diff --git a/.gitea/workflows/backed_build-image.yaml b/.gitea/workflows/backed_build-image.yaml
index 2f1d21c..5542a1f 100644
--- a/.gitea/workflows/backed_build-image.yaml
+++ b/.gitea/workflows/backed_build-image.yaml
@@ -18,7 +18,7 @@ jobs:
with:
registry: git.kluster.moll.re
username: ${{ gitea.repository_owner }}
- password: ${{ secrets.GITEA_TOKEN}}
+ password: ${{ secrets.DOCKER_PUSH_TOKEN }}
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..e934adf
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+cache/
diff --git a/backend/Pipfile b/backend/Pipfile
index f02576b..62e84e8 100644
--- a/backend/Pipfile
+++ b/backend/Pipfile
@@ -8,5 +8,7 @@ numpy = "*"
scipy = "*"
fastapi = "*"
osmpythontools = "*"
+pydantic = "*"
+shapely = "*"
[dev-packages]
diff --git a/backend/Pipfile.lock b/backend/Pipfile.lock
index f07cd8e..2e5aac3 100644
--- a/backend/Pipfile.lock
+++ b/backend/Pipfile.lock
@@ -1,7 +1,7 @@
{
"_meta": {
"hash": {
- "sha256": "15cc819367b908ccf7d61e3f2b45574c248556600dfeaa795e19045d6781fe90"
+ "sha256": "0f88c01cde3be9a6332acec33fa0ccf13b6e122a6df8ee5cfefa52ba1e98034f"
},
"pipfile-spec": 6,
"requires": {},
@@ -40,11 +40,11 @@
},
"certifi": {
"hashes": [
- "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f",
- "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"
+ "sha256:3cd43f1c6fa7dedc5899d69d3ad0398fd018ad1a17fba83ddaf78aa46c747516",
+ "sha256:ddc6c8ce995e6987e7faf5e3f1b02b302836a0e5d98ece18392cb1a36c72ad56"
],
"markers": "python_version >= '3.6'",
- "version": "==2024.2.2"
+ "version": "==2024.6.2"
},
"click": {
"hashes": [
@@ -122,11 +122,11 @@
},
"email-validator": {
"hashes": [
- "sha256:200a70680ba08904be6d1eef729205cc0d687634399a5924d842533efb824b84",
- "sha256:97d882d174e2a65732fb43bfce81a3a834cbc1bde8bf419e30ef5ea976370a05"
+ "sha256:561977c2d73ce3611850a06fa56b414621e0c8faa9d66f2611407d87465da631",
+ "sha256:cb690f344c617a714f22e66ae771445a1ceb46821152df8e165c5f9a364582b7"
],
"markers": "python_version >= '3.8'",
- "version": "==2.1.1"
+ "version": "==2.2.0"
},
"exceptiongroup": {
"hashes": [
@@ -155,51 +155,51 @@
},
"fonttools": {
"hashes": [
- "sha256:00d9abf4b400f98fb895566eb298f60432b4b29048e3dc02807427b09a06604e",
- "sha256:05e4291db6af66f466a203d9922e4c1d3e18ef16868f76f10b00e2c3b9814df2",
- "sha256:15efb2ba4b8c2d012ee0bb7a850c2e4780c530cc83ec8e843b2a97f8b3a5fd4b",
- "sha256:1dc626de4b204d025d029e646bae8fdbf5acd9217158283a567f4b523fda3bae",
- "sha256:21921e5855c399d10ddfc373538b425cabcf8b3258720b51450909e108896450",
- "sha256:309b617942041073ffa96090d320b99d75648ed16e0c67fb1aa7788e06c834de",
- "sha256:346d08ff92e577b2dc5a0c228487667d23fe2da35a8b9a8bba22c2b6ba8be21c",
- "sha256:35af630404223273f1d7acd4761f399131c62820366f53eac029337069f5826a",
- "sha256:46cc5d06ee05fd239c45d7935aaffd060ee773a88b97e901df50478247472643",
- "sha256:4b0b9eb0f55dce9c7278ad4175f1cbaed23b799dce5ecc20e3213da241584140",
- "sha256:4b419207e53db1599b3d385afd4bca6692c219d53732890d0814a2593104d0e2",
- "sha256:4c3ad89204c2d7f419436f1d6fde681b070c5e20b888beb57ccf92f640628cc9",
- "sha256:52f6001814ec5e0c961cabe89642f7e8d7e07892b565057aa526569b9ebb711c",
- "sha256:5ecb88318ff249bd2a715e7aec36774ce7ae3441128007ef72a39a60601f4a8f",
- "sha256:70d87f2099006304d33438bdaa5101953b7e22e23a93b1c7b7ed0f32ff44b423",
- "sha256:73ba38b98c012957940a04d9eb5439b42565ac892bba8cfc32e10d88e73921fe",
- "sha256:7467161f1eed557dbcec152d5ee95540200b1935709fa73307da16bc0b7ca361",
- "sha256:7dccf4666f716e5e0753f0fa28dad2f4431154c87747bc781c838b8a5dca990e",
- "sha256:859399b7adc8ac067be8e5c80ef4bb2faddff97e9b40896a9de75606a43d0469",
- "sha256:8873d6edd1dae5c088dd3d61c9fd4dd80c827c486fa224d368233e7f33dc98af",
- "sha256:890e7a657574610330e42dd1e38d3b9e0a8cb0eff3da080f80995460a256d3dd",
- "sha256:89b53386214197bd5b3e3c753895bad691de84726ced3c222a59cde1dd12d57b",
- "sha256:8b186cd6b8844f6cf04a7e0a174bc3649d3deddbfc10dc59846a4381f796d348",
- "sha256:9180775c9535389a665cae7c5282f8e07754beabf59b66aeba7f6bfeb32a3652",
- "sha256:95e8a5975d08d0b624a14eec0f987e204ad81b480e24c5436af99170054434b8",
- "sha256:9725687db3c1cef13c0f40b380c3c15bea0113f4d0231b204d58edd5f2a53d90",
- "sha256:9a5d1b0475050056d2e3bc378014f2ea2230e8ae434eeac8dfb182aa8efaf642",
- "sha256:9ed23a03b7d9f0e29ca0679eafe5152aeccb0580312a3fc36f0662e178b4791b",
- "sha256:a4daf2751a98c69d9620717826ed6c5743b662ef0ae7bb33dc6c205425e48eba",
- "sha256:a64e72d2c144630e017ac9c1c416ddf8ac43bef9a083bf81fe08c0695f0baa95",
- "sha256:a791f002d1b717268235cfae7e4957b7fd132e92e2c5400e521bf191f1b3a9a5",
- "sha256:b4cba644e2515d685d4ee3ca2fbb5d53930a0e9ec2cf332ed704dc341b145878",
- "sha256:b9a22cf1adaae7b2ba2ed7d8651a4193a4f348744925b4b740e6b38a94599c5b",
- "sha256:bb7d206fa5ba6e082ba5d5e1b7107731029fc3a55c71c48de65121710d817986",
- "sha256:cf694159528022daa71b1777cb6ec9e0ebbdd29859f3e9c845826cafaef4ca29",
- "sha256:d0184aa88865339d96f7f452e8c5b621186ef7638744d78bf9b775d67e206819",
- "sha256:d272c7e173c3085308345ccc7fb2ad6ce7f415d777791dd6ce4e8140e354d09c",
- "sha256:d2cc7906bc0afdd2689aaf88b910307333b1f936262d1d98f25dbf8a5eb2e829",
- "sha256:e03dae26084bb3632b4a77b1cd0419159d2226911aff6dc4c7e3058df68648c6",
- "sha256:e176249292eccd89f81d39f514f2b5e8c75dfc9cef8653bdc3021d06697e9eff",
- "sha256:ebb183ed8b789cece0bd6363121913fb6da4034af89a2fa5408e42a1592889a8",
- "sha256:fb8cd6559f0ae3a8f5e146f80ab2a90ad0325a759be8d48ee82758a0b89fa0aa"
+ "sha256:099634631b9dd271d4a835d2b2a9e042ccc94ecdf7e2dd9f7f34f7daf333358d",
+ "sha256:0c555e039d268445172b909b1b6bdcba42ada1cf4a60e367d68702e3f87e5f64",
+ "sha256:1e677bfb2b4bd0e5e99e0f7283e65e47a9814b0486cb64a41adf9ef110e078f2",
+ "sha256:2367d47816cc9783a28645bc1dac07f8ffc93e0f015e8c9fc674a5b76a6da6e4",
+ "sha256:28d072169fe8275fb1a0d35e3233f6df36a7e8474e56cb790a7258ad822b6fd6",
+ "sha256:31f0e3147375002aae30696dd1dc596636abbd22fca09d2e730ecde0baad1d6b",
+ "sha256:3e0ad3c6ea4bd6a289d958a1eb922767233f00982cf0fe42b177657c86c80a8f",
+ "sha256:45b4afb069039f0366a43a5d454bc54eea942bfb66b3fc3e9a2c07ef4d617380",
+ "sha256:4a2a6ba400d386e904fd05db81f73bee0008af37799a7586deaa4aef8cd5971e",
+ "sha256:4f520d9ac5b938e6494f58a25c77564beca7d0199ecf726e1bd3d56872c59749",
+ "sha256:52a6e0a7a0bf611c19bc8ec8f7592bdae79c8296c70eb05917fd831354699b20",
+ "sha256:5a4788036201c908079e89ae3f5399b33bf45b9ea4514913f4dbbe4fac08efe0",
+ "sha256:6b4f04b1fbc01a3569d63359f2227c89ab294550de277fd09d8fca6185669fa4",
+ "sha256:715b41c3e231f7334cbe79dfc698213dcb7211520ec7a3bc2ba20c8515e8a3b5",
+ "sha256:73121a9b7ff93ada888aaee3985a88495489cc027894458cb1a736660bdfb206",
+ "sha256:74ae2441731a05b44d5988d3ac2cf784d3ee0a535dbed257cbfff4be8bb49eb9",
+ "sha256:7d6166192dcd925c78a91d599b48960e0a46fe565391c79fe6de481ac44d20ac",
+ "sha256:7f193f060391a455920d61684a70017ef5284ccbe6023bb056e15e5ac3de11d1",
+ "sha256:907fa0b662dd8fc1d7c661b90782ce81afb510fc4b7aa6ae7304d6c094b27bce",
+ "sha256:93156dd7f90ae0a1b0e8871032a07ef3178f553f0c70c386025a808f3a63b1f4",
+ "sha256:93bc9e5aaa06ff928d751dc6be889ff3e7d2aa393ab873bc7f6396a99f6fbb12",
+ "sha256:95db0c6581a54b47c30860d013977b8a14febc206c8b5ff562f9fe32738a8aca",
