diff --git a/backend/Dockerfile b/backend/Dockerfile
index 056c3a0..9e15e47 100644
--- a/backend/Dockerfile
+++ b/backend/Dockerfile
@@ -1,4 +1,4 @@
-FROM python:3
+FROM python:3.11-slim
WORKDIR /app
COPY Pipfile Pipfile.lock .
diff --git a/backend/Pipfile.lock b/backend/Pipfile.lock
index d661287..f6188cf 100644
--- a/backend/Pipfile.lock
+++ b/backend/Pipfile.lock
@@ -1,7 +1,7 @@
{
"_meta": {
"hash": {
- "sha256": "b089081defe09c4ffad85fd22c5de336c76b31e7f6f67e5969b731bdaa58e528"
+ "sha256": "6f1907b01627126d80ae688e9f2e32fc696d85f0cca3913f14fd5d26cb3ac2db"
},
"pipfile-spec": 6,
"requires": {},
@@ -134,102 +134,6 @@
"markers": "python_full_version >= '3.7.0'",
"version": "==3.3.2"
},
- "charset-normalizer": {
- "hashes": [
- "sha256:06435b539f889b1f6f4ac1758871aae42dc3a8c0e24ac9e60c2384973ad73027",
- "sha256:06a81e93cd441c56a9b65d8e1d043daeb97a3d0856d177d5c90ba85acb3db087",
- "sha256:0a55554a2fa0d408816b3b5cedf0045f4b8e1a6065aec45849de2d6f3f8e9786",
- "sha256:0b2b64d2bb6d3fb9112bafa732def486049e63de9618b5843bcdd081d8144cd8",
- "sha256:10955842570876604d404661fbccbc9c7e684caf432c09c715ec38fbae45ae09",
- "sha256:122c7fa62b130ed55f8f285bfd56d5f4b4a5b503609d181f9ad85e55c89f4185",
- "sha256:1ceae2f17a9c33cb48e3263960dc5fc8005351ee19db217e9b1bb15d28c02574",
- "sha256:1d3193f4a680c64b4b6a9115943538edb896edc190f0b222e73761716519268e",
- "sha256:1f79682fbe303db92bc2b1136016a38a42e835d932bab5b3b1bfcfbf0640e519",
- "sha256:2127566c664442652f024c837091890cb1942c30937add288223dc895793f898",
- "sha256:22afcb9f253dac0696b5a4be4a1c0f8762f8239e21b99680099abd9b2b1b2269",
- "sha256:25baf083bf6f6b341f4121c2f3c548875ee6f5339300e08be3f2b2ba1721cdd3",
- "sha256:2e81c7b9c8979ce92ed306c249d46894776a909505d8f5a4ba55b14206e3222f",
- "sha256:3287761bc4ee9e33561a7e058c72ac0938c4f57fe49a09eae428fd88aafe7bb6",
- "sha256:34d1c8da1e78d2e001f363791c98a272bb734000fcef47a491c1e3b0505657a8",
- "sha256:37e55c8e51c236f95b033f6fb391d7d7970ba5fe7ff453dad675e88cf303377a",
- "sha256:3d47fa203a7bd9c5b6cee4736ee84ca03b8ef23193c0d1ca99b5089f72645c73",
- "sha256:3e4d1f6587322d2788836a99c69062fbb091331ec940e02d12d179c1d53e25fc",
- "sha256:42cb296636fcc8b0644486d15c12376cb9fa75443e00fb25de0b8602e64c1714",
- "sha256:45485e01ff4d3630ec0d9617310448a8702f70e9c01906b0d0118bdf9d124cf2",
- "sha256:4a78b2b446bd7c934f5dcedc588903fb2f5eec172f3d29e52a9096a43722adfc",
- "sha256:4ab2fe47fae9e0f9dee8c04187ce5d09f48eabe611be8259444906793ab7cbce",
- "sha256:4d0d1650369165a14e14e1e47b372cfcb31d6ab44e6e33cb2d4e57265290044d",
- "sha256:549a3a73da901d5bc3ce8d24e0600d1fa85524c10287f6004fbab87672bf3e1e",
- "sha256:55086ee1064215781fff39a1af09518bc9255b50d6333f2e4c74ca09fac6a8f6",
- "sha256:572c3763a264ba47b3cf708a44ce965d98555f618ca42c926a9c1616d8f34269",
- "sha256:573f6eac48f4769d667c4442081b1794f52919e7edada77495aaed9236d13a96",
- "sha256:5b4c145409bef602a690e7cfad0a15a55c13320ff7a3ad7ca59c13bb8ba4d45d",
- "sha256:6463effa3186ea09411d50efc7d85360b38d5f09b870c48e4600f63af490e56a",
- "sha256:65f6f63034100ead094b8744b3b97965785388f308a64cf8d7c34f2f2e5be0c4",
- "sha256:663946639d296df6a2bb2aa51b60a2454ca1cb29835324c640dafb5ff2131a77",
- "sha256:6897af51655e3691ff853668779c7bad41579facacf5fd7253b0133308cf000d",
- "sha256:68d1f8a9e9e37c1223b656399be5d6b448dea850bed7d0f87a8311f1ff3dabb0",
- "sha256:6ac7ffc7ad6d040517be39eb591cac5ff87416c2537df6ba3cba3bae290c0fed",
- "sha256:6b3251890fff30ee142c44144871185dbe13b11bab478a88887a639655be1068",
- "sha256:6c4caeef8fa63d06bd437cd4bdcf3ffefe6738fb1b25951440d80dc7df8c03ac",
- "sha256:6ef1d82a3af9d3eecdba2321dc1b3c238245d890843e040e41e470ffa64c3e25",
- "sha256:753f10e867343b4511128c6ed8c82f7bec3bd026875576dfd88483c5c73b2fd8",
- "sha256:7cd13a2e3ddeed6913a65e66e94b51d80a041145a026c27e6bb76c31a853c6ab",
- "sha256:7ed9e526742851e8d5cc9e6cf41427dfc6068d4f5a3bb03659444b4cabf6bc26",
- "sha256:7f04c839ed0b6b98b1a7501a002144b76c18fb1c1850c8b98d458ac269e26ed2",
- "sha256:802fe99cca7457642125a8a88a084cef28ff0cf9407060f7b93dca5aa25480db",
- "sha256:80402cd6ee291dcb72644d6eac93785fe2c8b9cb30893c1af5b8fdd753b9d40f",
- "sha256:8465322196c8b4d7ab6d1e049e4c5cb460d0394da4a27d23cc242fbf0034b6b5",
- "sha256:86216b5cee4b06df986d214f664305142d9c76df9b6512be2738aa72a2048f99",
- "sha256:87d1351268731db79e0f8e745d92493ee2841c974128ef629dc518b937d9194c",
- "sha256:8bdb58ff7ba23002a4c5808d608e4e6c687175724f54a5dade5fa8c67b604e4d",
- "sha256:8c622a5fe39a48f78944a87d4fb8a53ee07344641b0562c540d840748571b811",
- "sha256:8d756e44e94489e49571086ef83b2bb8ce311e730092d2c34ca8f7d925cb20aa",
- "sha256:8f4a014bc36d3c57402e2977dada34f9c12300af536839dc38c0beab8878f38a",
- "sha256:9063e24fdb1e498ab71cb7419e24622516c4a04476b17a2dab57e8baa30d6e03",
- "sha256:90d558489962fd4918143277a773316e56c72da56ec7aa3dc3dbbe20fdfed15b",
- "sha256:923c0c831b7cfcb071580d3f46c4baf50f174be571576556269530f4bbd79d04",
- "sha256:95f2a5796329323b8f0512e09dbb7a1860c46a39da62ecb2324f116fa8fdc85c",
- "sha256:96b02a3dc4381e5494fad39be677abcb5e6634bf7b4fa83a6dd3112607547001",
- "sha256:9f96df6923e21816da7e0ad3fd47dd8f94b2a5ce594e00677c0013018b813458",
- "sha256:a10af20b82360ab00827f916a6058451b723b4e65030c5a18577c8b2de5b3389",
- "sha256:a50aebfa173e157099939b17f18600f72f84eed3049e743b68ad15bd69b6bf99",
- "sha256:a981a536974bbc7a512cf44ed14938cf01030a99e9b3a06dd59578882f06f985",
- "sha256:a9a8e9031d613fd2009c182b69c7b2c1ef8239a0efb1df3f7c8da66d5dd3d537",
- "sha256:ae5f4161f18c61806f411a13b0310bea87f987c7d2ecdbdaad0e94eb2e404238",
- "sha256:aed38f6e4fb3f5d6bf81bfa990a07806be9d83cf7bacef998ab1a9bd660a581f",
- "sha256:b01b88d45a6fcb69667cd6d2f7a9aeb4bf53760d7fc536bf679ec94fe9f3ff3d",
- "sha256:b261ccdec7821281dade748d088bb6e9b69e6d15b30652b74cbbac25e280b796",
- "sha256:b2b0a0c0517616b6869869f8c581d4eb2dd83a4d79e0ebcb7d373ef9956aeb0a",
- "sha256:b4a23f61ce87adf89be746c8a8974fe1c823c891d8f86eb218bb957c924bb143",
- "sha256:bd8f7df7d12c2db9fab40bdd87a7c09b1530128315d047a086fa3ae3435cb3a8",
- "sha256:beb58fe5cdb101e3a055192ac291b7a21e3b7ef4f67fa1d74e331a7f2124341c",
- "sha256:c002b4ffc0be611f0d9da932eb0f704fe2602a9a949d1f738e4c34c75b0863d5",
- "sha256:c083af607d2515612056a31f0a8d9e0fcb5876b7bfc0abad3ecd275bc4ebc2d5",
- "sha256:c180f51afb394e165eafe4ac2936a14bee3eb10debc9d9e4db8958fe36afe711",
- "sha256:c235ebd9baae02f1b77bcea61bce332cb4331dc3617d254df3323aa01ab47bd4",
- "sha256:cd70574b12bb8a4d2aaa0094515df2463cb429d8536cfb6c7ce983246983e5a6",
- "sha256:d0eccceffcb53201b5bfebb52600a5fb483a20b61da9dbc885f8b103cbe7598c",
- "sha256:d965bba47ddeec8cd560687584e88cf699fd28f192ceb452d1d7ee807c5597b7",
- "sha256:db364eca23f876da6f9e16c9da0df51aa4f104a972735574842618b8c6d999d4",
- "sha256:ddbb2551d7e0102e7252db79ba445cdab71b26640817ab1e3e3648dad515003b",
