, + 0.8793965979622785, + 0.8510159535833302, + 0.7990260008469061, + 0.7272615971278159, + 0.6406156067799761, + 0.5448734597954773, + 0.4464812406718255, + 0.35224216753239357, + 0.268943513927532, + 0.2029266008149191, + 0.1596250262394631, + 0.14310855095943203, + 0.15567932084337355, + 0.19757079332390093, + 0.2667959717405234, + 0.3591796916391114, + 0.46859062322581346, + 0.5873647434068364, + 0.706886854566207, + 0.8182744137664693, + 0.9130924031028975, + 0.984022150433438, + 1.0254122303744797, + 1.0336552539615218, + 1.0073580512410036, + 0.9473005967782636, + 0.8562064454435379, + 0.7383700044148318 + ], + [ + -0.8390715290764524, + -0.7351166052477462, + -0.6091062600580377, + -0.4656909588052966, + -0.31018976747643384, + -0.14835723621446145, + 0.013869947878438135, + 0.17063682310064537, + 0.3164355098651272, + 0.44636377807416927, + 0.5563686977323318, + 0.6434663478089435, + 0.7059265125468465, + 0.7434085271256221, + 0.7570318911552572, + 0.7493642423222715, + 0.724311101069413, + 0.6868974327793558, + 0.6429407776171763, + 0.598628760204414, + 0.5600285099292028, + 0.5325694182604577, + 0.5205509114400323, + 0.5267309443437804, + 0.5520469830917194, + 0.595508921181755, + 0.6542838111008449, + 0.7239681242382231, + 0.7990182011874346, + 0.8732877812668267, + 0.9406068096093816, + 0.9953308065172889, + 1.0327959653028875, + 1.0496309186421515, + 1.0438990889346167, + 1.0150717295258667, + 0.9638566441739292, + 0.8919269358740894, + 0.8016048898442831, + 0.6955568187474679, + 0.5765458607550705, + 0.44727351965821543, + 0.31032059615194507, + 0.16817801374715918, + 0.02334154729757361, + -0.12156568956722467, + -0.2636816669407506, + -0.3998385998368002, + -0.526564088601513, + -0.6401596680529436, + -0.7368520040282801, + -0.8129999694301123, + -0.8653328215159501, + -0.891191488285395, + -0.888746330795734, + -0.8571696426489194, + -0.7967480837388367, + -0.7089276748623016, + -0.5962906480154612, + -0.46246860564443365, + -0.31199989525813077, + -0.15014110326591354, + 0.017356400646541384, + 0.18449339336251644, + 0.3452813659305132, + 0.4940006828896118, + 0.6254464322488424, + 0.7351508664830012, + 0.8195730134698027, + 0.8762485723070095, + 0.9038962661457617, + 0.9024797915975794, + 0.8732266544287831, + 0.8186059039902139, + 0.7422658218952145, + 0.6489302584021496, + 0.5442493762504698, + 0.4345983127345933, + 0.3268170961934043, + 0.22788819268147206, + 0.1445548199782104, + 0.08289324754832658, + 0.04786430835250127, + 0.04288105318529395, + 0.06943820869924999, + 0.126852295371076, + 0.21215707292289476, + 0.3201867842445109, + 0.4438603593760206, + 0.5746557213032066, + 0.7032381782934263, + 0.8201847230403743, + 0.9167307997082086, + 0.9854606657096707, + 1.0208681793014478, + 1.019731080246859, + 0.9812661320626189, + 0.9070609640452008, + 0.8008064805445245, + 0.6678768327859934 + ], + [ + -0.8390715290764524, + -0.7333897527901145, + -0.6050852308456429, + -0.4590047489012647, + -0.30069251925843776, + -0.1361420075133062, + 0.028473445550557933, + 0.18708047212023254, + 0.3339859527754969, + 0.46414847310693236, + 0.5734319904853581, + 0.6588320135540039, + 0.718662764976141, + 0.7526912746471541, + 0.7622020670287399, + 0.7499753405752787, + 0.7201636013202416, + 0.6780575640884796, + 0.6297420113295614, + 0.5816554805796192, + 0.5400824274444673, + 0.5106203976679509, + 0.49767491819150633, + 0.5040387053306681, + 0.5306076542219739, + 0.5762735089172022, + 0.6380132747016903, + 0.7111709724343509, + 0.7899019902888317, + 0.8677282373263807, + 0.9381373675913931, + 0.9951542318626925, + 1.033818457720716, + 1.050517763713428, + 1.043149595203548, + 1.0111099437781252, + 0.9551332494828657, + 0.8770268727596321, + 0.7793546567599743, + 0.6651251461199947, + 0.5375315381130802, + 0.3997745945038929, + 0.25497994317810657, + 0.10620136158479546, + -0.043514602930552704, + -0.19103727974975393, + -0.3330708029065414, + -0.4661154665351296, + -0.5864931282934797, + -0.6904446223439806, + -0.7742931886984773, + -0.8346562287193678, + -0.8686800546997215, + -0.8742694968118345, + -0.8502860189766223, + -0.796693316619714, + -0.7146367071148921, + -0.6064504082687768, + -0.4755937565420791, + -0.32652277946553127, + -0.16450709749681736, + 0.0045958690589149, + 0.17459648953394352, + 0.3392450754922497, + 0.49250730729875253, + 0.6288298239844826, + 0.7433834630695091, + 0.832272933239558, + 0.8927038938636858, + 0.9231014554971585, + 0.9231776422291342, + 0.8939487340562807, + 0.8377058854780725, + 0.7579433664198316, + 0.6592479273332297, + 0.5471504182670544, + 0.4279377232994427, + 0.3084205677434724, + 0.19565221625438628, + 0.09659566172652495, + 0.017743136902028833, + -0.035298713096153095, + -0.058233244221765595, + -0.04847193475401115, + -0.005462142661519731, + 0.06910230163548092, + 0.1712299091601316, + 0.29476954547664874, + 0.4317480050321832, + 0.5728935086661865, + 0.7083064912370066, + 0.8282171806894469, + 0.9237546336657354, + 0.987646986369186, + 1.0147789914965903, + 1.0025498066406475, + 0.9509989772693267, + 0.862697687483654, + 0.7424309915775856, + 0.596720396021226 + ], + [ + -0.8390715290764524, + -0.7316581286246869, + -0.601048454016343, + -0.45229166100564105, + -0.2911639685976527, + -0.12390463417670798, + 0.043070275341327784, + 0.20346448702304926, + 0.3513973627647132, + 0.48168870522942103, + 0.590122664532254, + 0.6736803446287616, + 0.7307291116471359, + 0.7611543500660229, + 0.7664174117446987, + 0.7495230183784207, + 0.7148817047450331, + 0.6680589694153667, + 0.6154128549147274, + 0.5636349784164945, + 0.5192248591972508, + 0.487941252067904, + 0.4742842758461083, + 0.481065849325546, + 0.5091215886449266, + 0.557204483117425, + 0.6220805245145546, + 0.6988216840121966, + 0.7812659714980492, + 0.8625919686103343, + 0.9359400393107838, + 0.9950071148103066, + 1.0345475670685658, + 1.050728342549784, + 1.0413095453404644, + 1.0056480520670108, + 0.9445469762048266, + 0.8599936541988276, + 0.7548401827721809, + 0.632481944322246, + 0.49658144856401243, + 0.3508693505404478, + 0.1990350619347478, + 0.04469984734292602, + -0.10855069117550215, + -0.25710762716638064, + -0.39728843780882034, + -0.525325814706149, + -0.6374155174301808, + -0.7298297845401561, + -0.799089162149868, + -0.8421742856507037, + -0.8567519348629645, + -0.8413873369204251, + -0.7957169461112835, + -0.7205617007892471, + -0.6179685027616694, + -0.49117579139936784, + -0.34450629798894, + -0.183195584159114, + -0.01316857221857443, + 0.15922179864072825, + 0.32747892410102974, + 0.48525499329680344, + 0.6266369928134311, + 0.7464123457710661, + 0.8403013993608337, + 0.9051458238925904, + 0.9390447293821746, + 0.9414338669470725, + 0.9131072710855952, + 0.8561844536655552, + 0.774029009508966, + 0.6711255914884003, + 0.5529213835985949, + 0.42563570633924214, + 0.2960380649853117, + 0.17119206937931875, + 0.058161631380168874, + -0.03632213737667144, + -0.1062292919872085, + -0.14662178526319997, + -0.15404287992772905, + -0.12687102126976332, + -0.06559626829021925, + 0.027024939013147453, + 0.14597605821123671, + 0.2841745637342608, + 0.43286128767716636, + 0.5821685352967682, + 0.7218258559436144, + 0.8419409431251574, + 0.9337788744468015, + 0.9904586560290606, + 1.0074930625685032, + 0.9831153913004844, + 0.9183624386027288, + 0.8169127936877835, + 0.6847087469681558, + 0.529414207305735 + ], + [ + -0.8390715290764524, + -0.729921744018056, + -0.596996034629081, + -0.44555208821744535, + -0.2816051074269917, + -0.1116471067018624, + 0.057657018308168224, + 0.21978364451938398, + 0.3686624898314717, + 0.4989752308548964, + 0.6064298608796657, + 0.6879996521300927, + 0.7421142482612171, + 0.7687884484254695, + 0.7696725502563027, + 0.7480079378211593, + 0.708474207821673, + 0.6569204466018852, + 0.5999835098771532, + 0.544609572025677, + 0.4975100762985333, + 0.46459703945479, + 0.45045262423375837, + 0.4578914233175082, + 0.48766928396983705, + 0.5383793614303364, + 0.6065556120485491, + 0.6869788103380923, + 0.7731541784219922, + 0.8579070672654847, + 0.9340276854719799, + 0.9948903747492077, + 1.0349784383297884, + 1.0502611639872745, + 1.0383927703727267, + 0.9987295584675008, + 0.9321870278914275, + 0.8409788864113985, + 0.728291606212625, + 0.5979499906790556, + 0.45412152118264, + 0.3010921878483189, + 0.14312743189052124, + -0.015587389286758235, + -0.17094835991502472, + -0.31890718299632065, + -0.45545146960429367, + -0.5766196572274086, + -0.6785688472369948, + -0.7577006876026076, + -0.8108371409056498, + -0.8354270908154783, + -0.8297580088407411, + -0.7931450618285655, + -0.7260727933196234, + -0.630270745717741, + -0.5087125371337364, + -0.3655363500197192, + -0.20589221352139422, + -0.03572707206754395, + 0.13847779838865576, + 0.3100007027645181, + 0.4721879987556019, + 0.6187607213710848, + 0.7441037816284927, + 0.8435233678078389, + 0.9134598957441521, + 0.9516463059930789, + 0.9572048252568257, + 0.930679390494927, + 0.8740053566527419, + 0.7904221860337443, + 0.6843377576141927, + 0.5611540796155658, + 0.42706325638554965, + 0.2888198162900862, + 0.1534917963150037, + 0.028189590839560658, + -0.08023005106476419, + -0.1654861435653993, + -0.2222180425387279, + -0.24634065293919072, + -0.2353882935535936, + -0.18881527463209094, + -0.10821401634082001, + 0.0025904679425812205, + 0.13760589938902096, + 0.28892146986495776, + 0.44714434926386026, + 0.602004843416202, + 0.7430869031495942, + 0.8606198975269655, + 0.9462540160763445, + 0.993738192347889, + 0.9994272602053642, + 0.9625634508439793, + 0.8853037341146538, + 0.7724948954525798, + 0.6312279078158832, + 0.47022755532267513 + ], + [ + -0.8390715290764524, + -0.7281806102677915, + -0.5929280781499051, + -0.4387864251865809, + -0.2720169308347276, + -0.09937141886418643, + 0.07223025787109298, + 0.23603274199632016, + 0.38577414488575895, + 0.5159989400982735, + 0.6223429700257669, + 0.7017786636128089, + 0.7528075087081283, + 0.7755851762099434, + 0.7719633318894976, + 0.7454323166121927, + 0.7009517816044288, + 0.6446629366099738, + 0.5834864965843881, + 0.524623939577082, + 0.4749945803202427, + 0.4406547256518314, + 0.42625499159632874, + 0.4345951678751415, + 0.46633111132064364, + 0.5198746700073076, + 0.5915067956730259, + 0.6756985017270363, + 0.765607971728841, + 0.8536991567441059, + 0.9324114988574602, + 0.9948047412313766, + 1.0351082024050466, + 1.0491195362397747, + 1.0344211947031423, + 0.990409568917282, + 0.918157553685468, + 0.8201518126353688, + 0.6999581612733985, + 0.5618707385713565, + 0.4105933891322462, + 0.2509870341863899, + 0.08789810538152844, + -0.07393580208707379, + -0.22992245120125293, + -0.3756226061115536, + -0.506760057372303, + -0.619260651433298, + -0.7093369710585616, + -0.7736225040399591, + -0.8093462918961739, + -0.8145286666428417, + -0.7881724915002103, + -0.7304230252745416, + -0.6426728032068799, + -0.5275946263435393, + -0.3890942809359532, + -0.23218332849426387, + -0.06277988271888559, + 0.1125490859241419, + 0.2868869355522969, + 0.45328954833020213, + 0.6051120021394208, + 0.7363220458543853, + 0.8417841157825873, + 0.9174994718344701, + 0.9607904027316608, + 0.9704195364490869, + 0.9466391945437324, + 0.8911703069915591, + 0.8071152083171377, + 0.698812955722242, + 0.571648837821685, + 0.4318308118967275, + 0.2861444369498582, + 0.141694734161708, + 0.005639169335140689, + -0.11508807094279831, + -0.2140626714595883, + -0.2856692083232001, + -0.32540998545513977, + -0.33021360466418853, + -0.2987236697711039, + -0.2315385579927556, + -0.1313663224966896, + -0.0030569744752715344, + 0.146520601889575, + 0.30877745187187144, + 0.47388054884705416, + 0.6313845119416543, + 0.7709740528163616, + 0.8832521453357933, + 0.9604961997948922, + 0.9973019076522637, + 0.9910430729033695, + 0.9420949025458749, + 0.8537952698499588, + 0.7321493462052567, + 0.585312978701647, + 0.4229146996479872 + ], + [ + -0.8390715290764524, + -0.7264347387023588, + -0.5888446904492234, + -0.4319950680907154, + -0.2624004369609003, + -0.0870795673930045, + 0.08678658061294389, + 0.25220659917636296, + 0.40272520274233503, + 0.5327508615769667, + 0.6378516388628326, + 0.715006531963551, + 0.7627988750584543, + 0.7815370605735812, + 0.7732868356355911, + 0.7417999248332706, + 0.6923269539523607, + 0.6313094841516217, + 0.565956585724003, + 0.5037250141802636, + 0.4517369562789327, + 0.4161829922110813, + 0.40176755837921135, + 0.41125724276828424, + 0.44518701422530665, + 0.5017656324161035, + 0.5770002405060632, + 0.6650342412186372, + 0.7586658282569623, + 0.8499912516598428, + 0.9311009388177145, + 0.9947507494124572, + 1.0349359952171653, + 1.0473115434726499, + 1.0294246713117066, + 0.980754352296872, + 0.9025767707247075, + 0.797697806757171, + 0.6701058382324687, + 0.5246009411310739, + 0.36644979616052553, + 0.20110140135456295, + 0.03398035712809297, + -0.1296441460825167, + -0.2847308864048565, + -0.42650746742220097, + -0.5505086204921129, + -0.6526366695947471, + -0.7292592292915548, + -0.7773468298631441, + -0.794640832333505, + -0.7798321805079418, + -0.732725934987257, + -0.6543658162987444, + -0.5470967899544736, + -0.4145508802773233, + -0.2615502777785832, + -0.09393074320923465, + 0.08170403913074005, + 0.2582809613002884, + 0.4285894381933123, + 0.5856262727216683, + 0.7229333597745274, + 0.8349096128470247, + 0.9170805786910649, + 0.9663117500899174, + 0.9809546547653389, + 0.9609185363810913, + 0.9076649132557383, + 0.8241273278254487, + 0.7145637078568398, + 0.584353218168908, + 0.43975247004491447, + 0.287626806422762, + 0.13517082146633902, + -0.01037140366812511, + -0.14198077027320893, + -0.25313153756242784, + -0.33805594520251014, + -0.39200763357203056, + -0.4115230143998844, + -0.39467296193559537, + -0.3412882438634457, + -0.25313305195016556, + -0.13399434775670088, + 0.01034705163131966, + 0.17229289735258485, + 0.3427686731773406, + 0.5117284194573343, + 0.6687998921807037, + 0.8040240492914594, + 0.9086237419174507, + 0.975724701633197, + 1.0009501973306456, + 0.9828202056956488, + 0.922905790803639, + 0.8257169587193958, + 0.6983334655008984, + 0.549818148052379, + 0.3904767348786848 + ], + [ + -0.8390715290764524, + -0.7246841406810508, + -0.5847459777990467, + -0.42517841461208844, + -0.25275662689341594, + -0.07477355164677962, + 0.10132257707891214, + 0.26830005976892, + 0.41950860508762017, + 0.549222167138756, + 0.6529457774129876, + 0.7276728439398539, + 0.7720789869488799, + 0.7866375575564275, + 0.773641373875872, + 0.7371160794207139, + 0.6826140886654083, + 0.6168851943636918, + 0.5474307250175545, + 0.4819618736693952, + 0.4277977201983538, + 0.3912520393917105, + 0.3770674173954485, + 0.3879579511473565, + 0.42431620910714063, + 0.48412586384896206, + 0.5630997275063481, + 0.6550365909993416, + 0.7523631448279844, + 0.8468036319123066, + 0.9301036759067756, + 0.9947287367073699, + 1.0344629634648757, + 1.0448499885566607, + 1.0234407573609303, + 0.9698408125966826, + 0.8855759680039174, + 0.7738167233689786, + 0.6390148863754609, + 0.48650912365144894, + 0.3221498875323222, + 0.15198040239261926, + -0.018007577068195085, + -0.18204290526664954, + -0.3346840038865533, + -0.4708920735956497, + -0.5860955415802599, + -0.676268586246155, + -0.7380373463690856, + -0.7688155600566509, + -0.7669596363292904, + -0.7319239764735598, + -0.66439239477679, + -0.56636137210185, + -0.4411552135029352, + -0.2933607641338626, + -0.1286785134189762, + 0.04630399882066196, + 0.22440293082143656, + 0.3981741415583738, + 0.5602726880662299, + 0.7038134321710738, + 0.822711435514716, + 0.9119829896697895, + 0.9679909630189714, + 0.9886214512099445, + 0.9733825095475982, + 0.9234201279579626, + 0.8414516048682216, + 0.7316218786265666, + 0.5992936817531616, + 0.4507868483287013, + 0.29308490445456026, + 