refactored landmark manager and clean up
This commit is contained in:
@@ -1,4 +1,4 @@
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FROM python:3
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FROM python:3.11-slim
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WORKDIR /app
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WORKDIR /app
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COPY Pipfile Pipfile.lock .
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COPY Pipfile Pipfile.lock .
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605
backend/Pipfile.lock
generated
605
backend/Pipfile.lock
generated
@@ -1,7 +1,7 @@
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{
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{
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"_meta": {
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"_meta": {
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"hash": {
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"hash": {
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"sha256": "b089081defe09c4ffad85fd22c5de336c76b31e7f6f67e5969b731bdaa58e528"
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"sha256": "6f1907b01627126d80ae688e9f2e32fc696d85f0cca3913f14fd5d26cb3ac2db"
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},
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},
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"pipfile-spec": 6,
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"pipfile-spec": 6,
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"requires": {},
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"requires": {},
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@@ -134,102 +134,6 @@
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"markers": "python_full_version >= '3.7.0'",
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"markers": "python_full_version >= '3.7.0'",
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"version": "==3.3.2"
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"version": "==3.3.2"
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},
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},
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"charset-normalizer": {
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"sha256:6ef1d82a3af9d3eecdba2321dc1b3c238245d890843e040e41e470ffa64c3e25",
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"sha256:753f10e867343b4511128c6ed8c82f7bec3bd026875576dfd88483c5c73b2fd8",
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"sha256:7cd13a2e3ddeed6913a65e66e94b51d80a041145a026c27e6bb76c31a853c6ab",
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"sha256:7ed9e526742851e8d5cc9e6cf41427dfc6068d4f5a3bb03659444b4cabf6bc26",
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"sha256:7f04c839ed0b6b98b1a7501a002144b76c18fb1c1850c8b98d458ac269e26ed2",
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"sha256:802fe99cca7457642125a8a88a084cef28ff0cf9407060f7b93dca5aa25480db",
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"sha256:80402cd6ee291dcb72644d6eac93785fe2c8b9cb30893c1af5b8fdd753b9d40f",
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"sha256:8465322196c8b4d7ab6d1e049e4c5cb460d0394da4a27d23cc242fbf0034b6b5",
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"sha256:86216b5cee4b06df986d214f664305142d9c76df9b6512be2738aa72a2048f99",
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"sha256:8c622a5fe39a48f78944a87d4fb8a53ee07344641b0562c540d840748571b811",
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"sha256:8d756e44e94489e49571086ef83b2bb8ce311e730092d2c34ca8f7d925cb20aa",
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"sha256:fb69256e180cb6c8a894fee62b3afebae785babc1ee98b81cdf68bbca1987f33",
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"sha256:ff8fa367d09b717b2a17a052544193ad76cd49979c805768879cb63d9ca50561"
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],
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"markers": "python_full_version >= '3.7.0'",
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"version": "==3.3.2"
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},
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"click": {
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"click": {
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"hashes": [
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"hashes": [
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"sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28",
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"sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28",
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@@ -254,14 +158,6 @@
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"markers": "python_version >= '3.8'",
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"markers": "python_version >= '3.8'",
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"version": "==2.2.0"
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"version": "==2.2.0"
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},
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},
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"exceptiongroup": {
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"hashes": [
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"sha256:3111b9d131c238bec2f8f516e123e14ba243563fb135d3fe885990585aa7795b",
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"sha256:47c2edf7c6738fafb49fd34290706d1a1a2f4d1c6df275526b62cbb4aa5393cc"
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],
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"markers": "python_version < '3.11'",
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"version": "==1.2.2"
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},
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"fastapi": {
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"fastapi": {
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"hashes": [
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"hashes": [
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"sha256:4f51cfa25d72f9fbc3280832e84b32494cf186f50158d364a8765aabf22587bf",
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"sha256:4f51cfa25d72f9fbc3280832e84b32494cf186f50158d364a8765aabf22587bf",
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@@ -279,54 +175,6 @@
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"markers": "python_version >= '3.8'",
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"markers": "python_version >= '3.8'",
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"version": "==0.0.4"
|