+ "sha256:973d030180eca8255b1bce6ffc09ef38a05dcec0e8320cc9b7bcaa65346f341d",
+ "sha256:9cd7a6beec6495d1dffb1033d50a3f82dfece23e9eb3c20cd3c2444d27514068",
+ "sha256:9fe9096a60113e1d755e9e6bda15ef7e03391ee0554d22829aa506cdf946f796",
+ "sha256:a209d2e624ba492df4f3bfad5996d1f76f03069c6133c60cd04f9a9e715595ec",
+ "sha256:a239afa1126b6a619130909c8404070e2b473dd2b7fc4aacacd2e763f8597fea",
+ "sha256:ba9f09ff17f947392a855e3455a846f9855f6cf6bec33e9a427d3c1d254c712f",
+ "sha256:bb7273789f69b565d88e97e9e1da602b4ee7ba733caf35a6c2affd4334d4f005",
+ "sha256:bd5bc124fae781a4422f61b98d1d7faa47985f663a64770b78f13d2c072410c2",
+ "sha256:bff98816cb144fb7b85e4b5ba3888a33b56ecef075b0e95b95bcd0a5fbf20f06",
+ "sha256:c4ee5a24e281fbd8261c6ab29faa7fd9a87a12e8c0eed485b705236c65999109",
+ "sha256:c93ed66d32de1559b6fc348838c7572d5c0ac1e4a258e76763a5caddd8944002",
+ "sha256:d1a24f51a3305362b94681120c508758a88f207fa0a681c16b5a4172e9e6c7a9",
+ "sha256:d8f191a17369bd53a5557a5ee4bab91d5330ca3aefcdf17fab9a497b0e7cff7a",
+ "sha256:daaef7390e632283051e3cf3e16aff2b68b247e99aea916f64e578c0449c9c68",
+ "sha256:e40013572bfb843d6794a3ce076c29ef4efd15937ab833f520117f8eccc84fd6",
+ "sha256:eceef49f457253000e6a2d0f7bd08ff4e9fe96ec4ffce2dbcb32e34d9c1b8161",
+ "sha256:ee595d7ba9bba130b2bec555a40aafa60c26ce68ed0cf509983e0f12d88674fd",
+ "sha256:ef50ec31649fbc3acf6afd261ed89d09eb909b97cc289d80476166df8438524d",
+ "sha256:fa1f3e34373aa16045484b4d9d352d4c6b5f9f77ac77a178252ccbc851e8b2ee",
+ "sha256:fca66d9ff2ac89b03f5aa17e0b21a97c21f3491c46b583bb131eb32c7bab33af"
],
"markers": "python_version >= '3.8'",
- "version": "==4.52.4"
+ "version": "==4.53.0"
},
"geojson": {
"hashes": [
@@ -667,98 +667,107 @@
},
"numpy": {
"hashes": [
- "sha256:03a8c78d01d9781b28a6989f6fa1bb2c4f2d51201cf99d3dd875df6fbd96b23b",
- "sha256:08beddf13648eb95f8d867350f6a018a4be2e5ad54c8d8caed89ebca558b2818",
- "sha256:1af303d6b2210eb850fcf03064d364652b7120803a0b872f5211f5234b399f20",
- "sha256:1dda2e7b4ec9dd512f84935c5f126c8bd8b9f2fc001e9f54af255e8c5f16b0e0",
- "sha256:2a02aba9ed12e4ac4eb3ea9421c420301a0c6460d9830d74a9df87efa4912010",
- "sha256:2e4ee3380d6de9c9ec04745830fd9e2eccb3e6cf790d39d7b98ffd19b0dd754a",
- "sha256:3373d5d70a5fe74a2c1bb6d2cfd9609ecf686d47a2d7b1d37a8f3b6bf6003aea",
- "sha256:47711010ad8555514b434df65f7d7b076bb8261df1ca9bb78f53d3b2db02e95c",
- "sha256:4c66707fabe114439db9068ee468c26bbdf909cac0fb58686a42a24de1760c71",
- "sha256:50193e430acfc1346175fcbdaa28ffec49947a06918b7b92130744e81e640110",
- "sha256:52b8b60467cd7dd1e9ed082188b4e6bb35aa5cdd01777621a1658910745b90be",
- "sha256:60dedbb91afcbfdc9bc0b1f3f402804070deed7392c23eb7a7f07fa857868e8a",
- "sha256:62b8e4b1e28009ef2846b4c7852046736bab361f7aeadeb6a5b89ebec3c7055a",
- "sha256:666dbfb6ec68962c033a450943ded891bed2d54e6755e35e5835d63f4f6931d5",
- "sha256:675d61ffbfa78604709862923189bad94014bef562cc35cf61d3a07bba02a7ed",
- "sha256:679b0076f67ecc0138fd2ede3a8fd196dddc2ad3254069bcb9faf9a79b1cebcd",
- "sha256:7349ab0fa0c429c82442a27a9673fc802ffdb7c7775fad780226cb234965e53c",
- "sha256:7ab55401287bfec946ced39700c053796e7cc0e3acbef09993a9ad2adba6ca6e",
- "sha256:7e50d0a0cc3189f9cb0aeb3a6a6af18c16f59f004b866cd2be1c14b36134a4a0",
- "sha256:95a7476c59002f2f6c590b9b7b998306fba6a5aa646b1e22ddfeaf8f78c3a29c",
- "sha256:96ff0b2ad353d8f990b63294c8986f1ec3cb19d749234014f4e7eb0112ceba5a",
- "sha256:9fad7dcb1aac3c7f0584a5a8133e3a43eeb2fe127f47e3632d43d677c66c102b",
- "sha256:9ff0f4f29c51e2803569d7a51c2304de5554655a60c5d776e35b4a41413830d0",
- "sha256:a354325ee03388678242a4d7ebcd08b5c727033fcff3b2f536aea978e15ee9e6",
- "sha256:a4abb4f9001ad2858e7ac189089c42178fcce737e4169dc61321660f1a96c7d2",
- "sha256:ab47dbe5cc8210f55aa58e4805fe224dac469cde56b9f731a4c098b91917159a",
- "sha256:afedb719a9dcfc7eaf2287b839d8198e06dcd4cb5d276a3df279231138e83d30",
- "sha256:b3ce300f3644fb06443ee2222c2201dd3a89ea6040541412b8fa189341847218",
- "sha256:b97fe8060236edf3662adfc2c633f56a08ae30560c56310562cb4f95500022d5",
- "sha256:bfe25acf8b437eb2a8b2d49d443800a5f18508cd811fea3181723922a8a82b07",
- "sha256:cd25bcecc4974d09257ffcd1f098ee778f7834c3ad767fe5db785be9a4aa9cb2",
- "sha256:d209d8969599b27ad20994c8e41936ee0964e6da07478d6c35016bc386b66ad4",
- "sha256:d5241e0a80d808d70546c697135da2c613f30e28251ff8307eb72ba696945764",
- "sha256:edd8b5fe47dab091176d21bb6de568acdd906d1887a4584a15a9a96a1dca06ef",
- "sha256:f870204a840a60da0b12273ef34f7051e98c3b5961b61b0c2c1be6dfd64fbcd3",
- "sha256:ffa75af20b44f8dba823498024771d5ac50620e6915abac414251bd971b4529f"
+ "sha256:04494f6ec467ccb5369d1808570ae55f6ed9b5809d7f035059000a37b8d7e86f",
+ "sha256:0a43f0974d501842866cc83471bdb0116ba0dffdbaac33ec05e6afed5b615238",
+ "sha256:0e50842b2295ba8414c8c1d9d957083d5dfe9e16828b37de883f51fc53c4016f",
+ "sha256:0ec84b9ba0654f3b962802edc91424331f423dcf5d5f926676e0150789cb3d95",
+ "sha256:17067d097ed036636fa79f6a869ac26df7db1ba22039d962422506640314933a",
+ "sha256:1cde1753efe513705a0c6d28f5884e22bdc30438bf0085c5c486cdaff40cd67a",
+ "sha256:1e72728e7501a450288fc8e1f9ebc73d90cfd4671ebbd631f3e7857c39bd16f2",
+ "sha256:2635dbd200c2d6faf2ef9a0d04f0ecc6b13b3cad54f7c67c61155138835515d2",
+ "sha256:2ce46fd0b8a0c947ae047d222f7136fc4d55538741373107574271bc00e20e8f",
+ "sha256:34f003cb88b1ba38cb9a9a4a3161c1604973d7f9d5552c38bc2f04f829536609",
+ "sha256:354f373279768fa5a584bac997de6a6c9bc535c482592d7a813bb0c09be6c76f",
+ "sha256:38ecb5b0582cd125f67a629072fed6f83562d9dd04d7e03256c9829bdec027ad",
+ "sha256:3e8e01233d57639b2e30966c63d36fcea099d17c53bf424d77f088b0f4babd86",
+ "sha256:3f6bed7f840d44c08ebdb73b1825282b801799e325bcbdfa6bc5c370e5aecc65",
+ "sha256:4554eb96f0fd263041baf16cf0881b3f5dafae7a59b1049acb9540c4d57bc8cb",
+ "sha256:46e161722e0f619749d1cd892167039015b2c2817296104487cd03ed4a955995",
+ "sha256:49d9f7d256fbc804391a7f72d4a617302b1afac1112fac19b6c6cec63fe7fe8a",
+ "sha256:4d2f62e55a4cd9c58c1d9a1c9edaedcd857a73cb6fda875bf79093f9d9086f85",
+ "sha256:5f64641b42b2429f56ee08b4f427a4d2daf916ec59686061de751a55aafa22e4",
+ "sha256:63b92c512d9dbcc37f9d81b123dec99fdb318ba38c8059afc78086fe73820275",
+ "sha256:6d7696c615765091cc5093f76fd1fa069870304beaccfd58b5dcc69e55ef49c1",
+ "sha256:79e843d186c8fb1b102bef3e2bc35ef81160ffef3194646a7fdd6a73c6b97196",
+ "sha256:821eedb7165ead9eebdb569986968b541f9908979c2da8a4967ecac4439bae3d",
+ "sha256:84554fc53daa8f6abf8e8a66e076aff6ece62de68523d9f665f32d2fc50fd66e",
+ "sha256:8d83bb187fb647643bd56e1ae43f273c7f4dbcdf94550d7938cfc32566756514",
+ "sha256:903703372d46bce88b6920a0cd86c3ad82dae2dbef157b5fc01b70ea1cfc430f",
+ "sha256:9416a5c2e92ace094e9f0082c5fd473502c91651fb896bc17690d6fc475128d6",
+ "sha256:9a1712c015831da583b21c5bfe15e8684137097969c6d22e8316ba66b5baabe4",
+ "sha256:9c27f0946a3536403efb0e1c28def1ae6730a72cd0d5878db38824855e3afc44",
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"markers": "python_version >= '3.9'",
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+ "version": "==2.0.0"
},
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],
"markers": "python_version >= '3.8'",
- "version": "==3.10.3"
+ "version": "==3.10.5"
},
