most static helpers up and running
This commit is contained in:
parent
795e2b5b92
commit
65c21c6033
1
.gitignore
vendored
Normal file
1
.gitignore
vendored
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@ -0,0 +1 @@
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*.pyc
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1
Pipfile
1
Pipfile
@ -9,6 +9,7 @@ ipython = "*"
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jupyter = "*"
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matplotlib = "*"
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scipy = "*"
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spacepy = "*"
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[dev-packages]
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532
Pipfile.lock
generated
532
Pipfile.lock
generated
@ -1,7 +1,7 @@
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{
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"_meta": {
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"hash": {
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"sha256": "ee1fe0bf58915e71ceca2822901cddf7d578638631d3698ada321e7c0472519f"
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"sha256": "d33c15770abf2c4b05283382da131b41c3adb982d6e486352357938ed088724a"
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},
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"pipfile-spec": 6,
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"requires": {
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@ -301,74 +301,63 @@
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},
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"contourpy": {
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"hashes": [
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"sha256:00ccd0dbaad6d804ab259820fa7cb0b8036bda0686ef844d24125d8287178ce0",
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"sha256:710a26b3dc80c0e4febf04555de66f5fd17e9cf7170a7b08000601a10570bda6",
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"sha256:732896af21716b29ab3e988d4ce14bc5133733b85956316fb0c56355f398099b",
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"sha256:75ee7cb1a14c617f34a51d11fa7524173e56551646828353c4af859c56b766e2",
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"sha256:76a896b2f195b57db25d6b44e7e03f221d32fe318d03ede41f8b4d9ba1bff53c",
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"sha256:76c905ef940a4474a6289c71d53122a4f77766eef23c03cd57016ce19d0f7b42",
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"sha256:7a52040312b1a858b5e31ef28c2e865376a386c60c0e248370bbea2d3f3b760d",
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"sha256:81cb5ed4952aae6014bc9d0421dec7c5835c9c8c31cdf51910b708f548cf58e5",
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"sha256:834e0cfe17ba12f79963861e0f908556b2cedd52e1f75e6578801febcc6a9f49",
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"sha256:87ddffef1dbe5e669b5c2440b643d3fdd8622a348fe1983fad7a0f0ccb1cd67b",
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"sha256:880ea32e5c774634f9fcd46504bf9f080a41ad855f4fef54f5380f5133d343c7",
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"sha256:8ca947601224119117f7c19c9cdf6b3ab54c5726ef1d906aa4a69dfb6dd58102",
|
||||
"sha256:90f73a5116ad1ba7174341ef3ea5c3150ddf20b024b98fb0c3b29034752c8aeb",
|
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"sha256:92f8557cbb07415a4d6fa191f20fd9d2d9eb9c0b61d1b2f52a8926e43c6e9af7",
|
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"sha256:94e848a6b83da10898cbf1311a815f770acc9b6a3f2d646f330d57eb4e87592e",
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"sha256:9c0da700bf58f6e0b65312d0a5e695179a71d0163957fa381bb3c1f72972537c",
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"sha256:a11077e395f67ffc2c44ec2418cfebed032cd6da3022a94fc227b6faf8e2acb8",
|
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"sha256:aea348f053c645100612b333adc5983d87be69acdc6d77d3169c090d3b01dc35",
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"sha256:b11b39aea6be6764f84360fce6c82211a9db32a7c7de8fa6dd5397cf1d079c3b",
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"sha256:c6c7c2408b7048082932cf4e641fa3b8ca848259212f51c8c59c45aa7ac18f14",
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"sha256:c6ec93afeb848a0845a18989da3beca3eec2c0f852322efe21af1931147d12cb",
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"sha256:cacd81e2d4b6f89c9f8a5b69b86490152ff39afc58a95af002a398273e5ce589",
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"sha256:d402880b84df3bec6eab53cd0cf802cae6a2ef9537e70cf75e91618a3801c20c",
|
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"sha256:d51fca85f9f7ad0b65b4b9fe800406d0d77017d7270d31ec3fb1cc07358fdea0",
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"sha256:d78ab28a03c854a873787a0a42254a0ccb3cb133c672f645c9f9c8f3ae9d0800",
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"sha256:e1fd23e9d01591bab45546c089ae89d926917a66dceb3abcf01f6105d927e2cb",