"version": "==0.0.4"
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},
|
},
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"geopandas": {
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||||||
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||||||
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|
||||||
"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": {
|
"geographiclib": {
|
||||||
"hashes": [
|
"hashes": [
|
||||||
"sha256:6b7225248e45ff7edcee32becc4e0a1504c606ac5ee163a5656d482e0cd38734",
|
"sha256:6b7225248e45ff7edcee32becc4e0a1504c606ac5ee163a5656d482e0cd38734",
|
||||||
@@ -335,14 +183,6 @@
|
|||||||
"markers": "python_version >= '3.7'",
|
"markers": "python_version >= '3.7'",
|
||||||
"version": "==2.0"
|
"version": "==2.0"
|
||||||
},
|
},
|
||||||
"geojson": {
|
|
||||||
"hashes": [
|
|
||||||
"sha256:58a7fa40727ea058efc28b0e9ff0099eadf6d0965e04690830208d3ef571adac",
|
|
||||||
"sha256:68a9771827237adb8c0c71f8527509c8f5bef61733aa434cefc9c9d4f0ebe8f3"
|
|
||||||
],
|
|
||||||
"markers": "python_version >= '3.7'",
|
|
||||||
"version": "==3.1.0"
|
|
||||||
},
|
|
||||||
"geopy": {
|
"geopy": {
|
||||||
"hashes": [
|
"hashes": [
|
||||||
"sha256:50283d8e7ad07d89be5cb027338c6365a32044df3ae2556ad3f52f4840b3d0d1",
|
"sha256:50283d8e7ad07d89be5cb027338c6365a32044df3ae2556ad3f52f4840b3d0d1",
|
||||||
@@ -507,41 +347,6 @@
|
|||||||
"markers": "python_version >= '3.7'",
|
"markers": "python_version >= '3.7'",
|
||||||
"version": "==2.1.5"
|
"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",
|
|
||||||
"sha256:83c6a792f1465d174c86d06f3ae85a8fe36e6f5964633ae8106312ec0921fdf5",
|
|
||||||
"sha256:84b3ba8429935a444f1fdc80ed930babbe06725bcf09fbeb5c8757a2cd74af04",
|
|
||||||
"sha256:a0c977c5c382f6696caf0bd277ef4f936da7e2aa202ff66cad5f0ac1428ee15b",
|
|
||||||
"sha256:a973c53ad0668c53e0ed76b27d2eeeae8799836fd0d0caaa4ecc66bf4e6676c0",
|
|
||||||
"sha256:ab38a4f3772523179b2f772103d8030215b318fef6360cb40558f585bf3d017f",
|
|
||||||
"sha256:b3fce58971b465e01b5c538f9d44915640c20ec5ff31346e963c9e1cd66fa812",
|
|
||||||
"sha256:b918770bf3e07845408716e5bbda17eadfc3fcbd9307dc67f37d6cf834bb3d98",
|
|
||||||
"sha256:d12cb1837cffaac087ad6b44399d5e22b78c729de3cdae4629e252067b705e2b",
|
|
||||||
"sha256:dc23f48ab630474264276be156d0d7710ac6c5a09648ccdf49fef9200d8cbe80",
|
|
||||||
"sha256:dd2a59ff4b83d33bca3b5ec58203cc65985367812cb8c257f3e101632be86d92",
|
|
||||||
"sha256:de06b19b8db95dd33d0dc17c926c7c9ebed9f572074b6fac4f65068a6814d010",
|
|
||||||
"sha256:f1f2e5d29e9435c97ad4c36fb6668e89aee13d48c75893e25cef064675038ac9"
|
|
||||||
],
|
|
||||||
"markers": "python_version >= '3.9'",
|
|
||||||
"version": "==3.9.1"
|
|
||||||
},
|
|
||||||
"mdurl": {
|
"mdurl": {
|
||||||
"hashes": [
|
"hashes": [
|
||||||
"sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8",
|
"sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8",
|
||||||
@@ -594,64 +399,55 @@
|
|||||||
},
|
},
|
||||||
"numpy": {
|
"numpy": {
|
||||||
"hashes": [
|
"hashes": [
|
||||||
"sha256:04494f6ec467ccb5369d1808570ae55f6ed9b5809d7f035059000a37b8d7e86f",
|
"sha256:08458fbf403bff5e2b45f08eda195d4b0c9b35682311da5a5a0a0925b11b9bd8",
|
||||||
"sha256:0a43f0974d501842866cc83471bdb0116ba0dffdbaac33ec05e6afed5b615238",
|
"sha256:0fbb536eac80e27a2793ffd787895242b7f18ef792563d742c2d673bfcb75134",
|
||||||
"sha256:0e50842b2295ba8414c8c1d9d957083d5dfe9e16828b37de883f51fc53c4016f",
|
"sha256:12f5d865d60fb9734e60a60f1d5afa6d962d8d4467c120a1c0cda6eb2964437d",
|
||||||
"sha256:0ec84b9ba0654f3b962802edc91424331f423dcf5d5f926676e0150789cb3d95",
|
"sha256:15eb4eca47d36ec3f78cde0a3a2ee24cf05ca7396ef808dda2c0ddad7c2bde67",
|
||||||
"sha256:17067d097ed036636fa79f6a869ac26df7db1ba22039d962422506640314933a",
|
"sha256:173a00b9995f73b79eb0191129f2455f1e34c203f559dd118636858cc452a1bf",
|
||||||
"sha256:1cde1753efe513705a0c6d28f5884e22bdc30438bf0085c5c486cdaff40cd67a",
|
"sha256:1b902ce0e0a5bb7704556a217c4f63a7974f8f43e090aff03fcf262e0b135e02",
|
||||||
"sha256:1e72728e7501a450288fc8e1f9ebc73d90cfd4671ebbd631f3e7857c39bd16f2",
|
"sha256:1f682ea61a88479d9498bf2091fdcd722b090724b08b31d63e022adc063bad59",
|
||||||
"sha256:2635dbd200c2d6faf2ef9a0d04f0ecc6b13b3cad54f7c67c61155138835515d2",
|
"sha256:1f87fec1f9bc1efd23f4227becff04bd0e979e23ca50cc92ec88b38489db3b55",
|
||||||
"sha256:2ce46fd0b8a0c947ae047d222f7136fc4d55538741373107574271bc00e20e8f",
|
"sha256:24a0e1befbfa14615b49ba9659d3d8818a0f4d8a1c5822af8696706fbda7310c",
|
||||||
"sha256:34f003cb88b1ba38cb9a9a4a3161c1604973d7f9d5552c38bc2f04f829536609",
|
"sha256:2c3a346ae20cfd80b6cfd3e60dc179963ef2ea58da5ec074fd3d9e7a1e7ba97f",
|
||||||
"sha256:354f373279768fa5a584bac997de6a6c9bc535c482592d7a813bb0c09be6c76f",
|
"sha256:36d3a9405fd7c511804dc56fc32974fa5533bdeb3cd1604d6b8ff1d292b819c4",
|
||||||
"sha256:38ecb5b0582cd125f67a629072fed6f83562d9dd04d7e03256c9829bdec027ad",
|
"sha256:3fdabe3e2a52bc4eff8dc7a5044342f8bd9f11ef0934fcd3289a788c0eb10018",
|
||||||
"sha256:3e8e01233d57639b2e30966c63d36fcea099d17c53bf424d77f088b0f4babd86",
|
"sha256:4127d4303b9ac9f94ca0441138acead39928938660ca58329fe156f84b9f3015",
|
||||||
"sha256:3f6bed7f840d44c08ebdb73b1825282b801799e325bcbdfa6bc5c370e5aecc65",
|
"sha256:4658c398d65d1b25e1760de3157011a80375da861709abd7cef3bad65d6543f9",
|
||||||
"sha256:4554eb96f0fd263041baf16cf0881b3f5dafae7a59b1049acb9540c4d57bc8cb",
|
"sha256:485b87235796410c3519a699cfe1faab097e509e90ebb05dcd098db2ae87e7b3",
|
||||||
"sha256:46e161722e0f619749d1cd892167039015b2c2817296104487cd03ed4a955995",
|
"sha256:529af13c5f4b7a932fb0e1911d3a75da204eff023ee5e0e79c1751564221a5c8",
|
||||||
"sha256:49d9f7d256fbc804391a7f72d4a617302b1afac1112fac19b6c6cec63fe7fe8a",
|
"sha256:5a3d94942c331dd4e0e1147f7a8699a4aa47dffc11bf8a1523c12af8b2e91bbe",
|
||||||
"sha256:4d2f62e55a4cd9c58c1d9a1c9edaedcd857a73cb6fda875bf79093f9d9086f85",
|
"sha256:5daab361be6ddeb299a918a7c0864fa8618af66019138263247af405018b04e1",
|
||||||
"sha256:5f64641b42b2429f56ee08b4f427a4d2daf916ec59686061de751a55aafa22e4",
|
"sha256:61728fba1e464f789b11deb78a57805c70b2ed02343560456190d0501ba37b0f",
|
||||||
"sha256:63b92c512d9dbcc37f9d81b123dec99fdb318ba38c8059afc78086fe73820275",
|
"sha256:6790654cb13eab303d8402354fabd47472b24635700f631f041bd0b65e37298a",
|
||||||
"sha256:6d7696c615765091cc5093f76fd1fa069870304beaccfd58b5dcc69e55ef49c1",
|
"sha256:69ff563d43c69b1baba77af455dd0a839df8d25e8590e79c90fcbe1499ebde42",
|
||||||
"sha256:79e843d186c8fb1b102bef3e2bc35ef81160ffef3194646a7fdd6a73c6b97196",
|
"sha256:6bf4e6f4a2a2e26655717a1983ef6324f2664d7011f6ef7482e8c0b3d51e82ac",
|
||||||
"sha256:821eedb7165ead9eebdb569986968b541f9908979c2da8a4967ecac4439bae3d",
|
"sha256:6e4eeb6eb2fced786e32e6d8df9e755ce5be920d17f7ce00bc38fcde8ccdbf9e",
|
||||||
"sha256:84554fc53daa8f6abf8e8a66e076aff6ece62de68523d9f665f32d2fc50fd66e",