- "sha256:deb6be0ac38ece9ba87dea880e438f25ca3eddfac8b002a2ec3d9183a454e8ae",
- "sha256:e06ed3eb3218bc64786f7db41917d4e686cc4856944f53d5bdf83a6884432e12",
- "sha256:e27ad930a842b4c5eb8ac0016b0a54f5aebbe679340c26101df33424142c143c",
- "sha256:e537484df0d8f426ce2afb2d0f8e1c3d0b114b83f8850e5f2fbea0e797bd82ae",
- "sha256:eb00ed941194665c332bf8e078baf037d6c35d7c4f3102ea2d4f16ca94a26dc8",
- "sha256:eb6904c354526e758fda7167b33005998fb68c46fbc10e013ca97f21ca5c8887",
- "sha256:eb8821e09e916165e160797a6c17edda0679379a4be5c716c260e836e122f54b",
- "sha256:efcb3f6676480691518c177e3b465bcddf57cea040302f9f4e6e191af91174d4",
- "sha256:f27273b60488abe721a075bcca6d7f3964f9f6f067c8c4c605743023d7d3944f",
- "sha256:f30c3cb33b24454a82faecaf01b19c18562b1e89558fb6c56de4d9118a032fd5",
- "sha256:fb69256e180cb6c8a894fee62b3afebae785babc1ee98b81cdf68bbca1987f33",
- "sha256:fd1abc0d89e30cc4e02e4064dc67fcc51bd941eb395c502aac3ec19fab46b519",
- "sha256:ff8fa367d09b717b2a17a052544193ad76cd49979c805768879cb63d9ca50561"
- ],
- "markers": "python_full_version >= '3.7.0'",
- "version": "==3.3.2"
- },
"click": {
"hashes": [
"sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28",
@@ -254,14 +158,6 @@
"markers": "python_version >= '3.8'",
"version": "==2.2.0"
},
- "exceptiongroup": {
- "hashes": [
- "sha256:3111b9d131c238bec2f8f516e123e14ba243563fb135d3fe885990585aa7795b",
- "sha256:47c2edf7c6738fafb49fd34290706d1a1a2f4d1c6df275526b62cbb4aa5393cc"
- ],
- "markers": "python_version < '3.11'",
- "version": "==1.2.2"
- },
"fastapi": {
"hashes": [
"sha256:4f51cfa25d72f9fbc3280832e84b32494cf186f50158d364a8765aabf22587bf",
@@ -279,54 +175,6 @@
"markers": "python_version >= '3.8'",
"version": "==0.0.4"
},
- "geopandas": {
- "hashes": [
- "sha256:02569e9a810f9d11f4ae82c391ebc6fb5730d95a0657d24d754ed7763fb2d122",
- "sha256:0679a30b59d74b6242909945429dbddb08496935b82f91ea9bf6ad240ec23397",
- "sha256:10f5e6c3510b79ea27bb1ebfcc67048cde9ec67afa87c7dd7efa5c700491ac7f",
- "sha256:2af40ae9cdcb204fc1d8f26b190aa16534fcd4f0df756268df674a270eab575d",
- "sha256:32f029c095ad66c425b0ee85553d0dc326d45d7059dbc227330fc29b43e8ba60",
- "sha256:35250099b0cfb32d799fb5d6c651220a642fe2e3c7d2560490e6f1d3f9ae9169",
- "sha256:3b3c8ebafbee8d9002bd8f1195d09ed2bd9ff134ddec37ee8f6a6375e6a4f0e8",
- "sha256:4824c198f714ab5559c5be10fd1adf876712aa7989882a4ec887bf1ef3e00e31",
- "sha256:5ff7e5e9bad94e3a70c5cd2fa27f20b9bb9385e10cddab567b85ce5d306ea923",
- "sha256:651390c3b26b0c7d1f4407cad281ee7a5a85a31a110cbac5269de72a51551ba2",
- "sha256:6e08f572625a1ee682115223eabebc4c6a2035a6917eac6f60350aba297ccadb",
- "sha256:6ed170b5e17da0264b9f6fae86073be3db15fa1bd74061c8331022bca6d09bab",
- "sha256:73379d3ffdeecb376640cd8ed03e9d2d0e568c9d1a4e9b16504a834ebadc2dfb",
- "sha256:75a157d8d26c06e64ace9df037ee93a4938a4606a38cb7ffaf6635e60e253b7a",
- "sha256:791b31ebbc05197d7aa096bbc7bd76d591f05905d2fd908bf103af4488e60670",
- "sha256:7b6b35e52ddc8fb0db562133894e6ef5b4e54e1283dff606fda3eed938c36fc8",
- "sha256:84ec3fb43befb54be490147b4a922b5314e16372a643004f182babee9f9c3407",
- "sha256:8959a59de5af6d2bec27489e98ef25a397cfa1774b375d5787509c06659b3671",
- "sha256:9dfdae43b7996af46ff9da520998a32b105c7f098aeea06b2226b30e74fbba88",
- "sha256:9e6ceba2a01b448e36754983d376064730690401da1dd104ddb543519470a15f",
- "sha256:9efd176f874cb6402e607e4cc9b4a9cd584d82fc34a4b0c811970b32ba62501f",
- "sha256:a1c7c5aa18dd3b17995898b4a9b5929d69ef6ae2af5b96d585ff4005033d82f0",
- "sha256:aae7bd54187e8bf7fd69f8ab87b2885253d3575163ad4d669a262fe97f0136cb",
- "sha256:b21952c092ffd827504de7e66b62aba26fdb5f9d1e435c52477e6486e9d128b2",
- "sha256:b96cd370a61f4d083c9c0053bf634279b094308d52fdc2dd9a22d8372fdd590d",
- "sha256:becc5d7cb89c7b7afa8321b6bb3dbee0eec2b57855c90b3e9bf5fb816671fa7c",
- "sha256:bee32ea8765e859670c4447b0817514ca79054463b6b79784b08a8df3a4d78e3",
- "sha256:c6e7170d675d12eac12ad1a981d90f118c06cf680b42a2d74c6c931e54b50719",
- "sha256:c818c058404eb2bba05e728d38049438afd649e3c409796723dfc17cd3f08749",
- "sha256:c8696544c964500aa9439efb6761947393b70b17ef4e82d73277413f291260a4",
- "sha256:c9cd19cf4fe0595ebdd1d4915882b9440c3a6d30b008f3cc7587c1da7b95be5f",
- "sha256:d4d0096cb1ac7a77b3b41cd78c9b6bc4a400550e21dc7a92f2b5ab53ed74eb02",
- "sha256:d92d3c2a1b39631a6131c2fa25b5406855f97969b068e7e08413325bc0afba58",
- "sha256:da33440b1413bad53a8674393c5d29ce64d8c1a15ef8a77c642ffd900d07bfe1",
- "sha256:e013aae589c1c12505da64a7d8d023e584987e51e62006e1bb30d72f26522c41",
- "sha256:e128778a8e9bc11159ce5447f76766cefbd876f44bd79aff030287254e4752c4",
- "sha256:e54f1bba2f655924c1138bbc7fa91abd61f45c68bd65ab5ed985942712864bbb",
- "sha256:e5b708073ea3d684235648786f5f6153a48dc8762cdfe5563c57e80787c29fbb",
- "sha256:e8bf06b94694251861ba7fdeea15c8ec0967f84c3d4143ae9daf42bbc7717fe3",
- "sha256:f08df60fbd8d289152079a65da4e66a447efc1d5d5a4d3f299cdd39e3b2e4a7d",
- "sha256:f1f8758a2ad110bd6432203a344269f445a2907dc24ef6bccfd0ac4e14e0d71d",
- "sha256:f677ce218976496a587ab17140da141557beb91d2a5c1a14212c994093f2eae2"
- ],
- "markers": "python_version >= '3.8'",
- "version": "==4.53.1"
- },
"geographiclib": {
"hashes": [
"sha256:6b7225248e45ff7edcee32becc4e0a1504c606ac5ee163a5656d482e0cd38734",
@@ -335,14 +183,6 @@
"markers": "python_version >= '3.7'",
"version": "==2.0"
},
- "geojson": {
- "hashes": [
- "sha256:58a7fa40727ea058efc28b0e9ff0099eadf6d0965e04690830208d3ef571adac",
- "sha256:68a9771827237adb8c0c71f8527509c8f5bef61733aa434cefc9c9d4f0ebe8f3"
- ],
- "markers": "python_version >= '3.7'",
- "version": "==3.1.0"
- },
"geopy": {
"hashes": [
"sha256:50283d8e7ad07d89be5cb027338c6365a32044df3ae2556ad3f52f4840b3d0d1",
@@ -507,41 +347,6 @@
"markers": "python_version >= '3.7'",
"version": "==2.1.5"
},
- "matplotlib": {
- "hashes": [
- "sha256:0000354e32efcfd86bda75729716b92f5c2edd5b947200be9881f0a671565c33",
- "sha256:0c584210c755ae921283d21d01f03a49ef46d1afa184134dd0f95b0202ee6f03",
- "sha256:0e835c6988edc3d2d08794f73c323cc62483e13df0194719ecb0723b564e0b5c",
- "sha256:0fc001516ffcf1a221beb51198b194d9230199d6842c540108e4ce109ac05cc0",
- "sha256:11fed08f34fa682c2b792942f8902e7aefeed400da71f9e5816bea40a7ce28fe",
- "sha256:208cbce658b72bf6a8e675058fbbf59f67814057ae78165d8a2f87c45b48d0ff",
- "sha256:2315837485ca6188a4b632c5199900e28d33b481eb083663f6a44cfc8987ded3",
- "sha256:26040c8f5121cd1ad712abffcd4b5222a8aec3a0fe40bc8542c94331deb8780d",
- "sha256:3fda72d4d472e2ccd1be0e9ccb6bf0d2eaf635e7f8f51d737ed7e465ac020cb3",
- "sha256:421851f4f57350bcf0811edd754a708d2275533e84f52f6760b740766c6747a7",
- "sha256:44a21d922f78ce40435cb35b43dd7d573cf2a30138d5c4b709d19f00e3907fd7",
- "sha256:4db17fea0ae3aceb8e9ac69c7e3051bae0b3d083bfec932240f9bf5d0197a049",
- "sha256:565d572efea2b94f264dd86ef27919515aa6d629252a169b42ce5f570db7f37b",