0.1335275443728972, + -0.020494514733583737, + -0.161830177076192, + -0.28383769406322334, + -0.38063269828852164, + -0.4473177931267531, + -0.4801980010595175, + -0.4769837385007474, + -0.4369768364747894, + -0.3612266423986068, + -0.25263444872414276, + -0.11597790298623739, + 0.04217490658371059, + 0.2136875581949308, + 0.3892280344349241, + 0.5587889751102378, + 0.7123309744148946, + 0.8405027392198953, + 0.9353738099171425, + 0.991104729916342, + 1.0044787197390372, + 0.975229133613856, + 0.9061173983412547, + 0.8027440578590478, + 0.6731068714752686, + 0.5269498645506634, + 0.37497122865508514 + ], + [ + -0.8390715290764524, + -0.7229288275939126, + -0.580632046870229, + -0.41833686391422087, + -0.24308650456382203, + -0.062455373287891365, + 0.11583484257510425, + 0.28430799311416005, + 0.43611736341873647, + 0.5654041765141558, + 0.6676155653927771, + 0.7397676283676582, + 0.7806391503513912, + 0.790881059279369, + 0.7730264945335178, + 0.7313876363827149, + 0.6718293615628979, + 0.60141718560905, + 0.5279479613460263, + 0.4593856253517109, + 0.40323916164758294, + 0.3659333847816968, + 0.3522323311174937, + 0.3647774632293423, + 0.4037968885002463, + 0.4670270718688917, + 0.5498663730474892, + 0.6457529526732289, + 0.7467320577643588, + 0.8441537317688289, + 0.9294255469873672, + 0.994738840681681, + 1.0336922569871412, + 1.0417523024074806, + 1.0165144318914572, + 0.9577558763752203, + 0.8672984000890046, + 0.7487211189387579, + 0.6069771830559558, + 0.447971939593758, + 0.27815443437908693, + 0.10416079499091771, + -0.06746958918702536, + -0.23050233900323283, + -0.3791532370988443, + -0.508192280859874, + -0.6130314397560035, + -0.68981715621412, + -0.7355398965352535, + -0.7481617951030832, + -0.7267523546460014, + -0.6716136665241731, + -0.5843723179866814, + -0.4680156501410373, + -0.3268548847002506, + -0.16640560688090644, + 0.006814503464962643, + 0.18556166309531832, + 0.36219916970131544, + 0.5290662036466819, + 0.6788582897438439, + 0.8049954565548727, + 0.9019559252079188, + 0.9655561274612433, + 0.9931615113520873, + 0.9838166975302106, + 0.9382880687602119, + 0.8590168688762202, + 0.749986339759724, + 0.6165120984687833, + 0.46497032920472825, + 0.30248268189493827, + 0.1365792110346581, + -0.02513939012537341, + -0.17532183109320734, + -0.3071553267768491, + -0.4146171751146732, + -0.49268881997468494, + -0.5375373555072674, + -0.5466713277370501, + -0.5190757656291762, + -0.45532576110354633, + -0.35766975824447556, + -0.23006501357225764, + -0.0781409456025946, + 0.09093626020084755, + 0.2687206002216539, + 0.4458769364118208, + 0.6127009908764627, + 0.7597439152244764, + 0.8784960636221817, + 0.962067533766096, + 1.0057931866359282, + 1.007689841429063, + 0.9687041832484314, + 0.8927107259827515, + 0.7862472165192872, + 0.6580060360308158, + 0.5181296786894126, + 0.37738170839881746 + ], + [ + -0.8390715290764524, + -0.721168810861667, + -0.5765030047296827, + -0.4114708166185477, + -0.23339107664280254, + -0.0501270359570561, + 0.1303199779660201, + 0.30022529581867674, + 0.4525445619534677, + 0.5812883618908657, + 0.6818514586022645, + 0.751281363990822, + 0.7884713457182428, + 0.7942629001098688, + 0.7714429816500443, + 0.7246229807638126, + 0.6599907335443116, + 0.5849345384927319, + 0.5075493584508746, + 0.4360492859900442, + 0.3781251816644029, + 0.3402996581405848, + 0.3273404868607058, + 0.34179554044117244, + 0.3837059281009464, + 0.45053876491068934, + 0.5373583602077094, + 0.6372273425179934, + 0.7418012790316582, + 0.8420560445100272, + 0.9290705210684319, + 0.9947809981919038, + 1.03262900778891, + 1.0380404205528102, + 1.008697757729954, + 0.9445958003873404, + 0.8478980800051477, + 0.7226343599257457, + 0.5742934935931598, + 0.4093704462223529, + 0.2349210411373939, + 0.05816511620215409, + -0.1138385341286686, + -0.2744400503976048, + -0.417579023920302, + -0.5379171828929042, + -0.6309459004633864, + -0.6930878846112433, + -0.7218042715483749, + -0.7157077644179036, + -0.6746721106119445, + -0.5999204487430067, + -0.49407145456430046, + -0.36112332166279976, + -0.2063609510499075, + -0.03618001784132591, + 0.14216887104036943, + 0.32090363341620287, + 0.4920822441217882, + 0.6479982036504919, + 0.7815740351109378, + 0.8867276440355983, + 0.9586890107788977, + 0.9942485382539388, + 0.9919229139439691, + 0.9520292667667622, + 0.8766636243597442, + 0.7695852194804346, + 0.6360140029600004, + 0.4823545320021837, + 0.3158646811183647, + 0.14429161461748563, + -0.02450064107865171, + -0.18288849746260258, + -0.3238111562543633, + -0.4410443903250228, + -0.5294197839450695, + -0.5849910692038721, + -0.6051550744080874, + -0.588737499220755, + -0.5360524232156764, + -0.448939233137777, + -0.3307726346148283, + -0.18643259052892683, + -0.022213897011399864, + 0.1543478760826219, + 0.3347543720216888, + 0.5099370338813758, + 0.6707594754134738, + 0.8086056695206829, + 0.9160104897295858, + 0.9872731200734509, + 1.0189848841564884, + 1.0104037066482845, + 0.9636186825581231, + 0.8834691695571615, + 0.7772106980003088, + 0.6539507032019628, + 0.5239058755883956, + 0.397555275333599 + ], + [ + -0.8390715290764524, + -0.7194041019356405, + -0.5723589588375978, + -0.4045806747809489, + -0.22367135243538724, + -0.037790544947425585, + 0.14477459047070196, + 0.31604689338247566, + 0.4687833605099517, + 0.5968663524078689, + 0.6956441951345115, + 0.7622049869664353, + 0.7955682354950249, + 0.7967793617917206, + 0.768892854385563, + 0.7168320143712309, + 0.6471179206771789, + 0.5674682411889949, + 0.48627791038414736, + 0.4120076573013098, + 0.35252112648566314, + 0.31442439305185027, + 0.30247025062933663, + 0.31909126096971246, + 0.3641185987527593, + 0.43472796972165106, + 0.5256306829563571, + 0.6295001827916907, + 0.7375959498410942, + 0.8405220431607114, + 0.9290406760749877, + 0.9948549457801064, + 1.0312802958679552, + 1.0337406278011565, + 1.0000494901505947, + 0.9304654048972958, + 0.8275384814704817, + 0.6957886346802942, + 0.5412706477306417, + 0.37108633669473423, + 0.19289938592480935, + 0.014495972545639946, + -0.15658273275640902, + -0.3133279856798069, + -0.44947784769821253, + -0.5596755716184807, + -0.6395925693033517, + -0.686033818884391, + -0.6970361208524853, + -0.671959799091865, + -0.6115648908469187, + -0.5180558836444085, + -0.39507616182266514, + -0.2476350214742849, + -0.08195588314810864, + 0.09475713121060038, + 0.2746275116677561, + 0.4494739225857936, + 0.6112145257725592, + 0.7522815666320867, + 0.8660187100332568, + 0.9470352285406112, + 0.991494723463888, + 0.9973208677839773, + 0.9643081063233238, + 0.8941309778106884, + 0.7902516016173836, + 0.657730870011033, + 0.5029549197774122, + 0.3332942429618701, + 0.1567177956888974, + -0.018612066129431986, + -0.1847368338402196, + -0.33426600953376373, + -0.46068766498218683, + -0.5586121724690228, + -0.6239446372518584, + -0.6539901975284143, + -0.6475031338747276, + -0.6046941362460656, + -0.5272092390798612, + -0.4180889143952801, + -0.2817077067952095, + -0.12368559686624367, + 0.04924549584552379, + 0.22944052349955735, + 0.40862397365579567, + 0.5782664995563381, + 0.7300513916960093, + 0.8564088448218632, + 0.9510776128572902, + 1.0096385959150904, + 1.029956683761875, + 1.0124683111071942, + 0.960263600743553, + 0.8789327435113192, + 0.7761736548196508, + 0.6611878704605159, + 0.543919392200825, + 0.4342123029844012 + ], + [ + -0.8390715290764524, + -0.7176347122976889, + -0.5682000170446436, + -0.39766684186821294, + -0.21392834377587167, + -0.025447906878399467, + 0.15919529445742212, + 0.33176774181675045, + 0.4848269973548932, + 0.6121299385668254, + 0.7089848014014002, + 0.7725298980000695, + 0.8019231709948216, + 0.7984276775331883, + 0.7653793644441201, + 0.7080261412810457, + 0.6332323613619102, + 0.5490511311820526, + 0.46417845089009724, + 0.3873171972633054, + 0.32649361751725536, + 0.2883818159825189, + 0.2776999203999469, + 0.29674274766269765, + 0.34510828444395497, + 0.41965895889061045, + 0.5147349043619648, + 0.6226081100797153, + 0.734137512458712, + 0.8395601177396821, + 0.929336186686277, + 0.9949602213203574, + 1.0296551020703595, + 1.0288833721122128, + 0.9906346352316997, + 0.9154772387832741, + 0.8063911614107591, + 0.6684228868234048, + 0.5082186591631351, + 0.3334981658638733, + 0.1525265433588028, + -0.02636945210239333, + -0.19521206827646678, + -0.3466987804792344, + -0.4744483214012549, + -0.5731810858776528, + -0.6388525398270426, + -0.6687562228364952, + -0.6616062725988501, + -0.6176004323738148, + -0.5384558031345781, + -0.42740341972835394, + -0.2891255361628671, + -0.12962175159877473, + 0.04400376128558091, + 0.22383298667993076, + 0.4014923317224237, + 0.5685594394692426, + 0.7169932649374678, + 0.8395588645138792, + 0.9302182281985099, + 0.9844610929233624, + 0.9995542841683357, + 0.9746939473835714, + 0.9110513935057587, + 0.811709779762034, + 0.6814952922110656, + 0.5267128209307268, + 0.3548022113718444, + 0.17393674421391261, + -0.00740999130812161, + -0.18089992762827323, + -0.33873992514888795, + -0.4740384396897876, + -0.5810890571651101, + -0.6555689907220087, + -0.6946505231461754, + -0.6970330188001773, + -0.6629107169893279, + -0.5938954883905871, + -0.49291317457310235, + -0.3640870755987637, + -0.21261369613925987, + -0.04462601601068086, + 0.1329691355421856, + 0.312698010770247, + 0.48678894557804525, + 0.6475141149986985, + 0.7876018162687484, + 0.9007003707445329, + 0.9818574397917482, + 1.0279643257259399, + 1.0381071364673693, + 1.013768015778595, + 0.9588309003140302, + 0.8793665260949197, + 0.7831979608550101, + 0.6792767448823576, + 0.5769261375828767, + 0.4850276049709774 + ], + [ + -0.8390715290764524, + -0.7158606534601224, + -0.5640262875891617, + -0.3907297227344075, + -0.20416306492248928, + -0.01310112936924788, + 0.17357871223666899, + 0.3473828292519151, + 0.5006687920190985, + 0.6270710765584161, + 0.7218645979718665, + 0.7822479691153049, + 0.8075301986269968, + 0.7992060350490278, + 0.7609069919274039, + 0.6982182511453983, + 0.6183571806282584, + 0.5297178335300173, + 0.44129755890928457, + 0.3620358875320467, + 0.3001103779861165, + 0.2622466333551739, + 0.2531074796190032, + 0.2748268992168885, + 0.32674620737422577, + 0.40539298957292824, + 0.5047189298849537, + 0.616583801590692, + 0.7314436008748827, + 0.8391755293716252, + 0.9299553233133895, + 0.9950961669067231, + 1.0277642482885703, + 1.0235030489868735, + 0.9805239612272152, + 0.8997506830833633, + 0.784634314362145, + 0.6407806885001579, + 0.47544781517287077, + 0.29697760711221965, + 0.11422243846687591, + -0.06398461081876777, + -0.229283606052589, + -0.37415137671978005, + -0.49217623832449786, + -0.5782559802148988, + -0.6287359887023413, + -0.6415031229460548, + -0.6160451816158851, + -0.5534777511380763, + -0.4565324246673394, + -0.32949501425953565, + -0.1780808615578847, + -0.009237088398593036, + 0.16913197678169115, + 0.3485112685180386, + 0.5201791799510629, + 0.6756472406623459, + 0.8071074542260461, + 0.9078571080607691, + 0.9726717798731936, + 0.9981009749892341, + 0.9826665389078879, + 0.9269508340219559, + 0.8335689240980003, + 0.7070261999977845, + 0.553469770386973, + 0.3803484283334074, + 0.19600196064632786, + 0.00920570131345971, + -0.17129996817713236, + -0.3372636800546254, + -0.48133011929367353, + -0.5973735170264884, + -0.6807382825767275, + -0.7283771925259898, + -0.7388885665969245, + -0.7124644307019593, + -0.6507699269354709, + -0.5567785669996866, + -0.43458739696013615, + -0.28923077671109443, + -0.12650238402658717, + 0.04721572576025229, + 0.22512803612659127, + 0.4002218299285124, + 0.5655029575676448, + 0.7142836311178733, + 0.8405232036000299, + 0.9392073808986289, + 1.0067349343502874, + 1.0412673571470727, + 1.0429894530787618, + 1.0142300238692836, + 0.9594025538665705, + 0.8847451699691444, + 0.7978645197171113, + 0.7071155904476419, + 0.6208743287415768, + 0.5467779235536946 + ], + [ + -0.8390715290764524, + -0.7140819369656307, + -0.5598378790943485, + -0.3837697235971788, + -0.19437653245183098, + -0.0007522207125456617, + 0.1879214748523, + 0.36288717753542105, + 0.5163021480791852, + 0.6416818925013815, + 0.7342752052187628, + 0.7913515500522409, + 0.8123840654747896, + 0.7991135785530539, + 0.7554814396220997, + 0.6874227003251833, + 0.6025171516228577, + 0.5095046957681113, + 0.4176834604043286, + 0.3362230972809801, + 0.27344005672530447, + 0.23609381724314055, + 0.22877035169068882, + 0.2534191255783836, + 0.3091011611204991, + 0.39198805447354446, + 0.4956267967517345, + 0.6114558202248612, + 0.7295279508915531, + 0.8393703815120819, + 0.9308944622223426, + 0.9952619329647653, + 1.0256203254002352, + 1.017637757903676, + 0.9697934666258766, + 0.8834110001232354, + 0.7624512709432347, + 0.6131080724364102, + 0.44326576369750187, + 0.2618857772299846, + 0.07838547897258241, + -0.09793847316554938, + -0.2584066724169988, + -0.395355842630311, + -0.5024385257930709, + -0.5748334641779336, + -0.6093820357673253, + -0.6046657478524756, + -0.5610349874523344, + -0.4805921644693339, + -0.367125511153388, + -0.22598524436198367, + -0.06389291151036157, + 0.11132211716765428, + 0.29105850731177546, + 0.46634210693654843, + 0.6282704848987257, + 0.7684775586407119, + 0.879588318447031, + 0.9556323482440333, + 0.9923869167431153, + 0.9876253157582355, + 0.9412529792961002, + 0.8553217859263195, + 0.733921033507398, + 0.5829513457522547, + 0.4097949455293378, + 0.22290218104281492, + 0.03131978469516275, + -0.15580919389462172, + -0.3297409847849347, + -0.48258880585770353, + -0.6077114860774626, + -0.7000072496749807, + -0.756095475652525, + -0.7743802331840969, + -0.755001465001376, + -0.6996910575013682, + -0.6115597711971761, + -0.49484545973933847, + -0.3546522229327752, + -0.1967041575870478, + -0.027127329469498696, + 0.1477384114275541, + 0.32150743654716896, + 0.4879137032409765, + 0.6409933647343815, + 0.7753003371971101, + 0.8861602663196516, + 0.969953847424696, + 1.0244047593088916, + 1.0488341444560947, + 1.0443359961551024, + 1.0138284581871742, + 0.9619458539870283, + 0.8947543833347874, + 0.8192982696089228, + 0.7430088312905117, + 0.6730320346686546, + 0.6155463836080264 + ], + [ + -0.8390715290764524, + -0.7122985743872081, + -0.5556349005654326, + -0.3767872520139553, + -0.1845697651530082, + 0.01159681045248581, + 0.20222022287062189, + 0.3782758438187994, + 0.5317205559042946, + 0.655954686592008, + 0.7462085487706624, + 0.799833474289932, + 0.8164802242164824, + 0.7981504096990572, + 0.7491096257281867, + 0.6756552908758372, + 0.5857386543519925, + 0.4884497195735428, + 0.39338592671424966, + 0.3099394437818289, + 0.2465520495506624, + 0.20999839030362372, + 0.20476515622787742, + 0.23259308846555832, + 0.29223925290159547, + 0.37949864610037426, + 0.4874984803362721, + 0.6072484791495265, + 0.728400330085702, + 0.8401436084428751, + 0.9321481067433718, + 0.9954564835608478, + 1.0232376094276643, + 1.011329032526405, + 0.9585238088964866, + 0.8665883357992593, + 0.7400289530486989, + 0.5856513420947946, + 0.41197462517581596, + 0.22856966567742568, + 0.04538841138681271, + -0.12786001662452107, + -0.2822473342426308, + -0.4100573379424396, + -0.5051060521145243, + -0.5629585813437998, + -0.5810568308951365, + -0.5587729121197924, + -0.4973993014004964, + -0.40008079579475586, + -0.27168738014524635, + -0.11862334593190133, + 0.051432321093806535, + 0.22985582898841894, + 0.4074737451437206, + 0.5750101812001673, + 0.7235644992170831, + 0.845091487389743, + 0.932852327543205, + 0.9818045787289915, + 0.9889027526496678, + 0.9532872220794018, + 0.8763472227753615, + 0.7616525609352421, + 0.6147574575880194, + 0.44288815961131356, + 0.25453320956885495, + 0.05896145000345185, + -0.1343021820123671, + -0.3160094793862316, + -0.47769514961080134, + -0.6121218510243288, + -0.7136332688689956, + -0.7783913413116497, + -0.8044837030625585, + -0.7918996049091324, + -0.7423855761913823, + -0.6592040668068039, + -0.546827262610084, + -0.41060254249001177, + -0.2564246838942689, + -0.09044299517470705, + 