|
"sha256:72dc22e9ec8f6eaa206deb1b1355eb2e253899d7347f5e2fae5f0af613741d06",
|
||||||
"sha256:8d83bb187fb647643bd56e1ae43f273c7f4dbcdf94550d7938cfc32566756514",
|
"sha256:75b4e316c5902d8163ef9d423b1c3f2f6252226d1aa5cd8a0a03a7d01ffc6268",
|
||||||
"sha256:903703372d46bce88b6920a0cd86c3ad82dae2dbef157b5fc01b70ea1cfc430f",
|
"sha256:7b9853803278db3bdcc6cd5beca37815b133e9e77ff3d4733c247414e78eb8d1",
|
||||||
"sha256:9416a5c2e92ace094e9f0082c5fd473502c91651fb896bc17690d6fc475128d6",
|
"sha256:7d6fddc5fe258d3328cd8e3d7d3e02234c5d70e01ebe377a6ab92adb14039cb4",
|
||||||
"sha256:9a1712c015831da583b21c5bfe15e8684137097969c6d22e8316ba66b5baabe4",
|
"sha256:81b0893a39bc5b865b8bf89e9ad7807e16717f19868e9d234bdaf9b1f1393868",
|
||||||
"sha256:9c27f0946a3536403efb0e1c28def1ae6730a72cd0d5878db38824855e3afc44",
|
"sha256:8efc84f01c1cd7e34b3fb310183e72fcdf55293ee736d679b6d35b35d80bba26",
|
||||||
"sha256:a356364941fb0593bb899a1076b92dfa2029f6f5b8ba88a14fd0984aaf76d0df",
|
"sha256:8fae4ebbf95a179c1156fab0b142b74e4ba4204c87bde8d3d8b6f9c34c5825ef",
|
||||||
"sha256:a7039a136017eaa92c1848152827e1424701532ca8e8967fe480fe1569dae581",
|
"sha256:99d0d92a5e3613c33a5f01db206a33f8fdf3d71f2912b0de1739894668b7a93b",
|
||||||
"sha256:acd3a644e4807e73b4e1867b769fbf1ce8c5d80e7caaef0d90dcdc640dfc9787",
|
"sha256:9adbd9bb520c866e1bfd7e10e1880a1f7749f1f6e5017686a5fbb9b72cf69f82",
|
||||||
"sha256:ad0c86f3455fbd0de6c31a3056eb822fc939f81b1618f10ff3406971893b62a5",
|
"sha256:a1e01dcaab205fbece13c1410253a9eea1b1c9b61d237b6fa59bcc46e8e89343",
|
||||||
"sha256:b4c76e3d4c56f145d41b7b6751255feefae92edbc9a61e1758a98204200f30fc",
|
"sha256:a8fc2de81ad835d999113ddf87d1ea2b0f4704cbd947c948d2f5513deafe5a7b",
|
||||||
"sha256:b6f6a8f45d0313db07d6d1d37bd0b112f887e1369758a5419c0370ba915b3871",
|
"sha256:b83e16a5511d1b1f8a88cbabb1a6f6a499f82c062a4251892d9ad5d609863fb7",
|
||||||
"sha256:c5a59996dc61835133b56a32ebe4ef3740ea5bc19b3983ac60cc32be5a665d54",
|
"sha256:bb2124fdc6e62baae159ebcfa368708867eb56806804d005860b6007388df171",
|
||||||
"sha256:c73aafd1afca80afecb22718f8700b40ac7cab927b8abab3c3e337d70e10e5a2",
|
"sha256:bfc085b28d62ff4009364e7ca34b80a9a080cbd97c2c0630bb5f7f770dae9414",
|
||||||
"sha256:cee6cc0584f71adefe2c908856ccc98702baf95ff80092e4ca46061538a2ba98",
|
"sha256:cbab9fc9c391700e3e1287666dfd82d8666d10e69a6c4a09ab97574c0b7ee0a7",
|
||||||
"sha256:cef04d068f5fb0518a77857953193b6bb94809a806bd0a14983a8f12ada060c9",
|
"sha256:e5eeca8067ad04bc8a2a8731183d51d7cbaac66d86085d5f4766ee6bf19c7f87",
|
||||||
"sha256:cf5d1c9e6837f8af9f92b6bd3e86d513cdc11f60fd62185cc49ec7d1aba34864",
|
"sha256:e9e81fa9017eaa416c056e5d9e71be93d05e2c3c2ab308d23307a8bc4443c368",
|
||||||
"sha256:e61155fae27570692ad1d327e81c6cf27d535a5d7ef97648a17d922224b216de",
|
"sha256:ea2326a4dca88e4a274ba3a4405eb6c6467d3ffbd8c7d38632502eaae3820587",
|
||||||
"sha256:e7f387600d424f91576af20518334df3d97bc76a300a755f9a8d6e4f5cadd289",
|
"sha256:eacf3291e263d5a67d8c1a581a8ebbcfd6447204ef58828caf69a5e3e8c75990",
|
||||||
"sha256:ed08d2703b5972ec736451b818c2eb9da80d66c3e84aed1deeb0c345fefe461b",
|
"sha256:ec87f5f8aca726117a1c9b7083e7656a9d0d606eec7299cc067bb83d26f16e0c",
|
||||||
"sha256:fbd6acc766814ea6443628f4e6751d0da6593dae29c08c0b2606164db026970c",
|
"sha256:f1659887361a7151f89e79b276ed8dff3d75877df906328f14d8bb40bb4f5101",
|
||||||
"sha256:feff59f27338135776f6d4e2ec7aeeac5d5f7a08a83e80869121ef8164b74af9"
|
"sha256:f9cf5ea551aec449206954b075db819f52adc1638d46a6738253a712d553c7b4"
|
||||||
],
|
],
|
||||||
"index": "pypi",
|
"index": "pypi",
|
||||||
"markers": "python_version >= '3.9'",
|
"markers": "python_version >= '3.9'",
|
||||||
"version": "==2.0.0"
|
"version": "==2.0.1"
|
||||||
},
|
|
||||||
"osmpythontools": {
|
|
||||||
"hashes": [
|
|
||||||
"sha256:22548d86d68d36edff3cf9ab76c45745cda86a4ea0b28442e107d6b42992a426",
|
|
||||||
"sha256:ac67bea77b521941af648ef641ae1d006101948d1112475c256ea23ef31b426a"
|
|
||||||
],
|
|
||||||
"index": "pypi",
|
|
||||||
"markers": "python_version >= '3.8'",
|
|
||||||
"version": "==1.9.3"
|
|
||||||
},
|
},
|
||||||
"packaging": {
|
"packaging": {
|
||||||
"hashes": [
|
"hashes": [
|
||||||
@@ -662,127 +458,6 @@
|
|||||||
"markers": "python_version >= '3.8'",
|
"markers": "python_version >= '3.8'",
|
||||||
"version": "==24.1"
|
"version": "==24.1"
|
||||||
},
|
},
|
||||||
"pandas": {
|
|
||||||
"hashes": [
|
|
||||||
"sha256:001910ad31abc7bf06f49dcc903755d2f7f3a9186c0c040b827e522e9cef0863",
|
|
||||||
"sha256:0ca6377b8fca51815f382bd0b697a0814c8bda55115678cbc94c30aacbb6eff2",
|
|
||||||
"sha256:0cace394b6ea70c01ca1595f839cf193df35d1575986e484ad35c4aeae7266c1",
|
|
||||||
"sha256:1cb51fe389360f3b5a4d57dbd2848a5f033350336ca3b340d1c53a1fad33bcad",
|
|
||||||
"sha256:2925720037f06e89af896c70bca73459d7e6a4be96f9de79e2d440bd499fe0db",
|
|
||||||
"sha256:3e374f59e440d4ab45ca2fffde54b81ac3834cf5ae2cdfa69c90bc03bde04d76",
|
|
||||||
"sha256:40ae1dffb3967a52203105a077415a86044a2bea011b5f321c6aa64b379a3f51",
|
|
||||||
"sha256:43498c0bdb43d55cb162cdc8c06fac328ccb5d2eabe3cadeb3529ae6f0517c32",
|
|
||||||
"sha256:4abfe0be0d7221be4f12552995e58723c7422c80a659da13ca382697de830c08",
|
|
||||||
"sha256:58b84b91b0b9f4bafac2a0ac55002280c094dfc6402402332c0913a59654ab2b",
|
|
||||||
"sha256:640cef9aa381b60e296db324337a554aeeb883ead99dc8f6c18e81a93942f5f4",
|
|
||||||
"sha256:66b479b0bd07204e37583c191535505410daa8df638fd8e75ae1b383851fe921",
|
|
||||||
"sha256:696039430f7a562b74fa45f540aca068ea85fa34c244d0deee539cb6d70aa288",
|
|
||||||
"sha256:6d2123dc9ad6a814bcdea0f099885276b31b24f7edf40f6cdbc0912672e22eee",
|
|
||||||
"sha256:8635c16bf3d99040fdf3ca3db669a7250ddf49c55dc4aa8fe0ae0fa8d6dcc1f0",
|
|
||||||
"sha256:873d13d177501a28b2756375d59816c365e42ed8417b41665f346289adc68d24",
|
|
||||||
"sha256:8e5a0b00e1e56a842f922e7fae8ae4077aee4af0acb5ae3622bd4b4c30aedf99",
|
|
||||||
"sha256:8e90497254aacacbc4ea6ae5e7a8cd75629d6ad2b30025a4a8b09aa4faf55151",
|
|
||||||
"sha256:9057e6aa78a584bc93a13f0a9bf7e753a5e9770a30b4d758b8d5f2a62a9433cd",
|
|
||||||
"sha256:90c6fca2acf139569e74e8781709dccb6fe25940488755716d1d354d6bc58bce",
|
|
||||||
"sha256:92fd6b027924a7e178ac202cfbe25e53368db90d56872d20ffae94b96c7acc57",
|
|
||||||
"sha256:9dfde2a0ddef507a631dc9dc4af6a9489d5e2e740e226ad426a05cabfbd7c8ef",
|
|
||||||
"sha256:9e79019aba43cb4fda9e4d983f8e88ca0373adbb697ae9c6c43093218de28b54",
|
|
||||||
"sha256:a77e9d1c386196879aa5eb712e77461aaee433e54c68cf253053a73b7e49c33a",
|
|
||||||
"sha256:c7adfc142dac335d8c1e0dcbd37eb8617eac386596eb9e1a1b77791cf2498238",
|
|
||||||
"sha256:d187d355ecec3629624fccb01d104da7d7f391db0311145817525281e2804d23",
|
|
||||||
"sha256:ddf818e4e6c7c6f4f7c8a12709696d193976b591cc7dc50588d3d1a6b5dc8772",
|
|
||||||
"sha256:e9b79011ff7a0f4b1d6da6a61aa1aa604fb312d6647de5bad20013682d1429ce",