"osmpythontools": {
"hashes": [
@@ -769,11 +778,11 @@
},
"packaging": {
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- "sha256:2ddfb553fdf02fb784c234c7ba6ccc288296ceabec964ad2eae3777778130bc5",
- "sha256:eb82c5e3e56209074766e6885bb04b8c38a0c015d0a30036ebe7ece34c9989e9"
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+ "sha256:5b8f2217dbdbd2f7f384c41c628544e6d52f2d0f53c6d0c3ea61aa5d1d7ff124"
],
- "markers": "python_version >= '3.7'",
- "version": "==24.0"
+ "markers": "python_version >= '3.8'",
+ "version": "==24.1"
},
"pandas": {
"hashes": [
@@ -887,96 +896,97 @@
},
"pydantic": {
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],
+ "index": "pypi",
"markers": "python_version >= '3.8'",
- "version": "==2.7.2"
+ "version": "==2.7.4"
},
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+ "sha256:f76d0ad001edd426b92233d45c746fd08f467d56100fd8f30e9ace4b005266e4",
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],
"markers": "python_version >= '3.8'",
- "version": "==2.18.3"
+ "version": "==2.18.4"
},
"pygments": {
"hashes": [
@@ -1120,6 +1130,54 @@
"markers": "python_version >= '3.9'",
"version": "==1.13.1"
},
+ "shapely": {
+ "hashes": [
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+ "sha256:fb5cdcbbe3080181498931b52a91a21a781a35dcb859da741c0345c6402bf00c"
+ ],
+ "index": "pypi",
+ "markers": "python_version >= '3.7'",
+ "version": "==2.0.4"
+ },
"shellingham": {
"hashes": [
"sha256:7ecfff8f2fd72616f7481040475a65b2bf8af90a56c89140852d1120324e8686",
@@ -1170,11 +1228,11 @@
},
"typing-extensions": {
"hashes": [
- "sha256:8cbcdc8606ebcb0d95453ad7dc5065e6237b6aa230a31e81d0f440c30fed5fd8",
- "sha256:b349c66bea9016ac22978d800cfff206d5f9816951f12a7d0ec5578b0a819594"
+ "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d",
+ "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"
],
"markers": "python_version >= '3.8'",
- "version": "==4.12.0"
+ "version": "==4.12.2"
},
"tzdata": {
"hashes": [
@@ -1273,11 +1331,11 @@
"standard"
],
"hashes": [
- "sha256:78fa0b5f56abb8562024a59041caeb555c86e48d0efdd23c3fe7de7a4075bdab",
- "sha256:f678dec4fa3a39706bbf49b9ec5fc40049d42418716cea52b53f07828a60aa37"
+ "sha256:cd17daa7f3b9d7a24de3617820e634d0933b69eed8e33a516071174427238c81",
+ "sha256:d46cd8e0fd80240baffbcd9ec1012a712938754afcf81bce56c024c1656aece8"
],
"markers": "python_version >= '3.8'",
- "version": "==0.30.0"
+ "version": "==0.30.1"
},
"uvloop": {
"hashes": [
@@ -1474,11 +1532,11 @@
},
"xarray": {
"hashes": [
- "sha256:7ddedfe2294a0ab00f02d0fbdcb9c6300ec589f3cf436a9c7b7b577a12cd9bcf",
- "sha256:e0eb1cb265f265126795f388ed9591f3c752f2aca491f6c0576711fd15b708f2"
+ "sha256:0b91e0bc4dc0296947947640fe31ec6e867ce258d2f7cbc10bedf4a6d68340c7",
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],
"markers": "python_version >= '3.9'",
- "version": "==2024.5.0"
+ "version": "==2024.6.0"
}
},
"develop": {}
diff --git a/backend/app/__pycache__/__init__.cpython-310.pyc b/backend/app/__pycache__/__init__.cpython-310.pyc
deleted file mode 100644
index 678d7d0..0000000
Binary files a/backend/app/__pycache__/__init__.cpython-310.pyc and /dev/null differ
diff --git a/backend/app/__pycache__/main.cpython-310.pyc b/backend/app/__pycache__/main.cpython-310.pyc
deleted file mode 100644
index cd67059..0000000
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diff --git a/backend/app/dependencies.py b/backend/app/dependencies.py
deleted file mode 100644
index 2cb6801..0000000
--- a/backend/app/dependencies.py
+++ /dev/null
@@ -1 +0,0 @@
-import app.src
\ No newline at end of file
diff --git a/backend/app/main.py b/backend/app/main.py
deleted file mode 100644
index 257d572..0000000
--- a/backend/app/main.py
+++ /dev/null
@@ -1,34 +0,0 @@
-from .src.optimizer import solve_optimization
-from .src.landmarks_manager import Landmark
-from fastapi import FastAPI
-
-app = FastAPI()
-
-
-@app.get("/optimize/{max_steps}/{print_details}")
-def main(max_steps: int, print_details: bool):
-
- # CONSTRAINT TO RESPECT MAX NUMBER OF STEPS
- #max_steps = 16
-
-
- # Initialize all landmarks (+ start and goal). Order matters here
- landmarks = []
- landmarks.append(Landmark("départ", -1, (0, 0)))
- landmarks.append(Landmark("tour eiffel", 99, (0,2))) # PUT IN JSON
- landmarks.append(Landmark("arc de triomphe", 99, (0,4)))
- landmarks.append(Landmark("louvre", 99, (0,6)))
- landmarks.append(Landmark("montmartre", 99, (0,10)))
- landmarks.append(Landmark("concorde", 99, (0,8)))
- landmarks.append(Landmark("arrivée", -1, (0, 0)))
-
-
- visiting_order = solve_optimization(landmarks, max_steps, print_details)
-
- #return visiting_order
-
- return("max steps :", max_steps, "\n", visiting_order)
-
-
-"""if __name__ == "__main__":
- main()"""
\ No newline at end of file
diff --git a/backend/app/src/__init__.py b/backend/app/src/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/backend/app/src/__pycache__/__init__.cpython-310.pyc b/backend/app/src/__pycache__/__init__.cpython-310.pyc
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index 3c06a08..0000000
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diff --git a/backend/app/src/__pycache__/optimizer.cpython-310.pyc b/backend/app/src/__pycache__/optimizer.cpython-310.pyc
deleted file mode 100644
index e60b82c..0000000
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diff --git a/backend/app/src/landmarks_manager.py b/backend/app/src/landmarks_manager.py
deleted file mode 100644
index 0c3a0e2..0000000
--- a/backend/app/src/landmarks_manager.py
+++ /dev/null
@@ -1,57 +0,0 @@
-from OSMPythonTools.api import Api
-from OSMPythonTools.overpass import Overpass
-from dataclasses import dataclass
-
-
-# Defines the landmark class (aka some place there is to visit)
-@dataclass
-class Landmarkkkk :
- name : str
- attractiveness : int
- id : int
-
-@dataclass
-class Landmark :
- name : str
- attractiveness : int
- loc : tuple
-
-# Converts a OSM id to a landmark
-def add_from_id(id: int, score: int) :
-
- try :
- s = 'way/' + str(id) # prepare string for query
- obj = api.query(s) # object to add
- except :
- s = 'relation/' + str(id) # prepare string for query
- obj = api.query(s) # object to add
-
- return Landmarkkkk(obj.tag('name:fr'), score, id) # create Landmark out of it
-
-
-# take a lsit of tuples (id, score) to generate a list of landmarks
-def generate_landmarks(ids_and_scores: list) :
-
- L = []
- for tup in ids_and_scores :
- L.append(add_from_id(tup[0], tup[1]))
-
- return L
-
-api = Api()
-
-
-l = (7515426, 70)
-t = (5013364, 100)
-n = (201611261, 99)
-a = (226413508, 50)
-m = (23762981, 30)
-
-
-ids_and_scores = [t, l, n, a, m]
-
-landmarks = generate_landmarks(ids_and_scores)
-
-
-for obj in landmarks :
- print(obj)
\ No newline at end of file
diff --git a/backend/app/src/optimizer.py b/backend/app/src/optimizer.py
deleted file mode 100644
index d74d1b9..0000000
--- a/backend/app/src/optimizer.py
+++ /dev/null
@@ -1,323 +0,0 @@
-from scipy.optimize import linprog
-import numpy as np
-from scipy.linalg import block_diag
-
-
-# landmarks = [Landmark_1, Landmark_2, ...]