0.08117974708227203, + 0.252453174150231, + 0.4176995569748395, + 0.5716674838866781, + 0.7096420247468025, + 0.8275716690707243, + 0.9222232020931836, + 0.991361982948637, + 1.0339397682378415, + 1.050258821610471, + 1.042072951281685, + 1.012585810529489, + 0.9663152846194523, + 0.9088092950327084, + 0.8462203927877285, + 0.7847703238520367, + 0.7301570000997704, + 0.6869709433385449 + ], + [ + -0.8390715290764524, + -0.7105105773280779, + -0.5514174613868308, + -0.3697827168580883, + -0.17474378392160084, + 0.023943955466693125, + 0.21647160716726746, + 0.3935439221334735, + 0.5469175953666534, + 0.6698819371618675, + 0.7576568647650654, + 0.8076870646880172, + 0.8198148373855382, + 0.7963175874690384, + 0.74179967503744, + 0.6629332474169798, + 0.5680496317483013, + 0.46659248932051595, + 0.36845616965161443, + 0.28324665005448135, + 0.2195163186938397, + 0.1840352105657337, + 0.1811674678336502, + 0.21242044790745557, + 0.2762236559073339, + 0.36797553524638166, + 0.48036971840039344, + 0.6039817265236559, + 0.7280664880061098, + 0.8414909811009146, + 0.9337089194422923, + 0.9956786028760701, + 1.0206319664761963, + 1.0046215465933306, + 0.9467996982132264, + 0.8494166829638705, + 0.7175562987815977, + 0.5586548794209915, + 0.38186815628480963, + 0.19735870353877036, + 0.015574444005852506, + -0.153422280854922, + -0.30053222791265055, + -0.41807917662093175, + -0.5001452514596413, + -0.5427876165024075, + -0.5441498939791408, + -0.5044834249019806, + -0.42609087540794044, + -0.3131997420692765, + -0.17176831972909476, + -0.00922365288969349, + 0.16586886419652053, + 0.3442009733671189, + 0.5161724808594981, + 0.672380214593703, + 0.8041201477804446, + 0.9038723192529744, + 0.9657357928599438, + 0.9857821504813759, + 0.9623007378518866, + 0.8959163512087807, + 0.7895692151479635, + 0.6483568544817538, + 0.47924687354659323, + 0.2906780886089167, + 0.09207457323939254, + -0.10669716985701605, + -0.29589360175872964, + -0.4664454500405011, + -0.6104580177553673, + -0.7216259062796211, + -0.7955328560899037, + -0.8298158643424479, + -0.8241840571160846, + -0.7802960803490205, + -0.7015135328543233, + -0.5925602829651628, + -0.4591263829113781, + -0.3074590486203124, + -0.14398088070533543, + 0.025032711443959133, + 0.19369778177179978, + 0.3566839487890778, + 0.5093051825744073, + 0.6475614251044783, + 0.7681587099838076, + 0.8685339794245748, + 0.9469025825832249, + 1.002333206405731, + 1.0348396534370596, + 1.0454649720208455, + 1.0363243734076013, + 1.010571681911407, + 0.9722608468324272, + 0.9260886304835684, + 0.8770246146181313, + 0.8298565079567997, + 0.7886981923236113, + 0.7565210822927297 + ], + [ + -0.8390715290764524, + -0.7087179574216159, + -0.5471856713193056, + -0.36275652829490657, + -0.1648996116533862, + 0.03628720597772582, + 0.230672289711631, + 0.40868654495479056, + 0.5618869385148788, + 0.68345630464169, + 0.7686127048995895, + 0.8149061387430974, + 0.822384780965712, + 0.7936171270088814, + 0.733560908573392, + 0.6492751919197672, + 0.549479543134573, + 0.4439740976596913, + 0.3429467335636146, + 0.2562073999202036, + 0.19240321076151598, + 0.158278756690778, + 0.1580515781727757, + 0.1929706156697625, + 0.261114372619987, + 0.35746556460082796, + 0.4742718539656011, + 0.6016710509181834, + 0.7285281268573752, + 0.8434051302085026, + 0.935567765065641, + 0.995926902804371, + 1.0178207470844667, + 0.9975627975707552, + 0.9347092607181631, + 0.832032814169869, + 0.6952226703964497, + 0.5323589696926926, + 0.3532289921945473, + 0.16856150609407278, + -0.010746322858388002, + -0.17434594047252294, + -0.3130516937832244, + -0.419324950318111, + -0.48761854622561474, + -0.514586038799564, + -0.499168758369824, + -0.44257663248639095, + -0.34817733749846363, + -0.2213044767971057, + -0.06899140578990985, + 0.10036506367479527, + 0.2774063577619297, + 0.4522709120778896, + 0.6150956757954992, + 0.7565389316084803, + 0.8682965660835346, + 0.9435796607082515, + 0.9775202909033488, + 0.9674751465761693, + 0.913202754941421, + 0.8168988651083257, + 0.6830847992716694, + 0.5183541027977075, + 0.3309926416852072, + 0.1304957266687317, + -0.07298746105562531, + -0.26924703101581077, + -0.4486050940881686, + -0.602469088583146, + -0.7238081764682345, + -0.8075191925744509, + -0.8506556073819108, + -0.85250294865289, + -0.8144967169701938, + -0.7399930360035423, + -0.6339165361581903, + -0.502322323337588, + -0.35191731694440903, + -0.18958910044862431, + -0.02198683969064681, + 0.14481100689355714, + 0.3055221566947125, + 0.4557762828194548, + 0.5921348555676916, + 0.7120418148605234, + 0.8137388032245005, + 0.8961782718708609, + 0.958960140037846, + 1.0023045178872136, + 1.0270571973844957, + 1.0347097273709822, + 1.0274053864368873, + 1.0078988841952865, + 0.9794423626199499, + 0.9455827014546224, + 0.9098730399289116, + 0.8755213270029772, + 0.8450165423800902, + 0.8197851761190034 + ], + [ + -0.8390715290764524, + -0.7069207263312796, + -0.5429396404971082, + -0.3557090977577048, + -0.15503827313784385, + 0.04862455426669563, + 0.2448189443487317, + 0.42369888475381257, + 0.5766223522089005, + 0.6966706354292718, + 0.7790689412778484, + 0.8214850134557263, + 0.8241876473177709, + 0.7900519974127479, + 0.7244038317059606, + 0.6347011164488687, + 0.5300593151621533, + 0.420637068262007, + 0.31691138458455437, + 0.22888519079855735, + 0.16528327369447296, + 0.13280291432080496, + 0.13549026208221418, + 0.17431051641680634, + 0.24696801001480057, + 0.34801145832868174, + 0.4692316975072365, + 0.6003274078804429, + 0.7297828928206608, + 0.8458755865786287, + 0.9377137640082411, + 0.9961998316273188, + 1.014822670690089, + 0.9902027703110057, + 0.9223433761070868, + 0.8145751922743396, + 0.6732162586682413, + 0.5069976628314593, + 0.3263259932301125, + 0.14246282121860288, + -0.033300122286198997, + -0.1904023573093802, + -0.3196621801989388, + -0.4137796870321041, + -0.4676835615669386, + -0.47872500790968536, + -0.44673199142530856, + -0.3739412304806419, + -0.26482520727122266, + -0.12582870263144055, + 0.03497386308164362, + 0.20829083831885178, + 0.38408537070724724, + 0.5520935205814331, + 0.7023694656260832, + 0.8258326529091492, + 0.9147865235503813, + 0.963375607160459, + 0.9679483591409095, + 0.9272974148582133, + 0.8427565809227301, + 0.7181439387983795, + 0.5595516590216134, + 0.37499425404432324, + 0.17393698849106765, + -0.033265680017137166, + -0.23598578008429696, + -0.42395206308101757, + -0.5878537663728907, + -0.7198776752862563, + -0.8141414718776503, + -0.8669922178530722, + -0.8771475041776469, + -0.8456682468771063, + -0.7757658085491222, + -0.6724610045334526, + -0.5421269919198458, + -0.3919600593606195, + -0.22942993317119473, + -0.061762786352513706, + 0.10449511120745111, + 0.26379691787263254, + 0.41177849030938274, + 0.5453051821271219, + 0.6624004743450558, + 0.7620893773951614, + 0.8441977187577288, + 0.9091487321699043, + 0.9577908963633275, + 0.9912773895746853, + 1.0110007502681384, + 1.0185699756325217, + 1.0158049088561347, + 1.004717121469216, + 0.9874489383384639, + 0.9661524054541254, + 0.9428058087179805, + 0.9189834815680821, + 0.8956111588150337, + 0.8727503296441737 + ], + [ + -0.8390715290764524, + -0.7051188957505247, + -0.5386794794251122, + -0.3486408379236415, + -0.1451607949514793, + 0.06095399357475852, + 0.25890825757826924, + 0.43857615553635665, + 0.5911177007153925, + 0.70951796565937, + 0.7890187710468825, + 0.8274185098041827, + 0.8252217474350789, + 0.7856261184586248, + 0.7143401207559006, + 0.619232353898951, + 0.5098212903056538, + 0.39662527587213026, + 0.29040499731319136, + 0.2013441845937101, + 0.1382270732028114, + 0.10768076412831035, + 0.11355454845698709, + 0.15650435743138086, + 0.23383756748179058, + 0.33965164839228, + 0.465271409076886, + 0.5999571679909111, + 0.7318243880554196, + 0.8488888383748138, + 0.940134355990255, + 0.9964956837113761, + 1.0116577009800616, + 0.9825935830965873, + 0.9097949945178342, + 0.7971828675792727, + 0.6517224971327323, + 0.4827966902131193, + 0.30141172084980844, + 0.11932071378412118, + -0.05185236824788719, + -0.20141607880143858, + -0.3202878894957495, + -0.4015100310438866, + -0.4405911419776699, + -0.4356764892202956, + -0.38756068815481676, + -0.29956250624835684, + -0.17728243079759873, + -0.02826198348323925, + 0.1384386847683605, + 0.31272980804558415, + 0.4840318231409199, + 0.6418471765269277, + 0.776340656260288, + 0.8788997893573631, + 0.9426430509439476, + 0.9628424244100995, + 0.937229120929724, + 0.8661572125042764, + 0.7526090553154342, + 0.6020376319696721, + 0.42205298767247235, + 0.22197144588793144, + 0.01225617893963744, + -0.19611472448213849, + -0.39231162196648744, + -0.566304763452381, + -0.7094607470433064, + -0.8150436393183875, + -0.8785856089635168, + -0.8980996730085782, + -0.8741175084321468, + -0.8095480511829646, + -0.7093671800688073, + -0.5801640196001383, + -0.4295836383542595, + -0.2657171914572991, + -0.0964968967860857, + 0.07084688032004485, + 0.23021641141248717, + 0.3769291764994598, + 0.5078351098556445, + 0.621272710278677, + 0.7168885371024512, + 0.7953606499445616, + 0.8580756063085107, + 0.9068092241354455, + 0.9434535174057135, + 0.9698176901935586, + 0.9875128036040485, + 0.997911418046699, + 1.0021588523401954, + 1.0012046050825048, + 0.9958224741707828, + 0.9865957872252682, + 0.9738580287356483, + 0.9575958295432256, + 0.9373369519706884, + 0.9120569182206104 + ], + [ + -0.8390715290764524, + -0.7033124774027346, + -0.534405298975935, + -0.3415521626895797, + -0.135268205350961, + 0.07327351842952334, + 0.2729369293307399, + 0.4533136143687784, + 0.6053669482626639, + 0.7219915248736182, + 0.7984557208231104, + 0.8327019568214955, + 0.8254861125259347, + 0.7803443562986203, + 0.7033826081061607, + 0.6028915467685229, + 0.4887991729997551, + 0.37198386382180826, + 0.263483439153771, + 0.17364905702031694, + 0.11130500915492361, + 0.08298437217509214, + 0.09231349663273414, + 0.13961340768478248, + 0.22177223826336945, + 0.3324201183212519, + 0.4624084008713022, + 0.6005620866585364, + 0.7346422033209075, + 0.8524284050123226, + 0.9428153735710263, + 0.9968126101670363, + 1.0083469129559337, + 0.9747891185769628, + 0.8971584378557945, + 0.7799943702931738, + 0.6309224995601989, + 0.45997145551838153, + 0.2787200666347686, + 0.0993640139027922, + -0.06621009557428137, + -0.20726675520347027, + -0.3149216471179451, + -0.3826634419706093, + -0.40668219488267354, + -0.3860070423171456, + -0.32246855493203386, + -0.22050819473342562, + -0.08685969640289587, + 0.06987349488861053, + 0.2397223863703673, + 0.411917034192784, + 0.5754899091966548, + 0.7198940018004896, + 0.8356080396665243, + 0.9146975966000261, + 0.9512987294940338, + 0.9419913636832735, + 0.8860337166813362, + 0.785436673975485, + 0.6448675815354629, + 0.4713852383823764, + 0.27402053623391187, + 0.06322881390195514, + -0.14975053451681558, + -0.3535850226886062, + -0.5375448884751816, + -0.6921575284061292, + -0.8097765940914684, + -0.8850266397303556, + -0.915091480880037, + -0.8998240611922179, + -0.8416675321189521, + -0.7453911240804382, + -0.6176595274199348, + -0.4664697109456667, + -0.30050271963127695, + -0.12844806832094213, + 0.0416370001488467, + 0.2028733083423669, + 0.3499600647033131, + 0.4793955099405174, + 0.5895049441909932, + 0.6802865174194751, + 0.7531071069436128, + 0.8102977147126103, + 0.854707319478739, + 0.8892744889319806, + 0.9166673694399213, + 0.9390266468622981, + 0.9578257841956647, + 0.9738421347893449, + 0.987215262527171, + 0.9975580719821343, + 1.0040838745123863, + 1.0057182729332783, + 1.001177008310611, + 0.9890066151705312, + 0.9676001407343101, + 0.9352116915694699 + ], + [ + -0.8390715290764524, + -0.7015014830411419, + -0.5301172103870601, + -0.3344434871478483, + -0.12536153416606777, + 0.08558112497125127, + 0.2869016737403902, + 0.46790656289005017, + 0.6193641615539072, + 0.7340847395885118, + 0.8073736509039284, + 0.8373311952725281, + 0.8249804949211796, + 0.7742125181087407, + 0.6915452658391216, + 0.5857026140168334, + 0.467027973507787, + 0.34675915915821826, + 0.23620345256452463, + 0.1458648457227849, + 0.0845871323979832, + 0.058784583181647865, + 0.07183397896894511, + 0.12369578701722916, + 0.21081722515171536, + 0.3263462650658036, + 0.46065526067621204, + 0.6021392957978632, + 0.7382219710511502, + 0.8564749272965576, + 0.9457411250694477, + 0.9971486304032211, + 1.0049123525989074, + 0.9668446422113879, + 0.8845286908083101, + 0.7631466071352214, + 0.6109915338289911, + 0.43872511750635024, + 0.25846405654865745, + 0.08279005424560021, + -0.07622396702135475, + -0.20789045468145367, + -0.3036249838835854, + -0.35746642256116634, + -0.36638340095533295, + -0.33037036451941537, + -0.2523507196441594, + -0.13791315071777005, + 0.0050891889274643064, + 0.1670466732364123, + 0.33717972489873266, + 0.5041763285456263, + 0.6568529420926258, + 0.7848097514994872, + 0.8790489364215176, + 0.9325226645069954, + 0.9405772488368709, + 0.901262424208227, + 0.8154806410810648, + 0.6869605665072339, + 0.5220508615477463, + 0.32934350597286194, + 0.11914896233210258, + -0.09714311628058775, + -0.30777523331924916, + -0.5013577109390194, + -0.6675791228910615, + -0.7978434981271355, + -0.8857908153214328, + -0.9276644131126435, + -0.9224983236626644, + -0.8721089792191572, + -0.7808890094859076, + -0.6554155256819021, + -0.5039006055953134, + -0.33552643006390936, + -0.15972003150213324, + 0.014568974728637779, + 0.1795217687832149, + 0.32897350961944183, + 0.4587571263927064, + 0.5668373386115819, + 0.6532280366130856, + 0.7197113265443664, + 0.7693997771998206, + 0.806201147924961, + 0.8342542952194203, + 0.8574045175946947, + 0.8787764255546501, + 0.9004843344607966, + 0.9234973879521401, + 0.9476531499560704, + 0.9717933128657532, + 0.9939817654257153, + 1.0117604596411005, + 1.0224024730318328, + 1.023132794869489, + 1.0113027070339589, + 0.9845194885495663, + 0.9407459227622799 + ], + [ + -0.8390715290764524, + -0.6996859244487528, + -0.5258153252579312, + -0.32731522756194076, + -0.11544181269248721, + 0.09787481127881006, + 0.30079921991484615, + 0.48235034880960603, + 0.6331035122377895, + 0.7457912367595906, + 0.8157667592622171, + 0.8413025809282068, + 0.8237053683062249, + 0.7672373457040339, + 0.6788431879205993, + 0.5676907160523487, + 0.4445439496155963, + 0.3209985855468788, + 0.20862253546181997, + 0.11805679754461722, + 0.058142962486593286, + 0.0351508173001443, + 0.052180470317372274, + 0.10880626615409517, + 0.20101357113637547, + 0.32145477949494333, + 0.46001969652161084, + 0.6046813174274126, + 0.7425454386127455, + 0.8610062733056292, + 0.9488944864055266, + 0.9975016445047296, + 1.0013768900693558, + 0.9588164109197029, + 0.8720006868771729, + 0.7467737708705604, + 0.592097545058572, + 0.4192467817754457, + 0.2408338510807786, + 0.069762718808767, + -0.08178982655312961, + -0.20328036191203724, + -0.2865274304551376, + -0.3262217965327857, + -0.3202018451393765, + -0.2694986875991916, + -0.1781714221562772, + -0.0529630575483135, + 0.09718756871328059, + 0.2617415056223889, + 0.42922761244890095, + 0.5879485796347288, + 0.7266915795462519, + 0.8354098044008823, + 0.905840462198131, + 0.9320247388503717, + 0.9106969983389167, + 0.8415150683485858, + 0.727111306707839, + 0.572955039449529, + 0.3870300154800168, + 0.17934436532695558, + -0.038696382645250015, + -0.25501183480380624, + -0.4576158846121311, + -0.6353798973163799, + -0.7787376196734828, + -0.8802833947343277, + -0.9352222136289846, + -0.9416395810061247, + -0.9005706246187329, + -0.8158609624658668, + -0.6938261873606487, + -0.5427319300004187, + -0.37213150260500866, + -0.19211188976465432, + -0.012509756486133304, + 0.15783458071722656, + 