|
|
||||||
"sha256:eee3a87076c0756de40b05c5e9a6069c035ba43e8dd71c379e68cab2c20f16ad"
|
|
||||||
],
|
|
||||||
"markers": "python_version >= '3.9'",
|
|
||||||
"version": "==2.2.2"
|
|
||||||
},
|
|
||||||
"pillow": {
|
|
||||||
"hashes": [
|
|
||||||
"sha256:02a2be69f9c9b8c1e97cf2713e789d4e398c751ecfd9967c18d0ce304efbf885",
|
|
||||||
"sha256:030abdbe43ee02e0de642aee345efa443740aa4d828bfe8e2eb11922ea6a21ea",
|
|
||||||
"sha256:06b2f7898047ae93fad74467ec3d28fe84f7831370e3c258afa533f81ef7f3df",
|
|
||||||
"sha256:0755ffd4a0c6f267cccbae2e9903d95477ca2f77c4fcf3a3a09570001856c8a5",
|
|
||||||
"sha256:0a9ec697746f268507404647e531e92889890a087e03681a3606d9b920fbee3c",
|
|
||||||
"sha256:0ae24a547e8b711ccaaf99c9ae3cd975470e1a30caa80a6aaee9a2f19c05701d",
|
|
||||||
"sha256:134ace6dc392116566980ee7436477d844520a26a4b1bd4053f6f47d096997fd",
|
|
||||||
"sha256:166c1cd4d24309b30d61f79f4a9114b7b2313d7450912277855ff5dfd7cd4a06",
|
|
||||||
"sha256:1b5dea9831a90e9d0721ec417a80d4cbd7022093ac38a568db2dd78363b00908",
|
|
||||||
"sha256:1d846aea995ad352d4bdcc847535bd56e0fd88d36829d2c90be880ef1ee4668a",
|
|
||||||
"sha256:1ef61f5dd14c300786318482456481463b9d6b91ebe5ef12f405afbba77ed0be",
|
|
||||||
"sha256:297e388da6e248c98bc4a02e018966af0c5f92dfacf5a5ca22fa01cb3179bca0",
|
|
||||||
"sha256:298478fe4f77a4408895605f3482b6cc6222c018b2ce565c2b6b9c354ac3229b",
|
|
||||||
"sha256:29dbdc4207642ea6aad70fbde1a9338753d33fb23ed6956e706936706f52dd80",
|
|
||||||
"sha256:2db98790afc70118bd0255c2eeb465e9767ecf1f3c25f9a1abb8ffc8cfd1fe0a",
|
|
||||||
"sha256:32cda9e3d601a52baccb2856b8ea1fc213c90b340c542dcef77140dfa3278a9e",
|
|
||||||
"sha256:37fb69d905be665f68f28a8bba3c6d3223c8efe1edf14cc4cfa06c241f8c81d9",
|
|
||||||
"sha256:416d3a5d0e8cfe4f27f574362435bc9bae57f679a7158e0096ad2beb427b8696",
|
|
||||||
"sha256:43efea75eb06b95d1631cb784aa40156177bf9dd5b4b03ff38979e048258bc6b",
|
|
||||||
"sha256:4b35b21b819ac1dbd1233317adeecd63495f6babf21b7b2512d244ff6c6ce309",
|
|
||||||
"sha256:4d9667937cfa347525b319ae34375c37b9ee6b525440f3ef48542fcf66f2731e",
|
|
||||||
"sha256:5161eef006d335e46895297f642341111945e2c1c899eb406882a6c61a4357ab",
|
|
||||||
"sha256:543f3dc61c18dafb755773efc89aae60d06b6596a63914107f75459cf984164d",
|
|
||||||
"sha256:551d3fd6e9dc15e4c1eb6fc4ba2b39c0c7933fa113b220057a34f4bb3268a060",
|
|
||||||
"sha256:59291fb29317122398786c2d44427bbd1a6d7ff54017075b22be9d21aa59bd8d",
|
|
||||||
"sha256:5b001114dd152cfd6b23befeb28d7aee43553e2402c9f159807bf55f33af8a8d",
|
|
||||||
"sha256:5b4815f2e65b30f5fbae9dfffa8636d992d49705723fe86a3661806e069352d4",
|
|
||||||
"sha256:5dc6761a6efc781e6a1544206f22c80c3af4c8cf461206d46a1e6006e4429ff3",
|
|
||||||
"sha256:5e84b6cc6a4a3d76c153a6b19270b3526a5a8ed6b09501d3af891daa2a9de7d6",
|
|
||||||
"sha256:6209bb41dc692ddfee4942517c19ee81b86c864b626dbfca272ec0f7cff5d9fb",
|
|
||||||
"sha256:673655af3eadf4df6b5457033f086e90299fdd7a47983a13827acf7459c15d94",
|
|
||||||
"sha256:6c762a5b0997f5659a5ef2266abc1d8851ad7749ad9a6a5506eb23d314e4f46b",
|
|
||||||
"sha256:7086cc1d5eebb91ad24ded9f58bec6c688e9f0ed7eb3dbbf1e4800280a896496",
|
|
||||||
"sha256:73664fe514b34c8f02452ffb73b7a92c6774e39a647087f83d67f010eb9a0cf0",
|
|
||||||
"sha256:76a911dfe51a36041f2e756b00f96ed84677cdeb75d25c767f296c1c1eda1319",
|
|
||||||
"sha256:780c072c2e11c9b2c7ca37f9a2ee8ba66f44367ac3e5c7832afcfe5104fd6d1b",
|
|
||||||
"sha256:7928ecbf1ece13956b95d9cbcfc77137652b02763ba384d9ab508099a2eca856",
|
|
||||||
"sha256:7970285ab628a3779aecc35823296a7869f889b8329c16ad5a71e4901a3dc4ef",
|
|
||||||
"sha256:7a8d4bade9952ea9a77d0c3e49cbd8b2890a399422258a77f357b9cc9be8d680",
|
|
||||||
"sha256:7c1ee6f42250df403c5f103cbd2768a28fe1a0ea1f0f03fe151c8741e1469c8b",
|
|
||||||
"sha256:7dfecdbad5c301d7b5bde160150b4db4c659cee2b69589705b6f8a0c509d9f42",
|
|
||||||
"sha256:812f7342b0eee081eaec84d91423d1b4650bb9828eb53d8511bcef8ce5aecf1e",
|
|
||||||
"sha256:866b6942a92f56300012f5fbac71f2d610312ee65e22f1aa2609e491284e5597",
|
|
||||||
"sha256:86dcb5a1eb778d8b25659d5e4341269e8590ad6b4e8b44d9f4b07f8d136c414a",
|
|
||||||
"sha256:87dd88ded2e6d74d31e1e0a99a726a6765cda32d00ba72dc37f0651f306daaa8",
|
|
||||||
"sha256:8bc1a764ed8c957a2e9cacf97c8b2b053b70307cf2996aafd70e91a082e70df3",
|
|
||||||
"sha256:8d4d5063501b6dd4024b8ac2f04962d661222d120381272deea52e3fc52d3736",
|
|
||||||
"sha256:8f0aef4ef59694b12cadee839e2ba6afeab89c0f39a3adc02ed51d109117b8da",
|
|
||||||
"sha256:930044bb7679ab003b14023138b50181899da3f25de50e9dbee23b61b4de2126",
|
|
||||||
"sha256:950be4d8ba92aca4b2bb0741285a46bfae3ca699ef913ec8416c1b78eadd64cd",
|
|
||||||
"sha256:961a7293b2457b405967af9c77dcaa43cc1a8cd50d23c532e62d48ab6cdd56f5",
|
|
||||||
"sha256:9b885f89040bb8c4a1573566bbb2f44f5c505ef6e74cec7ab9068c900047f04b",
|
|
||||||
"sha256:9f4727572e2918acaa9077c919cbbeb73bd2b3ebcfe033b72f858fc9fbef0026",
|
|
||||||
"sha256:a02364621fe369e06200d4a16558e056fe2805d3468350df3aef21e00d26214b",
|
|
||||||
"sha256:a985e028fc183bf12a77a8bbf36318db4238a3ded7fa9df1b9a133f1cb79f8fc",
|
|
||||||
"sha256:ac1452d2fbe4978c2eec89fb5a23b8387aba707ac72810d9490118817d9c0b46",
|
|
||||||
"sha256:b15e02e9bb4c21e39876698abf233c8c579127986f8207200bc8a8f6bb27acf2",
|
|
||||||
"sha256:b2724fdb354a868ddf9a880cb84d102da914e99119211ef7ecbdc613b8c96b3c",
|
|
||||||
"sha256:bbc527b519bd3aa9d7f429d152fea69f9ad37c95f0b02aebddff592688998abe",
|
|
||||||
"sha256:bcd5e41a859bf2e84fdc42f4edb7d9aba0a13d29a2abadccafad99de3feff984",
|
|
||||||
"sha256:bd2880a07482090a3bcb01f4265f1936a903d70bc740bfcb1fd4e8a2ffe5cf5a",
|
|
||||||
"sha256:bee197b30783295d2eb680b311af15a20a8b24024a19c3a26431ff83eb8d1f70",
|
|
||||||
"sha256:bf2342ac639c4cf38799a44950bbc2dfcb685f052b9e262f446482afaf4bffca",
|
|
||||||
"sha256:c76e5786951e72ed3686e122d14c5d7012f16c8303a674d18cdcd6d89557fc5b",
|
|
||||||
"sha256:cbed61494057c0f83b83eb3a310f0bf774b09513307c434d4366ed64f4128a91",
|
|
||||||
"sha256:cfdd747216947628af7b259d274771d84db2268ca062dd5faf373639d00113a3",
|
|
||||||
"sha256:d7480af14364494365e89d6fddc510a13e5a2c3584cb19ef65415ca57252fb84",
|
|
||||||
"sha256:dbc6ae66518ab3c5847659e9988c3b60dc94ffb48ef9168656e0019a93dbf8a1",
|
|
||||||
"sha256:dc3e2db6ba09ffd7d02ae9141cfa0ae23393ee7687248d46a7507b75d610f4f5",
|
|
||||||
"sha256:dfe91cb65544a1321e631e696759491ae04a2ea11d36715eca01ce07284738be",
|
|
||||||
"sha256:e4d49b85c4348ea0b31ea63bc75a9f3857869174e2bf17e7aba02945cd218e6f",
|
|
||||||
"sha256:e4db64794ccdf6cb83a59d73405f63adbe2a1887012e308828596100a0b2f6cc",
|
|
||||||
"sha256:e553cad5179a66ba15bb18b353a19020e73a7921296a7979c4a2b7f6a5cd57f9",
|
|
||||||
"sha256:e88d5e6ad0d026fba7bdab8c3f225a69f063f116462c49892b0149e21b6c0a0e",
|
|
||||||
"sha256:ecd85a8d3e79cd7158dec1c9e5808e821feea088e2f69a974db5edf84dc53141",
|
|
||||||