-
-# Convert the solution of the optimization into the list of edges to follow. Order is taken into account
-def untangle(resx: list) :
- 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
-
- order = []
- nonzeroind = np.nonzero(resx)[0] # the return is a little funny so I use the [0]
-
- nonzero_tup = np.unravel_index(nonzeroind, (L,L))
-
- indx = nonzero_tup[0].tolist()
- indy = nonzero_tup[1].tolist()
-
- vert = (indx[0], indy[0])
-
- order.append(vert[0])
- order.append(vert[1])
-
- while len(order) < n_edges + 1 :
- ind = indx.index(vert[1])
-
- vert = (indx[ind], indy[ind])
-
- order.append(vert[1])
-
- return order
-
-# Just to print the result
-def print_res(res, landmarks: list, P) :
- X = abs(res.x)
- order = untangle(X)
- things = []
-
- """N = int(np.sqrt(len(X)))
- for i in range(N):
- print(X[i*N:i*N+N])
- print("Optimal value:", -res.fun) # Minimization, so we negate to get the maximum
- print("Optimal point:", res.x)
- for i,x in enumerate(X) : X[i] = round(x,0)
- print(order)"""
-
- if (X.sum()+1)**2 == len(X) :
- 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 : ')
-
- for idx in order :
- print('- ' + landmarks[idx].name)
- things.append(landmarks[idx].name)
-
- steps = path_length(P, abs(res.x))
- print("\nSteps walked : " + str(steps))
-
- return things
-
-# Checks for cases of circular symmetry in the result
-def has_circle(resx: list) :
- 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))
-
- indx = nonzero_tup[0].tolist()
- indy = nonzero_tup[1].tolist()
-
-
- verts = []
-
- for i, x in enumerate(indx) :
- verts.append((x, indy[i]))
-
-
- for vert in verts :
- visited = []
- visited.append(vert)
-
- while len(visited) < n_edges + 1 :
-
- try :
- ind = indx.index(vert[1])
-
- vert = (indx[ind], indy[ind])
-
- if vert in visited :
- return visited
- else :
- visited.append(vert)
- except :
- break
-
- return []
-
-# Constraint to not have d14 and d41 simultaneously. Does not prevent circular symmetry with more elements
-def break_sym(landmarks, A_ub, b_ub):
- L = len(landmarks)
- 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)
-
- """for i in range(7):
- print(l[i*7:i*7+7])
- print("\n")"""
-
- return A_ub, b_ub
-
-# Constraint to not have circular paths. Want to go from start -> finish without unconnected loops
-def break_circle(landmarks, A_ub, b_ub, circle) :
- N = len(landmarks)
- l = [0]*N*N
-
- for index in circle :
- x = index[0]
- y = index[1]
- l[x*N+y] = 1
-
- A_ub = np.vstack((A_ub,l))
- b_ub.append(len(circle)-1)
-
- """print("\n\nPREVENT CIRCLE")
- for i in range(7):
- print(l[i*7:i*7+7])
- print("\n")"""
-
- return A_ub, b_ub
-
-# Constraint to respect max number of travels
-def respect_number(landmarks, A_ub, b_ub):
- h = []
- for i in range(len(landmarks)) : h.append([1]*len(landmarks))
- T = block_diag(*h)
- """for l in T :
- for i in range(7):
- print(l[i*7:i*7+7])
- print("\n")"""
- return np.vstack((A_ub, T)), b_ub + [1]*len(landmarks)
-
-# Constraint to tie the problem together. Necessary but not sufficient to avoid circles
-def respect_order(landmarks: list, A_eq, b_eq):
- N = len(landmarks)
- for i in range(N-1) : # Prevent stacked ones
- if i == 0 :
- 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)
-
- """for i in range(7):
- print(l[i*7:i*7+7])
- print("\n")"""
-
- return A_eq, b_eq
-
-# Compute manhattan distance between 2 locations
-def manhattan_distance(loc1: tuple, loc2: tuple):
- x1, y1 = loc1
- x2, y2 = loc2
- return abs(x1 - x2) + abs(y1 - y2)
-
-# Constraint to not stay in position
-def init_eq_not_stay(landmarks):
- L = len(landmarks)
- l = [0]*L*L
-
-
- for i in range(L) :
- for j in range(L) :
- if j == i :
- l[j + i*L] = 1
- l[L-1] = 1 # cannot skip from start to finish
- #A_eq = np.array([np.array(xi) for xi in A_eq]) # Must convert A_eq into an np array
- l = np.array(np.array(l))
-
- """for i in range(7):
- print(l[i*7:i*7+7])"""
-
- return [l], [0]
-
-# 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, max_steps: int):
- # Objective function coefficients. a*x1 + b*x2 + c*x3 + ...
- c = []
- # Coefficients of inequality constraints (left-hand side)
- A = []
- for i, spot1 in enumerate(landmarks) :
- dist_table = [0]*len(landmarks)
- c.append(-spot1.attractiveness)
- for j, spot2 in enumerate(landmarks) :
- dist_table[j] = manhattan_distance(spot1.loc, spot2.loc)
- A.append(dist_table)
- c = c*len(landmarks)
- A_ub = []
- for line in A :
- #print(line)
- A_ub += line
- return c, A_ub, [max_steps]
-
-# Go through the landmarks and force the optimizer to use landmarks where attractiveness is set to -1
-def respect_user_mustsee(landmarks: list, A_eq: list, b_eq: list) :
- L = len(landmarks)
- H = 0 # sort of heuristic to get an idea of the number of steps needed
- for i in landmarks :
- if i.name == "départ" : elem_prev = i # list of all matches
- for i, elem in enumerate(landmarks) :
- if elem.attractiveness == -1 :
- l = [0]*L*L
- if elem.name != "arrivée" :
- for j in range(L) :
- l[j +i*L] = 1
-
- else : # This ensures we go to goal
- for k in range(L-1) :
- l[k*L+L-1] = 1
-
- H += manhattan_distance(elem.loc, elem_prev.loc)
- elem_prev = elem
-
- """for i in range(7):
- print(l[i*7:i*7+7])
- print("\n")"""
-
- A_eq = np.vstack((A_eq,l))
- b_eq.append(1)
- return A_eq, b_eq, H
-
-# Computes the path length given path matrix (dist_table) and a result
-def path_length(P: list, resx: list) :
- return np.dot(P, resx)
-
-# Main optimization pipeline
-def solve_optimization (landmarks, max_steps, printing_details) :
-
- # SET CONSTRAINTS FOR INEQUALITY
- c, A_ub, b_ub = init_ub_dist(landmarks, max_steps) # Add the distances from each landmark to the other
- P = A_ub # store the paths for later. Needed to compute path length
- A_ub, b_ub = respect_number(landmarks, A_ub, b_ub) # Respect max number of visits.
-
- # TODO : Problems with circular symmetry
- A_ub, b_ub = break_sym(landmarks, A_ub, b_ub) # break the symmetry. Only use the upper diagonal values
-
- # SET CONSTRAINTS FOR EQUALITY
- A_eq, b_eq = init_eq_not_stay(landmarks) # Force solution not to stay in same place
- A_eq, b_eq, H = 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_order(landmarks, A_eq, b_eq) # Respect order of visit (only works when max_steps is limiting factor)
-
- # Bounds for variables (x can only be 0 or 1)
- x_bounds = [(0, 1)] * len(c)
-
- # 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 :
-
- # Override the max_steps using the heuristic
- for i, val in enumerate(b_ub) :
- if val == max_steps : b_ub[i] = H
-
- # Solve problem again :
- 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)
-
- if not res.success :
- s = "No solution could be found, even when increasing max_steps using the heuristic"
- return s
- #raise ValueError("No solution could be found, even when increasing max_steps using the heuristic")
-
- # If there is a solution, we're good to go, just check for
- else :
- circle = has_circle(res.x)
- i = 0
-
- # Break the circular symmetry if needed
- while len(circle) != 0 :
- A_ub, b_ub = break_circle(landmarks, A_ub, b_ub, circle)
- 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)
- circle = has_circle(res.x)
- i += 1
-
- if printing_details is True :
- if i != 0 :
- print(f"Neded to recompute paths {i} times because of unconnected loops...")