0.31171724947213064, + 0.44404432500127894, + 0.5520928972309742, + 0.6355351757655969, + 0.6962267220047018, + 0.7377866124821567, + 0.7650214249194492, + 0.7832627701466963, + 0.7976968163990179, + 0.8127623727717358, + 0.8316818329007447, + 0.8561685924205876, + 0.886328846639479, + 0.9207491838328051, + 0.9567383993985106, + 0.990675995264055, + 1.0184130102991902, + 1.0356734749983878, + 1.0384154240080816, + 1.0231261208313958, + 0.9870432458513987, + 0.928308570816742 + ], + [ + -0.8390715290764524, + -0.6978658134382698, + -0.5214997555470556, + -0.3201678013421309, + -0.10551007358446193, + 0.11015257769528732, + 0.3146263127012406, + 0.4966403673905215, + 0.6465792793353448, + 0.7571048471399936, + 0.8236295853211515, + 0.8446129874343331, + 0.8216619272772823, + 0.759426508126119, + 0.6652925709533063, + 0.5488822179040403, + 0.42138454624776817, + 0.29475057411286704, + 0.18079882003203923, + 0.09029021530647734, + 0.032041306793836516, + 0.01215087097393766, + 0.03341484503841391, + 0.09499607824606188, + 0.19239800563658238, + 0.3177655460254329, + 0.46050450279187327, + 0.6081760991248454, + 0.7475905613719698, + 0.8659976594379943, + 0.9522570013256163, + 0.997869446355387, + 0.9977640674182645, + 0.9507612747123376, + 0.8596685947958654, + 0.7310062714622806, + 0.5743997404466245, + 0.40170981760617996, + 0.2259949600469372, + 0.060410822422040766, + -0.0828497826869069, + -0.19348685255531695, + -0.26382503210510433, + -0.2893050691642255, + -0.2687186359333469, + -0.2041931409126041, + -0.10095075436808867, + 0.03312259379955229, + 0.18805654824699192, + 0.35248061198747277, + 0.5143708317327801, + 0.6618181005268113, + 0.783778933695597, + 0.8707707806341268, + 0.9154751153491032, + 0.9132136036358779, + 0.8622666186520095, + 0.7640100852225848, + 0.6228563679298149, + 0.445997299969476, + 0.24296146266056431, + 0.025012009348365994, + -0.19557592665223422, + -0.4063093352872311, + -0.5952880774483337, + -0.7519758184418118, + -0.8678773829579733, + -0.9370752404551553, + -0.9565873460365838, + -0.9265203529188223, + -0.8500056108391042, + -0.7329198329142115, + -0.5834073682129333, + -0.41123608142693874, + -0.2270330868473006, + -0.041458840301932094, + 0.1356131243528707, + 0.2958398348144062, + 0.4330077499647205, + 0.5434289444394818, + 0.6260942677055136, + 0.6825668421809152, + 0.7166267041890758, + 0.7337052186079613, + 0.7401722880452737, + 0.7425566643393761, + 0.7467870283114239, + 0.7575375283213465, + 0.7777465241862201, + 0.808353529807359, + 0.8482704161575922, + 0.8945733067495338, + 0.9428757980824942, + 0.987825918534851, + 1.0236608978199893, + 1.044755810669803, + 1.0461130768707787, + 1.0237567298952346, + 0.9750145294860659, + 0.8986885475242214 + ], + [ + -0.8390715290764524, + -0.6960411618520155, + -0.5171706135690873, + -0.31300162702103684, + -0.09556735074727878, + 0.12241242715326275, + 0.3283797134486553, + 0.510772062917513, + 0.6597858516221465, + 0.7680196085316069, + 0.8309570135068685, + 0.847259808772712, + 0.8188520862222122, + 0.7507885932112517, + 0.6509106935243321, + 0.529304650629412, + 0.39758833310681074, + 0.26806447238795295, + 0.15279095020660974, + 0.06263030445279445, + 0.006350081475425734, + -0.01014927754508782, + 0.0155961822045243, + 0.08231274258166205, + 0.18500280689437437, + 0.31529356178948875, + 0.46210754793968567, + 0.6126070711708045, + 0.7533316150966569, + 0.8714217859631084, + 0.9558089894247, + 0.9982497374248848, + 0.9940979418246726, + 0.9427362741204826, + 0.8476251106952998, + 0.7159696973534974, + 0.5580472477348696, + 0.3862703148669269, + 0.2140866888072545, + 0.05482683702235153, + -0.07939281063274484, + -0.17861694278714246, + -0.23577810080675102, + -0.24715991438484788, + -0.21258159322709552, + -0.13531320777299938, + -0.021750632209878794, + 0.11910801097983334, + 0.2763356393717827, + 0.4378483274749486, + 0.5912263242486914, + 0.7245365528504557, + 0.827112124409697, + 0.8902473920664126, + 0.9077703903170544, + 0.8764588888517333, + 0.7962726041117107, + 0.6703829855223404, + 0.5049935408164978, + 0.3089573527462536, + 0.09321313313843255, + -0.1299246724043289, + -0.34757420251917204, + -0.5471369403799996, + -0.7171309722124359, + -0.8479476529054245, + -0.9324779944636741, + -0.9665643419105949, + -0.9492450420186259, + -0.8827738673483833, + -0.772411079721315, + -0.6259985202310123, + -0.453346418569541, + -0.2654741094261484, + -0.07375972739314428, + 0.11093585739787158, + 0.27909944073542414, + 0.4232780956548247, + 0.5386078273681848, + 0.6231053638062409, + 0.6776904386001554, + 0.7059317660192963, + 0.7135387557801595, + 0.7076494006335655, + 0.6959888078064985, + 0.6859888822343693, + 0.6839649609720801, + 0.6944384865730873, + 0.7196766594582632, + 0.7594928270145564, + 0.8113190454306847, + 0.8705295359209336, + 0.9309654250469586, + 0.9855911687693482, + 1.0272038619676103, + 1.0491187693900264, + 1.0457664826763233, + 1.0131561258257114, + 0.9491810749658097, + 0.8537646761051517 + ], + [ + -0.8390715290764524, + -0.6942119815618548, + -0.5128280119919051, + -0.3058171242291113, + -0.08561467922964314, + 0.13465236549963208, + 0.34205620076669024, + 0.5247409301493217, + 0.6727177299647894, + 0.7785297689271053, + 0.8377442765766702, + 0.8492409613126608, + 0.8152784775270384, + 0.7413330981481954, + 0.6357158941729674, + 0.50898667101574, + 0.37319494043910767, + 0.24099045153485488, + 0.1246579580584277, + 0.035142019927060966, + -0.018863865247219086, + -0.03168565746346963, + -0.0012194203960826377, + 0.07079990107880635, + 0.1788556810440649, + 0.3140488756698207, + 0.46482178385781864, + 0.6179532251105023, + 0.7597393271197301, + 0.8772489863337142, + 0.9595296613334918, + 0.998640141133149, + 0.9904029254029569, + 0.9347982362771354, + 0.8359607613386125, + 0.7017838151529313, + 0.543177858607818, + 0.37306569471719014, + 0.20522083048762663, + 0.05306597728748769, + -0.07145486696423764, + -0.1588331199060578, + -0.20270823042508698, + -0.20029284259677574, + -0.15249709670019962, + -0.06376541383037442, + 0.05833980751327376, + 0.2037588405462289, + 0.36070312967725693, + 0.516512788905258, + 0.6585456565544966, + 0.7750440427592412, + 0.8559298973507738, + 0.893484217904442, + 0.8828722347676036, + 0.8224828074126697, + 0.714059208509667, + 0.5626094889558781, + 0.3760978405994043, + 0.16493312069007834, + -0.05871426534050609, + -0.28172218900100854, + -0.49089757186718586, + -0.6738657639960994, + -0.8199046459627061, + -0.9206632264078701, + -0.9707128780791515, + -0.9678915014409742, + -0.913415645116004, + -0.8117517586129592, + -0.670254276419071, + -0.4985943242813865, + -0.30801785548336597, + -0.11048535784754601, + 0.08224380601792633, + 0.2595117950843968, + 0.4125737233358511, + 0.5352490487473454, + 0.6243510948852183, + 0.6798491517016714, + 0.7047403484707025, + 0.7046360805480374, + 0.6870974792627539, + 0.6607826879309104, + 0.6344916551361794, + 0.6162081940071249, + 0.6122415821280245, + 0.6265599398392577, + 0.6603858467339175, + 0.7120938225648247, + 0.7774135952863513, + 0.8499075270113308, + 0.9216601797275016, + 0.9840969148771623, + 1.0288391904608745, + 1.0485074091740327, + 1.0373963205316203, + 0.9919699578248252, + 0.9111487553332717, + 0.796386515857321 + ], + [ + -0.8390715290764524, + -0.6923782844691184, + -0.5084720638336799, + -0.2986147136700675, + -0.07565309511593458, + 0.14687040181998226, + 0.3556525712799892, + 0.5385425157550061, + 0.6853695296106974, + 0.7886297895412246, + 0.8439869587206039, + 0.8505548854512859, + 0.8109444491098105, + 0.7310704190362808, + 0.6197275480069683, + 0.4879580196335202, + 0.34824499303465534, + 0.21357941202302883, + 0.09645913938019184, + 0.007889913635372586, + -0.04353492702514217, + -0.052396488954473075, + -0.016979022869870353, + 0.06049716811789718, + 0.17397965831112672, + 0.31403654744983855, + 0.46863527686753415, + 0.6241892133605738, + 0.7667810255956067, + 0.8834473894431228, + 0.9633972403961735, + 0.999038217702472, + 0.9867036226466648, + 0.9270033725092732, + 0.8247632236623572, + 0.6885616156915096, + 0.5299168676130056, + 0.36221348647397084, + 0.19948061648204424, + 0.055145654671085065, + -0.059118515627875734, + -0.13435156679312255, + -0.16499460923273426, + -0.14926711331351564, + -0.08922119735496117, + 0.009508603671262106, + 0.13821918461078056, + 0.2858598875664586, + 0.4398958711455779, + 0.5872467138201469, + 0.7152352997038466, + 0.8124870323266223, + 0.8697259972836378, + 0.8804221906245285, + 0.841252284958443, + 0.7523459640147329, + 0.6173010635459191, + 0.44296383259611954, + 0.23898532387130467, + 0.017180413345212996, + -0.2092690843380841, + -0.42671289621330444, + -0.6219689083673916, + -0.7832302214910091, + -0.9008762451828575, + -0.9681282031650656, + -0.9815009461272579, + -0.9410180507822155, + -0.8501744003965175, + -0.7156483954318268, + -0.5467834437432214, + -0.35487452159915756, + -0.15230971244359157, + 0.048371837377430955, + 0.23543575896099136, + 0.3988478991757757, + 0.5310355865426686, + 0.6274471356167843, + 0.6868546040699108, + 0.7113639392265106, + 0.7061215202401072, + 0.6787348999500422, + 0.6384554397219088, + 0.5951979258234246, + 0.5584933180546113, + 0.5364820603732732, + 0.5350544860762015, + 0.5572309912397583, + 0.6028489540983742, + 0.6685888463879173, + 0.7483331052523166, + 0.8338133998049145, + 0.9154701544108254, + 0.9834268504034328, + 1.02847331724731, + 1.0429574538040134, + 1.0215019856388197, + 0.9614886082494178, + 0.8632817560209199, + 0.7301936121907626 + ], + [ + -0.8390715290764524, + -0.6905400825045247, + -0.5041028824599318, + -0.2913948170962508, + -0.06568363541833881, + 0.15906454876242304, + 0.3691656403785618, + 0.5521724197336781, + 0.6977359824302928, + 0.7983143477296601, + 0.8496809984344008, + 0.8512005468412325, + 0.8058540612841224, + 0.720011839455134, + 0.6029660419970767, + 0.46624947730351995, + 0.322780042570484, + 0.18588288793331598, + 0.06825392870691593, + -0.01906201714355643, + -0.06759891008868424, + -0.07222236038132807, + -0.03163300988654538, + 0.05143999423324397, + 0.1703930067293833, + 0.31525662724508713, + 0.47353126018738, + 0.6312854693889249, + 0.7744208060884418, + 0.889983093940046, + 0.9673890901266043, + 0.9994414794045888, + 0.983024666591326, + 0.9194068802907102, + 0.8141166657388683, + 0.6764084140481754, + 0.5183760163863821, + 0.35381028152788296, + 0.19691993508831884, + 0.06104530523745605, + -0.04251206918093525, + -0.10543979962640937, + -0.12306967203240624, + -0.09469596577438644, + -0.023550104028708968, + 0.08354448873857939, + 0.21678902136325529, + 0.36423256020881867, + 0.5127281919729433, + 0.648946547912213, + 0.7603743924282287, + 0.8362327636836822, + 0.8682580616971224, + 0.8512996738719106, + 0.7836989317137103, + 0.6674265147502204, + 0.5079690677049131, + 0.3139707799143873, + 0.09665156461979557, + -0.13096067388985108, + -0.3549317625829061, + -0.5613936120235106, + -0.7375168771991739, + -0.8724121781826388, + -0.9578926550568805, + -0.9890407836163619, + -0.9645371670157846, + -0.8867269777085699, + -0.7614191862043376, + -0.5974330815575385, + -0.4059245389636216, + -0.19954005752366658, + 0.008540866511779474, + 0.20560539886593002, + 0.3803742535213653, + 0.5238602288626706, + 0.6300470593494211, + 0.69632711311433, + 0.7236523170001781, + 0.7163743519191119, + 0.6817766400889121, + 0.6293299749484063, + 0.5697329249736638, + 0.5138238190302892, + 0.4714694763831033, + 0.45054363467760977, + 0.4561031349091897, + 0.4898518788496188, + 0.5499528481294003, + 0.6312104022437078, + 0.725603446273691, + 0.8231104083420807, + 0.9127346721410976, + 0.9836185058318155, + 1.0261271759267645, + 1.0327932053716373, + 0.9990317935083757, + 0.9235685995108668, + 0.8085556110893457, + 0.6593846371729641 + ], + [ + -0.8390715290764524, + -0.6886973876281027, + -0.4997205815805859, + -0.2841578572839322, + -0.0557073379688987, + 0.17123282286083905, + 0.38259224296368566, + 0.565626296817239, + 0.7098119391106209, + 0.8075783397940679, + 0.8548226911618968, + 0.8511774372049441, + 0.8000120829552274, + 0.7081695180586399, + 0.5854527489812993, + 0.44389282004116243, + 0.29684249841043114, + 0.15795295007137994, + 0.0401017740459946, + -0.04565048004591424, + -0.09099320027880292, + -0.09110639872701526, + -0.0451352468810266, + 0.043659544131800176, + 0.16810916369976497, + 0.31770415529952084, + 0.4794882076516843, + 0.6392083478977783, + 0.782619714642807, + 0.8968203536475787, + 0.9714818466970162, + 0.9998474061074146, + 0.9793905547872065, + 0.9120625523743165, + 0.8041011141135631, + 0.6654210107132121, + 0.5086525520791448, + 0.3479308736198097, + 0.1975628256125485, + 0.07070659299863424, + -0.021808254190031406, + -0.07241374466345979, + -0.07741414173995817, + -0.03723524889082583, + 0.04368983540511728, + 0.1573678584740591, + 0.2929688484652969, + 0.43775178887727445, + 0.5781096484190636, + 0.700649682111466, + 0.7932296995214155, + 0.8458799521428741, + 0.8515518784991665, + 0.8066481126591363, + 0.7113023862375334, + 0.5693930869865618, + 0.38829024037593224, + 0.17835234320409843, + -0.04779379175624508, + -0.27614126590686655, + -0.4922971037188895, + -0.6825102584922059, + -0.8346575824323915, + -0.9391135678534489, + -0.9894377529190732, + -0.9828274082679297, + -0.9203011899765648, + -0.8066001128158731, + -0.6498135585730999, + -0.4607583132553331, + -0.2521564015702018, + -0.037671782510520654, + 0.16912377479397503, + 0.3557793821795934, + 0.5119106015507123, + 0.6299874732866353, + 0.705898846344665, + 0.7392403767341267, + 0.7332924023034187, + 0.6946776229867306, + 0.6327159357792074, + 0.5585228279675307, + 0.48392552303373015, + 0.42029414959059924, + 0.37740004628937596, + 0.36241695905926513, + 0.37917154882907655, + 0.4277272110328632, + 0.504351547725501, + 0.6018766044359698, + 0.7104172573392682, + 0.8183726378469678, + 0.913603685805996, + 0.9846611464924324, + 1.021935002026113, + 1.0186085942433851, + 0.9713263999062508, + 0.880519519178075, + 0.7503722364845778, + 0.5884510641955795 + ], + [ + -0.8390715290764524, + -0.6868502118291138, + -0.49532527524700376, + -0.276904258008558, + -0.04572524131148004, + 0.18337324485752665, + 0.3959292341892645, + 0.5788998578556704, + 0.7215923712994843, + 0.8164168836716816, + 0.8594086917052292, + 0.8504855747347884, + 0.7934239871523124, + 0.6955564752067824, + 0.567210000412097, + 0.42092077254417765, + 0.2704755569765309, + 0.12984210807207558, + 0.012062011578879595, + -0.07181303625494537, + -0.11365692596827187, + -0.10899443274170306, + -0.057443225297067524, + 0.03718258945894487, + 0.16713668564704626, + 0.3213691821474476, + 0.48647992835462917, + 0.647920284345266, + 0.7913359464033536, + 0.9039217730722191, + 0.9756515556837551, + 1.0002534610242861, + 0.9758254861742377, + 0.9050223958714311, + 0.7947918522726912, + 0.6556869205704323, + 0.5008284079200541, + 0.3446275931301761, + 0.20140325269159226, + 0.08403398674302648, + 0.0027775853282434038, + -0.035634286034446294, + -0.02855151728258082, + 0.022424460900392892, + 0.11165253207931047, + 0.23000712688474623, + 0.3657110633218557, + 0.5053621770144537, + 0.6350613509043845, + 0.7415494707168427, + 0.8132675224436687, + 0.8412655676021413, + 0.8199009329886267, + 0.7472823352412833, + 0.6254340286592596, + 0.46017199162079275, + 0.2607023589625335, + 0.03897034365844016, + -0.19119496661228377, + -0.41507941250633607, + -0.6181538805139, + -0.7871364849165654, + -0.9109665445430412, + -0.9816156099793081, + -0.9946727234535253, + -0.9496584884388356, + -0.8500437830293138, + -0.7029720411336714, + -0.5187068175475515, + -0.3098469281967078, + -0.09036834123868485, + 0.12543573791201576, + 0.3240379087465218, + 0.49370190956883736, + 0.625372202866267, + 0.7133573795780497, + 0.7557492901176502, + 0.754536805969267, + 0.7153945162798943, + 