"sha256:f5b92f4d70791b4a67157321c4e8225d60b119c5cc9aee8ecf153aace4aad4ef",
|
|
||||||
"sha256:f5f0c3e969c8f12dd2bb7e0b15d5c468b51e5017e01e2e867335c81903046a22",
|
|
||||||
"sha256:f7baece4ce06bade126fb84b8af1c33439a76d8a6fd818970215e0560ca28c27",
|
|
||||||
"sha256:ff25afb18123cea58a591ea0244b92eb1e61a1fd497bf6d6384f09bc3262ec3e",
|
|
||||||
"sha256:ff337c552345e95702c5fde3158acb0625111017d0e5f24bf3acdb9cc16b90d1"
|
|
||||||
],
|
|
||||||
"markers": "python_version >= '3.8'",
|
|
||||||
"version": "==10.4.0"
|
|
||||||
},
|
|
||||||
"pydantic": {
|
"pydantic": {
|
||||||
"hashes": [
|
"hashes": [
|
||||||
"sha256:6f62c13d067b0755ad1c21a34bdd06c0c12625a22b0fc09c6b149816604f7c2a",
|
"sha256:6f62c13d067b0755ad1c21a34bdd06c0c12625a22b0fc09c6b149816604f7c2a",
|
||||||
@@ -895,79 +570,6 @@
|
|||||||
"markers": "python_version >= '3.8'",
|
"markers": "python_version >= '3.8'",
|
||||||
"version": "==2.18.0"
|
"version": "==2.18.0"
|
||||||
},
|
},
|
||||||
"pyogrio": {
|
|
||||||
"hashes": [
|
|
||||||
"sha256:019731a856a9abfe909e86f50eb13f8362f6742337caf757c54b7c8acfe75b89",
|
|
||||||
"sha256:083351b258b3e08b6c6085dac560bd321b68de5cb4a66229095da68d5f3d696b",
|
|
||||||
"sha256:13642608a1cd67797ae8b5d792b0518d8ef3eb76506c8232ab5eaa1ea1159dff",
|
|
||||||
"sha256:17420febc17651876d5140b54b24749aa751d482b5f9ef6267b8053e6e962876",
|
|
||||||
"sha256:1a495ca4fb77c69595747dd688f8f17bb7d2ea9cd86603aa71c7fc98cc8b4174",
|
|
||||||
"sha256:2829615cf58b1b24a9f96fea42abedaa1a800dd351c67374cc2f6341138608f3",
|
|
||||||
"sha256:2e98913fa183f7597c609e774820a149e9329fd2a0f8d33978252fbd00ae87e6",
|
|
||||||
"sha256:2f2ec57ab74785db9c2bf47c0a6731e5175595a13f8253f06fa84136adb310a9",
|
|
||||||
"sha256:30cbeeaedb9bced7012487e7438919aa0c7dfba18ac3d4315182b46eb3139b9d",
|
|
||||||
"sha256:3a2fcaa269031dbbc8ebd91243c6452c5d267d6df939c008ab7533413c9cf92d",
|
|
||||||
"sha256:3f964002d445521ad5b8e732a6b5ef0e2d2be7fe566768e5075c1d71398da64a",
|
|
||||||
"sha256:4a289584da6df7ca318947301fe0ba9177e7f863f63110e087c80ac5f3658de8",
|
|
||||||
"sha256:4da0b9deb380bd9a200fee13182c4f95b02b4c554c923e2e0032f32aaf1439ed",
|
|
||||||
"sha256:4e0f90a6c3771ee1f1fea857778b4b6a1b64000d851b819f435f9091b3c38c60",
|
|
||||||
"sha256:6a6fa2e8cf95b3d4a7c0fac48bce6e5037579e28d3eb33b53349d6e11f15e5a8",
|
|
||||||
"sha256:6dc94a67163218581c7df275223488ac9b31dc582ccd756da607c3338908566c",
|
|
||||||
"sha256:796e4f6a4e769b2eb6fea9a10546ea4bdee16182d1e29802b4d6349363c3c1d7",
|
|
||||||
"sha256:7fcafed24371fe6e23bcf5abebbb29269f8d79915f1dd818ac85453657ea714a",
|
|
||||||
"sha256:9440466c0211ac81f3417f274da5903f15546b486f76b2f290e74a56aaf0e737",
|
|
||||||
"sha256:959022f3ad04053f8072dc9a2ad110c46edd9e4f92352061ba835fc91df3ca96",
|
|
||||||
"sha256:d668cb10f2bf6ccd7c402f91e8b06290722dd09dbe265ae95b2c13db29ebeba0",
|
|
||||||
"sha256:e38c3c6d37cf2cc969407e4d051dcb507cfd948eb26c7b0840c4f7d7d4a71bd4",
|
|
||||||
"sha256:f47c9b6818cc0f420015b672d5dcc488530a5ee63e5ba35a184957b21ea3922a",
|
|
||||||
"sha256:f5d80eb846be4fc4e642cbedc1ed0c143e8d241653382ecc76a7620bbd2a5c3a",
|
|
||||||
"sha256:f8bf193269ea9d347ac3ddada960a59f1ab2e4a5c009be95dc70e6505346b2fc",
|
|
||||||
"sha256:fb04bd80964428491951766452f0071b0bc37c7d38c45ef02502dbd83e5d74a0"
|
|
||||||
],
|
|
||||||
"markers": "python_version >= '3.8'",
|
|
||||||
"version": "==0.9.0"
|
|
||||||
},
|
|
||||||
"pyproj": {
|
|
||||||
"hashes": [
|
|
||||||
"sha256:18faa54a3ca475bfe6255156f2f2874e9a1c8917b0004eee9f664b86ccc513d3",
|
|
||||||
"sha256:1e9fbaf920f0f9b4ee62aab832be3ae3968f33f24e2e3f7fbb8c6728ef1d9746",
|
|
||||||
"sha256:2d6ff73cc6dbbce3766b6c0bce70ce070193105d8de17aa2470009463682a8eb",
|
|
||||||
"sha256:36b64c2cb6ea1cc091f329c5bd34f9c01bb5da8c8e4492c709bda6a09f96808f",
|
|
||||||
"sha256:38a3361941eb72b82bd9a18f60c78b0df8408416f9340521df442cebfc4306e2",
|
|
||||||
"sha256:447db19c7efad70ff161e5e46a54ab9cc2399acebb656b6ccf63e4bc4a04b97a",
|
|
||||||
"sha256:44aa7c704c2b7d8fb3d483bbf75af6cb2350d30a63b144279a09b75fead501bf",
|
|
||||||
"sha256:4ba1f9b03d04d8cab24d6375609070580a26ce76eaed54631f03bab00a9c737b",
|
|
||||||
"sha256:4bc0472302919e59114aa140fd7213c2370d848a7249d09704f10f5b062031fe",
|
|
||||||
"sha256:50100b2726a3ca946906cbaa789dd0749f213abf0cbb877e6de72ca7aa50e1ae",
|
|
||||||
"sha256:5279586013b8d6582e22b6f9e30c49796966770389a9d5b85e25a4223286cd3f",
|
|
||||||
"sha256:6420ea8e7d2a88cb148b124429fba8cd2e0fae700a2d96eab7083c0928a85110",
|
|
||||||
"sha256:65ad699e0c830e2b8565afe42bd58cc972b47d829b2e0e48ad9638386d994915",
|
|
||||||
"sha256:6d227a865356f225591b6732430b1d1781e946893789a609bb34f59d09b8b0f8",
|
|
||||||
"sha256:7a27151ddad8e1439ba70c9b4b2b617b290c39395fa9ddb7411ebb0eb86d6fb0",
|
|
||||||
"sha256:80fafd1f3eb421694857f254a9bdbacd1eb22fc6c24ca74b136679f376f97d35",
|
|
||||||
"sha256:83039e5ae04e5afc974f7d25ee0870a80a6bd6b7957c3aca5613ccbe0d3e72bf",
|
|
||||||
"sha256:8b8acc31fb8702c54625f4d5a2a6543557bec3c28a0ef638778b7ab1d1772132",
|
|
||||||
"sha256:9274880263256f6292ff644ca92c46d96aa7e57a75c6df3f11d636ce845a1877",
|
|
||||||
"sha256:ab7aa4d9ff3c3acf60d4b285ccec134167a948df02347585fdd934ebad8811b4",
|
|
||||||
"sha256:c41e80ddee130450dcb8829af7118f1ab69eaf8169c4bf0ee8d52b72f098dc2f",
|
|
||||||
"sha256:db3aedd458e7f7f21d8176f0a1d924f1ae06d725228302b872885a1c34f3119e",
|
|
||||||
"sha256:e7e13c40183884ec7f94eb8e0f622f08f1d5716150b8d7a134de48c6110fee85",
|
|
||||||
"sha256:ebfbdbd0936e178091309f6cd4fcb4decd9eab12aa513cdd9add89efa3ec2882",
|
|
||||||
"sha256:fd43bd9a9b9239805f406fd82ba6b106bf4838d9ef37c167d3ed70383943ade1",
|
|
||||||
"sha256:fd93c1a0c6c4aedc77c0fe275a9f2aba4d59b8acf88cebfc19fe3c430cfabf4f",
|
|
||||||
"sha256:fffb059ba3bced6f6725961ba758649261d85ed6ce670d3e3b0a26e81cf1aa8d"
|
|
||||||
],
|
|
||||||
"markers": "python_version >= '3.9'",
|
|
||||||
"version": "==3.6.1"
|
|
||||||
},
|
|
||||||
"python-dateutil": {
|
|
||||||
"hashes": [
|
|
||||||
"sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3",
|
|
||||||
"sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427"
|
|
||||||
],
|
|
||||||
"markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
|
|
||||||
"version": "==2.9.0.post0"
|
|
||||||
},
|
|
||||||
"python-dotenv": {
|
"python-dotenv": {
|
||||||
"hashes": [
|
"hashes": [
|
||||||
"sha256:e324ee90a023d808f1959c46bcbc04446a10ced277783dc6ee09987c37ec10ca",
|
"sha256:e324ee90a023d808f1959c46bcbc04446a10ced277783dc6ee09987c37ec10ca",
|
||||||
@@ -983,13 +585,6 @@
|
|||||||
"markers": "python_version >= '3.8'",
|
"markers": "python_version >= '3.8'",
|
||||||
"version": "==0.0.9"
|
"version": "==0.0.9"
|
||||||
},
|
},
|
||||||
"pytz": {
|
|
||||||
"hashes": [
|
|
||||||
"sha256:2a29735ea9c18baf14b448846bde5a48030ed267578472d8955cd0e7443a9812",
|
|
||||||
"sha256:328171f4e3623139da4983451950b28e95ac706e13f3f2630a879749e7a8b319"