- X = print_res(res, landmarks, P)
- return X
- else :
- return untangle(res.x)
-
-
-
-
-
-
diff --git a/backend/src/amenities/nature.am b/backend/src/amenities/nature.am
new file mode 100644
index 0000000..dcc4061
--- /dev/null
+++ b/backend/src/amenities/nature.am
@@ -0,0 +1,11 @@
+'leisure'='park'
+geological
+'natural'='geyser'
+'natural'='hot_spring'
+'natural'='arch'
+'natural'='volcano'
+'natural'='stone'
+'tourism'='alpine_hut'
+'tourism'='viewpoint'
+'tourism'='zoo'
+'waterway'='waterfall'
\ No newline at end of file
diff --git a/backend/src/amenities/shopping.am b/backend/src/amenities/shopping.am
new file mode 100644
index 0000000..14504a0
--- /dev/null
+++ b/backend/src/amenities/shopping.am
@@ -0,0 +1,2 @@
+'shop'='department_store'
+'shop'='mall'
\ No newline at end of file
diff --git a/backend/src/amenities/sightseeing.am b/backend/src/amenities/sightseeing.am
new file mode 100644
index 0000000..33bcb1a
--- /dev/null
+++ b/backend/src/amenities/sightseeing.am
@@ -0,0 +1,8 @@
+'tourism'='museum'
+'tourism'='attraction'
+'tourism'='gallery'
+historic
+'amenity'='planetarium'
+'amenity'='place_of_worship'
+'amenity'='fountain'
+'water'='reflecting_pool'
\ No newline at end of file
diff --git a/backend/src/landmarks_manager.py b/backend/src/landmarks_manager.py
new file mode 100644
index 0000000..618e169
--- /dev/null
+++ b/backend/src/landmarks_manager.py
@@ -0,0 +1,365 @@
+import math as m
+import json, os
+
+from typing import List, Tuple, Optional
+from OSMPythonTools.overpass import Overpass, overpassQueryBuilder
+
+from structs.landmarks import Landmark, LandmarkType
+from structs.preferences import Preferences, Preference
+
+
+SIGHTSEEING = LandmarkType(landmark_type='sightseeing')
+NATURE = LandmarkType(landmark_type='nature')
+SHOPPING = LandmarkType(landmark_type='shopping')
+
+
+# Include the json here
+# Create a list of all things to visit given some preferences and a city. Ready for the optimizer
+def generate_landmarks(preferences: Preferences, coordinates: Tuple[float, float]) :
+
+ l_sights, l_nature, l_shop = get_amenities()
+ L = []
+
+ # List for sightseeing
+ if preferences.sightseeing.score != 0 :
+ L1 = get_landmarks(l_sights, SIGHTSEEING, coordinates=coordinates)
+ correct_score(L1, preferences.sightseeing)
+ L += L1
+
+ # List for nature
+ if preferences.nature.score != 0 :
+ L2 = get_landmarks(l_nature, NATURE, coordinates=coordinates)
+ correct_score(L2, preferences.nature)
+ L += L2
+
+ # List for shopping
+ if preferences.shopping.score != 0 :
+ L3 = get_landmarks(l_shop, SHOPPING, coordinates=coordinates)
+ correct_score(L3, preferences.shopping)
+ L += L3
+
+ L = remove_duplicates(L)
+
+ return L, take_most_important(L)
+
+
+"""def generate_landmarks(preferences: Preferences, city_country: str = None, coordinates: Tuple[float, float] = None) -> Tuple[List[Landmark], List[Landmark]] :
+
+ l_sights, l_nature, l_shop = get_amenities()
+ L = []
+
+ # List for sightseeing
+ if preferences.sightseeing.score != 0 :
+ L1 = get_landmarks(l_sights, SIGHTSEEING, city_country=city_country, coordinates=coordinates)
+ correct_score(L1, preferences.sightseeing)
+ L += L1
+
+ # List for nature
+ if preferences.nature.score != 0 :
+ L2 = get_landmarks(l_nature, NATURE, city_country=city_country, coordinates=coordinates)
+ correct_score(L2, preferences.nature)
+ L += L2
+
+ # List for shopping
+ if preferences.shopping.score != 0 :
+ L3 = get_landmarks(l_shop, SHOPPING, city_country=city_country, coordinates=coordinates)
+ correct_score(L3, preferences.shopping)
+ L += L3
+
+ return remove_duplicates(L), take_most_important(L)
+"""
+# Helper function to gather the amenities list
+def get_amenities() -> List[List[str]] :
+
+ # Get the list of amenities from the files
+ sightseeing = get_list('/amenities/sightseeing.am')
+ nature = get_list('/amenities/nature.am')
+ shopping = get_list('/amenities/shopping.am')
+
+ return sightseeing, nature, shopping
+
+
+# Helper function to read a .am file and generate the corresponding list
+def get_list(path: str) -> List[str] :
+
+ with open(os.path.dirname(os.path.abspath(__file__)) + path) as f :
+ content = f.readlines()
+
+ amenities = []
+ for line in content :
+ amenities.append(line.strip('\n'))
+
+ return amenities
+
+
+# Take the most important landmarks from the list
+def take_most_important(L: List[Landmark], N = 0) -> List[Landmark] :
+
+ # Read the parameters from the file
+ with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/landmarks_manager.params', "r") as f :
+ parameters = json.loads(f.read())
+ N_important = parameters['N important']
+
+ L_copy = []
+ L_clean = []
+ scores = [0]*len(L)
+ names = []
+ name_id = {}
+
+ for i, elem in enumerate(L) :
+ if elem.name not in names :
+ names.append(elem.name)
+ name_id[elem.name] = [i]
+ L_copy.append(elem)
+ else :
+ name_id[elem.name] += [i]
+ scores = []
+ for j in name_id[elem.name] :
+ scores.append(L[j].attractiveness)
+ best_id = max(range(len(scores)), key=scores.__getitem__)
+ t = name_id[elem.name][best_id]
+ if t == i :
+ for old in L_copy :
+ if old.name == elem.name :
+ old.attractiveness = L[t].attractiveness
+
+ scores = [0]*len(L_copy)
+ for i, elem in enumerate(L_copy) :
+ scores[i] = elem.attractiveness
+
+ res = sorted(range(len(scores)), key = lambda sub: scores[sub])[-(N_important-N):]
+
+ for i, elem in enumerate(L_copy) :
+ if i in res :
+ L_clean.append(elem)
+
+ return L_clean
+
+
+# Remove duplicate elements and elements with low score
+def remove_duplicates(L: List[Landmark]) -> List[Landmark] :
+ """
+ Removes duplicate landmarks based on their names from the given list.
+
+ Parameters:
+ L (List[Landmark]): A list of Landmark objects.
+
+ Returns:
+ List[Landmark]: A list of unique Landmark objects based on their names.
+ """
+
+ L_clean = []
+ names = []
+
+ for landmark in L :
+ if landmark.name in names:
+ continue
+
+
+ else :
+ names.append(landmark.name)
+ L_clean.append(landmark)
+
+ return L_clean
+
+
+# Correct the score of a list of landmarks by taking into account preference settings
+def correct_score(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(coordinates: Tuple[float, float], radius: int) -> int:
+
+ lat = coordinates[0]
+ lon = coordinates[1]
+
+ 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)
+ return radius_result.countElements()
+
+ except :
+ return None
+
+
+# Creates a bounding box around given coordinates
+def create_bbox(coordinates: Tuple[float, float], side_length: int) -> Tuple[float, float, float, float]:
+
+ lat = coordinates[0]
+ lon = coordinates[1]
+
+ # Half the side length in km (since it's a square bbox)
+ half_side_length_km = side_length / 2.0
+
+ # Convert distance to degrees
+ lat_diff = half_side_length_km / 111 # 1 degree latitude is approximately 111 km
+ lon_diff = half_side_length_km / (111 * m.cos(m.radians(lat))) # Adjust for longitude based on latitude
+
+ # Calculate bbox
+ min_lat = lat - lat_diff
+ max_lat = lat + lat_diff
+ min_lon = lon - lon_diff
+ max_lon = lon + lon_diff
+
+ return min_lat, min_lon, max_lat, max_lon
+
+
+def get_landmarks(list_amenity: list, landmarktype: LandmarkType, coordinates: Tuple[float, float]) -> List[Landmark] :
+
+ # Read the parameters from the file
+ with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/landmarks_manager.params', "r") as f :
+ parameters = json.loads(f.read())
+ tag_coeff = parameters['tag coeff']
+ park_coeff = parameters['park coeff']
+ church_coeff = parameters['church coeff']
+ radius = parameters['radius close to']
+ bbox_side = parameters['city bbox side']
+
+ # Create bbox around start location
+ bbox = create_bbox(coordinates, bbox_side)
+
+ # 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()
+
+ for elem in result.elements():
+
+ name = elem.tag('name') # Add name
+ location = (elem.centerLat(), elem.centerLon()) # Add coordinates (lat, lon)
+
+ # skip if unprecise location
+ if name is None or location[0] is None:
+ continue
+
+ # skip if unused
+ if 'disused:leisure' in elem.tags().keys():
+ continue
+
+ # skip if part of another building
+ 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
+
+ # 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_within_radius(location, radius) + n_tags*tag_coeff )*church_coeff)
+ elif amenity == "'leisure'='park'" :
+ score = int((count_elements_within_radius(location, radius) + n_tags*tag_coeff )*park_coeff)
+ else :
+ score = count_elements_within_radius(location, radius) + n_tags*tag_coeff
+
+ if score is not None :
+ # 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=n_tags)
+ L.append(landmark)
+
+ return L
+
+
+
+"""def get_landmarks(list_amenity: list, landmarktype: LandmarkType, city_country: str = None, coordinates: Tuple[float, float] = None) -> List[Landmark] :
+
+ if city_country is None and coordinates is None :
+ raise ValueError("Either one of 'city_country' and 'coordinates' arguments must be specified")
+
+ if city_country is not None and coordinates is not None :
+ raise ValueError("Cannot specify both 'city_country' and 'coordinates' at the same time, please choose either one")
+
+ # Read the parameters from the file
+ with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/landmarks_manager.params', "r") as f :
+ parameters = json.loads(f.read())
+ tag_coeff = parameters['tag coeff']
+ park_coeff = parameters['park coeff']
+ church_coeff = parameters['church coeff']
+ radius = parameters['radius close to']
+ bbox_side = parameters['city bbox side']
+
+ # If city_country is specified :
+ if city_country is not None :
+ nominatim = Nominatim()
+ areaId = nominatim.query(city_country).areaId()
+ bbox = None
+
+ # If coordinates are specified :
+ elif coordinates is not None :
+ bbox = create_bbox(coordinates, bbox_side)
+ areaId = None
+
+ else :
+ raise ValueError("Argument number is not corresponding.")
+
+ # Initialize some variables
+ N = 0
+ L = []
+ overpass = Overpass()
+
+ for amenity in list_amenity :
+ query = overpassQueryBuilder(area=areaId, bbox=bbox, elementType=['way', 'relation'], selector=amenity, includeCenter=True, out='body')
+ result = overpass.query(query)
+ N += result.countElements()
+
+ for elem in result.elements():
+
+ name = elem.tag('name') # Add name
+ location = (elem.centerLat(), elem.centerLon()) # Add coordinates (lat, lon)
+
+ # skip if unprecise location
+ if name is None or location[0] is None:
+ continue
+
+ # skip if unused
+ if 'disused:leisure' in elem.tags().keys():
+ continue
+
+ # skip if part of another building
+ 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
+
+ # 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_within_radius(location, radius) + n_tags*tag_coeff )*church_coeff)
+ elif amenity == "'leisure'='park'" :
+ score = int((count_elements_within_radius(location, radius) + n_tags*tag_coeff )*park_coeff)
+ else :
+ score = count_elements_within_radius(location, radius) + n_tags*tag_coeff
+
+ if score is not None :
+ # 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=n_tags)
+ L.append(landmark)
+
+ return L
+"""
\ No newline at end of file
diff --git a/backend/src/main.py b/backend/src/main.py
new file mode 100644
index 0000000..692c7f9
--- /dev/null
+++ b/backend/src/main.py
@@ -0,0 +1,70 @@
+from optimizer import solve_optimization
+from refiner import refine_optimization
+from landmarks_manager import generate_landmarks
+from structs.landmarks import Landmark
+from structs.landmarktype import LandmarkType
+from structs.preferences import Preferences, Preference
+from fastapi import FastAPI, Query, Body
+from typing import List
+
+app = FastAPI()
+
+
+# Assuming frontend is calling like this :
+#"http://127.0.0.1:8000/process?param1={param1}¶m2={param2}"
+@app.post("/optimizer_coords/{start_lat}/{start_lon}/{finish_lat}/{finish_lon}")
+def main1(start_lat: float, start_lon: float, preferences: Preferences = Body(...), finish_lat: float = None, finish_lon: float = None) -> List[Landmark]:
+
+ if preferences is None :
+ raise ValueError("Please provide preferences in the form of a 'Preference' BaseModel class.")