0.6471517990911051, + 0.5609752557199179, + 0.469325361306286, + 0.3847735455867441, + 0.3187855093752301, + 0.2805871642744925, + 0.2762285398324804, + 0.3079469437175713, + 0.3739038919352524, + 0.468333060876133, + 0.5820927477528413, + 0.703571366990619, + 0.8198542120424666, + 0.9180295580620345, + 0.9864963738134946, + 1.0161366526534403, + 1.0012324737616198, + 0.9400388123129922, + 0.8349633503174185, + 0.6923484583161268, + 0.5218922700046043 + ], + [ + -0.8390715290764524, + -0.6849985671259727, + -0.49091707784902144, + -0.269634444019932, + -0.035738384593647574, + 0.1954838400251211, + 0.40917349019841737, + 0.5919888711844372, + 0.7330723736992194, + 0.8248253215081235, + 0.8634360164012284, + 0.8491255040787362, + 0.7860959459011173, + 0.6821865786500575, + 0.5482610578812229, + 0.39736696029162477, + 0.24372312980963676, + 0.10160321167878753, + -0.015806259401545328, + -0.09748824712866949, + -0.13553111644883187, + -0.12583514833981785, + -0.06851819641525025, + 0.03203141668029086, + 0.16747921596317417, + 0.3262368090591797, + 0.4944756818037839, + 0.657379973049234, + 0.8005250587707393, + 0.9112485119336053, + 0.9798738122697093, + 1.0006571065672822, + 0.9723531999453419, + 0.8983362639758744, + 0.7862588547630373, + 0.6472836758394048, + 0.494969512818086, + 0.34392984130607684, + 0.2084051629521858, + 0.10089570465197123, + 0.030989727738263695, + 0.004496677730800924, + 0.022957928984556536, + 0.08356615914750698, + 0.17948280257867716, + 0.30050629188106953, + 0.43401533637125767, + 0.566093151721607, + 0.6827306199211203, + 0.7710078162674447, + 0.8201623686709689, + 0.8224675896288773, + 0.7738612520623833, + 0.6742856836451672, + 0.527720430503113, + 0.34190937197351207, + 0.12780430299270437, + -0.10123397880029349, + -0.3304189941225757, + -0.5446342801125366, + -0.7295599045989399, + -0.8727443726317458, + -0.9645392116165377, + -0.9988230997585985, + -0.9734583636220397, + -0.890443732470067, + -0.7557514676355109, + -0.5788620814024831, + -0.3720334942342621, + -0.14936163252559573, + 0.07429532577527055, + 0.28444740361415055, + 0.46807283032043295, + 0.6146046657415056, + 0.7167285312523712, + 0.7709280393124637, + 0.7777308290908795, + 0.7416282155028029, + 0.670664809131968, + 0.5757202066077495, + 0.46953158834627184, + 0.36553174075648714, + 0.2765989892369126, + 0.2138307932836031, + 0.18545843485434046, + 0.19601412501179466, + 0.24584300839245649, + 0.3310216860796495, + 0.4437045857302475, + 0.5728740616763545, + 0.7054249832453591, + 0.8274756625054677, + 0.9257696727048865, + 0.9890213962403414, + 1.0090638829661311, + 0.9816801876258194, + 0.9070357653104167, + 0.7896747748128825, + 0.6380911817743113, + 0.46393013541407635 + ], + [ + -0.8390715290764524, + -0.6831424655661745, + -0.48649610411197075, + -0.26234884101734934, + -0.0257478074584954, + 0.207562638487812, + 0.42232190885517623, + 0.6048891639735499, + 0.7442471661092357, + 0.8327992221120655, + 0.866902045062598, + 0.8470982959116035, + 0.7780348244417011, + 0.6680745282822008, + 0.5286300834585345, + 0.37326586032438386, + 0.21662977043907314, + 0.07328935138335824, + -0.04344430105429967, + -0.12261581847885689, + -0.15655885537217645, + -0.14158023580098578, + -0.07832529334404453, + 0.028223750396487113, + 0.16913547135729, + 0.33228724860597975, + 0.5034403130768019, + 0.6675425630288196, + 0.8101401980069641, + 0.9187604975963235, + 0.9841239040828389, + 1.0010558202055542, + 0.9689968174711335, + 0.8920515029404117, + 0.7785662612171154, + 0.6402782085298008, + 0.49112523582294465, + 0.34584382857192336, + 0.21850282347478778, + 0.12112501736379166, + 0.06253473359102141, + 0.04754062535059542, + 0.07652357551597389, + 0.14545503051225112, + 0.24632712982374935, + 0.3679375171766217, + 0.4969423673233641, + 0.6190728967026066, + 0.720403752330156, + 0.7885651248108566, + 0.8138022389651791, + 0.7898036896649115, + 0.7142416362319485, + 0.5889902441994866, + 0.4200125431646014, + 0.21692903307873423, + -0.007696883313195482, + -0.239302031586007, + -0.4624241689402244, + -0.6618771534083663, + -0.8239108068121256, + -0.937265513126717, + -0.9940384939740085, + -0.9902926296634166, + -0.9263587842104398, + -0.8068071955823453, + -0.6400903499940813, + -0.4378863148753834, + -0.21419527498868768, + 0.015739062683133097, + 0.23659884859362657, + 0.43416208618605806, + 0.5963840168333447, + 0.7143077500531211, + 0.7827343119001269, + 0.8005988782822597, + 0.7710199625190518, + 0.7010109723794963, + 0.6008680642375565, + 0.4832736163767394, + 0.3621795274650333, + 0.2515570043146038, + 0.1641171413364728, + 0.11011678163542346, + 0.09636444013333562, + 0.12552973156855374, + 0.19583629160717914, + 0.3011837511229488, + 0.4317018359057197, + 0.5746936128102733, + 0.7158808457346579, + 0.8408281911805391, + 0.9363997343353478, + 0.9920947866168749, + 1.0011213646508927, + 0.9610942400307038, + 0.8742863442094779, + 0.7474121762850977, + 0.5909731767570716, + 0.41824124782229355 + ], + [ + -0.8390715290764524, + -0.6812819192262081, + -0.4820624690936934, + -0.2550478756246596, + -0.01575454993641558, + 0.21960767554175273, + 0.43537141047107153, + 0.6175966235578889, + 0.7551120954164536, + 0.8403343832904395, + 0.8698045226826149, + 0.8444055460921946, + 0.7692481747967771, + 0.653235839977993, + 0.508342108882589, + 0.34865275077916985, + 0.18924060018312971, + 0.04495375861339761, + -0.0707938607918212, + -0.14713674216243322, + -0.17668542884552718, + -0.15618452835256552, + -0.0868336407898529, + 0.025772692354879945, + 0.17209924666358206, + 0.3394959050980923, + 0.5133344073870378, + 0.6783598706552638, + 0.8201323381346975, + 0.9264166442419255, + 0.9883769558347878, + 1.0014471102295568, + 0.9657786883385979, + 0.8862126168055259, + 0.7717718942374245, + 0.6347263173266203, + 0.48932797012459883, + 0.35035251925281163, + 0.2316014388847107, + 0.14452189660060205, + 0.09708449773938999, + 0.09302720462533229, + 0.13153122380572707, + 0.20734727995374658, + 0.3113444029867245, + 0.43141334349909216, + 0.5536268021411347, + 0.6635408675192724, + 0.7475167069071331, + 0.7939474762515537, + 0.7942904466792035, + 0.7438258626222546, + 0.6420894510482904, + 0.49295253883095125, + 0.3043506360446946, + 0.08768678000439273, + -0.14304113171665886, + -0.3723203016900724, + -0.5843265000383848, + -0.7641576418281314, + -0.8990009202504455, + -0.9791409837402287, + -0.998728135831878, + -0.9562440152918116, + -0.8546264095654799, + -0.7010427376785484, + -0.5063307371436193, + -0.2841522267822382, + -0.049928906209744606, + 0.18035404444441563, + 0.3913815209870531, + 0.5696827531650402, + 0.7046558033987901, + 0.7893641857798899, + 0.8210448041554198, + 0.8012872999122752, + 0.7358681015354025, + 0.634246909472413, + 0.5087580696958012, + 0.3735532568659466, + 0.24337380982356177, + 0.13224926103939086, + 0.052231042421555046, + 0.012274850768711182, + 0.01737977189260198, + 0.06807577101332951, + 0.16032354052006353, + 0.28585348849513814, + 0.43292703956821066, + 0.5874580289681328, + 0.7343903290137648, + 0.8591955974200535, + 0.9493370272636431, + 0.9955444035569804, + 0.9927635324518812, + 0.9406775353996407, + 0.8437445020505403, + 0.7107496356859515, + 0.5539234179402818, + 0.3877236921522642 + ], + [ + -0.8390715290764524, + -0.6794169402114835, + -0.4776162881815451, + -0.24773197536529087, + -0.005759652336836695, + 0.2316169919746287, + 0.4483189385264845, + 0.63010719874833, + 0.7656626375328357, + 0.8474268340629595, + 0.8721415609022407, + 0.8410493744070104, + 0.7597442286966346, + 0.6376868285339113, + 0.487423003642313, + 0.3235636592499423, + 0.16160123300395854, + 0.016649705654615855, + -0.09779729405869858, + -0.17099343465280664, + -0.1958584677964355, + -0.16960613173624428, + -0.09401645226047228, + 0.02468667636788896, + 0.1763594380889576, + 0.34783347456887814, + 0.5241144633785058, + 0.6897806081037212, + 0.8304505309124894, + 0.9341750775824081, + 0.992608074913025, + 1.001828531322666, + 0.9627202415289469, + 0.8808609522546409, + 0.765926824763661, + 0.6306722231525661, + 0.48959286009720937, + 0.3574157832026041, + 0.24757804127737026, + 0.17085499305615537, + 0.13427966201738212, + 0.1404593720960034, + 0.1873501409572167, + 0.2684990718325941, + 0.3737165012979682, + 0.49009836840116316, + 0.6032891331350206, + 0.6988587094967308, + 0.7636635491539665, + 0.7870708979113121, + 0.7619439395445375, + 0.685311098591528, + 0.5586722275435426, + 0.38792512072610175, + 0.18292564850526954, + -0.04327783756595543, + -0.27547157069893974, + -0.4974818366530756, + -0.6934627188783398, + -0.849163594245356, + -0.9530748427495421, + -0.9973590134528993, + -0.9784910910905527, + -0.8975546310964664, + -0.7601686743941861, + -0.5760511107286137, + -0.35825466934368627, + -0.12213903019439094, + 0.11583595606300139, + 0.3393972068521659, + 0.5337217744785144, + 0.686576645558027, + 0.7892465375609223, + 0.8371790904394063, + 0.8302990943560097, + 0.772968185839673, + 0.6735921504637834, + 0.5439032691685157, + 0.3979689692932855, + 0.2509994100963947, + 0.1180444590453503, + 0.012683468496096514, + -0.05418223544110548, + -0.07532584228286368, + -0.04788331428254694, + 0.026342508573378, + 0.1410068281869728, + 0.2857827708805774, + 0.4473187231250012, + 0.6105122906655144, + 0.7599822292717534, + 0.8815926934319348, + 0.9638723586520783, + 0.9991770331278713, + 0.9844685834832015, + 0.921623088734627, + 0.8172324794515692, + 0.6819201521847214, + 0.5292450122844815, + 0.3743132228649984 + ], + [ + -0.8390715290764524, + -0.6775475406562509, + -0.47315767708939904, + -0.24040156863721235, + 0.004235844860085708, + 0.24358863438434925, + 0.4611614603865485, + 0.6424169011232886, + 0.7758943992791909, + 0.8540728367548097, + 0.8739116392386945, + 0.8370324229015107, + 0.7495318898672839, + 0.6214445897303876, + 0.4658994419904436, + 0.2980353100522811, + 0.13375769954201142, + -0.01156959450364814, + -0.12439768582887381, + -0.19412987226570705, + -0.214028084236408, + -0.18180654438701027, + -0.09985111439594324, + 0.024969439293215212, + 0.18190008481343833, + 0.35726606389860105, + 0.5357330843882544, + 0.701750626522917, + 0.8410421656112816, + 0.9419933638861339, + 0.996792497074384, + 1.0021976998428712, + 0.9598418427256318, + 0.8760344058274145, + 0.7610749881898811, + 0.6281482169475255, + 0.4919176736874632, + 0.366970752987145, + 0.2662826446231645, + 0.19986392398732442, + 0.1737333529875683, + 0.18931882436490788, + 0.24334029184088138, + 0.32817546950287957, + 0.43265858856562406, + 0.5432202409539202, + 0.6452464185236596, + 0.7245194215481693, + 0.7686025289403107, + 0.768042674627735, + 0.7172881512019128, + 0.615248252304039, + 0.4654553949231605, + 0.27582459269802717, + 0.05803768774099452, + -0.17339142629386214, + -0.40229068108392574, + -0.6121454768951089, + -0.7874484653297162, + -0.9149733674251, + -0.9848684734012713, + -0.9914794287162698, + -0.9338324446213416, + -0.8157368019483662, + -0.6454973031906196, + -0.435260401211549, + -0.20004898324818984, + 0.04343284081340555, + 0.2781246857966964, + 0.48795459639504923, + 0.6590936676705412, + 0.7810202693969883, + 0.8473110639512578, + 0.85609938715296, + 0.810168439924423, + 0.7166748124048304, + 0.5865251712515058, + 0.4334539865862126, + 0.27287187957226067, + 0.12057171751332457, + -0.008607793184977619, + -0.10205443918599119, + -0.15048417835187253, + -0.14877346564098823, + -0.09644010056266972, + 0.002284092294839113, + 0.13881197574492082, + 0.3009750666244595, + 0.47415479561228036, + 0.6426733441360819, + 0.7913137239843783, + 0.9068181476138314, + 0.9792089348459626, + 1.0027892108122023, + 0.9767111169667572, + 0.9050443138375907, + 0.796332082912404, + 0.662679638264746, + 0.5184720320658698, + 0.37886047741087875 + ], + [ + -0.8390715290764524, + -0.6756737327235225, + -0.4686867518546245, + -0.23305708468785508, + 0.014230901111770363, + 0.25552065549676894, + 0.47389596801147954, + 0.6545218063002365, + 0.7858031202144603, + 0.8602688889663684, + 0.8751136060746814, + 0.8323578538002416, + 0.7386207256890425, + 0.6045269815353886, + 0.4437988689308032, + 0.2721050704679422, + 0.10575637045655056, + -0.039651088211418634, + -0.15053897056520202, + -0.21649172272198564, + -0.23114700106877734, + -0.1927507678797556, + -0.10431925816129223, + 0.026620008175738452, + 0.18870042878741855, + 0.36775532859361815, + 0.548139186835235, + 0.7142131727697144, + 0.8518532372676645, + 0.9498287420635468, + 1.00090573138727, + 1.0025523087190382, + 0.9571626587026804, + 0.871767155564409, + 0.7572528541171264, + 0.6271744024705981, + 0.4962828212226161, + 0.37893238344345226, + 0.2875396528146, + 0.2312618478893958, + 0.21503520587965644, + 0.23907166172913835, + 0.2988596779138646, + 0.3856592679539789, + 0.4874289888710713, + 0.590079826622095, + 0.67892167404853, + 0.74015463436872, + 0.762259699980639, + 0.7371596749514131, + 0.6610484661063227, + 0.5348213322436064, + 0.3640765366887762, + 0.15869663187795974, + -0.06794754127337731, + -0.30009731515865723, + -0.5209152426936031, + -0.7138915894835556, + -0.864230380122381, + -0.9600992666609592, + -0.993638563787122, + -0.9616442886727284, + -0.8658668061408897, + -0.71289883311612, + -0.5136615210002093, + -0.28253200960228164, + -0.03618692949949346, + 0.2077392662423675, + 0.4320669761681479, + 0.6214362382830311, + 0.7635107959617746, + 0.8499244587838902, + 0.8768964192740525, + 0.8454709555106414, + 0.7613679473767335, + 0.6344611496133467, + 0.47792897929556655, + 0.30714610822790034, + 0.13840243008727357, + -0.012451532140843415, + -0.13132731937861547, + -0.20708389016277756, + -0.2324770216170323, + -0.2047881964591701, + -0.1260699088547641, + -0.0029729493791383343, + 0.15384417918939813, + 0.3306852696278309, + 0.5120887790192724, + 0.6822908109839765, + 0.8267408306261397, + 0.9335189213719697, + 0.9945060397811447, + 1.0061786176080578, + 0.969934979825123, + 0.8919099628908406, + 0.7822903083184617, + 0.6541999726583133, + 0.522274153053456, + 0.4010770195741181 + ] + ] + } + ], + "layout": { + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import plotly.graph_objects as go\n", "\n", @@ -3127,7 +103696,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 121, "metadata": {}, "outputs": [], "source": [ @@ -3144,9 +103713,1192 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 122, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:35.722873\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import waterfall_chart\n", "\n", @@ -3164,9 +104916,888 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 123, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "connector": { + "line": { + "color": "rgb(63, 63, 63)" + } + }, + "measure": [ + "relative", + "relative", + "relative", + "relative", + "relative", + "relative", + "total" + ], + "name": "20", + "orientation": "v", + "text": [ + "10", + "-30", + "-7.5", + "-25", + "95", + "-7", + "net" + ], + "textposition": "outside", + "type": "waterfall", + "x": [ + "sales", + "returns", + "credit fees", + "rebates", + "late charges", + "shipping", + "net" + ], + "y": [ + 10, + -30, + -7.5, + -25, + 95, + -7, + 35.5 + ] + } + ], + "layout": { + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import plotly.graph_objects as go\n", "\n", @@ -3199,9 +105830,487 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 124, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:35.777914\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from matplotlib_venn import venn2\n", "\n", @@ -3224,9 +106333,101 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 125, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
NamevariableDate
0ElgarBorn1857-06-02
1BrahmsBorn1833-05-07
2SchumannBorn1810-06-08
............
13BeethovenDied1827-03-26
14MozartDied1791-12-05
15HaydnDied1809-05-31
\n", + "