- "sha256:591d3a88903a30a6d23b040c1e44d1afdd0d778758d07110eb7596f811f31842",
- "sha256:6d397fd8ccc64af2ec0af1f0efc3bacd745ebfb9d507f3f552e8adb689ed730a",
- "sha256:7ccd6270066feb9a9d8e0705aa027f1ff39f354c72a87efe8fa07632f30fc6bb",
- "sha256:82cd5acf8f3ef43f7532c2f230249720f5dc5dd40ecafaf1c60ac8200d46d7eb",
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@@ -594,64 +399,55 @@
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},
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@@ -662,127 +458,6 @@
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- "sha256:b0111b27f2d5c820e7f2dbad7d48e3338c824e7ac4d2a12da3dc6061cc39c8e6",
- "sha256:b3cd8f3c5d8c7738257f1018880444f7b7d9b66232c64649f562d7ba86ad4bc1",
- "sha256:b9500e61fce0cfc86168b248104e954fead61f9be213087153d272e817ec7b4f",
- "sha256:ba17799fcddaddf5c1f75a4ba3fd6441f6a4f1e9173f8a786b42450851bd74f1",
- "sha256:ba43cc34cce49cf2d4bc76401a754a81202d8aa926d0e2b79f0ee258cb15d3a4",
- "sha256:baed37ea46d756aca2955e99525cc02d9181de67f25515c468856c38d52b5f3b",
- "sha256:beeaf1c48e32f07d8820c705ff8e645f8afa690cca1544adba4ebfa067efdc88",
- "sha256:c18610b9ccd2874950faf474692deee4223a994251bc0a083c114671b64e6518",
- "sha256:c66962ca7565605b355a9ed478292da628b8f18c0f2793021ca4425abf8b01e5",
- "sha256:caf270c6dba1be7a41125cd1e4fc7ba384bf564650beef0df2dd21a00b7f5770",
- "sha256:cc6139531f13148055d691e442e4bc6601f6dba1e6d521b1585d4788ab0bfad4",
- "sha256:d2c75269f8205b2690db4572a4a36fe47cd1338e4368bc73a7a0e48789e2e35a",
- "sha256:d47ebb01bd865fdea43da56254a3930a413f0c5590372a1241514abae8aa7c76",
- "sha256:d4dc2fd6b3067c0782e7002ac3b38cf48608ee6366ff176bbd02cf969c9c20fe",
- "sha256:d7d0e0ceeb8fe2468c70ec0c37b439dd554e2aa539a8a56365fd761edb418988",
- "sha256:d8640fb4072d36b08e95a3a380ba65779d356b2fee8696afeb7794cf0902d0a1",
- "sha256:dee5e97c2496874acbf1d3e37b521dd1f307349ed955e62d1d2f05382bc36dd5",
- "sha256:dfef2814c6b3291c3c5f10065f745a1307d86019dbd7ea50e83504950136ed5b",
- "sha256:e1402f0564a97d2a52310ae10a64d25bcef94f8dd643fcf5d310219d915484f7",
- "sha256:e7ce306a42b6b93ca47ac4a3b96683ca554f6d35dd8adc5acfcd55096c8dfcb8",
- "sha256:e82d4bb2138ab05e18f089a83b6564fee28048771eb63cdecf4b9b549de8a2cc",
- "sha256:ecb24f0bdd899d368b715c9e6664166cf694d1e57be73f17759573a6986dd95a",
- "sha256:f00ea7e00447918ee0eff2422c4add4c5752b1b60e88fcb3c067d4a21049a720",
- "sha256:f3caf9cd64abfeb11a3b661329085c5e167abbe15256b3b68cb5d914ba7396f3",
- "sha256:f44bd4b23a0e723bf8b10628288c2c7c335161d6840013d4d5de20e48551773b",
- "sha256:f77b74475c462cb8b88680471193064d3e715c7c6074b1c8c412cb526466efe9",
- "sha256:f8ccb77b3e40b151e20519c6ae6d89bfe3f4c14e8e210d910287f778368bb3d1",
- "sha256:fbd8fd427f57a03cff3ad6574b5e299131585d9727c8c366da4624a9069ed746"
- ],
- "markers": "python_version >= '3.8'",
- "version": "==5.10.0"
- },
"urllib3": {
"hashes": [
"sha256:a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472",
@@ -1300,11 +795,11 @@
"standard"
],
"hashes": [
- "sha256:cd17daa7f3b9d7a24de3617820e634d0933b69eed8e33a516071174427238c81",
- "sha256:d46cd8e0fd80240baffbcd9ec1012a712938754afcf81bce56c024c1656aece8"
+ "sha256:0d114d0831ff1adbf231d358cbf42f17333413042552a624ea6a9b4c33dcfd81",
+ "sha256:94a3608da0e530cea8f69683aa4126364ac18e3826b6630d1a65f4638aade503"
],
"markers": "python_version >= '3.8'",
- "version": "==0.30.1"
+ "version": "==0.30.3"
},
"uvloop": {
"hashes": [
diff --git a/backend/src/constants.py b/backend/src/constants.py
index 2e66727..951a51d 100644
--- a/backend/src/constants.py
+++ b/backend/src/constants.py
@@ -14,6 +14,6 @@ OSM_CACHE_DIR = Path(cache_dir_string)
logger = logging.getLogger(__name__)
logging.basicConfig(
- level = logging.INFO,
+ level = logging.DEBUG,
format = '%(asctime)s - %(name)s\t- %(levelname)s\t- %(message)s'
)
diff --git a/backend/src/example_landmarks_manager.py b/backend/src/example_landmarks_manager.py
deleted file mode 100644
index 30df6dc..0000000
--- a/backend/src/example_landmarks_manager.py
+++ /dev/null
@@ -1,151 +0,0 @@
-import yaml
-import logging
-from OSMPythonTools import cachingStrategy, overpass
-from structs.landmarks import Landmark, LandmarkType
-from structs.preferences import Preferences, Preference
-import constants
-
-SIGHTSEEING = LandmarkType(landmark_type='sightseeing')
-NATURE = LandmarkType(landmark_type='nature')
-SHOPPING = LandmarkType(landmark_type='shopping')
-
-
-class LandmarkManager:
- logger = logging.getLogger(__name__)
-
- def __init__(self) -> None:
- strategy = cachingStrategy.JSON(cacheDir=constants.OSM_CACHE_DIR)
- self.query_builder = overpass.Overpass()
- with constants.AMENITY_SELECTORS_PATH.open('r') as f:
- self.amenity_selectors = yaml.safe_load(f)
- with constants.LANDMARK_PARAMETERS_PATH.open('r') as f:
- self.parameters = yaml.safe_load(f)
- # max_distance = parameters['city_bbox_side']
-
-
- def get_landmark_lists(self, preferences: Preferences, center_coordinates: tuple[float, float]) -> tuple[list[Landmark], list[Landmark]]:
- '''
- Generate a list of landmarks based on the preferences of the user and the center (ie. start) coordinates.
- The list is then used by the pathfinding algorithm to generate a path that goes through the most interesting landmarks.
- :param preferences: the preferences specified by the user
- :param center_coordinates: the coordinates of the starting point
- '''
-
- L = []
-
- # List for sightseeing
- if preferences.sightseeing.score != 0:
- score_func = lambda loc, n_tags: int((10 + n_tags * self.parameters['tag_coeff']) * self.parameters['church_coeff'])
- L1 = self.fetch_landmarks(self.amenity_selectors['sightseeing'], SIGHTSEEING, center_coordinates, self.parameters['city_bbox_side'], score_func)
- self.correct_score(L1, preferences.sightseeing)
- L += L1
-
- # List for nature
- if preferences.nature.score != 0:
- score_func = lambda loc, n_tags: int((10 + n_tags * self.parameters['tag_coeff']) * self.parameters['park_coeff'])
- L2 = self.fetch_landmarks(self.amenity_selectors['nature'], NATURE, center_coordinates, self.parameters['city_bbox_side'], score_func)
- self.correct_score(L2, preferences.nature)
- L += L2
-
- # List for shopping
- if preferences.shopping.score != 0:
- score_func = lambda loc, n_tags: int((10 + n_tags * self.parameters['tag_coeff']))
- L3 = self.fetch_landmarks(self.amenity_selectors['shopping'], SHOPPING, center_coordinates, self.parameters['city_bbox_side'], score_func)
- self.correct_score(L3, preferences.shopping)
- L += L3
-
- # remove duplicates
- L = list(set(L))
- L_constrained = self.take_most_important(L, self.parameters['N_important'])
- self.logger.info(f'Generated {len(L)} landmarks around {center_coordinates}, and constrained to {len(L_constrained)} most important ones.')