+ if bool(start_lat) ^ bool(start_lon) :
+ raise ValueError("Please provide both latitude and longitude for the starting point")
+ if bool(finish_lat) ^ bool(finish_lon) :
+ raise ValueError("Please provide both latitude and longitude for the finish point")
+
+ start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(start_lat, start_lon), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+
+ if bool(finish_lat) and bool(finish_lon) :
+ finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(finish_lat, finish_lon), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+ else :
+ finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(start_lat, start_lon), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+
+
+ start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(48.8375946, 2.2949904), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+ finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.8375946, 2.2949904), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+
+ # Generate the landmarks from the start location
+ landmarks, landmarks_short = generate_landmarks(preferences=preferences, coordinates=start.location)
+
+ # 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 = 4 # 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 refined_tour
+
+
+
+# input city, country in the form of 'Paris, France'
+@app.post("/test2/{city_country}")
+def test2(city_country: str, preferences: Preferences = Body(...)) -> List[Landmark]:
+
+ landmarks = generate_landmarks(city_country, preferences)
+
+ max_steps = 9000000
+
+ visiting_order = solve_optimization(landmarks, max_steps, True)
+
+
+
+
diff --git a/backend/app/src/main_example.py b/backend/src/main_example.py
similarity index 100%
rename from backend/app/src/main_example.py
rename to backend/src/main_example.py
diff --git a/backend/src/optimizer.py b/backend/src/optimizer.py
new file mode 100644
index 0000000..d45d51b
--- /dev/null
+++ b/backend/src/optimizer.py
@@ -0,0 +1,411 @@
+import numpy as np
+import json, os
+
+from typing import List, Tuple
+from scipy.optimize import linprog
+from math import radians, sin, cos, acos
+from shapely import Polygon
+
+from structs.landmarks import Landmark
+
+
+# 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.name != 'start' :
+ print('- ' + elem.name + ', time to reach = ' + str(elem.time_to_reach))
+ dist += elem.time_to_reach
+ 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):
+
+ with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
+ parameters = json.loads(f.read())
+ 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 open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
+ parameters = json.loads(f.read())
+ 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 add_time_to_reach(order: List[int], landmarks: List[Landmark])->List[Landmark] :
+
+ # Read the parameters from the file
+ with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
+ parameters = json.loads(f.read())
+ detour_factor = parameters['detour factor']
+ speed = parameters['average walking speed']
+
+ j = 0
+ L = []
+ prev = landmarks[0]
+
+ while(len(L) != len(order)) :
+
+ elem = landmarks[order[j]]
+ if elem != prev :
+ elem.time_to_reach = get_distance(elem.location, prev.location, detour_factor, speed)[1]
+ elem.must_do = True
+ L.append(elem)
+ prev = elem
+ j += 1
+
+ return L
+
+
+def add_time_to_reach_simple(ordered_visit: List[Landmark])-> List[Landmark] :
+
+ # Read the parameters from the file
+ with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
+ parameters = json.loads(f.read())
+ detour_factor = parameters['detour factor']
+ speed = parameters['average walking speed']
+
+ L = []
+ prev = ordered_visit[0]
+ L.append(prev)
+ total_dist = 0
+
+ for elem in ordered_visit[1:] :
+ elem.time_to_reach = get_distance(elem.location, prev.location, detour_factor, speed)[1]
+ elem.must_do = True
+ L.append(elem)
+ prev = elem
+ total_dist += get_distance(elem.location, prev.location, detour_factor, speed)[1]
+
+
+ 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 = add_time_to_reach(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/parameters/landmarks_manager.params b/backend/src/parameters/landmarks_manager.params
new file mode 100644
index 0000000..942c214
--- /dev/null
+++ b/backend/src/parameters/landmarks_manager.params
@@ -0,0 +1,8 @@
+{
+ "city bbox side" : 10,
+ "radius close to" : 27.5,
+ "church coeff" : 0.6,
+ "park coeff" : 1.5,
+ "tag coeff" : 100,
+ "N important" : 40
+}
\ No newline at end of file
diff --git a/backend/src/parameters/optimizer.params b/backend/src/parameters/optimizer.params
new file mode 100644
index 0000000..18ca240
--- /dev/null
+++ b/backend/src/parameters/optimizer.params
@@ -0,0 +1,5 @@
+{
+ "detour factor" : 1.4,
+ "average walking speed" : 4.8,
+ "max landmarks" : 10
+}
\ No newline at end of file
diff --git a/backend/src/refiner.py b/backend/src/refiner.py
new file mode 100644
index 0000000..dec6101
--- /dev/null
+++ b/backend/src/refiner.py
@@ -0,0 +1,293 @@
+from collections import defaultdict
+from heapq import heappop, heappush
+from itertools import permutations
+import os, json
+
+from shapely import buffer, LineString, Point, Polygon, MultiPoint, convex_hull, concave_hull, LinearRing
+from typing import List, Tuple
+from math import pi
+
+from structs.landmarks import Landmark
+from landmarks_manager import take_most_important
+from optimizer import solve_optimization, add_time_to_reach_simple, print_res, get_distance
+
+
+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
+
+
+def create_linestring(landmarks: List[Landmark])->List[Point] :
+
+ points = []
+
+ for landmark in landmarks :
+ points.append(Point(landmark.location))
+
+ return LineString(points)
+
+
+def is_in_area(area: Polygon, coordinates) -> bool :
+ point = Point(coordinates)
+ return point.within(area)
+
+
+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))
+
+
+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
+
+"""
+def find_shortest_path(landmarks: List[Landmark]) -> List[Landmark]:
+
+ # Read from data
+ with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
+ parameters = json.loads(f.read())
+ detour = parameters['detour factor']
+ speed = parameters['average walking speed']
+
+ # 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 from data
+ with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
+ parameters = json.loads(f.read())
+ 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_distance(current_landmark.location, lm.location, detour, speed)[1])
+ 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
+
+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)
+
+ return take_most_important(second_order_landmarks, len(visited_landmarks))
+
+
+
+"""def refine_optimization(landmarks: List[Landmark], base_tour: List[Landmark], max_time: int, print_infos: bool) -> List[Landmark] :
+
+ minor_landmarks = get_minor_landmarks(landmarks, base_tour, 200)
+
+ if print_infos : print("There are " + str(len(minor_landmarks)) + " minor landmarks around the predicted path")
+
+ full_set = base_tour[:-1] + minor_landmarks # create full set of possible landmarks (without finish)
+ full_set.append(base_tour[-1]) # add finish back
+
+ new_tour = solve_optimization(full_set, max_time, print_infos)
+
+ return new_tour"""
+
+
+def refine_optimization(landmarks: List[Landmark], base_tour: List[Landmark], max_time: int, print_infos: bool) -> List[Landmark] :
+
+ # Read from the file
+ with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
+ parameters = json.loads(f.read())
+ max_landmarks = parameters['max landmarks']
+
+ if len(base_tour)-2 >= max_landmarks :
+ return base_tour
+
+
+ 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, False)
+ new_tour, new_dist = add_time_to_reach_simple(new_tour)
+
+ """#if base_tour[0].location == base_tour[-1].location :
+ if False :
+ coords = [] # Coordinates of the new tour
+ coords_dict = {} # maps the location of an element to the element itself. Used to access the elements back once we get the geometry
+
+ # Iterate through the new tour without finish
+ for elem in new_tour[:-1] :
+ coords.append(Point(elem.location))
+ coords_dict[elem.location] = elem # if start = goal, only finish remains
+
+ # Create a concave polygon using the coordinates
+ 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]) :
+ better_tour.append(coords_dict[tuple((x,ys[i]))])
+ name_index[coords_dict[tuple((x,ys[i]))].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(new_tour[-1])
+
+ # Rearrange only if polygon
+ better_tour = rearrange(better_tour)
+
+ # Add the time to reach
+ better_tour = add_time_to_reach_simple(better_tour)
+ """
+
+
+ """
+ if not better_poly.is_simple :
+
+ coords_dict = {}
+ better_tour2 = []
+ for elem in better_tour :
+ coords_dict[elem.location] = elem
+
+ better_poly2 = better_poly.buffer(0)
+ new_coords = better_poly2.exterior.coords[:]
+ start_coords = base_tour[0].location
+ start_index = new_coords.