16 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " Name variable Date\n", + "0 Elgar Born 1857-06-02\n", + "1 Brahms Born 1833-05-07\n", + "2 Schumann Born 1810-06-08\n", + ".. ... ... ...\n", + "13 Beethoven Died 1827-03-26\n", + "14 Mozart Died 1791-12-05\n", + "15 Haydn Died 1809-05-31\n", + "\n", + "[16 rows x 3 columns]" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df = (\n", " pd.read_csv(\n", @@ -3258,9 +106459,1232 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 126, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:37.051949\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from pywaffle import Waffle\n", "\n", @@ -3289,9 +107713,84 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 127, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
xyfilled
0001
1011
2021
3031
4041
\n", + "
" + ], + "text/plain": [ + " x y filled\n", + "0 0 0 1\n", + "1 0 1 1\n", + "2 0 2 1\n", + "3 0 3 1\n", + "4 0 4 1" + ] + }, + "execution_count": 127, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import itertools\n", "\n", @@ -3303,9 +107802,103 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 128, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "g = (\n", " ggplot(df, aes(x=\"x\", y=\"y\", fill=as_discrete(\"filled\")))\n", @@ -3334,9 +107927,84 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 129, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
StageGenderUsers
0Stage 01: BrowsersMale-1.492762e+07
1Stage 02: Unbounced UsersMale-1.286266e+07
2Stage 03: Email SignupsMale-1.136190e+07
3Stage 04: Email ConfirmedMale-9.411708e+06
4Stage 05: Campaign-Email OpensMale-8.074317e+06
\n", + "
" + ], + "text/plain": [ + " Stage Gender Users\n", + "0 Stage 01: Browsers Male -1.492762e+07\n", + "1 Stage 02: Unbounced Users Male -1.286266e+07\n", + "2 Stage 03: Email Signups Male -1.136190e+07\n", + "3 Stage 04: Email Confirmed Male -9.411708e+06\n", + "4 Stage 05: Campaign-Email Opens Male -8.074317e+06" + ] + }, + "execution_count": 129, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df = pd.read_csv(\n", " \"https://raw.githubusercontent.com/selva86/datasets/master/email_campaign_funnel.csv\"\n", @@ -3353,9 +108021,2851 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 130, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:37.718538\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots()\n", "group_col = \"Gender\"\n", @@ -3398,9 +110908,69 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 131, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "g = (\n", " ggplot(df, aes(x=\"Stage\", y=\"Users\", fill=\"Gender\", weight=\"Users\"))\n", @@ -3421,9 +110991,968 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 132, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "Users=%{x}
Stage=%{y}", + "legendgroup": "", + "marker": { + "color": "#636efa" + }, + "name": "", + "orientation": "h", + "showlegend": false, + "type": "funnel", + "x": [ + -14927618.7065238, + -12862663.40577, + -11361896.414205, + -9411708.10306551, + -8074316.61565254, + -6958512.21751809, + -6045363.48273234, + -5029954.21439611, + -4008034.11281016, + -3172555.22488748, + -2484808.19938415, + -1903727.48070039, + -1490277.0164728, + -1152003.96468187, + -770748.058053054, + -434430.028171115, + -195031.889918096, + -58570.2215626985, + -12215.4458488971, + 888.495352178812, + -443.619050107896, + 14226434.2934762, + 12276042.59423, + 10850385.585795, + 8999931.89693449, + 7732693.38434746, + 6666393.78248191, + 5743259.51726766, + 4723254.78560389, + 3680878.88718984, + 3002640.77511252, + 2467804.80061585, + 1977277.51929961, + 1593649.9835272, + 1229651.03531813, + 828496.941946946, + 486621.971828885, + 227106.110081904, + 73466.7784373015, + 12654.5541511029, + 3425.49535217881, + -306.619050107896 + ], + "xaxis": "x", + "y": [ + "Stage 01: Browsers", + "Stage 02: Unbounced Users", + "Stage 03: Email Signups", + "Stage 04: Email Confirmed", + "Stage 05: Campaign-Email Opens", + "Stage 06: Campaign-Email Clickthroughs", + "Stage 07: Buy Button Page", + "Stage 08: Buy Button Clickers", + "Stage 09: Cart Confirmation Page", + "Stage 10: Address Verification Page", + "Stage 11: Submit Order Page", + "Stage 12: Payment", + "Stage 13: Payment Successful", + "Stage 14: 1st Successful Purchase", + "Stage 15: 2nd Purchase", + "Stage 16: 3rd Purchase", + "Stage 17: 4th Purchase", + "Stage 18: 5th Purchase", + "Stage 18: 5th Purchase", + "Stage 18: 5th Purchase", + "Stage 18: 5th Purchase", + "Stage 01: Browsers", + "Stage 02: Unbounced Users", + "Stage 03: Email Signups", + "Stage 04: Email Confirmed", + "Stage 05: Campaign-Email Opens", + "Stage 06: Campaign-Email Clickthroughs", + "Stage 07: Buy Button Page", + "Stage 08: Buy Button Clickers", + "Stage 09: Cart Confirmation Page", + "Stage 10: Address Verification Page", + "Stage 11: Submit Order Page", + "Stage 12: Payment", + "Stage 13: Payment Successful", + "Stage 14: 1st Successful Purchase", + "Stage 15: 2nd Purchase", + "Stage 16: 3rd Purchase", + "Stage 17: 4th Purchase", + "Stage 18: 5th Purchase", + "Stage 18: 5th Purchase", + "Stage 18: 5th Purchase", + "Stage 18: 5th Purchase" + ], + "yaxis": "y" + } + ], + "layout": { + "legend": { + "tracegroupgap": 0 + }, + "margin": { + "t": 60 + }, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Users" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Stage" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig = px.funnel(df, y=\"Stage\", x=\"Users\")\n", "fig.show()" @@ -3447,9 +111976,903 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 133, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "link": { + "source": [ + 0, + 1, + 0, + 2, + 3, + 3, + 2 + ], + "target": [ + 2, + 3, + 3, + 4, + 4, + 5, + 5 + ], + "value": [ + 7, + 3, + 2, + 6, + 4, + 2, + 1 + ] + }, + "node": { + "color": [ + "#636EFA", + "#EF553B", + "#00CC96", + "#AB63FA", + "#FFA15A", + "#19D3F3" + ], + "label": [ + "A1", + "A2", + "B1", + "B2", + "C1", + "C2" + ], + "line": { + "color": "black", + "width": 0.5 + }, + "pad": 15, + "thickness": 20 + }, + "type": "sankey" + } + ], + "layout": { + "font": { + "size": 10 + }, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "Basic Sankey Diagram" + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import plotly.graph_objects as go\n", "\n", @@ -3497,9 +112920,4564 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 134, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-03-24T10:32:38.433116\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.8.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Data\n", "df = (\n", @@ -3529,9 +117507,2267 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 135, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "branchvalues": "total", + "customdata": [ + [ + "AFG", + 43.828 + ], + [ + "ALB", + 76.423 + ], + [ + "DZA", + 72.301 + ], + [ + "AGO", + 42.731 + ], + [ + "ARG", + 75.32 + ], + [ + "AUS", + 81.235 + ], + [ + "AUT", + 79.829 + ], + [ + "BHR", + 75.635 + ], + [ + "BGD", + 64.062 + ], + [ + "BEL", + 79.441 + ], + [ + "BEN", + 56.728 + ], + [ + "BOL", + 65.554 + ], + [ + "BIH", + 74.852 + ], + [ + "BWA", + 50.728 + ], + [ + "BRA", + 72.39 + ], + [ + "BGR", + 73.005 + ], + [ + "BFA", + 52.295 + ], + [ + "BDI", + 49.58 + ], + [ + "KHM", + 59.723 + ], + [ + "CMR", + 50.43 + ], + [ + "CAN", + 80.65300000000002 + ], + [ + "CAF", + 44.74100000000001 + ], + [ + "TCD", + 50.651 + ], + [ + "CHL", + 78.553 + ], + [ + "CHN", + 72.961 + ], + [ + "COL", + 72.889 + ], + [ + "COM", + 65.152 + ], + [ + "COD", + 46.462 + ], + [ + "COG", + 55.322 + ], + [ + "CRI", + 78.782 + ], + [ + "CIV", + 48.328 + ], + [ + "HRV", + 75.748 + ], + [ + "CUB", + 78.273 + ], + [ + "CZE", + 76.486 + ], + [ + "DNK", + 78.332 + ], + [ + "DJI", + 54.791 + ], + [ + "DOM", + 72.235 + ], + [ + "ECU", + 74.994 + ], + [ + "EGY", + 71.33800000000002 + ], + [ + "SLV", + 71.878 + ], + [ + "GNQ", + 51.57899999999999 + ], + [ + "ERI", + 58.03999999999999 + ], + [ + "ETH", + 52.947 + ], + [ + "FIN", + 79.313 + ], + [ + "FRA", + 80.65699999999998 + ], + [ + "GAB", + 56.73500000000001 + ], + [ + "GMB", + 59.448 + ], + [ + "DEU", + 79.406 + ], + [ + "GHA", + 60.02199999999999 + ], + [ + "GRC", + 79.483 + ], + [ + "GTM", + 70.259 + ], + [ + "GIN", + 56.007 + ], + [ + "GNB", + 46.38800000000001 + ], + [ + "HTI", + 60.916 + ], + [ + "HND", + 70.19800000000001 + ], + [ + "HKG", + 82.208 + ], + [ + "HUN", + 73.33800000000002 + ], + [ + "ISL", + 81.757 + ], + [ + "IND", + 64.69800000000001 + ], + [ + "IDN", + 70.65 + ], + [ + "IRN", + 70.964 + ], + [ + "IRQ", + 59.545 + ], + [ + "IRL", + 78.885 + ], + [ + "ISR", + 80.745 + ], + [ + "ITA", + 80.546 + ], + [ + "JAM", + 72.567 + ], + [ + "JPN", + 82.603 + ], + [ + "JOR", + 72.535 + ], + [ + "KEN", + 54.11 + ], + [ + "KOR", + 67.297 + ], + [ + "KOR", + 78.623 + ], + [ + "KWT", + 77.58800000000002 + ], + [ + "LBN", + 71.993 + ], + [ + "LSO", + 42.592 + ], + [ + "LBR", + 45.678 + ], + [ + "LBY", + 73.952 + ], + [ + "MDG", + 59.443000000000005 + ], + [ + "MWI", + 48.303 + ], + [ + "MYS", + 74.241 + ], + [ + "MLI", + 54.467 + ], + [ + "MRT", + 64.164 + ], + [ + "MUS", + 72.801 + ], + [ + "MEX", + 76.195 + ], + [ + "MNG", + 66.803 + ], + [ + "MNE", + 74.543 + ], + [ + "MAR", + 71.164 + ], + [ + "MOZ", + 42.082 + ], + [ + "MMR", + 62.06900000000001 + ], + [ + "NAM", + 52.90600000000001 + ], + [ + "NPL", + 63.785 + ], + [ + "NLD", + 79.762 + ], + [ + "NZL", + 80.204 + ], + [ + "NIC", + 72.899 + ], + [ + "NER", + 56.867 + ], + [ + "NGA", + 46.859 + ], + [ + "NOR", + 80.196 + ], + [ + "OMN", + 75.64 + ], + [ + "PAK", + 65.483 + ], + [ + "PAN", + 75.53699999999998 + ], + [ + "PRY", + 71.752 + ], + [ + "PER", + 71.421 + ], + [ + "PHL", + 71.688 + ], + [ + "POL", + 75.563 + ], + [ + "PRT", + 78.098 + ], + [ + "PRI", + 78.74600000000002 + ], + [ + "REU", + 76.442 + ], + [ + "ROU", + 72.476 + ], + [ + "RWA", + 46.242 + ], + [ + "STP", + 65.528 + ], + [ + "SAU", + 72.777 + ], + [ + "SEN", + 63.062 + ], + [ + "SRB", + 74.002 + ], + [ + "SLE", + 42.56800000000001 + ], + [ + "SGP", + 79.972 + ], + [ + "SVK", + 74.663 + ], + [ + "SVN", + 77.926 + ], + [ + "SOM", + 48.159 + ], + [ + "ZAF", + 49.339 + ], + [ + "ESP", + 80.941 + ], + [ + "LKA", + 72.396 + ], + [ + "SDN", + 58.55599999999999 + ], + [ + "SWZ", + 39.613 + ], + [ + "SWE", + 80.884 + ], + [ + "CHE", + 81.70100000000002 + ], + [ + "SYR", + 74.143 + ], + [ + "TWN", + 78.4 + ], + [ + "TZA", + 52.517 + ], + [ + "THA", + 70.616 + ], + [ + "TGO", + 58.42 + ], + [ + "TTO", + 69.819 + ], + [ + "TUN", + 73.923 + ], + [ + "TUR", + 71.777 + ], + [ + "UGA", + 51.542 + ], + [ + "GBR", + 79.425 + ], + [ + "USA", + 78.242 + ], + [ + "URY", + 76.384 + ], + [ + "VEN", + 73.747 + ], + [ + "VNM", + 74.249 + ], + [ + "PSE", + 73.422 + ], + [ + "YEM", + 62.698 + ], + [ + "ZMB", + 42.38399999999999 + ], + [ + "ZWE", + 43.487 + ], + [ + "(?)", + 54.56441057558197 + ], + [ + "(?)", + 75.35668222743027 + ], + [ + "(?)", + 69.44386304205017 + ], + [ + "(?)", + 77.89057081069897 + ], + [ + "(?)", + 81.06215400970112 + ], + [ + "(?)", + 68.91909251904043 + ] + ], + "domain": { + "x": [ + 0, + 1 + ], + "y": [ + 0, + 1 + ] + }, + "hovertemplate": "labels=%{label}
pop=%{value}
parent=%{parent}
id=%{id}
iso_alpha=%{customdata[0]}
lifeExp=%{color}", + "ids": [ + "world/Asia/Afghanistan", + "world/Europe/Albania", + "world/Africa/Algeria", + "world/Africa/Angola", + "world/Americas/Argentina", + "world/Oceania/Australia", + "world/Europe/Austria", + "world/Asia/Bahrain", + "world/Asia/Bangladesh", + "world/Europe/Belgium", + "world/Africa/Benin", + "world/Americas/Bolivia", + "world/Europe/Bosnia and Herzegovina", + "world/Africa/Botswana", + "world/Americas/Brazil", + "world/Europe/Bulgaria", + "world/Africa/Burkina Faso", + "world/Africa/Burundi", + "world/Asia/Cambodia", + "world/Africa/Cameroon", + "world/Americas/Canada", + "world/Africa/Central African Republic", + "world/Africa/Chad", + "world/Americas/Chile", + "world/Asia/China", + "world/Americas/Colombia", + "world/Africa/Comoros", + "world/Africa/Congo, Dem. Rep.", + "world/Africa/Congo, Rep.", + "world/Americas/Costa Rica", + "world/Africa/Cote d'Ivoire", + "world/Europe/Croatia", + "world/Americas/Cuba", + "world/Europe/Czech Republic", + "world/Europe/Denmark", + "world/Africa/Djibouti", + "world/Americas/Dominican Republic", + "world/Americas/Ecuador", + "world/Africa/Egypt", + "world/Americas/El Salvador", + "world/Africa/Equatorial Guinea", + "world/Africa/Eritrea", + "world/Africa/Ethiopia", + "world/Europe/Finland", + "world/Europe/France", + "world/Africa/Gabon", + "world/Africa/Gambia", + "world/Europe/Germany", + "world/Africa/Ghana", + "world/Europe/Greece", + "world/Americas/Guatemala", + "world/Africa/Guinea", + "world/Africa/Guinea-Bissau", + "world/Americas/Haiti", + "world/Americas/Honduras", + "world/Asia/Hong Kong, China", + "world/Europe/Hungary", + "world/Europe/Iceland", + "world/Asia/India", + "world/Asia/Indonesia", + "world/Asia/Iran", + "world/Asia/Iraq", + "world/Europe/Ireland", + "world/Asia/Israel", + "world/Europe/Italy", + "world/Americas/Jamaica", + "world/Asia/Japan", + "world/Asia/Jordan", + "world/Africa/Kenya", + "world/Asia/Korea, Dem. Rep.", + "world/Asia/Korea, Rep.", + "world/Asia/Kuwait", + "world/Asia/Lebanon", + "world/Africa/Lesotho", + "world/Africa/Liberia", + "world/Africa/Libya", + "world/Africa/Madagascar", + "world/Africa/Malawi", + "world/Asia/Malaysia", + "world/Africa/Mali", + "world/Africa/Mauritania", + "world/Africa/Mauritius", + "world/Americas/Mexico", + "world/Asia/Mongolia", + "world/Europe/Montenegro", + "world/Africa/Morocco", + "world/Africa/Mozambique", + "world/Asia/Myanmar", + "world/Africa/Namibia", + "world/Asia/Nepal", + "world/Europe/Netherlands", + "world/Oceania/New Zealand", + "world/Americas/Nicaragua", + "world/Africa/Niger", + "world/Africa/Nigeria", + "world/Europe/Norway", + "world/Asia/Oman", + "world/Asia/Pakistan", + "world/Americas/Panama", + "world/Americas/Paraguay", + "world/Americas/Peru", + "world/Asia/Philippines", + "world/Europe/Poland", + "world/Europe/Portugal", + "world/Americas/Puerto Rico", + "world/Africa/Reunion", + "world/Europe/Romania", + "world/Africa/Rwanda", + "world/Africa/Sao Tome and Principe", + "world/Asia/Saudi Arabia", + "world/Africa/Senegal", + "world/Europe/Serbia", + "world/Africa/Sierra Leone", + "world/Asia/Singapore", + "world/Europe/Slovak Republic", + "world/Europe/Slovenia", + "world/Africa/Somalia", + "world/Africa/South Africa", + "world/Europe/Spain", + "world/Asia/Sri Lanka", + "world/Africa/Sudan", + "world/Africa/Swaziland", + "world/Europe/Sweden", + "world/Europe/Switzerland", + "world/Asia/Syria", + "world/Asia/Taiwan", + "world/Africa/Tanzania", + "world/Asia/Thailand", + "world/Africa/Togo", + "world/Americas/Trinidad and Tobago", + "world/Africa/Tunisia", + "world/Europe/Turkey", + "world/Africa/Uganda", + "world/Europe/United Kingdom", + "world/Americas/United States", + "world/Americas/Uruguay", + "world/Americas/Venezuela", + "world/Asia/Vietnam", + "world/Asia/West Bank and Gaza", + "world/Asia/Yemen, Rep.", + "world/Africa/Zambia", + "world/Africa/Zimbabwe", + "world/Africa", + "world/Americas", + "world/Asia", + "world/Europe", + "world/Oceania", + "world" + ], + "labels": [ + "Afghanistan", + "Albania", + "Algeria", + "Angola", + "Argentina", + "Australia", + "Austria", + "Bahrain", + "Bangladesh", + "Belgium", + "Benin", + "Bolivia", + "Bosnia and Herzegovina", + "Botswana", + "Brazil", + "Bulgaria", + "Burkina Faso", + "Burundi", + "Cambodia", + "Cameroon", + "Canada", + "Central African Republic", + "Chad", + "Chile", + "China", + "Colombia", + "Comoros", + "Congo, Dem. Rep.", + "Congo, Rep.", + "Costa Rica", + "Cote d'Ivoire", + "Croatia", + "Cuba", + "Czech Republic", + "Denmark", + "Djibouti", + "Dominican Republic", + "Ecuador", + "Egypt", + "El Salvador", + "Equatorial Guinea", + "Eritrea", + "Ethiopia", + "Finland", + "France", + "Gabon", + "Gambia", + "Germany", + "Ghana", + "Greece", + "Guatemala", + "Guinea", + "Guinea-Bissau", + "Haiti", + "Honduras", + "Hong Kong, China", + "Hungary", + "Iceland", + "India", + "Indonesia", + "Iran", + "Iraq", + "Ireland", + "Israel", + "Italy", + "Jamaica", + "Japan", + "Jordan", + "Kenya", + "Korea, Dem. Rep.", + "Korea, Rep.", + "Kuwait", + "Lebanon", + "Lesotho", + "Liberia", + "Libya", + "Madagascar", + "Malawi", + "Malaysia", + "Mali", + "Mauritania", + "Mauritius", + "Mexico", + "Mongolia", + "Montenegro", + "Morocco", + "Mozambique", + "Myanmar", + "Namibia", + "Nepal", + "Netherlands", + "New Zealand", + "Nicaragua", + "Niger", + "Nigeria", + "Norway", + "Oman", + "Pakistan", + "Panama", + "Paraguay", + "Peru", + "Philippines", + "Poland", + "Portugal", + "Puerto Rico", + "Reunion", + "Romania", + "Rwanda", + "Sao Tome and Principe", + "Saudi Arabia", + "Senegal", + "Serbia", + "Sierra Leone", + "Singapore", + "Slovak Republic", + "Slovenia", + "Somalia", + "South Africa", + "Spain", + "Sri Lanka", + "Sudan", + "Swaziland", + "Sweden", + "Switzerland", + "Syria", + "Taiwan", + "Tanzania", + "Thailand", + "Togo", + "Trinidad and Tobago", + "Tunisia", + "Turkey", + "Uganda", + "United Kingdom", + "United States", + "Uruguay", + "Venezuela", + "Vietnam", + "West Bank and Gaza", + "Yemen, Rep.", + "Zambia", + "Zimbabwe", + "Africa", + "Americas", + "Asia", + "Europe", + "Oceania", + "world" + ], + "marker": { + "coloraxis": "coloraxis", + "colors": [ + 43.828, + 76.423, + 72.301, + 42.731, + 75.32, + 81.235, + 79.829, + 75.635, + 64.062, + 79.441, + 56.728, + 65.554, + 74.852, + 50.728, + 72.39, + 73.005, + 52.295, + 49.58, + 59.723, + 50.43, + 80.65300000000002, + 44.74100000000001, + 50.651, + 78.553, + 72.961, + 72.889, + 65.152, + 46.462, + 55.322, + 78.782, + 48.328, + 75.748, + 78.273, + 76.486, + 78.332, + 54.791, + 72.235, + 74.994, + 71.33800000000002, + 71.878, + 51.57899999999999, + 58.03999999999999, + 52.947, + 79.313, + 80.65699999999998, + 56.73500000000001, + 59.448, + 79.406, + 60.02199999999999, + 79.483, + 70.259, + 56.007, + 46.38800000000001, + 60.916, + 70.19800000000001, + 82.208, + 73.33800000000002, + 81.757, + 64.69800000000001, + 70.65, + 70.964, + 59.545, + 78.885, + 80.745, + 80.546, + 72.567, + 82.603, + 72.535, + 54.11, + 67.297, + 78.623, + 77.58800000000002, + 71.993, + 42.592, + 45.678, + 73.952, + 59.443000000000005, + 48.303, + 74.241, + 54.467, + 64.164, + 72.801, + 76.195, + 66.803, + 74.543, + 71.164, + 42.082, + 62.06900000000001, + 52.90600000000001, + 63.785, + 79.762, + 80.204, + 72.899, + 56.867, + 46.859, + 80.196, + 75.64, + 65.483, + 75.53699999999998, + 71.752, + 71.421, + 71.688, + 75.563, + 78.098, + 78.74600000000002, + 76.442, + 72.476, + 46.242, + 65.528, + 72.777, + 63.062, + 74.002, + 42.56800000000001, + 79.972, + 74.663, + 77.926, + 48.159, + 49.339, + 80.941, + 72.396, + 58.55599999999999, + 39.613, + 80.884, + 81.70100000000002, + 74.143, + 78.4, + 52.517, + 70.616, + 58.42, + 69.819, + 73.923, + 71.777, + 51.542, + 79.425, + 78.242, + 76.384, + 73.747, + 74.249, + 73.422, + 62.698, + 42.38399999999999, + 43.487, + 54.56441057558197, + 75.35668222743027, + 69.44386304205017, + 77.89057081069897, + 81.06215400970112, + 68.91909251904043 + ] + }, + "name": "", + "parents": [ + "world/Asia", + "world/Europe", + "world/Africa", + "world/Africa", + "world/Americas", + "world/Oceania", + "world/Europe", + "world/Asia", + "world/Asia", + "world/Europe", + "world/Africa", + "world/Americas", + "world/Europe", + "world/Africa", + "world/Americas", + "world/Europe", + "world/Africa", + "world/Africa", + "world/Asia", + "world/Africa", + "world/Americas", + "world/Africa", + "world/Africa", + "world/Americas", + "world/Asia", + "world/Americas", + "world/Africa", + "world/Africa", + "world/Africa", + "world/Americas", + "world/Africa", + "world/Europe", + "world/Americas", + "world/Europe", + "world/Europe", + "world/Africa", + "world/Americas", + "world/Americas", + "world/Africa", + "world/Americas", + "world/Africa", + "world/Africa", + "world/Africa", + "world/Europe", + "world/Europe", + "world/Africa", + "world/Africa", + "world/Europe", + "world/Africa", + "world/Europe", + "world/Americas", + "world/Africa", + "world/Africa", + "world/Americas", + "world/Americas", + "world/Asia", + "world/Europe", + "world/Europe", + "world/Asia", + "world/Asia", + "world/Asia", + "world/Asia", + "world/Europe", + "world/Asia", + "world/Europe", + "world/Americas", + "world/Asia", + "world/Asia", + "world/Africa", + "world/Asia", + "world/Asia", + "world/Asia", + "world/Asia", + "world/Africa", + "world/Africa", + "world/Africa", + "world/Africa", + "world/Africa", + "world/Asia", + "world/Africa", + "world/Africa", + "world/Africa", + "world/Americas", + "world/Asia", + "world/Europe", + "world/Africa", + "world/Africa", + "world/Asia", + "world/Africa", + "world/Asia", + "world/Europe", + "world/Oceania", + "world/Americas", + "world/Africa", + "world/Africa", + "world/Europe", + "world/Asia", + "world/Asia", + "world/Americas", + "world/Americas", + "world/Americas", + "world/Asia", + "world/Europe", + "world/Europe", + "world/Americas", + "world/Africa", + "world/Europe", + "world/Africa", + "world/Africa", + "world/Asia", + "world/Africa", + "world/Europe", + "world/Africa", + "world/Asia", + "world/Europe", + "world/Europe", + "world/Africa", + "world/Africa", + "world/Europe", + "world/Asia", + "world/Africa", + "world/Africa", + "world/Europe", + "world/Europe", + "world/Asia", + "world/Asia", + "world/Africa", + "world/Asia", + "world/Africa", + "world/Americas", + "world/Africa", + "world/Europe", + "world/Africa", + "world/Europe", + "world/Americas", + "world/Americas", + "world/Americas", + "world/Asia", + "world/Asia", + "world/Asia", + "world/Africa", + "world/Africa", + "world", + "world", + "world", + "world", + "world", + "" + ], + "type": "treemap", + "values": [ + 31889923, + 3600523, + 33333216, + 12420476, + 40301927, + 20434176, + 8199783, + 708573, + 150448339, + 10392226, + 8078314, + 9119152, + 4552198, + 1639131, + 190010647, + 7322858, + 14326203, + 8390505, + 14131858, + 17696293, + 33390141, + 4369038, + 10238807, + 16284741, + 1318683096, + 44227550, + 710960, + 64606759, + 3800610, + 4133884, + 18013409, + 4493312, + 11416987, + 10228744, + 5468120, + 496374, + 9319622, + 13755680, + 80264543, + 6939688, + 551201, + 4906585, + 76511887, + 5238460, + 61083916, + 1454867, + 1688359, + 82400996, + 22873338, + 10706290, + 12572928, + 9947814, + 1472041, + 8502814, + 7483763, + 6980412, + 9956108, + 301931, + 1110396331, + 223547000, + 69453570, + 27499638, + 4109086, + 6426679, + 58147733, + 2780132, + 127467972, + 6053193, + 35610177, + 23301725, + 49044790, + 2505559, + 3921278, + 2012649, + 3193942, + 6036914, + 19167654, + 13327079, + 24821286, + 12031795, + 3270065, + 1250882, + 108700891, + 2874127, + 684736, + 33757175, + 19951656, + 47761980, + 2055080, + 28901790, + 16570613, + 4115771, + 5675356, + 12894865, + 135031164, + 4627926, + 3204897, + 169270617, + 3242173, + 6667147, + 28674757, + 91077287, + 38518241, + 10642836, + 3942491, + 798094, + 22276056, + 8860588, + 199579, + 27601038, + 12267493, + 10150265, + 6144562, + 4553009, + 5447502, + 2009245, + 9118773, + 43997828, + 40448191, + 20378239, + 42292929, + 1133066, + 9031088, + 7554661, + 19314747, + 23174294, + 38139640, + 65068149, + 5701579, + 1056608, + 10276158, + 71158647, + 29170398, + 60776238, + 301139947, + 3447496, + 26084662, + 85262356, + 4018332, + 22211743, + 11746035, + 12311143, + 929539692, + 898871184, + 3811953827, + 586098529, + 24549947, + 6251013179 + ] + } + ], + "layout": { + "coloraxis": { + "cmid": 68.91909251904043, + "colorbar": { + "title": { + "text": "lifeExp" + } + }, + "colorscale": [ + [ + 0, + "rgb(103,0,31)" + ], + [ + 0.1, + "rgb(178,24,43)" + ], + [ + 0.2, + "rgb(214,96,77)" + ], + [ + 0.3, + "rgb(244,165,130)" + ], + [ + 0.4, + "rgb(253,219,199)" + ], + [ + 0.5, + "rgb(247,247,247)" + ], + [ + 0.6, + "rgb(209,229,240)" + ], + [ + 0.7, + "rgb(146,197,222)" + ], + [ + 0.8, + "rgb(67,147,195)" + ], + [ + 0.9, + "rgb(33,102,172)" + ], + [ + 1, + "rgb(5,48,97)" + ] + ] + }, + "legend": { + "tracegroupgap": 0 + }, + "margin": { + "b": 25, + "l": 25, + "r": 25, + "t": 50 + }, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import plotly.express as px\n", "import numpy as np\n", diff --git a/coming-from-stata.html b/coming-from-stata.html index 8c4de30..a552e3a 100644 --- a/coming-from-stata.html +++ b/coming-from-stata.html @@ -680,22 +680,22 @@