- return L, L_constrained
-
-
-
- # Take the most important landmarks from the list
- def take_most_important(self, landmarks: list[Landmark], n_max: int) -> list[Landmark]:
-
- landmarks_sorted = sorted(landmarks, key=lambda x: x.attractiveness, reverse=True)
- return landmarks_sorted[:n_max]
-
-
-
- # Correct the score of a list of landmarks by taking into account preference settings
- def correct_score(self, L: list[Landmark], preference: Preference):
-
- if len(L) == 0 :
- return
-
- if L[0].type != preference.type :
- raise TypeError(f"LandmarkType {preference.type} does not match the type of Landmark {L[0].name}")
-
- for elem in L :
- elem.attractiveness = int(elem.attractiveness*preference.score/500) # arbitrary computation
-
-
- # Function to count elements within a certain radius of a location
- def count_elements_within_radius(self, point: Point, radius: int) -> int:
-
- center_coordinates = (point.x, point.y)
- try:
- landmarks = ox.features_from_point(
- center_point = center_coordinates,
- dist = radius,
- tags = {'building': True} # this is a common tag to give an estimation of the number of elements in the area
- )
- return len(landmarks)
- except ox._errors.InsufficientResponseError:
- return 0
-
-
-
-
- def fetch_landmarks(
- self,
- amenity_selectors: list[dict],
- landmarktype: LandmarkType,
- center_coordinates: tuple[float, float],
- distance: int,
- score_function: callable
- ) -> list[Landmark]:
-
- landmarks = ox.features_from_point(
- center_point = center_coordinates,
- dist = distance,
- tags = amenity_selectors
- )
- self.logger.info(f'Fetched {len(landmarks)} landmarks around {center_coordinates}.')
-
- # cleanup the list
- # remove rows where name is None
- landmarks = landmarks[landmarks['name'].notna()]
- # TODO: remove rows that are part of another building
-
- ret_landmarks = []
- for element, description in landmarks.iterrows():
- osm_type = element[0]
- osm_id = element[1]
- location = description['geometry']
- n_tags = len(description['nodes']) if type(description['nodes']) == list else 1
-
- if type(location) == Point:
- location = location
- elif type(location) == Polygon or type(location) == MultiPolygon:
- location = location.centroid
- elif type(location) == LineString:
- location = location.interpolate(location.length/2)
-
- score = score_function(location, n_tags)
- landmark = Landmark(
- name = description['name'],
- type = landmarktype,
- location = (location.x, location.y),
- osm_type = osm_type,
- osm_id = osm_id,
- attractiveness = score,
- must_do = False,
- n_tags = n_tags
- )
- ret_landmarks.append(landmark)
-
- return ret_landmarks
diff --git a/backend/src/example_optimizer.py b/backend/src/example_optimizer.py
deleted file mode 100644
index 922af99..0000000
--- a/backend/src/example_optimizer.py
+++ /dev/null
@@ -1,419 +0,0 @@
-import numpy as np
-import yaml
-
-from typing import List, Tuple
-from scipy.optimize import linprog
-from math import radians, sin, cos, acos
-
-from structs.landmarks import Landmark
-import constants
-
-# Function to print the result
-def print_res(L: List[Landmark], L_tot):
-
- if len(L) == L_tot:
- print('\nAll landmarks can be visited within max_steps, the following order is suggested : ')
- else :
- print('Could not visit all the landmarks, the following order is suggested : ')
-
- dist = 0
- for elem in L :
- if elem.time_to_reach_next is not None :
- print('- ' + elem.name + ', time to reach next = ' + str(elem.time_to_reach_next))
- dist += elem.time_to_reach_next
- else :
- print('- ' + elem.name)
-
- print("\nMinutes walked : " + str(dist))
- print(f"Visited {len(L)-2} out of {L_tot-2} landmarks")
-
-
-# Prevent the use of a particular solution
-def prevent_config(resx, A_ub, b_ub) -> bool:
-
- for i, elem in enumerate(resx):
- resx[i] = round(elem)
-
- N = len(resx) # Number of edges
- L = int(np.sqrt(N)) # Number of landmarks
-
- nonzeroind = np.nonzero(resx)[0] # the return is a little funky so I use the [0]
- nonzero_tup = np.unravel_index(nonzeroind, (L,L))
-
- ind_a = nonzero_tup[0].tolist()
- vertices_visited = ind_a
- vertices_visited.remove(0)
-
- ones = [1]*L
- h = [0]*N
- for i in range(L) :
- if i in vertices_visited :
- h[i*L:i*L+L] = ones
-
- A_ub = np.vstack((A_ub, h))
- b_ub.append(len(vertices_visited)-1)
-
- return A_ub, b_ub
-
-
-# Prevent the possibility of a given solution bit
-def break_cricle(circle_vertices: list, L: int, A_ub: list, b_ub: list) -> bool:
-
- if L-1 in circle_vertices :
- circle_vertices.remove(L-1)
-
- h = [0]*L*L
- for i in range(L) :
- if i in circle_vertices :
- h[i*L:i*L+L] = [1]*L
-
- A_ub = np.vstack((A_ub, h))
- b_ub.append(len(circle_vertices)-1)
-
- return A_ub, b_ub
-
-
-# Checks if the path is connected, returns a circle if it finds one and the RESULT
-def is_connected(resx) -> bool:
-
- # first round the results to have only 0-1 values
- for i, elem in enumerate(resx):
- resx[i] = round(elem)
-
- N = len(resx) # length of res
- L = int(np.sqrt(N)) # number of landmarks. CAST INTO INT but should not be a problem because N = L**2 by def.
- n_edges = resx.sum() # number of edges
-
- nonzeroind = np.nonzero(resx)[0] # the return is a little funny so I use the [0]
-
- nonzero_tup = np.unravel_index(nonzeroind, (L,L))
-
- ind_a = nonzero_tup[0].tolist()
- ind_b = nonzero_tup[1].tolist()
-
- edges = []
- edges_visited = []
- vertices_visited = []
-
- edge1 = (ind_a[0], ind_b[0])
- edges_visited.append(edge1)
- vertices_visited.append(edge1[0])
-
- for i, a in enumerate(ind_a) :
- edges.append((a, ind_b[i])) # Create the list of edges
-
- remaining = edges
- remaining.remove(edge1)
-
- break_flag = False
- while len(remaining) > 0 and not break_flag:
- for edge2 in remaining :
- if edge2[0] == edge1[1] :
- if edge1[1] in vertices_visited :
- edges_visited.append(edge2)
- break_flag = True
- break
- else :
- vertices_visited.append(edge1[1])
- edges_visited.append(edge2)
- remaining.remove(edge2)
- edge1 = edge2
-
- elif edge1[1] == L-1 or edge1[1] in vertices_visited:
- break_flag = True
- break
-
- vertices_visited.append(edge1[1])
-
-
- if len(vertices_visited) == n_edges +1 :
- return vertices_visited, []
- else:
- return vertices_visited, edges_visited
-
-
-# Function that returns the distance in meters from one location to another
-def get_distance(p1: Tuple[float, float], p2: Tuple[float, float], detour: float, speed: float) :
-
- # Compute the straight-line distance in km
- if p1 == p2 :
- return 0, 0
- else:
- dist = 6371.01 * acos(sin(radians(p1[0]))*sin(radians(p2[0])) + cos(radians(p1[0]))*cos(radians(p2[0]))*cos(radians(p1[1]) - radians(p2[1])))
-
- # Consider the detour factor for average cityto deterline walking distance (in km)
- walk_dist = dist*detour
-
- # Time to walk this distance (in minutes)
- walk_time = walk_dist/speed*60
-
- if walk_time > 15 :
- walk_time = 5*round(walk_time/5)
- else :
- walk_time = round(walk_time)
-
-
- return round(walk_dist, 1), walk_time
-
-
-# Initialize A and c. Compute the distances from all landmarks to each other and store attractiveness
-# We want to maximize the sightseeing : max(c) st. A*x < b and A_eq*x = b_eq
-def init_ub_dist(landmarks: List[Landmark], max_steps: int):
-
- # Read the parameters from the file
- with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
- parameters = yaml.safe_load(f)
- detour = parameters['detour_factor']
- speed = parameters['average_walking_speed']
-
- # Objective function coefficients. a*x1 + b*x2 + c*x3 + ...
- c = []
- # Coefficients of inequality constraints (left-hand side)
- A_ub = []
-
- for spot1 in landmarks :
- dist_table = [0]*len(landmarks)
- c.append(-spot1.attractiveness)
- for j, spot2 in enumerate(landmarks) :
- t = get_distance(spot1.location, spot2.location, detour, speed)[1]
- dist_table[j] = t
- A_ub += dist_table
- c = c*len(landmarks)
-
- return c, A_ub, [max_steps]
-
-
-# Constraint to respect only one travel per landmark. Also caps the total number of visited landmarks
-def respect_number(L:int, A_ub, b_ub):
-
- ones = [1]*L
- zeros = [0]*L
- for i in range(L) :
- h = zeros*i + ones + zeros*(L-1-i)
- A_ub = np.vstack((A_ub, h))
- b_ub.append(1)
-
- # Read the parameters from the file
- with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
- parameters = yaml.safe_load(f)
- max_landmarks = parameters['max_landmarks']
-
- A_ub = np.vstack((A_ub, ones*L))
- b_ub.append(max_landmarks+1)
-
- return A_ub, b_ub
-
-
-# Constraint to not have d14 and d41 simultaneously. Does not prevent circular symmetry with more elements
-def break_sym(L, A_ub, b_ub):
- upper_ind = np.triu_indices(L,0,L)
-
- up_ind_x = upper_ind[0]
- up_ind_y = upper_ind[1]
-
- for i, _ in enumerate(up_ind_x) :
- l = [0]*L*L
- if up_ind_x[i] != up_ind_y[i] :
- l[up_ind_x[i]*L + up_ind_y[i]] = 1
- l[up_ind_y[i]*L + up_ind_x[i]] = 1
-
- A_ub = np.vstack((A_ub,l))
- b_ub.append(1)
-
- return A_ub, b_ub
-
-
-# Constraint to not stay in position. Removes d11, d22, d33, etc.