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"sha256:e8134301d7e204c88ed7ab50028ba06c683000040ede1d617298611f9dc6240c",
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"sha256:eb8b141bb00fa977d9122636b16aa67d37fd40a3d8b52dd837e536d64b9a4d06",
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"sha256:eca7e17a65f72a5133bdbec9ecf22401c62bcf4821361ef7811faee695799779",
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"sha256:f317576606de89da6b7e0861cf6061f6146ead3528acabff9236458a6ba467f8",
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"sha256:fd2a0fc506eccaaa7595b7e1418951f213cf8255be2600f1ea1b61e46a60c55f",
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"sha256:fe41b41505a5a33aeaed2a613dccaeaa74e0e3ead6dd6fd3a118fb471644fd6c"
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"sha256:041b640d4ec01922083645a94bb3b2e777e6b626788f4095cf21abbe266413c1",
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"sha256:05e806338bfeaa006acbdeba0ad681a10be63b26e1b17317bfac3c5d98f36cda",
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"sha256:08d9d449a61cf53033612cb368f3a1b26cd7835d9b8cd326647efe43bca7568d",
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"sha256:0ffa84be8e0bd33410b17189f7164c3589c229ce5db85798076a3fa136d0e509",
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"sha256:113231fe3825ebf6f15eaa8bc1f5b0ddc19d42b733345eae0934cb291beb88b6",
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"sha256:14c102b0eab282427b662cb590f2e9340a9d91a1c297f48729431f2dcd16e14f",
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"sha256:174e758c66bbc1c8576992cec9599ce8b6672b741b5d336b5c74e35ac382b18e",
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"sha256:19c1555a6801c2f084c7ddc1c6e11f02eb6a6016ca1318dd5452ba3f613a1751",
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"sha256:19d40d37c1c3a4961b4619dd9d77b12124a453cc3d02bb31a07d58ef684d3d86",
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"sha256:1bf98051f1045b15c87868dbaea84f92408337d4f81d0e449ee41920ea121d3b",
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"sha256:20914c8c973f41456337652a6eeca26d2148aa96dd7ac323b74516988bea89fc",
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"sha256:287ccc248c9e0d0566934e7d606201abd74761b5703d804ff3df8935f523d546",
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"sha256:2ba94a401342fc0f8b948e57d977557fbf4d515f03c67682dd5c6191cb2d16ec",
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"sha256:31c1b55c1f34f80557d3830d3dd93ba722ce7e33a0b472cba0ec3b6535684d8f",
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"sha256:36987a15e8ace5f58d4d5da9dca82d498c2bbb28dff6e5d04fbfcc35a9cb3a82",
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"sha256:3a04ecd68acbd77fa2d39723ceca4c3197cb2969633836ced1bea14e219d077c",
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"sha256:3e8b974d8db2c5610fb4e76307e265de0edb655ae8169e8b21f41807ccbeec4b",
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||||
"sha256:3ea9924d28fc5586bf0b42d15f590b10c224117e74409dd7a0be3b62b74a501c",
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"sha256:4318af1c925fb9a4fb190559ef3eec206845f63e80fb603d47f2d6d67683901c",
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"sha256:44a29502ca9c7b5ba389e620d44f2fbe792b1fb5734e8b931ad307071ec58c53",
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||||
"sha256:47734d7073fb4590b4a40122b35917cd77be5722d80683b249dac1de266aac80",
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||||
"sha256:4d76d5993a34ef3df5181ba3c92fabb93f1eaa5729504fb03423fcd9f3177242",
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||||
"sha256:4dbbc03a40f916a8420e420d63e96a1258d3d1b58cbdfd8d1f07b49fcbd38e85",
|
||||
"sha256:500360b77259914f7805af7462e41f9cb7ca92ad38e9f94d6c8641b089338124",
|
||||
"sha256:523a8ee12edfa36f6d2a49407f705a6ef4c5098de4f498619787e272de93f2d5",
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"sha256:573abb30e0e05bf31ed067d2f82500ecfdaec15627a59d63ea2d95714790f5c2",
|
||||
"sha256:5b75aa69cb4d6f137b36f7eb2ace9280cfb60c55dc5f61c731fdf6f037f958a3",
|
||||
"sha256:61332c87493b00091423e747ea78200659dc09bdf7fd69edd5e98cef5d3e9a8d",
|
||||
"sha256:805617228ba7e2cbbfb6c503858e626ab528ac2a32a04a2fe88ffaf6b02c32bc",
|
||||
"sha256:841ad858cff65c2c04bf93875e384ccb82b654574a6d7f30453a04f04af71342",
|
||||
"sha256:89785bb2a1980c1bd87f0cb1517a71cde374776a5f150936b82580ae6ead44a1",
|
||||
"sha256:8eb96e79b9f3dcadbad2a3891672f81cdcab7f95b27f28f1c67d75f045b6b4f1",
|
||||
"sha256:974d8145f8ca354498005b5b981165b74a195abfae9a8129df3e56771961d595",
|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"sha256:abbb49fb7dac584e5abc6636b7b2a7227111c4f771005853e7d25176daaf8453",
|
||||
"sha256:ac4578ac281983f63b400f7fe6c101bedc10651650eef012be1ccffcbacf3697",
|
||||
"sha256:adce39d67c0edf383647a3a007de0a45fd1b08dedaa5318404f1a73059c2512b",
|
||||
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|
||||
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|
||||
"sha256:b2f926efda994cdf3c8d3fdb40b9962f86edbc4457e739277b961eced3d0b4c1",
|
||||
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|