|
|
||||||
],
|
|
||||||
"version": "==2024.1"
|
|
||||||
},
|
|
||||||
"pywikibot": {
|
"pywikibot": {
|
||||||
"hashes": [
|
"hashes": [
|
||||||
"sha256:3f4fbc57f1765aa0fa1ccf84125bcfa475cae95b9cc0291867b751f3d4ac8fa2",
|
"sha256:3f4fbc57f1765aa0fa1ccf84125bcfa475cae95b9cc0291867b751f3d4ac8fa2",
|
||||||
@@ -1155,14 +750,6 @@
|
|||||||
"markers": "python_version >= '3.7'",
|
"markers": "python_version >= '3.7'",
|
||||||
"version": "==1.5.4"
|
"version": "==1.5.4"
|
||||||
},
|
},
|
||||||
"six": {
|
|
||||||
"hashes": [
|
|
||||||
"sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926",
|
|
||||||
"sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"
|
|
||||||
],
|
|
||||||
"markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
|
|
||||||
"version": "==1.16.0"
|
|
||||||
},
|
|
||||||
"sniffio": {
|
"sniffio": {
|
||||||
"hashes": [
|
"hashes": [
|
||||||
"sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2",
|
"sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2",
|
||||||
@@ -1195,98 +782,6 @@
|
|||||||
"markers": "python_version >= '3.8'",
|
"markers": "python_version >= '3.8'",
|
||||||
"version": "==4.12.2"
|
"version": "==4.12.2"
|
||||||
},
|
},
|
||||||
"tzdata": {
|
|
||||||
"hashes": [
|
|
||||||
"sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd",
|
|
||||||
"sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"
|
|
||||||
],
|
|
||||||
"markers": "python_version >= '2'",
|
|
||||||
"version": "==2024.1"
|
|
||||||
},
|
|
||||||
"ujson": {
|
|
||||||
"hashes": [
|
|
||||||
"sha256:0de4971a89a762398006e844ae394bd46991f7c385d7a6a3b93ba229e6dac17e",
|
|
||||||
"sha256:129e39af3a6d85b9c26d5577169c21d53821d8cf68e079060602e861c6e5da1b",
|
|
||||||
"sha256:22cffecf73391e8abd65ef5f4e4dd523162a3399d5e84faa6aebbf9583df86d6",
|
|
||||||
"sha256:232cc85f8ee3c454c115455195a205074a56ff42608fd6b942aa4c378ac14dd7",
|
|
||||||
"sha256:2544912a71da4ff8c4f7ab5606f947d7299971bdd25a45e008e467ca638d13c9",
|
|
||||||
"sha256:2601aa9ecdbee1118a1c2065323bda35e2c5a2cf0797ef4522d485f9d3ef65bd",
|
|
||||||
"sha256:26b0e2d2366543c1bb4fbd457446f00b0187a2bddf93148ac2da07a53fe51569",
|
|
||||||
"sha256:2987713a490ceb27edff77fb184ed09acdc565db700ee852823c3dc3cffe455f",
|
|
||||||
"sha256:29b443c4c0a113bcbb792c88bea67b675c7ca3ca80c3474784e08bba01c18d51",
|
|
||||||
"sha256:2a890b706b64e0065f02577bf6d8ca3b66c11a5e81fb75d757233a38c07a1f20",
|
|
||||||
"sha256:2aff2985cef314f21d0fecc56027505804bc78802c0121343874741650a4d3d1",
|
|
||||||
"sha256:348898dd702fc1c4f1051bc3aacbf894caa0927fe2c53e68679c073375f732cf",
|
|
||||||
"sha256:38665e7d8290188b1e0d57d584eb8110951a9591363316dd41cf8686ab1d0abc",
|
|
||||||
"sha256:38d5d36b4aedfe81dfe251f76c0467399d575d1395a1755de391e58985ab1c2e",
|
|
||||||
"sha256:3ff201d62b1b177a46f113bb43ad300b424b7847f9c5d38b1b4ad8f75d4a282a",
|
|
||||||
"sha256:4573fd1695932d4f619928fd09d5d03d917274381649ade4328091ceca175539",
|
|
||||||
"sha256:4734ee0745d5928d0ba3a213647f1c4a74a2a28edc6d27b2d6d5bd9fa4319e27",
|
|
||||||
"sha256:4c4fc16f11ac1612f05b6f5781b384716719547e142cfd67b65d035bd85af165",
|
|
||||||
"sha256:502bf475781e8167f0f9d0e41cd32879d120a524b22358e7f205294224c71126",
|
|
||||||
"sha256:57aaf98b92d72fc70886b5a0e1a1ca52c2320377360341715dd3933a18e827b1",
|
|
||||||
"sha256:59e02cd37bc7c44d587a0ba45347cc815fb7a5fe48de16bf05caa5f7d0d2e816",
|
|
||||||
"sha256:5b6fee72fa77dc172a28f21693f64d93166534c263adb3f96c413ccc85ef6e64",
|
|
||||||
"sha256:5b91b5d0d9d283e085e821651184a647699430705b15bf274c7896f23fe9c9d8",
|
|
||||||
"sha256:604a046d966457b6cdcacc5aa2ec5314f0e8c42bae52842c1e6fa02ea4bda42e",
|
|
||||||
"sha256:618efd84dc1acbd6bff8eaa736bb6c074bfa8b8a98f55b61c38d4ca2c1f7f287",
|
|
||||||
"sha256:61d0af13a9af01d9f26d2331ce49bb5ac1fb9c814964018ac8df605b5422dcb3",
|
|
||||||
"sha256:61e1591ed9376e5eddda202ec229eddc56c612b61ac6ad07f96b91460bb6c2fb",
|
|
||||||
"sha256:621e34b4632c740ecb491efc7f1fcb4f74b48ddb55e65221995e74e2d00bbff0",
|
|
||||||
"sha256:6627029ae4f52d0e1a2451768c2c37c0c814ffc04f796eb36244cf16b8e57043",
|
|
||||||
"sha256:67079b1f9fb29ed9a2914acf4ef6c02844b3153913eb735d4bf287ee1db6e557",
|
|
||||||
"sha256:6dea1c8b4fc921bf78a8ff00bbd2bfe166345f5536c510671bccececb187c80e",
|
|
||||||
"sha256:6e32abdce572e3a8c3d02c886c704a38a1b015a1fb858004e03d20ca7cecbb21",
|
|
||||||
"sha256:7223f41e5bf1f919cd8d073e35b229295aa8e0f7b5de07ed1c8fddac63a6bc5d",
|
|
||||||
"sha256:73814cd1b9db6fc3270e9d8fe3b19f9f89e78ee9d71e8bd6c9a626aeaeaf16bd",
|
|
||||||
"sha256:7490655a2272a2d0b072ef16b0b58ee462f4973a8f6bbe64917ce5e0a256f9c0",
|
|
||||||
"sha256:7663960f08cd5a2bb152f5ee3992e1af7690a64c0e26d31ba7b3ff5b2ee66337",
|
|
||||||
"sha256:78778a3aa7aafb11e7ddca4e29f46bc5139131037ad628cc10936764282d6753",
|
|
||||||
"sha256:7c10f4654e5326ec14a46bcdeb2b685d4ada6911050aa8baaf3501e57024b804",
|
|
||||||
"sha256:7ec0ca8c415e81aa4123501fee7f761abf4b7f386aad348501a26940beb1860f",
|
|
||||||
"sha256:924f7318c31874d6bb44d9ee1900167ca32aa9b69389b98ecbde34c1698a250f",
|
|
||||||
"sha256:94a87f6e151c5f483d7d54ceef83b45d3a9cca7a9cb453dbdbb3f5a6f64033f5",
|
|
||||||
"sha256:98ba15d8cbc481ce55695beee9f063189dce91a4b08bc1d03e7f0152cd4bbdd5",
|
|
||||||
"sha256:a245d59f2ffe750446292b0094244df163c3dc96b3ce152a2c837a44e7cda9d1",
|
|
||||||
"sha256:a5b366812c90e69d0f379a53648be10a5db38f9d4ad212b60af00bd4048d0f00",
|
|
||||||
"sha256:a65b6af4d903103ee7b6f4f5b85f1bfd0c90ba4eeac6421aae436c9988aa64a2",
|
|
||||||
"sha256:a984a3131da7f07563057db1c3020b1350a3e27a8ec46ccbfbf21e5928a43050",
|
|
||||||
"sha256:a9d2edbf1556e4f56e50fab7d8ff993dbad7f54bac68eacdd27a8f55f433578e",
|
|
||||||
"sha256:ab13a2a9e0b2865a6c6db9271f4b46af1c7476bfd51af1f64585e919b7c07fd4",
|
|
||||||
"sha256:ac56eb983edce27e7f51d05bc8dd820586c6e6be1c5216a6809b0c668bb312b8",
|
|
||||||
"sha256:ad88ac75c432674d05b61184178635d44901eb749786c8eb08c102330e6e8996",
|
|
||||||
"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": {
|
"urllib3": {
|
||||||
"hashes": [
|
"hashes": [
|
||||||
"sha256:a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472",
|
"sha256:a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472",
|
||||||
@@ -1300,11 +795,11 @@
|
|||||||
"standard"
|
"standard"
|
||||||
],
|
],
|
||||||
"hashes": [
|
"hashes": [
|
||||||
"sha256:cd17daa7f3b9d7a24de3617820e634d0933b69eed8e33a516071174427238c81",
|
"sha256:0d114d0831ff1adbf231d358cbf42f17333413042552a624ea6a9b4c33dcfd81",
|
||||||
"sha256:d46cd8e0fd80240baffbcd9ec1012a712938754afcf81bce56c024c1656aece8"
|
"sha256:94a3608da0e530cea8f69683aa4126364ac18e3826b6630d1a65f4638aade503"
|
||||||
],
|
],
|
||||||
"markers": "python_version >= '3.8'",
|
"markers": "python_version >= '3.8'",
|
||||||
"version": "==0.30.1"
|
"version": "==0.30.3"
|
||||||
},
|
},
|
||||||
"uvloop": {
|
"uvloop": {