-def init_eq_not_stay(L: int):
- l = [0]*L*L
-
- for i in range(L) :
- for j in range(L) :
- if j == i :
- l[j + i*L] = 1
-
- l = np.array(np.array(l))
-
- return [l], [0]
-
-
-# Go through the landmarks and force the optimizer to use landmarks where attractiveness is set to -1
-def respect_user_mustsee(landmarks: List[Landmark], A_eq: list, b_eq: list) :
- L = len(landmarks)
-
- for i, elem in enumerate(landmarks) :
- if elem.must_do is True and elem.name not in ['finish', 'start']:
- l = [0]*L*L
- for j in range(L) : # sets the horizontal ones (go from)
- l[j +i*L] = 1 # sets the vertical ones (go to) double check if good
-
- for k in range(L-1) :
- l[k*L+L-1] = 1
-
- A_eq = np.vstack((A_eq,l))
- b_eq.append(2)
-
- return A_eq, b_eq
-
-
-# Constraint to ensure start at start and finish at goal
-def respect_start_finish(L: int, A_eq: list, b_eq: list):
- ls = [1]*L + [0]*L*(L-1) # sets only horizontal ones for start (go from)
- ljump = [0]*L*L
- ljump[L-1] = 1 # Prevent start finish jump
- lg = [0]*L*L
- ll = [0]*L*(L-1) + [1]*L
- for k in range(L-1) : # sets only vertical ones for goal (go to)
- ll[k*L] = 1
- if k != 0 : # Prevent the shortcut start -> finish
- lg[k*L+L-1] = 1
-
-
- A_eq = np.vstack((A_eq,ls))
- A_eq = np.vstack((A_eq,ljump))
- A_eq = np.vstack((A_eq,lg))
- A_eq = np.vstack((A_eq,ll))
- b_eq.append(1)
- b_eq.append(0)
- b_eq.append(1)
- b_eq.append(0)
-
- return A_eq, b_eq
-
-
-# Constraint to tie the problem together. Necessary but not sufficient to avoid circles
-def respect_order(N: int, A_eq, b_eq):
- for i in range(N-1) : # Prevent stacked ones
- if i == 0 or i == N-1: # Don't touch start or finish
- continue
- else :
- l = [0]*N
- l[i] = -1
- l = l*N
- for j in range(N) :
- l[i*N + j] = 1
-
- A_eq = np.vstack((A_eq,l))
- b_eq.append(0)
-
- return A_eq, b_eq
-
-
-# Computes the time to reach from each landmark to the next
-def link_list(order: List[int], landmarks: List[Landmark]) -> List[Landmark]:
-
- # Read the parameters from the file
- with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
- parameters = yaml.safe_load(f)
-
- detour_factor = parameters['detour_factor']
- speed = parameters['average_walking_speed']
-
- L = []
- j = 0
- total_dist = 0
- while j < len(order)-1 :
- elem = landmarks[order[j]]
- next = landmarks[order[j+1]]
-
- d = get_distance(elem.location, next.location, detour_factor, speed)[1]
- elem.time_to_reach_next = d
- L.append(elem)
- j += 1
- total_dist += d
-
- L.append(next)
-
- return L, total_dist
-
-
-def link_list_simple(ordered_visit: List[Landmark])-> List[Landmark] :
-
- # Read the parameters from the file
- with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
- parameters = yaml.safe_load(f)
-
- detour_factor = parameters['detour_factor']
- speed = parameters['average_walking_speed']
-
- L = []
- j = 0
- total_dist = 0
- while j < len(ordered_visit)-1 :
- elem = ordered_visit[j]
- next = ordered_visit[j+1]
-
- elem.next_uuid = next.uuid
- d = get_distance(elem.location, next.location, detour_factor, speed)[1]
- elem.time_to_reach_next = d
- if elem.name not in ['start', 'finish'] :
- elem.must_do = True
- L.append(elem)
- j += 1
- total_dist += d
-
- L.append(next)
-
- return L, total_dist
-
-
-# Main optimization pipeline
-def solve_optimization (landmarks :List[Landmark], max_steps: int, printing_details: bool) :
-
- L = len(landmarks)
-
- # SET CONSTRAINTS FOR INEQUALITY
- c, A_ub, b_ub = init_ub_dist(landmarks, max_steps) # Add the distances from each landmark to the other
- A_ub, b_ub = respect_number(L, A_ub, b_ub) # Respect max number of visits (no more possible stops than landmarks).
- A_ub, b_ub = break_sym(L, A_ub, b_ub) # break the 'zig-zag' symmetry
-
- # SET CONSTRAINTS FOR EQUALITY
- A_eq, b_eq = init_eq_not_stay(L) # Force solution not to stay in same place
- A_eq, b_eq = respect_user_mustsee(landmarks, A_eq, b_eq) # Check if there are user_defined must_see. Also takes care of start/goal
- A_eq, b_eq = respect_start_finish(L, A_eq, b_eq) # Force start and finish positions
- A_eq, b_eq = respect_order(L, A_eq, b_eq) # Respect order of visit (only works when max_steps is limiting factor)
-
- # SET BOUNDS FOR DECISION VARIABLE (x can only be 0 or 1)
- x_bounds = [(0, 1)]*L*L
-
- # Solve linear programming problem
- res = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq = b_eq, bounds=x_bounds, method='highs', integrality=3)
-
- # Raise error if no solution is found
- if not res.success :
- raise ArithmeticError("No solution could be found, the problem is overconstrained. Please adapt your must_dos")
-
- # If there is a solution, we're good to go, just check for connectiveness
- else :
- order, circle = is_connected(res.x)
- i = 0
- timeout = 80
- while len(circle) != 0 and i < timeout:
- A_ub, b_ub = prevent_config(res.x, A_ub, b_ub)
- A_ub, b_ub = break_cricle(order, len(landmarks), A_ub, b_ub)
- res = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq = b_eq, bounds=x_bounds, method='highs', integrality=3)
- order, circle = is_connected(res.x)
- if len(circle) == 0 :
- break
- print(i)
- i += 1
-
- if i == timeout :
- raise TimeoutError(f"Optimization took too long. No solution found after {timeout} iterations.")
-
- # Add the times to reach and stop optimizing
- L, total_dist = link_list(order, landmarks)
-
- if printing_details is True :
- if i != 0 :
- print(f"Neded to recompute paths {i} times because of unconnected loops...")
- print_res(L, len(landmarks))
- print("\nTotal score : " + str(int(-res.fun)))
-
- return L
-
-
-
-
-
-
diff --git a/backend/src/example_refiner.py b/backend/src/example_refiner.py
deleted file mode 100644
index 4b1c306..0000000
--- a/backend/src/example_refiner.py
+++ /dev/null
@@ -1,303 +0,0 @@
-from collections import defaultdict
-from heapq import heappop, heappush
-from itertools import permutations
-import os
-import yaml
-
-from shapely import buffer, LineString, Point, Polygon, MultiPoint, concave_hull
-from typing import List, Tuple
-from math import pi
-
-from structs.landmarks import Landmark
-from landmarks_manager import take_most_important
-from backend.src.example_optimizer import solve_optimization, link_list_simple, print_res, get_distance
-import constants
-
-
-# Create corridor from tour
-def create_corridor(landmarks: List[Landmark], width: float) :
-
- corrected_width = (180*width)/(6371000*pi)
-
- path = create_linestring(landmarks)
- obj = buffer(path, corrected_width, join_style="mitre", cap_style="square", mitre_limit=2)
-
- return obj
-
-
-# Create linestring from tour
-def create_linestring(landmarks: List[Landmark])->List[Point] :
-
- points = []
-
- for landmark in landmarks :
- points.append(Point(landmark.location))
-
- return LineString(points)
-
-
-# Check if some coordinates are in area. Used for the corridor
-def is_in_area(area: Polygon, coordinates) -> bool :
- point = Point(coordinates)
- return point.within(area)
-
-
-# Function to determine if two landmarks are close to each other
-def is_close_to(location1: Tuple[float], location2: Tuple[float]):
- """Determine if two locations are close by rounding their coordinates to 3 decimals."""