||||
"sha256:c414fc1ed8ee1dbd5da626cf3710c6013d3d27456651d156711fa24f24bd1291",
|
||||
"sha256:cb76c1a154b83991a3cbbf0dfeb26ec2833ad56f95540b442c73950af2013750",
|
||||
"sha256:dfd97abd83335045a913e3bcc4a09c0ceadbe66580cf573fe961f4a825efa699",
|
||||
"sha256:e914a8cb05ce5c809dd0fe350cfbb4e881bde5e2a38dc04e3afe1b3e58bd158e",
|
||||
"sha256:ece6df05e2c41bd46776fbc712e0996f7c94e0d0543af1656956d150c4ca7c81",
|
||||
"sha256:efa874e87e4a647fd2e4f514d5e91c7d493697127beb95e77d2f7561f6905bd9",
|
||||
"sha256:f611e628ef06670df83fce17805c344710ca5cde01edfdc72751311da8585375"
|
||||
],
|
||||
"markers": "python_version >= '3.9'",
|
||||
"version": "==1.3.0"
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"markers": "python_version >= '3.10'",
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||||
"version": "==1.3.1"
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},
|
||||
"cycler": {
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||||
"hashes": [
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@ -439,57 +428,59 @@
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},
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||||
"fonttools": {
|
||||
"hashes": [
|
||||
"sha256:07e005dc454eee1cc60105d6a29593459a06321c21897f769a281ff2d08939f6",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"sha256:357cacb988a18aace66e5e55fe1247f2ee706e01debc4b1a20d77400354cddeb",
|
||||
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|
||||
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|
||||
"sha256:4aa4817f0031206e637d1e685251ac61be64d1adef111060df84fdcbc6ab6c44",
|
||||
"sha256:4e10d2e0a12e18f4e2dd031e1bf7c3d7017be5c8dbe524d07706179f355c5dac",
|
||||
"sha256:5419771b64248484299fa77689d4f3aeed643ea6630b2ea750eeab219588ba20",
|
||||
"sha256:54471032f7cb5fca694b5f1a0aaeba4af6e10ae989df408e0216f7fd6cdc405d",
|
||||
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|
||||
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|
||||
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|
||||
"sha256:6e37561751b017cf5c40fce0d90fd9e8274716de327ec4ffb0df957160be3bff",
|
||||
"sha256:76ae5091547e74e7efecc3cbf8e75200bc92daaeb88e5433c5e3e95ea8ce5aa7",
|
||||
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|
||||
"sha256:7e3b7d44e18c085fd8c16dcc6f1ad6c61b71ff463636fcb13df7b1b818bd0c02",
|
||||
"sha256:7ed7ee041ff7b34cc62f07545e55e1468808691dddfd315d51dd82a6b37ddef2",
|
||||
"sha256:82834962b3d7c5ca98cb56001c33cf20eb110ecf442725dc5fdf36d16ed1ab07",
|
||||
"sha256:8583e563df41fdecef31b793b4dd3af8a9caa03397be648945ad32717a92885b",
|
||||
"sha256:8fa92cb248e573daab8d032919623cc309c005086d743afb014c836636166f08",
|
||||
"sha256:93d458c8a6a354dc8b48fc78d66d2a8a90b941f7fec30e94c7ad9982b1fa6bab",
|
||||
"sha256:957f669d4922f92c171ba01bef7f29410668db09f6c02111e22b2bce446f3285",
|
||||
"sha256:9dc080e5a1c3b2656caff2ac2633d009b3a9ff7b5e93d0452f40cd76d3da3b3c",
|
||||
"sha256:9ef1b167e22709b46bf8168368b7b5d3efeaaa746c6d39661c1b4405b6352e58",
|
||||
"sha256:a7a310c6e0471602fe3bf8efaf193d396ea561486aeaa7adc1f132e02d30c4b9",
|
||||
"sha256:ab774fa225238986218a463f3fe151e04d8c25d7de09df7f0f5fce27b1243dbc",
|
||||
"sha256:ada215fd079e23e060157aab12eba0d66704316547f334eee9ff26f8c0d7b8ab",
|
||||
"sha256:c39287f5c8f4a0c5a55daf9eaf9ccd223ea59eed3f6d467133cc727d7b943a55",
|
||||
"sha256:c9c563351ddc230725c4bdf7d9e1e92cbe6ae8553942bd1fb2b2ff0884e8b714",
|
||||
"sha256:d26732ae002cc3d2ecab04897bb02ae3f11f06dd7575d1df46acd2f7c012a8d8",
|
||||
"sha256:d3b659d1029946f4ff9b6183984578041b520ce0f8fb7078bb37ec7445806b33",
|
||||
"sha256:dd9cc95b8d6e27d01e1e1f1fae8559ef3c02c76317da650a19047f249acd519d",
|
||||
"sha256:e4564cf40cebcb53f3dc825e85910bf54835e8a8b6880d59e5159f0f325e637e",
|
||||
"sha256:e7d82b9e56716ed32574ee106cabca80992e6bbdcf25a88d97d21f73a0aae664",
|
||||
"sha256:e8a4b261c1ef91e7188a30571be6ad98d1c6d9fa2427244c545e2fa0a2494dd7",
|
||||
"sha256:e96bc94c8cda58f577277d4a71f51c8e2129b8b36fd05adece6320dd3d57de8a",
|
||||
"sha256:ed2f80ca07025551636c555dec2b755dd005e2ea8fbeb99fc5cdff319b70b23b",
|
||||
"sha256:f5b8a096e649768c2f4233f947cf9737f8dbf8728b90e2771e2497c6e3d21d13",
|
||||
"sha256:f8e953cc0bddc2beaf3a3c3b5dd9ab7554677da72dfaf46951e193c9653e515a",
|
||||
"sha256:fda582236fee135d4daeca056c8c88ec5f6f6d88a004a79b84a02547c8f57386",
|
||||
"sha256:fdb062893fd6d47b527d39346e0c5578b7957dcea6d6a3b6794569370013d9ac"
|
||||
"sha256:09fe922a3eff181fd07dd724cdb441fb6b9fc355fd1c0f1aa79aca60faf1fbdd",
|
||||
"sha256:0ecd1c2b1c2ec46bb73685bc5473c72e16ed0930ef79bc2919ccadc43a99fb16",
|
||||
"sha256:10aff204e2edee1d312fa595c06f201adf8d528a3b659cfb34cd47eceaaa6a26",
|
||||
"sha256:131591ac8d7a47043aaf29581aba755ae151d46e49d2bf49608601efd71e8b4d",
|
||||
"sha256:18f082445b8fe5e91c53e6184f4c1c73f3f965c8bcc614c6cd6effd573ce6c1a",
|
||||
"sha256:22ef222740eb89d189bf0612eb98fbae592c61d7efeac51bfbc2a1592d469557",
|
||||
"sha256:25062b6ca03464dd5179fc2040fb19e03391b7cc49b9cc4f879312e638605c5c",
|
||||
"sha256:27c0f91adbbd706e8acd1db73e3e510118e62d0ffb651864567dccc5b2339f90",
|
||||