Stata <==> Pythonpd.get_dummies(df['var'])

reg yvar xvar if condition, r

-

from pyfixest.estimation import feols
fit = feols(“yvar ~ xvar”, data=df[“condition”], vcov=”HC2”)

+

import pyfixest as pf
fit = pf.feols(“yvar ~ xvar”, data=df[“condition”], vcov=”HC2”)

reg yvar xvar if condition, vce(cluster clustervar)

-

from pyfixest.estimation import feols
fit = feols(“yvar ~ xvar”, data=df[“condition”], vcov={“CRV1”: “clustervar”})

+

import pyfixest as pf
fit = pf.feols(“yvar ~ xvar”, data=df[“condition”], vcov={“CRV1”: “clustervar”})

areg yvar xvar, absorb(fe_var)

-

from pyfixest.estimation import feols
fit = feols(“yvar ~ xvar | fe_var”, data=df)

+

import pyfixest as pf
fit = pf.feols(“yvar ~ xvar | fe_var”, data=df)

_b[var], _se[var]

-

results_sw.coef()["var"], results_sw.se()["var"] following creation of results_sw via results_sw = feols(...)

+

results_sw.coef()["var"], results_sw.se()["var"] following creation of results_sw via results_sw = pf.feols(...)

ivreg2 lwage exper expersq (educ=age)

-

feols(“lwage ~ exper + expersq | educ ~ age”, data=dfiv)

+

pf.feols(“lwage ~ exper + expersq | educ ~ age”, data=dfiv)

outreg2

-

results = feols(...) then results.tidy()

+

results = pf.feols(...) then results.tidy()

binscatter

binsreg from the binsreg package; see Regression diagnostics and visualisations.

@@ -706,7 +706,7 @@

Stata <==> Pythonpyfixest packages.

-

Note that, in the below, you need only import feols once in each Python session, and the syntax for looking at results is results = feols(...) and then results.summary().

+

Note that, in the below, you need only import pf.feols once in each Python session, and the syntax for looking at results is results = pf.feols(...) and then results.summary().

@@ -717,31 +717,31 @@

Stata <==> Python

- + - + - + - + - + - + - +

Command

Fixed Effects (absorbing)

reghdfe y x, absorb(fe)

from pyfixest.estimation import feols
fit = feols(“y ~ x | fe”, data=df)

import pyfixest as pf
fit = pf.feols(“y ~ x | fe”, data=df)

Categorical regression

reghdfe y x i.cat

from pyfixest.estimation import feols
fit = feols(“y ~ x + C(cat)”, data=df)
But if cat is of type categorical it can be run with y ~ x + cat

import pyfixest as pf
fit = pf.feols(“y ~ x + C(cat)”, data=df)
But if cat is of type categorical it can be run with y ~ x + cat

Interacting categoricals

reghdfe y x i.cat#i.cat2

from pyfixest.estimation import feols
fit = feols(“yvar ~ xvar + C(cat):C(cat2)”, data=df)
Note that a*b is a short-hand for a + b + a:b, with the last term representing the interaction.

import pyfixest as pf
fit = pf.feols(“yvar ~ xvar + C(cat):C(cat2)”, data=df)
Note that a*b is a short-hand for a + b + a:b, with the last term representing the interaction.

Robust standard errors

reghdfe y x, r

from pyfixest.estimation import feols
fit = feols(“y ~ x, data=df, vcov=”HC1”)
Note that a range of heteroskedasticity robust standard errors are available: see Regression for more.

import pyfixest as pf
fit = pf.feols(“y ~ x, data=df, vcov=”HC1”)
Note that a range of heteroskedasticity robust standard errors are available: see Regression for more.

Clustered standard errors

reghdfe y x, cluster(clust)

from pyfixest.estimation import feols
fit = feols(“y ~ x”, data=df, vcov={“CRV1”: “clust”})

import pyfixest as pf
fit = pf.feols(“y ~ x”, data=df, vcov={“CRV1”: “clust”})

Two-way clustered standard errors

reghdfe y x, cluster(clust1 clust2)

from pyfixest.estimation import feols
fit = feols(“y ~ x”, data=df, vcov={“CRV1”: “clust1 + clust2”})

import pyfixest as pf
fit = pf.feols(“y ~ x”, data=df, vcov={“CRV1”: “clust1 + clust2”})

Instrumental variables

ivreghdfe 2sls y exog (endog = instrument)

from pyfixest.estimation import feols
fit = feols(“y ~ exog | endog ~ instrument”, data=df)

import pyfixest as pf
fit = pf.feols(“y ~ exog | endog ~ instrument”, data=df)

diff --git a/data-sharing.html b/data-sharing.html index 8b78390..5eb162a 100644 --- a/data-sharing.html +++ b/data-sharing.html @@ -627,7 +627,6 @@

Run on the web

Here’s one I made earlier#

If you want to follow a full example of serving up data end-to-end, take a look at the particulate matter datasette github repo. These data were constructed by downloading files of estimates of 2.5 micron particulate matter concentration in the UK from the DEFRA website and combining them into a CSV.

-

You can see how the data in this repo get served up by datasette at this link.