- absx = abs(location1[0] - location2[0])
- absy = abs(location1[1] - location2[1])
-
- return absx < 0.001 and absy < 0.001
- #return (round(location1[0], 3), round(location1[1], 3)) == (round(location2[0], 3), round(location2[1], 3))
-
-
-# Rearrange some landmarks in the order of visit to group visit
-def rearrange(landmarks: List[Landmark]) -> List[Landmark]:
-
- i = 1
- while i < len(landmarks):
- j = i+1
- while j < len(landmarks):
- if is_close_to(landmarks[i].location, landmarks[j].location) and landmarks[i].name not in ['start', 'finish'] and landmarks[j].name not in ['start', 'finish']:
- # If they are not adjacent, move the j-th element to be adjacent to the i-th element
- if j != i + 1:
- landmarks.insert(i + 1, landmarks.pop(j))
- break # Move to the next i-th element after rearrangement
- j += 1
- i += 1
-
- return landmarks
-
-
- # Step 1: Build the graph
- graph = defaultdict(list)
- for i in range(len(landmarks)):
- for j in range(len(landmarks)):
- if i != j:
- distance = get_distance(landmarks[i].location, landmarks[j].location, detour, speed)[1]
- graph[i].append((distance, j))
-
- # Step 2: Dijkstra's algorithm to find the shortest path from start to finish
- start_idx = next(i for i, lm in enumerate(landmarks) if lm.name == 'start')
- finish_idx = next(i for i, lm in enumerate(landmarks) if lm.name == 'finish')
-
- distances = {i: float('inf') for i in range(len(landmarks))}
- previous_nodes = {i: None for i in range(len(landmarks))}
- distances[start_idx] = 0
- priority_queue = [(0, start_idx)]
-
- while priority_queue:
- current_distance, current_index = heappop(priority_queue)
-
- if current_distance > distances[current_index]:
- continue
-
- for neighbor_distance, neighbor_index in graph[current_index]:
- distance = current_distance + neighbor_distance
-
- if distance < distances[neighbor_index]:
- distances[neighbor_index] = distance
- previous_nodes[neighbor_index] = current_index
- heappush(priority_queue, (distance, neighbor_index))
-
- # Step 3: Backtrack from finish to start to find the path
- path = []
- current_index = finish_idx
- while current_index is not None:
- path.append(landmarks[current_index])
- current_index = previous_nodes[current_index]
- path.reverse()
-
- return path
-"""
-def total_path_distance(path: List[Landmark], detour, speed) -> float:
- total_distance = 0
- for i in range(len(path) - 1):
- total_distance += get_distance(path[i].location, path[i + 1].location, detour, speed)[1]
- return total_distance
-"""
-
-
-def find_shortest_path_through_all_landmarks(landmarks: List[Landmark]) -> List[Landmark]:
- # Read the parameters from the file
- with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
- parameters = yaml.safe_load(f)
-
- detour = parameters['detour_factor']
- speed = parameters['average_walking_speed']
-
- # Step 1: Find 'start' and 'finish' landmarks
- start_idx = next(i for i, lm in enumerate(landmarks) if lm.name == 'start')
- finish_idx = next(i for i, lm in enumerate(landmarks) if lm.name == 'finish')
-
- start_landmark = landmarks[start_idx]
- finish_landmark = landmarks[finish_idx]
-
-
- # Step 2: Create a list of unvisited landmarks excluding 'start' and 'finish'
- unvisited_landmarks = [lm for i, lm in enumerate(landmarks) if i not in [start_idx, finish_idx]]
-
- # Step 3: Initialize the path with the 'start' landmark
- path = [start_landmark]
- coordinates = [landmarks[start_idx].location]
-
- current_landmark = start_landmark
-
- # Step 4: Use nearest neighbor heuristic to visit all landmarks
- while unvisited_landmarks:
- nearest_landmark = min(unvisited_landmarks, key=lambda lm: get_time(current_landmark.location, lm.location, detour, speed))
- path.append(nearest_landmark)
- coordinates.append(nearest_landmark.location)
- current_landmark = nearest_landmark
- unvisited_landmarks.remove(nearest_landmark)
-
- # Step 5: Finally add the 'finish' landmark to the path
- path.append(finish_landmark)
- coordinates.append(landmarks[finish_idx].location)
-
- path_poly = Polygon(coordinates)
-
- return path, path_poly
-
-
-# Returns a list of minor landmarks around the planned path to enhance experience
-def get_minor_landmarks(all_landmarks: List[Landmark], visited_landmarks: List[Landmark], width: float) -> List[Landmark] :
-
- second_order_landmarks = []
- visited_names = []
- area = create_corridor(visited_landmarks, width)
-
- for visited in visited_landmarks :
- visited_names.append(visited.name)
-
- for landmark in all_landmarks :
- if is_in_area(area, landmark.location) and landmark.name not in visited_names:
- second_order_landmarks.append(landmark)
-
- with constants.LANDMARK_PARAMETERS_PATH.open('r') as f:
- parameters = yaml.safe_load(f)
- return take_most_important(second_order_landmarks, parameters, len(visited_landmarks))
-
-
-# Try fix the shortest path using shapely
-def fix_using_polygon(tour: List[Landmark])-> List[Landmark] :
-
- coords = []
- coords_dict = {}
- for landmark in tour :
- coords.append(landmark.location)
- if landmark.name != 'finish' :
- coords_dict[landmark.location] = landmark
-
- tour_poly = Polygon(coords)
-
- better_tour_poly = tour_poly.buffer(0)
- try :
- xs, ys = better_tour_poly.exterior.xy
-
- if len(xs) != len(tour) :
- better_tour_poly = concave_hull(MultiPoint(coords)) # Create concave hull with "core" of tour leaving out start and finish
- xs, ys = better_tour_poly.exterior.xy
-
- except :
- better_tour_poly = concave_hull(MultiPoint(coords)) # Create concave hull with "core" of tour leaving out start and finish
- xs, ys = better_tour_poly.exterior.xy
-
-
- # reverse the xs and ys
- xs.reverse()
- ys.reverse()
-
- better_tour = [] # List of ordered visit
- name_index = {} # Maps the name of a landmark to its index in the concave polygon
-
- # Loop through the polygon and generate the better (ordered) tour
- for i,x in enumerate(xs[:-1]) :
- y = ys[i]
- better_tour.append(coords_dict[tuple((x,y))])
- name_index[coords_dict[tuple((x,y))].name] = i
-
-
- # Scroll the list to have start in front again
- start_index = name_index['start']
- better_tour = better_tour[start_index:] + better_tour[:start_index]
-
- # Append the finish back and correct the time to reach
- better_tour.append(tour[-1])
-
- # Rearrange only if polygon still not simple
- if not better_tour_poly.is_simple :
- better_tour = rearrange(better_tour)
-
- return better_tour
-
-
-# Second stage of the optimization. Use linear programming again to refine the path
-def refine_optimization(landmarks: List[Landmark], base_tour: List[Landmark], max_time: int, detour: int, print_infos: bool) -> List[Landmark] :
-
- # Read the parameters from the file
- with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
- parameters = yaml.safe_load(f)
- max_landmarks = parameters['max_landmarks']
-
- if len(base_tour)-2 >= max_landmarks :
- return base_tour
-
-
- # No need to refine if no detour is taken
- # if detour == 0 :
- if False :
- new_tour = base_tour
-
- else :
- minor_landmarks = get_minor_landmarks(landmarks, base_tour, 200)
-
- if print_infos : print("Using " + str(len(minor_landmarks)) + " minor landmarks around the predicted path")
-
- # full set of visitable landmarks
- full_set = base_tour[:-1] + minor_landmarks # create full set of possible landmarks (without finish)
- full_set.append(base_tour[-1]) # add finish back
-
- # get a new tour
- new_tour = solve_optimization(full_set, max_time+detour, False, max_landmarks)
- if new_tour is None :
- new_tour = base_tour
-
- # Link the new tour
- new_tour, new_dist = link_list_simple(new_tour)
-
- # If the tour contains only one landmark, return
- if len(new_tour) < 4 :
- return new_tour
-
- # Find shortest path using the nearest neighbor heuristic
- better_tour, better_poly = find_shortest_path_through_all_landmarks(new_tour)
-
- # Fix the tour using Polygons if the path looks weird
- if base_tour[0].location == base_tour[-1].location and not better_poly.is_valid :
- better_tour = fix_using_polygon(better_tour)
-
- # Link the tour again
- better_tour, better_dist = link_list_simple(better_tour)
-
- # Choose the better tour depending on walked distance
- if new_dist < better_dist :
- final_tour = new_tour
- else :
- final_tour = better_tour
-
- if print_infos :
- print("\n\n\nRefined tour (result of second stage optimization): ")
- print_res(final_tour)
- total_score = 0
- for elem in final_tour :
- total_score += elem.attractiveness
-
- print("\nTotal score : " + str(total_score))
-
-
-
- return final_tour
-
-
diff --git a/backend/src/example_tester.py b/backend/src/example_tester.py
deleted file mode 100644
index 71d82e7..0000000
--- a/backend/src/example_tester.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import pandas as pd
-
-from typing import List
-from landmarks_manager import LandmarkManager
-from fastapi.encoders import jsonable_encoder
-
-from backend.src.example_optimizer import solve_optimization
-# from refiner import refine_optimization
-from structs.landmarks import Landmark
-from structs.landmarktype import LandmarkType
-from structs.preferences import Preferences, Preference
-
-
-# Helper function to create a .txt file with results
-def write_data(L: List[Landmark], file_name: str):
-
- data = pd.DataFrame()
- i = 0
-
- for landmark in L :
- data[i] = jsonable_encoder(landmark)
- i += 1
-
- data.to_json(file_name, indent = 2, force_ascii=False)
-
-def main(coordinates: tuple[float, float]) -> List[Landmark]:
-
- manager = LandmarkManager()
-
- preferences = Preferences(
- sightseeing=Preference(
- name='sightseeing',
- type=LandmarkType(landmark_type='sightseeing'),
- score = 5
- ),
- nature=Preference(
- name='nature',
- type=LandmarkType(landmark_type='nature'),
- score = 5
- ),
- shopping=Preference(
- name='shopping',
- type=LandmarkType(landmark_type='shopping'),
- score = 5
- )
- )
-
-
- # Create start and finish
- start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=coordinates, osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
- finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=coordinates, osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
-
- # Generate the landmarks from the start location
- landmarks, landmarks_short = manager.get_landmark_lists(preferences=preferences, center_coordinates=start.location)
- print([l.name for l in landmarks_short])
-
- #write_data(landmarks, "landmarks.txt")
-
- # Insert start and finish to the landmarks list
- landmarks_short.insert(0, start)
- landmarks_short.append(finish)
-
- # TODO use these parameters in another way
- max_walking_time = 3 # hours
- detour = 30 # minutes
-
- # First stage optimization
- base_tour = solve_optimization(landmarks_short, max_walking_time*60, True)
-
- # Second stage optimization
- # refined_tour = refine_optimization(landmarks, base_tour, max_walking_time*60+detour, True)
-
- return base_tour
-
-
-
-if __name__ == '__main__':
- start = (48.847132, 2.312359) # Café Chez César
- # start = (47.377859, 8.540585) # Zurich HB
- main(start)
diff --git a/backend/src/landmarks_manager.py b/backend/src/landmarks_manager.py
index 53fa012..403648f 100644
--- a/backend/src/landmarks_manager.py
+++ b/backend/src/landmarks_manager.py
@@ -2,9 +2,8 @@ import math as m
import yaml
import logging
-from typing import List, Tuple
from OSMPythonTools.overpass import Overpass, overpassQueryBuilder
-from OSMPythonTools import cachingStrategy
+from OSMPythonTools.cachingStrategy import CachingStrategy, JSON
from pywikibot import ItemPage, Site
from pywikibot import config
config.put_throttle = 0
@@ -33,10 +32,8 @@ class LandmarkManager:
tag_coeff: float # coeff to adjust weight of tags
N_important: int # number of important landmarks to consider
- preferences: Preferences # preferences of visit
- location: Tuple[float] # coordinates around which to find a path
- def __init__(self, preferences: Preferences, coordinates: Tuple[float, float]) -> None:
+ def __init__(self) -> None:
with constants.AMENITY_SELECTORS_PATH.open('r') as f:
self.amenity_selectors = yaml.safe_load(f)
@@ -49,12 +46,12 @@ class LandmarkManager:
self.park_coeff = parameters['park_coeff']
self.tag_coeff = parameters['tag_coeff']
self.N_important = parameters['N_important']
-
- self.preferences = preferences
- self.location = coordinates
+
+ self.overpass = Overpass()
+ CachingStrategy.use(JSON, cacheDir=constants.OSM_CACHE_DIR)
- def generate_landmarks_list(self) -> Tuple[List[Landmark], List[Landmark]] :
+ def generate_landmarks_list(self, center_coordinates: tuple[float, float], preferences: Preferences) -> tuple[list[Landmark], list[Landmark]]:
"""
Generate and prioritize a list of landmarks based on user preferences.