"sha256:2df61d9fc15199cc86dad29f64dd686874a3a52dda0c2d8597d21f509f95c332",
|
||||
"sha256:3d8ccce035320d63dba0c35f52499322f5531dbe85bba1514c7cea26297e4c54",
|
||||
"sha256:3d9bbc1e380fdaf04ad9eabd8e3e6a4301eaf3487940893e9fd98537ea2e283b",
|
||||
"sha256:42a9afedff07b6f75aa0f39b5e49922ac764580ef3efce035ca30284b2ee65c8",
|
||||
"sha256:42aca564b575252fd9954ed0d91d97a24de24289a16ce8ff74ed0bdf5ecebf11",
|
||||
"sha256:44cf2a98aa661dbdeb8c03f5e405b074e2935196780bb729888639f5276067d9",
|
||||
"sha256:45947e7b3f9673f91df125d375eb57b9a23f2a603f438a1aebf3171bffa7a205",
|
||||
"sha256:487e1e8b524143a799bda0169c48b44a23a6027c1bb1957d5a172a7d3a1dd704",
|
||||
"sha256:4c83381c3e3e3d9caa25527c4300543578341f21aae89e4fbbb4debdda8d82a2",
|
||||
"sha256:508ebb42956a7a931c4092dfa2d9b4ffd4f94cea09b8211199090d2bd082506b",
|
||||
"sha256:5b1a6e576db0c83c1b91925bf1363478c4bb968dbe8433147332fb5782ce6190",
|
||||
"sha256:5cfa67414d7414442a5635ff634384101c54f53bb7b0e04aa6a61b013fcce194",
|
||||
"sha256:616368b15716781bc84df5c2191dc0540137aaef56c2771eb4b89b90933f347a",
|
||||
"sha256:627cf10d6f5af5bec6324c18a2670f134c29e1b7dce3fb62e8ef88baa6cba7a9",
|
||||
"sha256:663eba5615d6abaaf616432354eb7ce951d518e43404371bcc2b0694ef21e8d6",
|
||||
"sha256:6b5917ef79cac8300b88fd6113003fd01bbbbea2ea060a27b95d8f77cb4c65c2",
|
||||
"sha256:6fc88cfb58b0cd7b48718c3e61dd0d0a3ee8e2c86b973342967ce09fbf1db6d4",
|
||||
"sha256:7bbae4f3915225c2c37670da68e2bf18a21206060ad31dfb95fec91ef641caa7",
|
||||
"sha256:803d5cef5fc47f44f5084d154aa3d6f069bb1b60e32390c225f897fa19b0f939",
|
||||
"sha256:81ccd2b3a420b8050c7d9db3be0555d71662973b3ef2a1d921a2880b58957db8",
|
||||
"sha256:8b02b10648d69d67a7eb055f4d3eedf4a85deb22fb7a19fbd9acbae7c7538199",
|
||||
"sha256:8bc5f100de0173cc39102c0399bd6c3bd544bbdf224957933f10ee442d43cddd",
|
||||
"sha256:8e2d89fbe9b08d96e22c7a81ec04a4e8d8439c31223e2dc6f2f9fc8ff14bdf9f",
|
||||
"sha256:9008438ad59e5a8e403a62fbefef2b2ff377eb3857d90a3f2a5f4d674ff441b2",
|
||||
"sha256:93f439ca27e55f585e7aaa04a74990acd983b5f2245e41d6b79f0a8b44e684d8",
|
||||
"sha256:944228b86d472612d3b48bcc83b31c25c2271e63fdc74539adfcfa7a96d487fb",
|
||||
"sha256:96e126df9615df214ec7f04bebcf60076297fbc10b75c777ce58b702d7708ffb",
|
||||
"sha256:9b1726872e09268bbedb14dc02e58b7ea31ecdd1204c6073eda4911746b44797",
|
||||
"sha256:9f0e55f5da594b85f269cfbecd2f6bd3e07d0abba68870bc3f34854de4fa4678",
|
||||
"sha256:bbea0ab841113ac8e8edde067e099b7288ffc6ac2dded538b131c2c0595d5f77",
|
||||
"sha256:bef0f8603834643b1a6419d57902f18e7d950ec1a998fb70410635c598dc1a1e",
|
||||
"sha256:c1b9de46ef7b683d50400abf9f1578eaceee271ff51c36bf4b7366f2be29f498",
|
||||
"sha256:c6457f650ebe15baa17fc06e256227f0a47f46f80f27ec5a0b00160de8dc2c13",
|
||||
"sha256:d0bf24d2b02dbc9376d795a63062632ff73e3e9e60c0229373f500aed7e86dd7",
|
||||
"sha256:d1100d8e665fe386a79cab59446992de881ea74d0d6c191bb988642692aa2421",
|
||||
"sha256:d337ec087da8216a828574aa0525d869df0a2ac217a2efc1890974ddd1fbc5b9",
|
||||
"sha256:d34525e8141286fa976e14806639d32294bfb38d28bbdb5f6be9f46a1cd695a6",
|
||||
"sha256:d4ff250ed4ff05015dfd9cf2adf7570c7a383ca80f4d9732ac484a5ed0d8453c",
|
||||
"sha256:d559eb1744c7dcfa90ae60cb1a4b3595e898e48f4198738c321468c01180cd83",
|
||||
"sha256:dbdc251c5e472e5ae6bc816f9b82718b8e93ff7992e7331d6cf3562b96aa268e",
|
||||
"sha256:e857fe1859901ad8c5cab32e0eebc920adb09f413d2d73b74b677cf47b28590c",
|
||||
"sha256:f1c76f423f1a241df08f87614364dff6e0b7ce23c962c1b74bd995ec7c0dad13"
|
||||
],
|
||||
"markers": "python_version >= '3.8'",
|
||||
"version": "==4.54.1"
|
||||
"version": "==4.55.2"
|
||||
},
|
||||
"fqdn": {
|
||||
"hashes": [
|
||||
@ -506,6 +497,38 @@
|
||||
"markers": "python_version >= '3.7'",
|
||||
"version": "==0.14.0"
|
||||
},
|
||||
"h5py": {
|
||||
"hashes": [
|
||||
"sha256:018a4597f35092ae3fb28ee851fdc756d2b88c96336b8480e124ce1ac6fb9166",
|
||||
"sha256:050a4f2c9126054515169c49cb900949814987f0c7ae74c341b0c9f9b5056834",
|
||||
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|
||||
"sha256:1473348139b885393125126258ae2d70753ef7e9cec8e7848434f385ae72069e",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"sha256:62be1fc0ef195891949b2c627ec06bc8e837ff62d5b911b6e42e38e0f20a897d",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"sha256:cb65f619dfbdd15e662423e8d257780f9a66677eae5b4b3fc9dca70b5fd2d2a3",
|
||||
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|
||||
"sha256:d2b8dd64f127d8b324f5d2cd1c0fd6f68af69084e9e47d27efeb9e28e685af3e",
|
||||
"sha256:d3e465aee0ec353949f0f46bf6c6f9790a2006af896cee7c178a8c3e5090aa32",
|
||||
"sha256:e4d51919110a030913201422fb07987db4338eba5ec8c5a15d6fab8e03d443fc"
|
||||
],
|
||||
"markers": "python_version >= '3.9'",
|
||||
"version": "==3.12.1"
|
||||
},
|
||||
"httpcore": {
|
||||
"hashes": [
|
||||
"sha256:27b59625743b85577a8c0e10e55b50b5368a4f2cfe8cc7bcfa9cf00829c2682f",
|
||||
@ -897,50 +920,51 @@
|
||||
},
|
||||
"matplotlib": {
|
||||
"hashes": [
|
||||
"sha256:039082812cacd6c6bec8e17a9c1e6baca230d4116d522e81e1f63a74d01d2e21",
|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
"sha256:3119b2f16de7f7b9212ba76d8fe6a0e9f90b27a1e04683cd89833a991682f639",