+
+ #for point in new_coords :
+ """
+
+
+ better_tour, better_poly = find_shortest_path_through_all_landmarks(new_tour)
+ better_tour, better_dist = add_time_to_reach_simple(better_tour)
+
+ if new_dist < better_dist :
+ final_tour = new_tour
+ else :
+ final_tour = better_tour
+
+ if print_infos :
+ print("\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n")
+ print("\nRefined tour (result of second stage optimization): ")
+ print_res(final_tour, len(full_set))
+
+
+
+ return final_tour
diff --git a/backend/app/__init__.py b/backend/src/structs/__init__.py
similarity index 100%
rename from backend/app/__init__.py
rename to backend/src/structs/__init__.py
diff --git a/backend/src/structs/landmarks.py b/backend/src/structs/landmarks.py
new file mode 100644
index 0000000..94209f3
--- /dev/null
+++ b/backend/src/structs/landmarks.py
@@ -0,0 +1,20 @@
+from typing import Optional
+from pydantic import BaseModel
+
+from .landmarktype import LandmarkType
+
+
+
+# Output to frontend
+class Landmark(BaseModel) :
+ name : str
+ type: LandmarkType # De facto mapping depending on how the query was executed with overpass. Should still EXACTLY correspond to the preferences
+ location : tuple
+ osm_type : str
+ osm_id : int
+ attractiveness : int
+ must_do : bool
+ n_tags : int
+ time_to_reach : Optional[int] = 0
+
+
diff --git a/backend/src/structs/landmarktype.py b/backend/src/structs/landmarktype.py
new file mode 100644
index 0000000..8a63a1b
--- /dev/null
+++ b/backend/src/structs/landmarktype.py
@@ -0,0 +1,4 @@
+from pydantic import BaseModel
+
+class LandmarkType(BaseModel):
+ landmark_type: str
\ No newline at end of file
diff --git a/backend/src/structs/preferences.py b/backend/src/structs/preferences.py
new file mode 100644
index 0000000..500ebec
--- /dev/null
+++ b/backend/src/structs/preferences.py
@@ -0,0 +1,28 @@
+from pydantic import BaseModel
+from .landmarktype import LandmarkType
+
+class Preference(BaseModel) :
+ name: str
+ type: LandmarkType # should match the attributes of the Preferences class
+ score: int # score could be from 1 to 5
+
+# Input for optimization
+class Preferences(BaseModel) :
+ # Sightseeing / History & Culture (Musées, bâtiments historiques, opéras, églises)
+ sightseeing : Preference
+
+ # Nature (parcs, jardins, rivières, plages)
+ nature: Preference
+
+ # Shopping (diriger plutôt vers des zones / rues commerçantes)
+ shopping : Preference
+
+""" # Food (price low or high. Combien on veut dépenser pour manger à midi/soir)
+ food_budget : Preference
+
+ # Tolérance au détour (ce qui détermine (+ ou -) le chemin emprunté)
+ detour_tol : Preference"""
+
+
+
+
diff --git a/backend/src/tester.py b/backend/src/tester.py
new file mode 100644
index 0000000..758e003
--- /dev/null
+++ b/backend/src/tester.py
@@ -0,0 +1,116 @@
+import pandas as pd
+
+from typing import List
+from landmarks_manager import generate_landmarks
+from fastapi.encoders import jsonable_encoder
+
+from 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 test3(city_country: str) -> List[Landmark]:
+
+
+ preferences = Preferences(
+ sightseeing=Preference(
+ name='sightseeing',
+ type=LandmarkType(landmark_type='sightseeing'),
+ score = 5),
+ nature=Preference(
+ name='nature',
+ type=LandmarkType(landmark_type='nature'),
+ score = 0),
+ shopping=Preference(
+ name='shopping',
+ type=LandmarkType(landmark_type='shopping'),
+ score = 5))
+
+ coordinates = None
+
+ landmarks, landmarks_short = generate_landmarks(preferences=preferences, city_country=city_country, coordinates=coordinates)
+
+ #write_data(landmarks)
+
+ start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(48.2044576, 16.3870242), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+ finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.2044576, 16.3870242), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+
+
+ test = landmarks_short
+
+ test.insert(0, start)
+ test.append(finish)
+
+ max_walking_time = 2 # hours
+
+ visiting_list = solve_optimization(test, max_walking_time*60, True)
+
+
+
+
+
+def test4(coordinates: tuple[float, float]) -> List[Landmark]:
+
+
+ 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)
+ #finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.8777055, 2.3640967), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+ #start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(48.847132, 2.312359), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+ #finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.843185, 2.344533), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
+ #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)
+
+ # Generate the landmarks from the start location
+ landmarks, landmarks_short = generate_landmarks(preferences=preferences, coordinates=start.location)
+ #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 = 2 # 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 refined_tour
+
+
+#test4(tuple((48.8344400, 2.3220540))) # Café Chez César
+#test4(tuple((48.8375946, 2.2949904))) # Point random
+test4(tuple((47.377859, 8.540585))) # Zurich HB
+#test3('Vienna, Austria')
\ No newline at end of file
diff --git a/landmarks.txt b/landmarks.txt
new file mode 100644
index 0000000..7ee474a
--- /dev/null
+++ b/landmarks.txt
@@ -0,0 +1,9730 @@
+{
+ "0":{
+ "name":"Musée du quai Branly - Jacques Chirac",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.8609823,
+ 2.2977973
+ ],
+ "osm_type":"way",
+ "osm_id":17954721,
+ "attractiveness":30,
+ "must_do":false,
+ "n_tags":30,
+ "time_to_reach":0
+ },
+ "1":{
+ "name":"Bourse de Commerce — Pinault Collection",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.8628167,
+ 2.3428183
+ ],
+ "osm_type":"way",
+ "osm_id":19856722,
+ "attractiveness":32,
+ "must_do":false,
+ "n_tags":32,
+ "time_to_reach":0
+ },
+ "2":{
+ "name":"Galerie de Minéralogie et de Géologie",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.8418045,
+ 2.3577296
+ ],
+ "osm_type":"way",
+ "osm_id":21999357,
+ "attractiveness":16,
+ "must_do":false,
+ "n_tags":16,
+ "time_to_reach":0
+ },
+ "3":{
+ "name":"Galeries de Paléontologie et d'Anatomie comparée",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.8431475,
+ 2.3628815
+ ],
+ "osm_type":"way",
+ "osm_id":21999454,
+ "attractiveness":15,
+ "must_do":false,
+ "n_tags":15,
+ "time_to_reach":0
+ },
+ "4":{
+ "name":"Institut du Monde Arabe",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.8489231,
+ 2.3572223
+ ],
+ "osm_type":"way",
+ "osm_id":22870791,
+ "attractiveness":21,
+ "must_do":false,
+ "n_tags":21,
+ "time_to_reach":0
+ },
+ "5":{
+ "name":"Jardin Tino Rossi - Musée de la Sculpture en Plein Air",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.8474186,
+ 2.3607371
+ ],
+ "osm_type":"way",
+ "osm_id":23644147,
+ "attractiveness":13,
+ "must_do":false,
+ "n_tags":8,
+ "time_to_reach":0
+ },
+ "6":{
+ "name":"Halle Saint-Pierre",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.8848435,
+ 2.3445246
+ ],
+ "osm_type":"way",
+ "osm_id":45073393,
+ "attractiveness":13,
+ "must_do":false,
+ "n_tags":13,
+ "time_to_reach":0
+ },
+ "7":{
+ "name":"Jeu de Paume",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.8658156,
+ 2.3240828
+ ],
+ "osm_type":"way",
+ "osm_id":54188994,
+ "attractiveness":21,
+ "must_do":false,
+ "n_tags":20,
+ "time_to_reach":0
+ },
+ "8":{
+ "name":"Musée de l'Orangerie",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.8637655,
+ 2.3226594
+ ],
+ "osm_type":"way",
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+ },
+ "location":[
+ 48.8738744,
+ 2.3290548
+ ],
+ "osm_type":"way",
+ "osm_id":69224137,
+ "attractiveness":20,
+ "must_do":false,
+ "n_tags":20,
+ "time_to_reach":0
+ },
+ "596":{
+ "name":"Marché Saint-Honoré",
+ "type":{
+ "landmark_type":"shopping"
+ },
+ "location":[
+ 48.8670006,
+ 2.3317785
+ ],
+ "osm_type":"way",
+ "osm_id":54155008,
+ "attractiveness":7,
+ "must_do":false,
+ "n_tags":7,
+ "time_to_reach":0
+ },
+ "597":{
+ "name":"Passage du Grand-Cerf",
+ "type":{
+ "landmark_type":"shopping"
+ },
+ "location":[
+ 48.8648328,
+ 2.3493274
+ ],
+ "osm_type":"way",
+ "osm_id":54970437,
+ "attractiveness":8,
+ "must_do":false,
+ "n_tags":7,
+ "time_to_reach":0
+ },
+ "598":{
+ "name":"Centre Commercial Émile Dubois",
+ "type":{
+ "landmark_type":"shopping"
+ },
+ "location":[
+ 48.9152909,
+ 2.4027961
+ ],
+ "osm_type":"way",
+ "osm_id":73004279,
+ "attractiveness":5,
+ "must_do":false,
+ "n_tags":4,
+ "time_to_reach":0
+ },
+ "599":{
+ "name":"Bistrot des rosiers",
+ "type":{
+ "landmark_type":"shopping"
+ },
+ "location":[
+ 48.902197,
+ 2.3421451
+ ],
+ "osm_type":"way",
+ "osm_id":73835282,
+ "attractiveness":4,
+ "must_do":false,
+ "n_tags":4,
+ "time_to_reach":0
+ },
+ "600":{
+ "name":"Marché Dauphine",
+ "type":{
+ "landmark_type":"shopping"
+ },
+ "location":[
+ 48.9018063,
+ 2.341632
+ ],
+ "osm_type":"way",
+ "osm_id":73836239,
+ "attractiveness":4,
+ "must_do":false,
+ "n_tags":4,
+ "time_to_reach":0
+ },
+ "601":{
+ "name":"Marché Malassis",
+ "type":{
+ "landmark_type":"shopping"
+ },
+ "location":[
+ 48.9016795,
+ 2.3421091
+ ],
+ "osm_type":"way",
+ "osm_id":73838196,
+ "attractiveness":5,
+ "must_do":false,
+ "n_tags":5,
+ "time_to_reach":0
+ },