|
||||||
"hashes": [
|
"hashes": [
|
||||||
|
|||||||
@@ -14,6 +14,6 @@ OSM_CACHE_DIR = Path(cache_dir_string)
|
|||||||
|
|
||||||
logger = logging.getLogger(__name__)
|
logger = logging.getLogger(__name__)
|
||||||
logging.basicConfig(
|
logging.basicConfig(
|
||||||
level = logging.INFO,
|
level = logging.DEBUG,
|
||||||
format = '%(asctime)s - %(name)s\t- %(levelname)s\t- %(message)s'
|
format = '%(asctime)s - %(name)s\t- %(levelname)s\t- %(message)s'
|
||||||
)
|
)
|
||||||
|
|||||||
@@ -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
|
|
||||||
@@ -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
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
@@ -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
|
|
||||||
|
|
||||||
|
|
||||||
@@ -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)
|
|
||||||
@@ -2,9 +2,8 @@ import math as m
|
|||||||
import yaml
|
import yaml
|
||||||
import logging
|
import logging
|
||||||
|
|
||||||
from typing import List, Tuple
|
|
||||||
from OSMPythonTools.overpass import Overpass, overpassQueryBuilder
|
from OSMPythonTools.overpass import Overpass, overpassQueryBuilder
|
||||||
from OSMPythonTools import cachingStrategy
|
from OSMPythonTools.cachingStrategy import CachingStrategy, JSON
|
||||||
from pywikibot import ItemPage, Site
|
from pywikibot import ItemPage, Site
|
||||||
from pywikibot import config
|
from pywikibot import config
|
||||||
config.put_throttle = 0
|
config.put_throttle = 0
|
||||||
@@ -33,10 +32,8 @@ class LandmarkManager:
|
|||||||
tag_coeff: float # coeff to adjust weight of tags
|
tag_coeff: float # coeff to adjust weight of tags
|
||||||
N_important: int # number of important landmarks to consider
|
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:
|
with constants.AMENITY_SELECTORS_PATH.open('r') as f:
|
||||||
self.amenity_selectors = yaml.safe_load(f)
|
self.amenity_selectors = yaml.safe_load(f)
|
||||||
@@ -50,11 +47,11 @@ class LandmarkManager:
|
|||||||
self.tag_coeff = parameters['tag_coeff']
|
self.tag_coeff = parameters['tag_coeff']
|
||||||
self.N_important = parameters['N_important']
|
self.N_important = parameters['N_important']
|
||||||
|
|
||||||
self.preferences = preferences
|
self.overpass = Overpass()
|
||||||
self.location = coordinates
|
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.
|
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
|
and current location. It scores and corrects these landmarks, removes duplicates, and then selects the most important
|
||||||
landmarks based on a predefined criterion.
|
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:
|
Returns:
|
||||||
Tuple[List[Landmark], List[Landmark]]:
|
tuple[list[Landmark], list[Landmark]]:
|
||||||
- A list of all existing landmarks.
|
- A list of all existing landmarks.
|
||||||
- A list of the most important landmarks based on the user's preferences.
|
- A list of the most important landmarks based on the user's preferences.
|
||||||
"""
|
"""
|
||||||
|
|
||||||
L = []
|
L = []
|
||||||
|
bbox = self.create_bbox(center_coordinates)
|
||||||
# List for sightseeing
|
# list for sightseeing
|
||||||
if self.preferences.sightseeing.score != 0 :
|
if preferences.sightseeing.score != 0:
|
||||||
L1 = self.fetch_landmarks(self.amenity_selectors['sightseeing'], SIGHTSEEING, coordinates=self.location)
|
score_function = lambda loc, n_tags: int((self.count_elements_close_to(loc) + ((n_tags**1.2)*self.tag_coeff) )*self.church_coeff)
|
||||||
self.correct_score(L1, self.preferences.sightseeing)
|
L1 = self.fetch_landmarks(bbox, self.amenity_selectors['sightseeing'], SIGHTSEEING, score_function)
|
||||||
|
self.correct_score(L1, preferences.sightseeing)
|
||||||
L += L1
|
L += L1
|
||||||
|
|
||||||
# List for nature
|
# list for nature
|
||||||
if self.preferences.nature.score != 0 :
|
if preferences.nature.score != 0:
|
||||||
L2 = self.fetch_landmarks(self.amenity_selectors['nature'], NATURE, coordinates=self.location)
|
score_function = lambda loc, n_tags: int((self.count_elements_close_to(loc) + ((n_tags**1.2)*self.tag_coeff) )*self.park_coeff)
|
||||||
self.correct_score(L2, self.preferences.nature)
|
L2 = self.fetch_landmarks(bbox, self.amenity_selectors['nature'], NATURE, score_function)
|
||||||
|
self.correct_score(L2, preferences.nature)
|
||||||
L += L2
|
L += L2
|
||||||
|
|
||||||
# List for shopping
|
# list for shopping
|
||||||
if self.preferences.shopping.score != 0 :
|
if preferences.shopping.score != 0:
|
||||||
L3 = self.fetch_landmarks(self.amenity_selectors['shopping'], SHOPPING, coordinates=self.location)
|
score_function = lambda loc, n_tags: int(self.count_elements_close_to(loc) + ((n_tags**1.2)*self.tag_coeff))
|
||||||
self.correct_score(L3, self.preferences.shopping)
|
L3 = self.fetch_landmarks(bbox, self.amenity_selectors['shopping'], SHOPPING, score_function)
|
||||||
|
self.correct_score(L3, preferences.shopping)
|
||||||
L += L3
|
L += L3
|
||||||
|
|
||||||
L = self.remove_duplicates(L)
|
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
|
Removes duplicate landmarks based on their names from the given list. Only retains the landmark with highest score
|
||||||
|
|
||||||
Parameters:
|
Parameters:
|
||||||
landmarks (List[Landmark]): A list of Landmark objects.
|
landmarks (list[Landmark]): A list of Landmark objects.
|
||||||
|
|
||||||
Returns:
|
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 = []
|
L_clean = []
|
||||||
names = []
|
names = []
|
||||||
|
|
||||||
for landmark in landmarks :
|
for landmark in landmarks:
|
||||||
if landmark.name in names:
|
if landmark.name in names:
|
||||||
continue
|
continue
|
||||||
else :
|
else:
|
||||||
names.append(landmark.name)
|
names.append(landmark.name)
|
||||||
L_clean.append(landmark)
|
L_clean.append(landmark)
|
||||||
|
|
||||||
return L_clean
|
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.
|
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.
|
The score adjustment is computed using a simple linear transformation based on the preference score.
|
||||||
|
|
||||||
Args:
|
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.
|
preference (Preference): The user's preference settings that influence the attractiveness score adjustment.
|
||||||
|
|
||||||
Raises:
|
Raises:
|
||||||
TypeError: If the type of any landmark in the list does not match the expected type in the preference.
|
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
|
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}")
|
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
|
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.
|
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.
|
OpenStreetMap data to count the number of elements within that bounding box.