|
||||
"sha256:3fb0b37c896172899a4a93d9442ffdc6f870165f59e05ce2e07c6fded1c15749",
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||||
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||||
"sha256:4a8d279f78844aad213c4935c18f8292a9432d51af2d88bca99072c903948045",
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||||
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||||
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|
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|
||||
"sha256:760a5e89ebbb172989e8273024a1024b0f084510b9105261b3b00c15e9c9f006",
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||||
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||||
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|
||||
"sha256:b651b0d3642991259109dc0351fc33ad44c624801367bb8307be9bfc35e427ad",
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||||
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||||
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|
||||
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|
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||||
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|
||||
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|
||||
"sha256:d796272408f8567ff7eaa00eb2856b3a00524490e47ad505b0b4ca6bb8a7411f",
|
||||
"sha256:e0fcb7da73fbf67b5f4bdaa57d85bb585a4e913d4a10f3e15b32baea56a67f0a",
|
||||
"sha256:e14485bb1b83eeb3d55b6878f9560240981e7bbc7a8d4e1e8c38b9bd6ec8d2de",
|
||||
"sha256:edd14cf733fdc4f6e6fe3f705af97676a7e52859bf0044aa2c84e55be739241c"
|
||||
],
|
||||
"index": "pypi",
|
||||
"markers": "python_version >= '3.9'",
|
||||
"version": "==3.9.2"
|
||||
"version": "==3.9.3"
|
||||
},
|
||||
"matplotlib-inline": {
|
||||
"hashes": [
|
||||
@ -1078,11 +1102,11 @@
|
||||
},
|
||||
"packaging": {
|
||||
"hashes": [
|
||||
"sha256:026ed72c8ed3fcce5bf8950572258698927fd1dbda10a5e981cdf0ac37f4f002",
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|
||||
],
|
||||
"markers": "python_version >= '3.8'",
|
||||
"version": "==24.1"
|
||||
"version": "==24.2"
|
||||
},
|
||||
"pandocfilters": {
|
||||
"hashes": [
|
||||
@ -1110,89 +1134,84 @@
|
||||
},
|
||||
"pillow": {
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||||
"hashes": [
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|
||||
"sha256:61b887f9ddba63ddf62fd02a3ba7add935d053b6dd7d58998c630e6dbade8527",
|
||||
"sha256:6619654954dc4936fcff82db8eb6401d3159ec6be81e33c6000dfd76ae189947",
|
||||
"sha256:674629ff60030d144b7bca2b8330225a9b11c482ed408813924619c6f302fdbb",
|
||||
"sha256:6ec0d5af64f2e3d64a165f490d96368bb5dea8b8f9ad04487f9ab60dc4bb6003",
|
||||
"sha256:6f4dba50cfa56f910241eb7f883c20f1e7b1d8f7d91c750cd0b318bad443f4d5",
|
||||
"sha256:70fbbdacd1d271b77b7721fe3cdd2d537bbbd75d29e6300c672ec6bb38d9672f",
|
||||
"sha256:72bacbaf24ac003fea9bff9837d1eedb6088758d41e100c1552930151f677739",
|
||||
"sha256:7326a1787e3c7b0429659e0a944725e1b03eeaa10edd945a86dead1913383944",
|
||||
"sha256:73853108f56df97baf2bb8b522f3578221e56f646ba345a372c78326710d3830",
|
||||
"sha256:73e3a0200cdda995c7e43dd47436c1548f87a30bb27fb871f352a22ab8dcf45f",
|
||||
"sha256:75acbbeb05b86bc53cbe7b7e6fe00fbcf82ad7c684b3ad82e3d711da9ba287d3",
|
||||
"sha256:8069c5179902dcdce0be9bfc8235347fdbac249d23bd90514b7a47a72d9fecf4",
|
||||
"sha256:846e193e103b41e984ac921b335df59195356ce3f71dcfd155aa79c603873b84",
|
||||
"sha256:8594f42df584e5b4bb9281799698403f7af489fba84c34d53d1c4bfb71b7c4e7",
|
||||
"sha256:86510e3f5eca0ab87429dd77fafc04693195eec7fd6a137c389c3eeb4cfb77c6",
|
||||
"sha256:8853a3bf12afddfdf15f57c4b02d7ded92c7a75a5d7331d19f4f9572a89c17e6",
|
||||
"sha256:88a58d8ac0cc0e7f3a014509f0455248a76629ca9b604eca7dc5927cc593c5e9",
|
||||
"sha256:8ba470552b48e5835f1d23ecb936bb7f71d206f9dfeee64245f30c3270b994de",
|
||||
"sha256:8c676b587da5673d3c75bd67dd2a8cdfeb282ca38a30f37950511766b26858c4",
|
||||
"sha256:8ec4a89295cd6cd4d1058a5e6aec6bf51e0eaaf9714774e1bfac7cfc9051db47",
|
||||
"sha256:94f3e1780abb45062287b4614a5bc0874519c86a777d4a7ad34978e86428b8dd",
|
||||
"sha256:9a0f748eaa434a41fccf8e1ee7a3eed68af1b690e75328fd7a60af123c193b50",
|
||||
"sha256:a5629742881bcbc1f42e840af185fd4d83a5edeb96475a575f4da50d6ede337c",
|
||||
"sha256:a65149d8ada1055029fcb665452b2814fe7d7082fcb0c5bed6db851cb69b2086",
|
||||
"sha256:b3c5ac4bed7519088103d9450a1107f76308ecf91d6dabc8a33a2fcfb18d0fba",
|
||||
"sha256:b4fd7bd29610a83a8c9b564d457cf5bd92b4e11e79a4ee4716a63c959699b306",
|
||||
"sha256:bcd1fb5bb7b07f64c15618c89efcc2cfa3e95f0e3bcdbaf4642509de1942a699",
|
||||
"sha256:c12b5ae868897c7338519c03049a806af85b9b8c237b7d675b8c5e089e4a618e",
|
||||
"sha256:c26845094b1af3c91852745ae78e3ea47abf3dbcd1cf962f16b9a5fbe3ee8488",
|
||||
"sha256:c6a660307ca9d4867caa8d9ca2c2658ab685de83792d1876274991adec7b93fa",
|
||||
"sha256:c809a70e43c7977c4a42aefd62f0131823ebf7dd73556fa5d5950f5b354087e2",
|
||||
"sha256:c8b2351c85d855293a299038e1f89db92a2f35e8d2f783489c6f0b2b5f3fe8a3",
|
||||
"sha256:cb929ca942d0ec4fac404cbf520ee6cac37bf35be479b970c4ffadf2b6a1cad9",
|
||||