@@ -62,62 +59,71 @@ class LandmarkManager:
and current location. It scores and corrects these landmarks, removes duplicates, and then selects the most important
landmarks based on a predefined criterion.
+ Parameters:
+ center_coordinates (tuple[float, float]): The latitude and longitude of the center location around which to search.
+ preferences (Preferences): The user's preference settings that influence the landmark selection.
+
Returns:
- Tuple[List[Landmark], List[Landmark]]:
+ tuple[list[Landmark], list[Landmark]]:
- A list of all existing landmarks.
- A list of the most important landmarks based on the user's preferences.
"""
L = []
-
- # List for sightseeing
- if self.preferences.sightseeing.score != 0 :
- L1 = self.fetch_landmarks(self.amenity_selectors['sightseeing'], SIGHTSEEING, coordinates=self.location)
- self.correct_score(L1, self.preferences.sightseeing)
+ bbox = self.create_bbox(center_coordinates)
+ # list for sightseeing
+ if preferences.sightseeing.score != 0:
+ score_function = lambda loc, n_tags: int((self.count_elements_close_to(loc) + ((n_tags**1.2)*self.tag_coeff) )*self.church_coeff)
+ L1 = self.fetch_landmarks(bbox, self.amenity_selectors['sightseeing'], SIGHTSEEING, score_function)
+ self.correct_score(L1, preferences.sightseeing)
L += L1
-
- # List for nature
- if self.preferences.nature.score != 0 :
- L2 = self.fetch_landmarks(self.amenity_selectors['nature'], NATURE, coordinates=self.location)
- self.correct_score(L2, self.preferences.nature)
+
+ # list for nature
+ if preferences.nature.score != 0:
+ score_function = lambda loc, n_tags: int((self.count_elements_close_to(loc) + ((n_tags**1.2)*self.tag_coeff) )*self.park_coeff)
+ L2 = self.fetch_landmarks(bbox, self.amenity_selectors['nature'], NATURE, score_function)
+ self.correct_score(L2, preferences.nature)
L += L2
-
- # List for shopping
- if self.preferences.shopping.score != 0 :
- L3 = self.fetch_landmarks(self.amenity_selectors['shopping'], SHOPPING, coordinates=self.location)
- self.correct_score(L3, self.preferences.shopping)
+
+ # list for shopping
+ if preferences.shopping.score != 0:
+ score_function = lambda loc, n_tags: int(self.count_elements_close_to(loc) + ((n_tags**1.2)*self.tag_coeff))
+ L3 = self.fetch_landmarks(bbox, self.amenity_selectors['shopping'], SHOPPING, score_function)
+ self.correct_score(L3, preferences.shopping)
L += L3
L = self.remove_duplicates(L)
+ L_constrained = take_most_important(L, self.N_important)
+ self.logger.info(f'Generated {len(L)} landmarks around {center_coordinates}, and constrained to {len(L_constrained)} most important ones.')
- return L, take_most_important(L, self.N_important)
+ return L, L_constrained
- def remove_duplicates(self, landmarks: List[Landmark]) -> List[Landmark] :
+ def remove_duplicates(self, landmarks: list[Landmark]) -> list[Landmark]:
"""
Removes duplicate landmarks based on their names from the given list. Only retains the landmark with highest score
Parameters:
- landmarks (List[Landmark]): A list of Landmark objects.
+ landmarks (list[Landmark]): A list of Landmark objects.
Returns:
- List[Landmark]: A list of unique Landmark objects based on their names.
+ list[Landmark]: A list of unique Landmark objects based on their names.
"""
L_clean = []
names = []
- for landmark in landmarks :
+ for landmark in landmarks:
if landmark.name in names:
continue
- else :
+ else:
names.append(landmark.name)
L_clean.append(landmark)
return L_clean
- def correct_score(self, landmarks: List[Landmark], preference: Preference) :
+ def correct_score(self, landmarks: list[Landmark], preference: Preference):
"""
Adjust the attractiveness score of each landmark in the list based on user preferences.
@@ -125,24 +131,24 @@ class LandmarkManager:
The score adjustment is computed using a simple linear transformation based on the preference score.
Args:
- landmarks (List[Landmark]): A list of landmarks whose scores need to be corrected.
+ landmarks (list[Landmark]): A list of landmarks whose scores need to be corrected.
preference (Preference): The user's preference settings that influence the attractiveness score adjustment.
Raises:
TypeError: If the type of any landmark in the list does not match the expected type in the preference.
"""
- if len(landmarks) == 0 :
+ if len(landmarks) == 0:
return
- if landmarks[0].type != preference.type :
+ if landmarks[0].type != preference.type:
raise TypeError(f"LandmarkType {preference.type} does not match the type of Landmark {landmarks[0].name}")
- for elem in landmarks :
+ for elem in landmarks:
elem.attractiveness = int(elem.attractiveness*preference.score/5) # arbitrary computation
- def count_elements_close_to(self, coordinates: Tuple[float, float]) -> int:
+ def count_elements_close_to(self, coordinates: tuple[float, float]) -> int:
"""
Count the number of OpenStreetMap elements (nodes, ways, relations) within a specified radius of the given location.
@@ -150,7 +156,7 @@ class LandmarkManager:
OpenStreetMap data to count the number of elements within that bounding box.
Args:
- coordinates (Tuple[float, float]): The latitude and longitude of the location to search around.
+ coordinates (tuple[float, float]): The latitude and longitude of the location to search around.
Returns:
int: The number of elements (nodes, ways, relations) within the specified radius. Returns 0 if no elements
@@ -164,32 +170,36 @@ class LandmarkManager:
alpha = (180*radius) / (6371000*m.pi)
bbox = {'latLower':lat-alpha,'lonLower':lon-alpha,'latHigher':lat+alpha,'lonHigher': lon+alpha}
-
- # Build the query to find elements within the radius
- radius_query = overpassQueryBuilder(bbox=[bbox['latLower'],bbox['lonLower'],bbox['latHigher'],bbox['lonHigher']],
- elementType=['node', 'way', 'relation'])
- try :
- overpass = Overpass()
- radius_result = overpass.query(radius_query)
+ # Build the query to find elements within the radius
+ radius_query = overpassQueryBuilder(
+ bbox=[bbox['latLower'],
+ bbox['lonLower'],
+ bbox['latHigher'],
+ bbox['lonHigher']],
+ elementType=['node', 'way', 'relation']
+ )
+
+ try:
+ radius_result = self.overpass.query(radius_query)
N_elem = radius_result.countWays() + radius_result.countRelations()
- #print(f"There are {N_elem} ways/relations within 50m")
- if N_elem is None :
+ self.logger.debug(f"There are {N_elem} ways/relations within 50m")
+ if N_elem is None:
return 0
return N_elem
- except :
+ except:
return 0
- def create_bbox(self, coordinates: Tuple[float, float]) -> Tuple[float, float, float, float]:
+ def create_bbox(self, coordinates: tuple[float, float]) -> tuple[float, float, float, float]:
"""
Create a bounding box around the given coordinates.
Args:
- coordinates (Tuple[float, float]): The latitude and longitude of the center of the bounding box.
+ coordinates (tuple[float, float]): The latitude and longitude of the center of the bounding box.
Returns:
- Tuple[float, float, float, float]: The minimum latitude, minimum longitude, maximum latitude, and maximum longitude
+ tuple[float, float, float, float]: The minimum latitude, minimum longitude, maximum latitude, and maximum longitude
defining the bounding box.
"""
@@ -212,44 +222,52 @@ class LandmarkManager:
return min_lat, min_lon, max_lat, max_lon
- def fetch_landmarks(self, list_amenity: list, landmarktype: str, coordinates: Tuple[float, float]) -> List[Landmark] :
+ def fetch_landmarks(self, bbox: tuple, amenity_selector: dict, landmarktype: str, score_function: callable) -> list[Landmark]:
"""
Fetches landmarks of a specified type from OpenStreetMap (OSM) within a bounding box centered on given coordinates.
Args:
- list_amenity (list): A list of OSM amenity queries to be used for fetching landmarks.
- These queries are typically used to filter results (e.g., [''amenity'='place_of_worship']).
+ bbox (tuple[float, float, float, float]): The bounding box coordinates (min_lat, min_lon, max_lat, max_lon).
+ amenity_selector (dict): The Overpass API query selector for the desired landmark type.
landmarktype (str): The type of the landmark (e.g., 'sightseeing', 'nature', 'shopping').
- coordinates (Tuple[float, float]): The central coordinates (latitude, longitude) for the bounding box.
+ score_function (callable): The function to compute the score of the landmark based on its attributes.
Returns:
- List[Landmark]: A list of Landmark objects that were fetched and filtered based on the provided criteria.
+ list[Landmark]: A list of Landmark objects that were fetched and filtered based on the provided criteria.
Notes:
- - The bounding box is created around the given coordinates with a side length defined by `self.city_bbox_side`.
- Landmarks are fetched using Overpass API queries.
+ - Selectors are translated from the dictionary to the Overpass query format. (e.g., 'amenity'='place_of_worship')
- Landmarks are filtered based on various conditions including tags and type.
- Scores are assigned to landmarks based on their attributes and surrounding elements.