+ "602":{
+ "name":"Marché Malik",
+ "type":{
+ "landmark_type":"shopping"
+ },
+ "location":[
+ 48.901541,
+ 2.3400264
+ ],
+ "osm_type":"way",
+ "osm_id":73839906,
+ "attractiveness":4,
+ "must_do":false,
+ "n_tags":4,
+ "time_to_reach":0
+ },
+ "603":{
+ "name":"Centre Commercial de la Grande Porte",
+ "type":{
+ "landmark_type":"shopping"
+ },
+ "location":[
+ 48.855103,
+ 2.4162425
+ ],
+ "osm_type":"way",
+ "osm_id":81767599,
+ "attractiveness":4,
+ "must_do":false,
+ "n_tags":4,
+ "time_to_reach":0
+ },
+ "604":{
+ "name":"Avenir",
+ "type":{
+ "landmark_type":"shopping"
+ },
+ "location":[
+ 48.9197328,
+ 2.4219237
+ ],
+ "osm_type":"way",
+ "osm_id":84437363,
+ "attractiveness":11,
+ "must_do":false,
+ "n_tags":11,
+ "time_to_reach":0
+ },
+ "605":{
+ "name":"Marques Avenue",
+ "type":{
+ "landmark_type":"shopping"
+ },
+ "location":[
+ 48.9225096,
+ 2.3303125
+ ],
+ "osm_type":"way",
+ "osm_id":434347031,
+ "attractiveness":11,
+ "must_do":false,
+ "n_tags":11,
+ "time_to_reach":0
+ },
+ "606":{
+ "name":"Qwartz",
+ "type":{
+ "landmark_type":"shopping"
+ },
+ "location":[
+ 48.92563,
+ 2.3290174
+ ],
+ "osm_type":"way",
+ "osm_id":843953196,
+ "attractiveness":18,
+ "must_do":false,
+ "n_tags":18,
+ "time_to_reach":0
+ },
+ "607":{
+ "name":"Galerie Hoche",
+ "type":{
+ "landmark_type":"shopping"
+ },
+ "location":[
+ 48.8908975,
+ 2.4039477
+ ],
+ "osm_type":"relation",
+ "osm_id":1132674,
+ "attractiveness":5,
+ "must_do":false,
+ "n_tags":5,
+ "time_to_reach":0
+ }
+}
\ No newline at end of file
diff --git a/minor_landmarks.txt b/minor_landmarks.txt
new file mode 100644
index 0000000..8c3f4ab
--- /dev/null
+++ b/minor_landmarks.txt
@@ -0,0 +1,482 @@
+{
+ "0":{
+ "name":"Atelier Brancusi",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.8614029,
+ 2.3519903
+ ],
+ "osm_type":"way",
+ "osm_id":55503399,
+ "attractiveness":13,
+ "must_do":false,
+ "n_tags":13,
+ "time_to_reach":0
+ },
+ "1":{
+ "name":"Musée de Cluny",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.8506604,
+ 2.3437398
+ ],
+ "osm_type":"way",
+ "osm_id":56640163,
+ "attractiveness":18,
+ "must_do":false,
+ "n_tags":18,
+ "time_to_reach":0
+ },
+ "2":{
+ "name":"Musée du Luxembourg",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.8485965,
+ 2.3340156
+ ],
+ "osm_type":"way",
+ "osm_id":170226810,
+ "attractiveness":15,
+ "must_do":false,
+ "n_tags":15,
+ "time_to_reach":0
+ },
+ "3":{
+ "name":"Musée national Eugène Delacroix",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.8546662,
+ 2.3353836
+ ],
+ "osm_type":"way",
+ "osm_id":395785603,
+ "attractiveness":15,
+ "must_do":false,
+ "n_tags":15,
+ "time_to_reach":0
+ },
+ "4":{
+ "name":"Hôtel de Sully",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.854635,
+ 2.3638126
+ ],
+ "osm_type":"relation",
+ "osm_id":403146,
+ "attractiveness":14,
+ "must_do":false,
+ "n_tags":13,
+ "time_to_reach":0
+ },
+ "5":{
+ "name":"Hôtel de la Monnaie",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.856403,
+ 2.3388625
+ ],
+ "osm_type":"relation",
+ "osm_id":967664,
+ "attractiveness":11,
+ "must_do":false,
+ "n_tags":11,
+ "time_to_reach":0
+ },
+ "6":{
+ "name":"Musée Bourdelle",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.8432078,
+ 2.3186583
+ ],
+ "osm_type":"relation",
+ "osm_id":1212876,
+ "attractiveness":23,
+ "must_do":false,
+ "n_tags":23,
+ "time_to_reach":0
+ },
+ "7":{
+ "name":"Musée Carnavalet",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.8576819,
+ 2.3627147
+ ],
+ "osm_type":"relation",
+ "osm_id":2405955,
+ "attractiveness":25,
+ "must_do":false,
+ "n_tags":25,
+ "time_to_reach":0
+ },
+ "8":{
+ "name":"Pont Neuf",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.8565248,
+ 2.3408132
+ ],
+ "osm_type":"way",
+ "osm_id":53574149,
+ "attractiveness":16,
+ "must_do":false,
+ "n_tags":15,
+ "time_to_reach":0
+ },
+ "9":{
+ "name":"Jardin du Luxembourg",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.8467137,
+ 2.3363649
+ ],
+ "osm_type":"way",
+ "osm_id":128206209,
+ "attractiveness":35,
+ "must_do":false,
+ "n_tags":23,
+ "time_to_reach":0
+ },
+ "10":{
+ "name":"Cathédrale Notre-Dame de Paris",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.8529372,
+ 2.3498701
+ ],
+ "osm_type":"way",
+ "osm_id":201611261,
+ "attractiveness":55,
+ "must_do":false,
+ "n_tags":54,
+ "time_to_reach":0
+ },
+ "11":{
+ "name":"Maison à l'enseigne du Faucheur",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.8558553,
+ 2.3568266
+ ],
+ "osm_type":"way",
+ "osm_id":1123456865,
+ "attractiveness":13,
+ "must_do":false,
+ "n_tags":11,
+ "time_to_reach":0
+ },
+ "12":{
+ "name":"Maison à l'enseigne du Mouton",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.8558431,
+ 2.3568914
+ ],
+ "osm_type":"way",
+ "osm_id":1123456866,
+ "attractiveness":13,
+ "must_do":false,
+ "n_tags":11,
+ "time_to_reach":0
+ },
+ "13":{
+ "name":"Palais de Justice de Paris",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.8556537,
+ 2.3446072
+ ],
+ "osm_type":"relation",
+ "osm_id":536982,
+ "attractiveness":24,
+ "must_do":false,
+ "n_tags":24,
+ "time_to_reach":0
+ },
+ "14":{
+ "name":"Mémorial des Martyrs de la Déportation",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.8517365,
+ 2.3524734
+ ],
+ "osm_type":"relation",
+ "osm_id":9396191,
+ "attractiveness":21,
+ "must_do":false,
+ "n_tags":21,
+ "time_to_reach":0
+ },
+ "15":{
+ "name":"Fontaine des Innocents",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.8606368,
+ 2.3480233
+ ],
+ "osm_type":"way",
+ "osm_id":52469222,
+ "attractiveness":16,
+ "must_do":false,
+ "n_tags":15,
+ "time_to_reach":0
+ },
+ "16":{
+ "name":"Hôtel de Sens",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.8535257,
+ 2.3588733
+ ],
+ "osm_type":"way",
+ "osm_id":55541122,
+ "attractiveness":21,
+ "must_do":false,
+ "n_tags":20,
+ "time_to_reach":0
+ },
+ "17":{
+ "name":"Hôtel d'Ourscamp",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.8555465,
+ 2.3571206
+ ],
+ "osm_type":"way",
+ "osm_id":55620201,
+ "attractiveness":10,
+ "must_do":false,
+ "n_tags":9,
+ "time_to_reach":0
+ },
+ "18":{
+ "name":"Hôtel de Choiseul-Praslin",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.8478008,
+ 2.3203873
+ ],
+ "osm_type":"way",
+ "osm_id":65756922,
+ "attractiveness":12,
+ "must_do":false,
+ "n_tags":12,
+ "time_to_reach":0
+ },
+ "19":{
+ "name":"Chapelle Notre-Dame-des-Anges",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.8462836,
+ 2.3235558
+ ],
+ "osm_type":"way",
+ "osm_id":219378497,
+ "attractiveness":16,
+ "must_do":false,
+ "n_tags":16,
+ "time_to_reach":0
+ },
+ "20":{
+ "name":"Fontaine du Palmier",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.8575005,
+ 2.3472864
+ ],
+ "osm_type":"way",
+ "osm_id":261092850,
+ "attractiveness":23,
+ "must_do":false,
+ "n_tags":14,
+ "time_to_reach":0
+ },
+ "21":{
+ "name":"Église Saint-Séverin",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.8520913,
+ 2.3457237
+ ],
+ "osm_type":"way",
+ "osm_id":19740659,
+ "attractiveness":15,
+ "must_do":false,
+ "n_tags":25,
+ "time_to_reach":0
+ },
+ "22":{
+ "name":"Église Orthodoxe Roumaine des Saints Archanges",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.849488,
+ 2.3471975
+ ],
+ "osm_type":"way",
+ "osm_id":55366403,
+ "attractiveness":10,
+ "must_do":false,
+ "n_tags":16,
+ "time_to_reach":0
+ },
+ "23":{
+ "name":"Église Saint-Merri",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.8590777,
+ 2.3508448
+ ],
+ "osm_type":"way",
+ "osm_id":55742120,
+ "attractiveness":16,
+ "must_do":false,
+ "n_tags":26,
+ "time_to_reach":0
+ },
+ "24":{
+ "name":"Église Saint-Germain des Prés",
+ "type":{
+ "landmark_type":"sightseeing"
+ },
+ "location":[
+ 48.8539667,
+ 2.334463
+ ],
+ "osm_type":"way",
+ "osm_id":62287173,
+ "attractiveness":12,
+ "must_do":false,
+ "n_tags":21,
+ "time_to_reach":0
+ },
+ "25":{
+ "name":"Square de la Tour Saint-Jacques",
+ "type":{
+ "landmark_type":"nature"
+ },
+ "location":[
+ 48.857913,
+ 2.3487608
+ ],
+ "osm_type":"way",
+ "osm_id":15895172,
+ "attractiveness":19,
+ "must_do":false,
+ "n_tags":12,
+ "time_to_reach":0
+ },
+ "26":{
+ "name":"Square Jean XXIII",
+ "type":{
+ "landmark_type":"nature"
+ },
+ "location":[
+ 48.8523775,
+ 2.3503406
+ ],
+ "osm_type":"way",
+ "osm_id":20444455,
+ "attractiveness":23,
+ "must_do":false,
+ "n_tags":15,
+ "time_to_reach":0
+ },
+ "27":{
+ "name":"Square Félix Desruelles",
+ "type":{
+ "landmark_type":"nature"
+ },
+ "location":[
+ 48.8536974,
+ 2.3345069
+ ],
+ "osm_type":"way",
+ "osm_id":62287123,
+ "attractiveness":12,
+ "must_do":false,
+ "n_tags":7,
+ "time_to_reach":0
+ },
+ "28":{
+ "name":"Jardin des Rosiers – Joseph-Migneret",
+ "type":{
+ "landmark_type":"nature"
+ },
+ "location":[
+ 48.8572946,
+ 2.3602516
+ ],
+ "osm_type":"way",
+ "osm_id":365878540,
+ "attractiveness":14,
+ "must_do":false,
+ "n_tags":8,
+ "time_to_reach":0
+ },
+ "29":{
+ "name":"Jardin du Père-Armand-David",
+ "type":{
+ "landmark_type":"nature"
+ },
+ "location":[
+ 48.8478674,
+ 2.3220972
+ ],
+ "osm_type":"way",
+ "osm_id":588324415,
+ "attractiveness":10,
+ "must_do":false,
+ "n_tags":7,
+ "time_to_reach":0
+ }
+}
\ No newline at end of file