"sha256:d2c0a187a92a1cb5ef2c8ed5412dd8d4334272617f532d4ad4de31e0495bd923",
|
||||
"sha256:d69bfd8ec3219ae71bcde1f942b728903cad25fafe3100ba2258b973bd2bc1b2",
|
||||
"sha256:daffdf51ee5db69a82dd127eabecce20729e21f7a3680cf7cbb23f0829189790",
|
||||
"sha256:e58876c91f97b0952eb766123bfef372792ab3f4e3e1f1a2267834c2ab131734",
|
||||
"sha256:eda2616eb2313cbb3eebbe51f19362eb434b18e3bb599466a1ffa76a033fb916",
|
||||
"sha256:ee217c198f2e41f184f3869f3e485557296d505b5195c513b2bfe0062dc537f1",
|
||||
"sha256:f02541ef64077f22bf4924f225c0fd1248c168f86e4b7abdedd87d6ebaceab0f",
|
||||
"sha256:f1b82c27e89fffc6da125d5eb0ca6e68017faf5efc078128cfaa42cf5cb38798",
|
||||
"sha256:fba162b8872d30fea8c52b258a542c5dfd7b235fb5cb352240c8d63b414013eb",
|
||||
"sha256:fbbcb7b57dc9c794843e3d1258c0fbf0f48656d46ffe9e09b63bbd6e8cd5d0a2",
|
||||
"sha256:fcb4621042ac4b7865c179bb972ed0da0218a076dc1820ffc48b1d74c1e37fe9"
|
||||
],
|
||||
"markers": "python_version >= '3.8'",
|
||||
"version": "==10.4.0"
|
||||
"markers": "python_version >= '3.9'",
|
||||
"version": "==11.0.0"
|
||||
},
|
||||
"platformdirs": {
|
||||
"hashes": [
|
||||
@ -1273,11 +1292,11 @@
|
||||
},
|
||||
"pyparsing": {
|
||||
"hashes": [
|
||||
"sha256:a6a7ee4235a3f944aa1fa2249307708f893fe5717dc603503c6c7969c070fb7c",
|
||||
"sha256:f86ec8d1a83f11977c9a6ea7598e8c27fc5cddfa5b07ea2241edbbde1d7bc032"
|
||||
"sha256:93d9577b88da0bbea8cc8334ee8b918ed014968fd2ec383e868fb8afb1ccef84",
|
||||
"sha256:cbf74e27246d595d9a74b186b810f6fbb86726dbf3b9532efb343f6d7294fe9c"
|
||||
],
|
||||
"markers": "python_full_version >= '3.6.8'",
|
||||
"version": "==3.1.4"
|
||||
"markers": "python_version >= '3.9'",
|
||||
"version": "==3.2.0"
|
||||
},
|
||||
"python-dateutil": {
|
||||
"hashes": [
|
||||
@ -1668,11 +1687,11 @@
|
||||
},
|
||||
"six": {
|
||||
"hashes": [
|
||||
"sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926",
|
||||
"sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"
|
||||
"sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274",
|
||||
"sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81"
|
||||
],
|
||||
"markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2'",
|
||||
"version": "==1.16.0"
|
||||
"version": "==1.17.0"
|
||||
},
|
||||
"sniffio": {
|
||||
"hashes": [
|
||||
@ -1690,6 +1709,19 @@
|
||||
"markers": "python_version >= '3.8'",
|
||||
"version": "==2.6"
|
||||
},
|
||||
"spacepy": {
|
||||
"hashes": [
|
||||
"sha256:44b2542837a3f07ee8a77a4ac16754ea5b26b90cdf895182696cb566123561b4",
|
||||
"sha256:61b160bb422e3a85dac2e5ee163d3f01112524d7f30da1d64ae839ebad7cbd27",
|
||||
"sha256:7cca4f39ac796d8b8c8dc362639fed019ee2c1fb164b7f9cf8404ead292ffead",
|
||||
"sha256:a55f30865bb4ebf6f292d2d1a0abfb89045ad0702e052ac40456eb4add25d852",
|
||||
"sha256:aba3d27e27c43dd310152eb618698f517c1d5117579e6d8cd062f7834ebf78d6",
|
||||
"sha256:d970de02b1b8ec8451bc2f8077b8c9cc2597e897f52d6ab160a4e05fe5b4d192"
|
||||
],
|
||||
"index": "pypi",
|
||||
"markers": "python_version >= '3.7'",
|
||||
"version": "==0.7.0"
|
||||
},
|
||||
"stack-data": {
|
||||
"hashes": [
|
||||
"sha256:836a778de4fec4dcd1dcd89ed8abff8a221f58308462e1c4aa2a3cf30148f0b9",
|
||||
|
||||
412
integration/Explicit_ODE_Sovlers_Solutions.ipynb
Normal file
412
integration/Explicit_ODE_Sovlers_Solutions.ipynb
Normal file
File diff suppressed because one or more lines are too long
11
nbody/checklist.md
Normal file
11
nbody/checklist.md
Normal file
@ -0,0 +1,11 @@
|
||||
# N-Body project - Checklist
|
||||
|
||||
### Task 1
|
||||
- [ ] Compute characteristic quantities/scales
|
||||
- [x] Compare analytical model and particle density distribution
|
||||
- [ ] Compute forces through nbody simulation
|
||||
- [ ] vary softening length and compare results
|
||||
- [ ] compare with the analytical expectation from Newtons 2nd law
|
||||
- [ ] compute the relaxation time
|
||||
|
||||
### Task 2
|
||||
@ -1,3 +1,4 @@
|
||||
ID MASS X Y Z VX VY VZ SOFTENING ?
|
||||
0 92.4259 -0.00381649 -0.0796699 -0.019072 3779.62 354.734 -73.4501 0.1 0.0130215
|
||||
1 92.4259 -0.0322979 -0.249461 -0.01089 3250.59 -674.28 -18.3347 0.1 0.0130215
|
||||
2 92.4259 0.067577 -0.810356 -0.00684857 2190.86 199.053 3.86061 0.1 0.0130215
|
||||
|
||||
422
nbody/notebook.ipynb
Normal file
422
nbody/notebook.ipynb
Normal file
File diff suppressed because one or more lines are too long
7
nbody/utils/__init__.py
Normal file
7
nbody/utils/__init__.py
Normal file
@ -0,0 +1,7 @@
|
||||
# Import all functions in all the files in the current directory
|
||||
from .load import *
|
||||
from .mesh import *
|
||||
from .model import *
|
||||
from .particles import *
|
||||
from .forces import *
|
||||
from .integrate import *
|
||||
77
nbody/utils/forces.py
Normal file
77
nbody/utils/forces.py
Normal file
@ -0,0 +1,77 @@
|
||||
import numpy as np
|
||||
import logging
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def n_body_forces(particles: np.ndarray, G: float, softening: float = 0):
|
||||
"""
|
||||
Computes the gravitational forces between a set of particles.
|
||||
Assumes that the particles array has the following columns: x, y, z, m.