"""
-
- # Create bbox around start location
- bbox = self.create_bbox(coordinates)
+ return_list = []
- # Initialize some variables
- N = 0
- L = []
- overpass = Overpass()
-
- for amenity in list_amenity :
- query = overpassQueryBuilder(bbox=bbox, elementType=['way', 'relation'], selector=amenity, includeCenter=True, out='body')
- result = overpass.query(query)
- N += result.countElements()
+ # caution, when applying a list of selectors, overpass will search for elements that match ALL selectors simultaneously
+ # we need to split the selectors into separate queries and merge the results
+ for sel in dict_to_selector_list(amenity_selector):
+ self.logger.debug(f"Current selector: {sel}")
+ query = overpassQueryBuilder(
+ bbox = bbox,
+ elementType = ['way', 'relation'],
+ selector = sel,
+ # conditions = [],
+ includeCenter = True,
+ out = 'body'
+ )
+ try:
+ result = self.overpass.query(query)
+ except Exception as e:
+ self.logger.error(f"Error fetching landmarks: {e}")
+ return
+
for elem in result.elements():
name = elem.tag('name') # Add name
location = (elem.centerLat(), elem.centerLon()) # Add coordinates (lat, lon)
+ # TODO: exclude these from the get go
# skip if unprecise location
if name is None or location[0] is None:
continue
@@ -262,65 +280,86 @@ class LandmarkManager:
if 'building:part' in elem.tags().keys() and elem.tag('building:part') == 'yes':
continue
- else :
- osm_type = elem.type() # Add type : 'way' or 'relation'
- osm_id = elem.id() # Add OSM id
- elem_type = landmarktype # Add the landmark type as 'sightseeing,
- n_tags = len(elem.tags().keys()) # Add number of tags
+ osm_type = elem.type() # Add type: 'way' or 'relation'
+ osm_id = elem.id() # Add OSM id
+ elem_type = landmarktype # Add the landmark type as 'sightseeing,
+ n_tags = len(elem.tags().keys()) # Add number of tags
- # remove specific tags
- skip = False
- for tag in elem.tags().keys() :
- if "pay" in tag :
- n_tags -= 1 # discard payment options for tags
+ # remove specific tags
+ skip = False
+ for tag in elem.tags().keys():
+ if "pay" in tag:
+ n_tags -= 1 # discard payment options for tags
- if "disused" in tag :
- skip = True # skip disused amenities
+ if "disused" in tag:
+ skip = True # skip disused amenities
+ break
+
+ if "wikipedia" in tag:
+ n_tags += 3 # wikipedia entries count more
+
+ if tag == "wikidata":
+ Q = elem.tag('wikidata')
+ site = Site("wikidata", "wikidata")
+ item = ItemPage(site, Q)
+ item.get()
+ n_languages = len(item.labels)
+ n_tags += n_languages/10
+
+ if elem_type != "nature":
+ if "leisure" in tag and elem.tag('leisure') == "park":
+ elem_type = "nature"
+
+ if landmarktype != SHOPPING:
+ if "shop" in tag:
+ skip = True
break
- if "wikipedia" in tag :
- n_tags += 3 # wikipedia entries count more
+ if tag == "building" and elem.tag('building') in ['retail', 'supermarket', 'parking']:
+ skip = True
+ break
- if tag == "wikidata" :
- Q = elem.tag('wikidata')
- site = Site("wikidata", "wikidata")
- item = ItemPage(site, Q)
- item.get()
- n_languages = len(item.labels)
- n_tags += n_languages/10
+ if skip:
+ continue
- if elem_type != "nature" :
- if "leisure" in tag and elem.tag('leisure') == "park":
- elem_type = "nature"
+ score = score_function(location, n_tags)
+ if score != 0:
+ # Generate the landmark and append it to the list
+ landmark = Landmark(
+ name=name,
+ type=elem_type,
+ location=location,
+ osm_type=osm_type,
+ osm_id=osm_id,
+ attractiveness=score,
+ must_do=False,
+ n_tags=int(n_tags)
+ )
+ return_list.append(landmark)
+
+ self.logger.debug(f"Fetched {len(return_list)} landmarks of type {landmarktype} in {bbox}")
- if amenity not in ["'shop'='department_store'", "'shop'='mall'"] :
- if "shop" in tag :
- skip = True
- break
-
- if tag == "building" and elem.tag('building') in ['retail', 'supermarket', 'parking']:
- skip = True
- break
-
- if skip:
- continue
-
- # Add score of given landmark based on the number of surrounding elements. Penalty for churches as there are A LOT
- if amenity == "'amenity'='place_of_worship'" :
- #score = int((count_elements_close_to(location, radius) + (n_tags*tag_coeff) )*church_coeff)
- score = int((self.count_elements_close_to(location) + ((n_tags**1.2)*self.tag_coeff) )*self.church_coeff)
- elif amenity == "'leisure'='park'" :
- score = int((self.count_elements_close_to(location) + ((n_tags**1.2)*self.tag_coeff) )*self.park_coeff)
- else :
- score = int(self.count_elements_close_to(location) + ((n_tags**1.2)*self.tag_coeff))
-
- if score is not None :
-
- # Generate the landmark and append it to the list
- #print(f"There are {n_tags} tags on this Landmark. Total score : {score}\n")
- landmark = Landmark(name=name, type=elem_type, location=location, osm_type=osm_type, osm_id=osm_id, attractiveness=score, must_do=False, n_tags=int(n_tags))
- L.append(landmark)
-
- return L
+ return return_list
+
+def dict_to_selector_list(d: dict) -> list:
+ """
+ Convert a dictionary of key-value pairs to a list of Overpass query strings.
+
+ Args:
+ d (dict): A dictionary of key-value pairs representing the selector.
+
+ Returns:
+ list: A list of strings representing the Overpass query selectors.
+ """
+ return_list = []
+ for key, value in d.items():
+ if type(value) == list:
+ val = '|'.join(value)
+ return_list.append(f'{key}~"{val}"')
+ elif type(value) == str and len(value) == 0:
+ return_list.append(f'{key}')
+ else:
+ return_list.append(f'{key}={value}')
+ return return_list
diff --git a/backend/src/tester.py b/backend/src/tester.py
index cd75435..3c1cd94 100644
--- a/backend/src/tester.py
+++ b/backend/src/tester.py
@@ -24,9 +24,9 @@ def write_data(L: List[Landmark], file_name: str):
def test(start_coords: tuple[float, float], finish_coords: tuple[float, float] = None) -> List[Landmark]:
+ manager = LandmarkManager()
-
preferences = Preferences(
sightseeing=Preference(
name='sightseeing',
@@ -56,10 +56,12 @@ def test(start_coords: tuple[float, float], finish_coords: tuple[float, float] =
#finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.847132, 2.312359), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
- manager = LandmarkManager(preferences=preferences, coordinates=start_coords)
# Generate the landmarks from the start location
- landmarks, landmarks_short = manager.generate_landmarks_list()
+ landmarks, landmarks_short = manager.generate_landmarks_list(
+ center_coordinates = start_coords,
+ preferences = preferences
+ )
# Store data to file for debug purposes
# write_data(landmarks, "landmarks_Strasbourg.txt")
diff --git a/deployment/deployment.yaml b/deployment/deployment.yaml
deleted file mode 100644
index aabc6fa..0000000
--- a/deployment/deployment.yaml
+++ /dev/null
@@ -1,40 +0,0 @@
-apiVersion: apps/v1
-kind: Deployment
-metadata:
- name: nav-backend
-spec:
- replicas: 1
- selector:
- matchLabels:
- app: nav-backend
- template:
- metadata:
- labels:
- app: nav-backend
- spec:
- containers:
- - name: worker
- image: backend-image
- ports:
- - containerPort: 8000
- env:
- - name: NUM_WORKERS
- value: "3"
- - name: OSM_CACHE_DIR
- value: "/osm-cache"
- volumeMounts:
- - name: osm-cache
- mountPath: /osm-cache
- resources:
- requests:
- cpu: 100m
- memory: 100Mi
- limits:
- cpu: 4
- memory: 10Gi
- volumes:
- - name: osm-cache
- persistentVolumeClaim:
- claimName: osm-cache
-
-
\ No newline at end of file
diff --git a/deployment/ingress.yaml b/deployment/ingress.yaml
deleted file mode 100644
index 8fa3b41..0000000
--- a/deployment/ingress.yaml
+++ /dev/null
@@ -1,15 +0,0 @@
-kind: IngressRoute
-apiVersion: traefik.io/v1alpha1
-metadata:
- name: nav-ingress
-spec:
- entryPoints:
- - websecure
- routes:
- - match: Host(`nav.kluster.moll.re`)
- kind: Rule
- services:
- - name: nav-service
- port: 8000
- tls:
- certResolver: default-tls
diff --git a/deployment/kustomization.yaml b/deployment/kustomization.yaml
deleted file mode 100644
index bbb5d0c..0000000
--- a/deployment/kustomization.yaml
+++ /dev/null
@@ -1,16 +0,0 @@
-apiVersion: kustomize.config.k8s.io/v1beta1
-kind: Kustomization
-
-
-namespace: nav
-resources:
- - namespace.yaml
- - pvc.yaml
- - deployment.yaml
- - service.yaml
- - ingress.yaml
-
-images:
- - name: backend-image
- newName: git.kluster.moll.re/remoll/fast_network_navigation/backend
- newTag: latest
\ No newline at end of file
diff --git a/deployment/namespace.yaml b/deployment/namespace.yaml
deleted file mode 100644
index 0a074bd..0000000
--- a/deployment/namespace.yaml
+++ /dev/null
@@ -1,4 +0,0 @@
-apiVersion: v1
-kind: Namespace
-metadata:
- name: placeholder
diff --git a/deployment/pvc.yaml b/deployment/pvc.yaml
deleted file mode 100644
index a7e747f..0000000
--- a/deployment/pvc.yaml
+++ /dev/null
@@ -1,11 +0,0 @@
-apiVersion: v1
-kind: PersistentVolumeClaim
-metadata:
- name: osm-cache
-spec:
- storageClassName: "nfs-client"
- accessModes:
- - ReadWriteOnce
- resources:
- requests:
- storage: "5Gi"
diff --git a/deployment/service.yaml b/deployment/service.yaml
deleted file mode 100644
index c4e5de3..0000000
--- a/deployment/service.yaml
+++ /dev/null
@@ -1,11 +0,0 @@
-apiVersion: v1
-kind: Service
-metadata:
- name: nav-service
-spec:
- selector:
- app: nav-backend
- ports:
- - protocol: TCP
- port: 8000
- targetPort: 8000