|
||||||
|
|
||||||
Args:
|
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:
|
Returns:
|
||||||
int: The number of elements (nodes, ways, relations) within the specified radius. Returns 0 if no elements
|
int: The number of elements (nodes, ways, relations) within the specified radius. Returns 0 if no elements
|
||||||
@@ -166,30 +172,34 @@ class LandmarkManager:
|
|||||||
bbox = {'latLower':lat-alpha,'lonLower':lon-alpha,'latHigher':lat+alpha,'lonHigher': lon+alpha}
|
bbox = {'latLower':lat-alpha,'lonLower':lon-alpha,'latHigher':lat+alpha,'lonHigher': lon+alpha}
|
||||||
|
|
||||||
# Build the query to find elements within the radius
|
# Build the query to find elements within the radius
|
||||||
radius_query = overpassQueryBuilder(bbox=[bbox['latLower'],bbox['lonLower'],bbox['latHigher'],bbox['lonHigher']],
|
radius_query = overpassQueryBuilder(
|
||||||
elementType=['node', 'way', 'relation'])
|
bbox=[bbox['latLower'],
|
||||||
|
bbox['lonLower'],
|
||||||
|
bbox['latHigher'],
|
||||||
|
bbox['lonHigher']],
|
||||||
|
elementType=['node', 'way', 'relation']
|
||||||
|
)
|
||||||
|
|
||||||
try :
|
try:
|
||||||
overpass = Overpass()
|
radius_result = self.overpass.query(radius_query)
|
||||||
radius_result = overpass.query(radius_query)
|
|
||||||
N_elem = radius_result.countWays() + radius_result.countRelations()
|
N_elem = radius_result.countWays() + radius_result.countRelations()
|
||||||
#print(f"There are {N_elem} ways/relations within 50m")
|
self.logger.debug(f"There are {N_elem} ways/relations within 50m")
|
||||||
if N_elem is None :
|
if N_elem is None:
|
||||||
return 0
|
return 0
|
||||||
return N_elem
|
return N_elem
|
||||||
except :
|
except:
|
||||||
return 0
|
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.
|
Create a bounding box around the given coordinates.
|
||||||
|
|
||||||
Args:
|
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:
|
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.
|
defining the bounding box.
|
||||||
"""
|
"""
|
||||||
|
|
||||||
@@ -212,44 +222,52 @@ class LandmarkManager:
|
|||||||
return min_lat, min_lon, max_lat, max_lon
|
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.
|
Fetches landmarks of a specified type from OpenStreetMap (OSM) within a bounding box centered on given coordinates.
|
||||||
|
|
||||||
Args:
|
Args:
|
||||||
list_amenity (list): A list of OSM amenity queries to be used for fetching landmarks.
|
bbox (tuple[float, float, float, float]): The bounding box coordinates (min_lat, min_lon, max_lat, max_lon).
|
||||||
These queries are typically used to filter results (e.g., [''amenity'='place_of_worship']).
|
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').
|
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:
|
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:
|
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.
|
- 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.
|
- Landmarks are filtered based on various conditions including tags and type.
|
||||||
- Scores are assigned to landmarks based on their attributes and surrounding elements.
|
- Scores are assigned to landmarks based on their attributes and surrounding elements.
|
||||||
"""
|
"""
|
||||||
|
return_list = []
|
||||||
|
|
||||||
# Create bbox around start location
|
# caution, when applying a list of selectors, overpass will search for elements that match ALL selectors simultaneously
|
||||||
bbox = self.create_bbox(coordinates)
|
# 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'
|
||||||
|
)
|
||||||
|
|
||||||
# Initialize some variables
|
try:
|
||||||
N = 0
|
result = self.overpass.query(query)
|
||||||
L = []
|
except Exception as e:
|
||||||
overpass = Overpass()
|
self.logger.error(f"Error fetching landmarks: {e}")
|
||||||
|
return
|
||||||
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():
|
for elem in result.elements():
|
||||||
|
|
||||||
name = elem.tag('name') # Add name
|
name = elem.tag('name') # Add name
|
||||||
location = (elem.centerLat(), elem.centerLon()) # Add coordinates (lat, lon)
|
location = (elem.centerLat(), elem.centerLon()) # Add coordinates (lat, lon)
|
||||||
|
|
||||||
|
# TODO: exclude these from the get go
|
||||||
# skip if unprecise location
|
# skip if unprecise location
|
||||||
if name is None or location[0] is None:
|
if name is None or location[0] is None:
|
||||||
continue
|
continue
|
||||||
@@ -262,65 +280,86 @@ class LandmarkManager:
|
|||||||
if 'building:part' in elem.tags().keys() and elem.tag('building:part') == 'yes':
|
if 'building:part' in elem.tags().keys() and elem.tag('building:part') == 'yes':
|
||||||
continue
|
continue
|
||||||
|
|
||||||
else :
|
osm_type = elem.type() # Add type: 'way' or 'relation'
|
||||||
osm_type = elem.type() # Add type : 'way' or 'relation'
|
osm_id = elem.id() # Add OSM id
|
||||||
osm_id = elem.id() # Add OSM id
|
elem_type = landmarktype # Add the landmark type as 'sightseeing,
|
||||||
elem_type = landmarktype # Add the landmark type as 'sightseeing,
|
n_tags = len(elem.tags().keys()) # Add number of tags
|
||||||
n_tags = len(elem.tags().keys()) # Add number of tags
|
|
||||||
|
|
||||||
# remove specific tags
|
# remove specific tags
|
||||||
skip = False
|
skip = False
|
||||||
for tag in elem.tags().keys() :
|
for tag in elem.tags().keys():
|
||||||
if "pay" in tag :
|
if "pay" in tag:
|
||||||
n_tags -= 1 # discard payment options for tags
|
n_tags -= 1 # discard payment options for tags
|
||||||
|
|
||||||
if "disused" in tag :
|
if "disused" in tag:
|
||||||
skip = True # skip disused amenities
|
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
|
break
|
||||||
|
|
||||||
if "wikipedia" in tag :
|
if tag == "building" and elem.tag('building') in ['retail', 'supermarket', 'parking']:
|
||||||
n_tags += 3 # wikipedia entries count more
|
skip = True
|
||||||
|
break
|
||||||
|
|
||||||
if tag == "wikidata" :
|
if skip:
|
||||||
Q = elem.tag('wikidata')
|
continue
|
||||||
site = Site("wikidata", "wikidata")
|
|
||||||
item = ItemPage(site, Q)
|
|
||||||
item.get()
|
|
||||||
n_languages = len(item.labels)
|
|
||||||
n_tags += n_languages/10
|
|
||||||
|
|
||||||
if elem_type != "nature" :
|
score = score_function(location, n_tags)
|
||||||
if "leisure" in tag and elem.tag('leisure') == "park":
|
if score != 0:
|
||||||
elem_type = "nature"
|
# 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)
|
||||||
|
|
||||||
if amenity not in ["'shop'='department_store'", "'shop'='mall'"] :
|
self.logger.debug(f"Fetched {len(return_list)} landmarks of type {landmarktype} in {bbox}")
|
||||||
if "shop" in tag :
|
|
||||||
skip = True
|
|
||||||
break
|
|
||||||
|
|
||||||
if tag == "building" and elem.tag('building') in ['retail', 'supermarket', 'parking']:
|
return return_list
|
||||||
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
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
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
|
||||||
|
|||||||
@@ -24,7 +24,7 @@ def write_data(L: List[Landmark], file_name: str):
|
|||||||
|
|
||||||
|
|
||||||
def test(start_coords: tuple[float, float], finish_coords: tuple[float, float] = None) -> List[Landmark]:
|
def test(start_coords: tuple[float, float], finish_coords: tuple[float, float] = None) -> List[Landmark]:
|
||||||
|
manager = LandmarkManager()
|
||||||
|
|
||||||
|
|
||||||
preferences = Preferences(
|
preferences = Preferences(
|
||||||
@@ -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)
|
#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
|
# 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
|
# Store data to file for debug purposes
|
||||||
# write_data(landmarks, "landmarks_Strasbourg.txt")
|
# write_data(landmarks, "landmarks_Strasbourg.txt")
|
||||||
|
|||||||
@@ -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
|
|
||||||
|
|
||||||
|
|
||||||
@@ -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
|
|
||||||
@@ -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
|
|
||||||
@@ -1,4 +0,0 @@
|
|||||||
apiVersion: v1
|
|
||||||
kind: Namespace
|
|
||||||
metadata:
|
|
||||||
name: placeholder
|
|
||||||
@@ -1,11 +0,0 @@
|
|||||||
apiVersion: v1
|
|
||||||
kind: PersistentVolumeClaim
|
|
||||||
metadata:
|
|
||||||
name: osm-cache
|
|
||||||
spec:
|
|
||||||
storageClassName: "nfs-client"
|
|
||||||
accessModes:
|
|
||||||
- ReadWriteOnce
|
|
||||||
resources:
|
|
||||||
requests:
|
|
||||||
storage: "5Gi"
|
|
||||||
@@ -1,11 +0,0 @@
|
|||||||
apiVersion: v1
|
|
||||||
kind: Service
|
|
||||||
metadata:
|
|
||||||
name: nav-service
|
|
||||||
spec:
|
|
||||||
selector:
|
|
||||||
app: nav-backend
|
|
||||||
ports:
|
|
||||||
- protocol: TCP
|
|
||||||
port: 8000
|
|
||||||
targetPort: 8000
|
|
||||||
Reference in New Issue
Block a user