|
||||
"""
|
||||
if particles.shape[1] != 4:
|
||||
raise ValueError("Particles array must have 4 columns: x, y, z, m")
|
||||
|
||||
x_vec = particles[:, 0:3]
|
||||
masses = particles[:, 3]
|
||||
|
||||
n = particles.shape[0]
|
||||
forces = np.zeros((n, 3))
|
||||
logger.debug(f"Computing forces for {n} particles using n^2 algorithm")
|
||||
|
||||
for i in range(n):
|
||||
# the current particle is at x_current
|
||||
x_current = x_vec[i, :]
|
||||
m_current = masses[i]
|
||||
|
||||
# first compute the displacement to all other particles
|
||||
displacements = x_vec - x_current
|
||||
# and its magnitude
|
||||
r = np.linalg.norm(displacements, axis=1)
|
||||
# add softening to the denominator
|
||||
r_adjusted = r**2 + softening**2
|
||||
# the numerator is tricky:
|
||||
# m is a list of scalars and displacements is a list of vectors (2D array)
|
||||
# we only want row_wise multiplication
|
||||
num = G * (masses * displacements.T).T
|
||||
|
||||
# a zero value is expected where we have the same particle
|
||||
r_adjusted[i] = 1
|
||||
num[i] = 0
|
||||
|
||||
f = np.sum((num.T / r_adjusted**1.5).T, axis=0) * m_current
|
||||
forces[i] = -f
|
||||
|
||||
if i % 1000 == 0:
|
||||
logger.debug(f"Particle {i} done")
|
||||
|
||||
return forces
|
||||
|
||||
|
||||
|
||||
def analytical_forces(particles: np.ndarray):
|
||||
"""
|
||||
Computes the interparticle forces without computing the n^2 interactions.
|
||||
This is done by using newton's second theorem for a spherical mass distribution.
|
||||
The force on a particle at radius r is simply the force exerted by a point mass with the enclosed mass.
|
||||
Assumes that the particles array has the following columns: x, y, z, m.
|
||||
"""
|
||||
n = particles.shape[0]
|
||||
forces = np.zeros((n, 3))
|
||||
|
||||
logger.debug(f"Computing forces for {n} particles using spherical approximation")
|
||||
|
||||
for i in range(n):
|
||||
r_current = np.linalg.norm(particles[i, 0:3])
|
||||
m_current = particles[i, 3]
|
||||
|
||||
r_particles = np.linalg.norm(particles[:, :3], axis=1)
|
||||
m_enclosed = np.sum(particles[r_particles < r_current, 3])
|
||||
|
||||
# the force is the same as the force exerted by a point mass at the center
|
||||
f = - m_current * m_enclosed / r_current**2
|
||||
forces[i] = f
|
||||
|
||||
if i % 1000 == 0:
|
||||
logger.debug(f"Particle {i} done")
|
||||
|
||||
return forces
|
||||
69
nbody/utils/integrate.py
Normal file
69
nbody/utils/integrate.py
Normal file
@ -0,0 +1,69 @@
|
||||
import numpy as np
|
||||
import scipy.integrate as spi
|
||||
|
||||
import logging
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def ode_setup(particles: np.ndarray, force_function: callable) -> tuple[np.ndarray, callable]:
|
||||
"""
|
||||
Linearizes the ODE system for the particles interacting gravitationally.
|
||||
Returns:
|
||||
- the Y0 array corresponding to the initial conditions (x0 and v0)
|
||||
- the function that computes the right hand side of the ODE with function signature f(t, y)
|
||||
Assumes that the particles array has the following columns: x, y, z, vx, vy, vz, m.
|
||||
"""
|
||||
if particles.shape[1] != 7:
|
||||
raise ValueError("Particles array must have 7 columns: x, y, z, vx, vy, vz, m")
|
||||
|
||||
n = particles.shape[0]
|
||||
# for scipy integrators we need to flatten the n 3D positions and n 3D velocities
|
||||
y0 = np.zeros(6*n)
|
||||
y0[:3*n] = particles[:, :3].flatten()
|
||||
y0[3*n:] = particles[:, 3:6].flatten()
|
||||
|
||||
# the masses don't change we can define them once
|
||||
masses = particles[:, 6]
|
||||
logger.debug(f"Reshaped {particles.shape} to y0 with {y0.shape} and masses with {masses.shape}")
|
||||
|
||||
|
||||
def f(y, t):
|
||||
"""
|
||||
Computes the right hand side of the ODE system.
|
||||
The ODE system is linearized around the current positions and velocities.
|
||||
"""
|
||||
n = y.size // 6
|
||||
logger.debug(f"y with shape {y.shape}")
|
||||
# unsqueeze and unstack to extract the positions and velocities
|
||||
y = y.reshape((2*n, 3))
|
||||
x = y[:n, ...]
|
||||
v = y[n:, ...]
|
||||
logger.debug(f"Unstacked y into x with shape {x.shape} and v with shape {v.shape}")
|
||||
|
||||
# compute the forces
|
||||
x_with_m = np.zeros((n, 4))
|
||||
x_with_m[:, :3] = x
|
||||
x_with_m[:, 3] = masses
|
||||
forces = force_function(x_with_m)
|
||||
|
||||
# compute the accelerations
|
||||
a = forces / masses[:, None]
|
||||
a.flatten()
|
||||
# the [:, None] is to force broadcasting in order to divide each row of forces by the corresponding mass
|
||||
|
||||
# reshape into a 1D array
|
||||
return np.vstack((v, a)).flatten()
|
||||
|
||||
return y0, f
|
||||
|
||||
|
||||
def to_particles(y: np.ndarray) -> np.ndarray:
|
||||
"""
|
||||
Converts the 1D array y into a 2D array with the shape (n, 6) where n is the number of particles.
|
||||
The columns are x, y, z, vx, vy, vz
|
||||
"""
|
||||
n = y.size // 6
|
||||
y = y.reshape((2*n, 3))
|
||||
x = y[:n, ...]
|
||||
v = y[n:, ...]
|
||||
return np.hstack((x, v))
|
||||
@ -0,0 +1,18 @@
|
||||
import numpy as np
|
||||
from pathlib import Path
|
||||
import logging
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def load_data(file: Path) -> tuple[np.ndarray, list]:
|
||||
try:
|
||||
data = np.loadtxt(file)
|
||||
columns = []
|
||||
except ValueError:
|
||||
data = np.loadtxt(file, skiprows=1)
|
||||
header = file.read_text().splitlines()[0]
|
||||
columns = header.split()
|
||||
logger.info(f"Loaded {data.shape[0]} rows and {data.shape[1]} columns from {file}")
|
||||
return data, columns
|
||||
|
||||
0
nbody/utils/mesh.py
Normal file
0
nbody/utils/mesh.py
Normal file
12
nbody/utils/model.py
Normal file
12
nbody/utils/model.py
Normal file
@ -0,0 +1,12 @@
|
||||
import numpy as np
|
||||
|
||||
M = 5
|
||||
a = 5
|
||||
|
||||
def model_density_distribution(r_bins: np.ndarray):
|
||||
"""
|
||||
Generate a density distribution for a spherical galaxy model, as per the Hernquist model.