Exercises

    diff --git a/econmt-regression.html b/econmt-regression.html index 3781b1e..7df7a02 100644 --- a/econmt-regression.html +++ b/econmt-regression.html @@ -649,8 +649,7 @@

    Importsfrom lets_plot import * import statsmodels.api as sm import statsmodels.formula.api as smf -from pyfixest.estimation import feols -from pyfixest.summarize import summary +import pyfixest as pf LetsPlot.setup_html() @@ -662,7 +661,7 @@

    Imports
    -
    +
    -
    +
    @@ -834,7 +833,7 @@

    Regression basics
    -
    results_sw = feols("mass ~ height", data=df)
+
    results_sw = pf.feols("mass ~ height", data=df)
    @@ -854,10 +853,10 @@

    Regression basics -_images/09020f9ee3c268962cddb346c785d4a639c6ac93fd3cd366b0c56030a770cbfa.svg

    +_images/242dba4ced800eb24022a700a8db671257000515b39d2c91dcac1f72e7eb9066.svg

    Oh dear, Jabba’s been on the paddy frogs again, and he’s a bit of different case. When we’re estimating statistical relationships, we have all kinds of choices and should be wary about arbitrary decisions of what to include or exclude in case we fool ourselves about the generality of the relationship we are capturing. Let’s say we knew that we weren’t interested in Hutts though, but only in other species: in that case, it’s fair enough to filter out Jabba and run the regression without this obvious outlier. We’ll exclude any entry that contains the string ‘Jabba’ in the name column:

    -
    results_outlier_free = feols(
+
    results_outlier_free = pf.feols(
     "mass ~ height", data=df[~df["name"].str.contains("Jabba")]
 )
 print(results_outlier_free.summary())
@@ -974,10 +973,10 @@ 
    Regression basics
 
    
-
    feols("mass ~ height", data=df, vcov="HC2").summary()
+
    pf.feols("mass ~ height", data=df, vcov="HC2").summary()
 
    
 
    
 
    
@@ -1003,10 +1002,10 @@ 
    Standard errors
 
    
 
    xf = df.dropna(subset=["homeworld", "mass", "height", "species"])
-feols("mass ~ height", data=xf, vcov={"CRV1": "homeworld"}).summary()
+pf.feols("mass ~ height", data=xf, vcov={"CRV1": "homeworld"}).summary()
 
    
 
    
 
    
@@ -1065,10 +1064,10 @@ 
    Clustered standard errors
 
    
-
    feols("mass ~ height", data=xf, vcov={"CRV1": "homeworld + species"}).summary()
+
    pf.feols("mass ~ height", data=xf, vcov={"CRV1": "homeworld + species"}).summary()
 
    
 
    
 
    
@@ -1090,10 +1089,10 @@ 
    Clustered standard errors
 
    
-
    feols("mpg ~ hp + C(cyl)", data=mpg).summary()
+
    pf.feols("mpg ~ hp + C(cyl)", data=mpg).summary()
 
    
 
    
 
    
@@ -1248,12 +1247,12 @@ 
    Fixed effects and categorical variables+C(cyl) has been added makes it so that the coefficients given are relative to the coefficient for the intercept. We can turn the intercept off to get a coefficient per unique cyl value:
    
 
    
 
    
-
    feols("mpg ~ hp + C(cyl) -1", data=mpg).tidy()
+
    pf.feols("mpg ~ hp + C(cyl) -1", data=mpg).tidy()
 
    
 
    
 
    
@@ -1304,8 +1303,8 @@ 
    Fixed effects and categorical variables
     
       0
-      33
-      158141
-      1.022579
-      -0.766596
-      -1.568515
+      1
+      186213
+      -0.761561
+      0.340281
+      -3.057208
     
     
       1
-      10
-      71833
-      -0.013286
-      0.743807
-      1.416374
+      17
+      159758
+      -0.680405
+      1.086975
+      0.445564
     
     
       2
-      46
-      41537
-      -0.128423
-      2.213870
-      8.859534
+      18
+      104489
+      0.683346
+      -0.106550
+      -1.247462
     
     
       3
-      26
-      172230
-      1.730427
-      -1.150454
-      0.762653
+      5
+      30840
+      -0.207784
+      0.169841
+      1.902407
     
     
       4
-      41
-      142970
-      -0.586274
-      -0.218835
-      -4.921580
+      25
+      144311
+      0.295865
+      1.241407
+      3.598971
     
   
 
@@ -1482,7 +1481,7 @@ 
    High dimensional fixed effects, aka absorbing regressionstate_id and firm_id variables entered after a vertical bar:
    
 
    
 
    
-
    results_hdfe = feols("y ~ exog_0 + exog_1 | state_id + firm_id", data=sim)
+
    results_hdfe = pf.feols("y ~ exog_0 + exog_1 | state_id + firm_id", data=sim)
 results_hdfe.summary()
 
    
 
    
@@ -1495,12 +1494,12 @@ 
    High dimensional fixed effects, aka absorbing regression
 
    
 
    mpg["lnhp"] = np.log(mpg["hp"])
-feols("mpg ~ lnhp", data=mpg).tidy()
+pf.feols("mpg ~ lnhp", data=mpg).tidy()
 
    
 
    
 
    
@@ -1544,8 +1543,8 @@ 
    Logs and arcsinh
 
    
-
    results_ln = feols("mpg ~ np.log(hp)", data=mpg)
+
    results_ln = pf.feols("mpg ~ np.log(hp)", data=mpg)
 results_ln.tidy()
 
    
 
    
@@ -1611,8 +1610,8 @@ 
    Logs and arcsinharcsinh(x) and log(x+1), but you can also pass both of these into the formula directly too. (For more on the pros and cons of arcsinh, see Bellemare and Wichman [2020].) Here it is with arcsinh:
    
 
    
 
    
-
    feols("mpg ~ np.arcsinh(hp)", data=mpg).tidy()
+
    pf.feols("mpg ~ np.arcsinh(hp)", data=mpg).tidy()
 
    
 
    
 
    
@@ -1677,8 +1676,8 @@ 
    Logs and arcsinhmpg on hp making use of the numpy power function:
    
 
    
 
    
-
    res_poly = feols("mpg ~ hp + np.power(hp, 2)", data=mpg)
+
    res_poly = pf.feols("mpg ~ hp + np.power(hp, 2)", data=mpg)
 res_poly.tidy()
 
    
 
    
@@ -1759,8 +1758,8 @@ 
    Interaction terms and powers
 
    
-
    res_inter = feols("mpg ~ hp * disp", data=mpg)
+
    res_inter = pf.feols("mpg ~ hp * disp", data=mpg)
 res_inter.tidy()
 
    
 
    
@@ -1835,8 +1834,8 @@ 
    Interaction terms and powers
 
    
-
    res_only_inter = feols("mpg ~ hp : disp", data=mpg)
+
    res_only_inter = pf.feols("mpg ~ hp : disp", data=mpg)
 res_only_inter.tidy()
 
    
 
    
@@ -1920,8 +1919,8 @@ 
    Interaction terms and powersLet’s see etable in action:
    
 
    
 
    
-
    from pyfixest.summarize import etable
-
-etable([results_ln, res_poly, res_inter], type="df")
+
    pf.etable([results_ln, res_poly, res_inter], type="df")
 
    
 
    
 
    
@@ -2014,7 +2011,7 @@ 
    Comparing a set of models you’ve already created
       1
       Intercept
-      72.64*** (6.004)
+      72.640*** (6.004)
       40.409*** (2.741)
       39.674*** (2.914)
     
@@ -2026,18 +2023,39 @@ 
    Comparing a set of models you’ve already created
     
     
-      ...
-      ...
-      ...
-      ...
-      ...
+      3
+      hp
+      
+      -0.213*** (0.035)
+      -0.098*** (0.025)
+    
+    
+      4
+      np.power(hp,2)
+      
+      0.000*** (0.000)
+      
+    
+    
+      5
+      disp
+      
+      
+      -0.073*** (0.014)
+    
+    
+      6
+      hp:disp
+      
+      
+      0.000** (0.000)
     
     
       7
       R2
-      0.72
+      0.720
       0.756
-      0.82
+      0.820
     
     
       8
@@ -2055,13 +2073,12 @@ 
    Comparing a set of models you’ve already created
   
 
-
    10 rows × 4 columns
    
 
    
 
    
 
    There are a few different type= options, including dataframe, markdown, and latex:
    
 
    
 
    
-
    etable([results_ln, res_poly, res_inter], type="md")
+
    pf.etable([results_ln, res_poly, res_inter], type="md")
 
    
 
    
 
    
@@ -2070,19 +2087,21 @@ 
    Comparing a set of models you’ve already created
    
 
    
 
    
@@ -2184,7 +2203,7 @@ 
    Stepwise multiple models
 
    
-
    feols("y ~ sw(x1, x2, x3)", data=iris).summary()
+
    pf.feols("y ~ sw(x1, x2, x3)", data=iris).summary()
 
    
 
    
 
    
@@ -2196,10 +2215,10 @@ 
    Stepwise multiple models
 
    
-
    feols("y ~ csw(x1, x2, x3)", data=iris).summary()
+
    pf.feols("y ~ csw(x1, x2, x3)", data=iris).summary()
 
    
 
    
 
    
@@ -2247,10 +2266,10 @@ 
    Stepwise multiple modelsetable again, but this time as a method rather than a stand-alone function:
    
 
    
 
    
-
    reg_iris_csw = feols("y ~ csw(x1, x2, x3)", data=iris)
+
    reg_iris_csw = pf.feols("y ~ csw(x1, x2, x3)", data=iris)
 reg_iris_csw.etable()
 
    
 
    
@@ -2380,7 +2399,7 @@ 
    Stepwise multiple models
-
       
    
+
       
    
    
@@ -933,7 +937,7 @@ 
    Matplotlib
-_images/da654374062485f7313d8a4d89dba262daf86294c39cfd1678bf9184f3e2e4d2.svg
    
+_images/ad5dd6f1b26f2ff31fa49163cdd80cb83eba36ad62ce0d74921eabb820a56dcc.svg
    
 
    
 
 
    
@@ -963,7 +967,7 @@ 
    Lets-Plot
-
       
    
+
       
    
    
    
 
    
 
@@ -1023,13 +1027,13 @@ 
    Altair
 
    
 
    
-
    
+
    
 
-        
                                
                
    
 
    
 
@@ -1433,13 +1437,13 @@ 
    Altair#<
 
    
 
    
 
    
-
    
+
    
 
    
 
    
 
@@ -1851,7 +1858,7 @@ 
    Matplotlib
-_images/ffa2bfbaefca34c24aa1fb95f71b5d3d053ead74429af06e92a3091820991105.svg
    
+_images/0d06e80606d49ef1eb22f54fab63c773159f43a36d01faa10382e65f9b331d3c.svg
    
 
    
 
 
    
@@ -1883,7 +1890,7 @@ 
    Lets-Plot
-
       
    
+
       
    
    
    
 
    
 
@@ -1936,13 +1943,13 @@ 
    Altair#
 
    
 
    
 
    
-
    
+
    
 
    
 
    
 
@@ -2251,13 +2258,13 @@ 
    Altair#
 
    
 
    
 
    
-
    
+
    
 
    
 
    
 
@@ -2522,13 +2529,13 @@ 
    Altair#
 
    
 
    
 
    
-
    
+
    
 
    
 
    
 
@@ -2807,13 +2814,13 @@ 
    Altair#
 
    
 
    
 
    
-
    
+
    
 
    
 
    
 
@@ -3033,13 +3040,13 @@ 
    Altair#
 
    
 
    
 
    
-
    
+
    
 
    
 
    
 
-
    
-
    Altair#
    
+
    
+
    Altair#
    
 
    
 
    
 
    alt.Chart(diamonds).transform_density(
@@ -3319,13 +3475,13 @@ 
    Altair#
 
    
 
    
 
    
-
    
+
    
 
    
 
    
 
    
-
    
-
    Plotly#
    
+
    
+
    Plotly#
    
 
    
 
    
 
    import plotly.figure_factory as ff
@@ -3392,9 +3548,9 @@ 
    Plotly#
 
    
 
    
 
    
-
                                
                
    
 
    
 
    
-
    
-
    Altair#
    
+
    
+
    Altair#
    
 
    
 
    
 
    alt.Chart(penguins).mark_bar().encode(
@@ -3608,13 +3764,13 @@ 
    Altair#
 
    
 
    
 
    
-
    
+
    
 
    
 
    
 
    
-
    
-
    Plotly#
    
+
    
+
    Plotly#
    
 
    
 
    
 
    fig = px.histogram(penguins, x="flipper_length_mm", nbins=30)
@@ -3674,9 +3830,9 @@ 
    Plotly#
 
    
 
    
 
    
-
                                
                
    
 
    
 
    
-
    
-
    Altair#
    
+
    
+
    Altair#
    
 
    This is a bit fiddly.
    
 
    
 
    
@@ -3889,13 +4047,13 @@ 
    Altair#
 
    
 
    
 
    
-
    
+
    
 
    
 
    
 
    
-
    
-
    Plotly#
    
+
    
+
    Plotly#
    
 
    
 
    
 
    fig = px.scatter(
@@ -3961,9 +4119,9 @@ 
    Plotly#
 
    
 
    
 
    
-
                                
                
    
 
    
 
    
-
    
-
    Altair#
    
+
    
+
    Altair#
    
 
    
 
    
 
    alt.Chart(flights).mark_rect().encode(
@@ -4235,13 +4393,13 @@ 
    Altair#
 
    
 
    
 
    
-
    
+
    
 
    
 
    
 
    
-
    
-
    Altair#
    
+
    
+
    Altair#
    
 
    
 
    
 
    alt.Chart(tips).mark_boxplot(size=50).encode(
@@ -4507,13 +4665,13 @@ 
    Altair#
 
    
 
    
 
    
-
    
+
    
 
    
 
    
 
    
-
    
-
    Plotly#
    
+
    
+
    Plotly#
    
 
    
 
    
 
    fig = px.box(tips, x="time", y="tip", color="time")
@@ -4573,9 +4731,9 @@ 
    Plotly#
 
    
 
    
 
    
-
                                
                
    
 
    
 
    
-
    
-
    Altair#
    
+
    
+
    Altair#
    
 
    
 
    
 
    alt.Chart(tips).transform_density(
@@ -4723,13 +4881,13 @@ 
    Altair#
 
    
 
    
 
    
-
    
+
    
 
    
 
    
 
    
-
    
-
    Plotly#
    
+
    
+
    Plotly#
    
 
    
 
    
 
    fig = px.violin(
@@ -4797,9 +4955,9 @@ 
    Plotly#
 
    
 
    
 
    
-
                                
                
    
 
    
 
    
-
    
-
    Plotly#
    
+
    
+
    Plotly#
    
 
    
 
    
 
    import plotly.graph_objects as go
@@ -4963,9 +5121,9 @@ 
    Plotly#
 
    
 
    
 
    
-
                                
                
    
 
    
 
    
-
    
-
    Altair#
    
+
    
+
    Altair#
    
 
    
 
    
 
    alt.Chart(
@@ -5203,13 +5361,13 @@ 
    Altair#
 
    
 
    
 
    
-
    
+
    
 
    
 
    
 
    
-
    
-
    Altair#
    
+
    
+
    Altair#
    
 
    
 
    
 
    alt.Chart(df).mark_line().encode(x="Year:O", y="GDP per capita", color="Continent")
@@ -5550,13 +5708,13 @@ 
    Altair#
 
    
 
    
 
    
-
    
+
    
 
    
 
    
 
    
-
    
-
    Plotly#
    
+
    
+
    Plotly#
    
 
    
 
    
 
    import plotly.graph_objects as go
@@ -5654,9 +5812,9 @@ 
    Plotly#
 
    
 
    
 
    
-
                                
                
    
 
    
 
    
-
    
-
    Plotly#
    
+
    
+
    Plotly#
    
 
    
 
    
 
    import plotly.graph_objects as go
@@ -5952,9 +6110,9 @@ 
    Plotly#
 
    
 
    
 
    
-
                                
                
    
 
    
 
    
@@ -6546,8 +6704,8 @@ 
    Lets-Plot#
    
 
    Contour plots can help you show how a third variable, Z, varies with both X and Y (ie Z is a surface). The way that Z is depicted could be via the density of lines drawn in the X-Y plane (use ax.contour() for this) or via colour, as in the example below (using ax.contourf()).
    
 
    The heatmap (or contour plot) below, which has a colour bar legend and a title that’s rendered with latex, uses a perceptually uniform distribution that makes equal changes look equal; matplotlib has a few of these. If you need more colours, check out the packages colorcet and palettable.
    
-
    
-
    Maplotlib#
    
+
    
+
    Maplotlib#
    
 
    Note that, in the below, Z is returned by a function that accepts a grid of X and Y values.
    
 
    
 
    
@@ -6569,11 +6727,11 @@ 
    Maplotlib
-_images/a520ee35278c73e2d082f15d39e5caa3dfced3c53baeac88283d2248b0f673d1.svg
    
+_images/aa19cdc6e30ec55d3405e116c8c8ac517a19885725dcd2fe117121d3a845985e.svg
    
 
    
 
-
    
-
    Lets-Plot#
    
+
    
+
    Lets-Plot#
    
 
    
 
    
 
    contour_data = {"x": X.flatten(), "y": Y.flatten(), "z": Z.flatten()}
@@ -6587,7 +6745,7 @@ 
    Lets-Plot
-
       
    
+
       
    
    
    
 
    
 
    
-
    
-
    Plotly#
    
+
    
+
    Plotly#
    
 
    
 
    
 
    import plotly.graph_objects as go
@@ -6646,9 +6804,9 @@ 
    Plotly#
 
    
 
    
 
    
-
                                
                
    
 
    
 
    
@@ -7174,11 +7332,11 @@ 
    Matplotlib/Seaborn
-_images/e27c10e4e35ff77bac78f1a62b2b0a9c95d8f952d9c62f2780299844b852d4be.svg
    
+_images/c0285b269063bb81e9cdac0db7d9b21d3dbf0207ae3eaf0857e9f251977c3f1f.svg
    
 
    
 
-
    
-
    Lets-Plot#
    
+
    
+
    Lets-Plot#
    
 
    Unfortunately, the 20 character limit is hardcoded, so y labels are cut off. But the full text can be seen in the axial tooltip.
    
 
    
 
    
@@ -7194,7 +7352,7 @@ 
    Lets-Plot
-
       
    
+
       
    
    
    
 
    
 
    
-
    
-
    Plotly#
    
+
    
+
    Plotly#
    
 
    
 
    
 
    fig = px.funnel(df, y="Stage", x="Users")
@@ -7253,9 +7411,9 @@ 
    Plotly#
 
    
 
    
 
    
-