|
||||
See https://doi.org/10.1086%2F168845 for more information.
|
||||
"""
|
||||
rho = M / (2 * np.pi) * a / (r_bins * (r_bins + a)**3)
|
||||
return rho
|
||||
124
nbody/utils/particles.py
Normal file
124
nbody/utils/particles.py
Normal file
@ -0,0 +1,124 @@
|
||||
import numpy as np
|
||||
import logging
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def density_distribution(r_bins: np.ndarray, particles: np.ndarray, ret_error: bool = False):
|
||||
"""
|
||||
Computes the radial density distribution of a set of particles.
|
||||
Assumes that the particles array has the following columns: x, y, z, m.
|
||||
"""
|
||||
if particles.shape[1] != 4:
|
||||
raise ValueError("Particles array must have 4 columns: x, y, z, m")
|
||||
|
||||
m = particles[:, 3]
|
||||
r = np.linalg.norm(particles[:, :3], axis=1)
|
||||
density = [np.sum(m[(r >= r_bins[i]) & (r < r_bins[i + 1])]) for i in range(len(r_bins) - 1)]
|
||||
|
||||
# add the first volume which should be wrt 0
|
||||
volume = 4/3 * np.pi * (r_bins[1:]**3 - r_bins[:-1]**3)
|
||||
volume = np.insert(volume, 0, 4/3 * np.pi * r_bins[0]**3)
|
||||
density = r_bins / volume
|
||||
if ret_error:
|
||||
return density, density / np.sqrt(r_bins)
|
||||
else:
|
||||
return density
|
||||
|
||||
|
||||
|
||||
def r_distribution(particles: np.ndarray):
|
||||
"""
|
||||
Computes the distribution of distances (to the origin) of a set of particles.
|
||||
Assumes that the particles array has the following columns: x, y, z ...
|
||||
"""
|
||||
if particles.shape[1] < 3:
|
||||
raise ValueError("Particles array must have at least 3 columns: x, y, z")
|
||||
|
||||
r = np.linalg.norm(particles[:, :3], axis=1)
|
||||
return r
|
||||
|
||||
|
||||
|
||||
def remove_outliers(particles: np.ndarray, std_threshold: float = 3):
|
||||
"""
|
||||
Removes outliers from a set of particles.
|
||||
Assumes that the particles array has the following columns: x, y, z ...
|
||||
"""
|
||||
if particles.shape[1] < 3:
|
||||
raise ValueError("Particles array must have at least 3 columns: x, y, z")
|
||||
|
||||
r = np.linalg.norm(particles[:, :3], axis=1)
|
||||
r_std = np.std(r)
|
||||
r_mean = np.mean(r)
|
||||
mask = np.abs(r - r_mean) < std_threshold * r_std
|
||||
return particles[mask]
|
||||
|
||||
|
||||
|
||||
def mean_interparticle_distance(particles: np.ndarray):
|
||||
"""
|
||||
Computes the mean interparticle distance of a set of particles.
|
||||
Assumes that the particles array has the following columns: x, y, z ...
|
||||
"""
|
||||
if particles.shape[1] < 3:
|
||||
raise ValueError("Particles array must have at least 3 columns: x, y, z")
|
||||
|
||||
|
||||
r_half_mass = half_mass_radius(particles)
|
||||
r = np.linalg.norm(particles[:, :3], axis=1)
|
||||
|
||||
n_half_mass = np.sum(r < r_half_mass)
|
||||
logger.debug(f"Number of particles within half mass radius: {n_half_mass} of {particles.shape[0]}")
|
||||
|
||||
rho = n_half_mass / (4/3 * np.pi * r_half_mass**3)
|
||||
# the mean distance between particles is the inverse of the density
|
||||
return (1 / rho)**(1/3)
|
||||
# TODO: check if this is correct
|
||||
|
||||
|
||||
|
||||
def half_mass_radius(particles: np.ndarray):
|
||||
"""
|
||||
Computes the half mass radius of a set of particles.
|
||||
Assumes that the particles array has the following columns: x, y, z ...
|
||||
"""
|
||||
if particles.shape[1] < 3:
|
||||
raise ValueError("Particles array must have at least 3 columns: x, y, z")
|
||||
|
||||
# even though in the simple example, all the masses are the same, we will consider the general case
|
||||
total_mass = np.sum(particles[:, 3])
|
||||
half_mass = total_mass / 2
|
||||
|
||||
# sort the particles by distance
|
||||
r = np.linalg.norm(particles[:, :3], axis=1)
|
||||
indices = np.argsort(r)
|
||||
r = r[indices]
|
||||
masses = particles[indices, 3]
|
||||
masses_cumsum = np.cumsum(masses)
|
||||
|
||||
i = np.argmin(np.abs(masses_cumsum - half_mass))
|
||||
logger.debug(f"Half mass radius: {r[i]} for {i}th particle of {particles.shape[0]}")
|
||||
r_hm = r[i]
|
||||
|
||||
return r_hm
|
||||
|
||||
|
||||
|
||||
def relaxation_timescale(particles: np.ndarray, G:float) -> float:
|
||||
"""
|
||||
Computes the relaxation timescale of a set of particles using the velocity at the half mass radius.
|
||||
Assumes that the particles array has the following columns: x, y, z ...
|
||||
"""
|
||||
m_half = np.sum(particles[:, 3]) / 2 # enclosed mass at half mass radius
|
||||
r_half = half_mass_radius(particles)
|
||||
n_half = np.sum(np.linalg.norm(particles[:, :3], axis=1) < r_half) # number of enclosed particles
|
||||
v_c = np.sqrt(G * m_half / r_half)
|
||||
|
||||
# the crossing time for the half mass system is
|
||||
t_c = r_half / v_c
|
||||
logger.debug(f"Crossing time for half mass system: {t_c}")
|
||||
|
||||
# the relaxation timescale is t_c * N/(10 * log(N))
|
||||
t_rel = t_c * n_half / (10 * np.ln(n_half))
|
||||
|
||||
return t_rel
|
||||
Loading…
x
Reference in New Issue
Block a user