cleanup-backend #13
@ -14,13 +14,13 @@ jobs:
|
||||
steps:
|
||||
|
||||
- uses: https://gitea.com/actions/checkout@v4
|
||||
|
||||
|
||||
- name: Login to Docker Registry
|
||||
uses: docker/login-action@v3
|
||||
with:
|
||||
registry: git.kluster.moll.re
|
||||
username: ${{ gitea.repository_owner }}
|
||||
password: ${{ secrets.DOCKER_PUSH_TOKEN }}
|
||||
password: ${{ secrets.PACKAGE_REGISTRY_ACCESS }}
|
||||
|
||||
- name: Set up Docker Buildx
|
||||
uses: docker/setup-buildx-action@v3
|
||||
@ -29,5 +29,5 @@ jobs:
|
||||
uses: docker/build-push-action@v5
|
||||
with:
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||||
context: backend
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||||
tags: git.kluster.moll.re/remoll/fast_network_navigation/backend:latest
|
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tags: git.kluster.moll.re/anydev/anyway-backend:latest
|
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push: true
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|
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@ -44,7 +44,7 @@ jobs:
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- name: Add required secrets
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run: |
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echo ${{ secrets.ANDROID_SECRETS_BASE64 }} | base64 -d > ./android/secrets.properties
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echo ${{ secrets.ANDROID_SECRETS_PROPERTIES }} > ./android/secrets.properties
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working-directory: ./frontend
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|
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- name: Sanity check
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@ -1,34 +1,34 @@
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on:
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pull_request:
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branches:
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- main
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paths:
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- frontend/**
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# on:
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# pull_request:
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# branches:
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# - main
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# paths:
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# - frontend/**
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name: Build web
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# name: Build web
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jobs:
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build:
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name: Build Web
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runs-on: ubuntu-latest
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steps:
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# jobs:
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# build:
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# name: Build Web
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# runs-on: ubuntu-latest
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# steps:
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- name: Install prerequisites
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run: |
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sudo apt-get update
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sudo apt-get install -y xz-utils
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# - name: Install prerequisites
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# run: |
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# sudo apt-get update
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# sudo apt-get install -y xz-utils
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- uses: actions/checkout@v4
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# - uses: actions/checkout@v4
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|
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- uses: https://github.com/subosito/flutter-action@v2
|
||||
with:
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channel: stable
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flutter-version: 3.19.6
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cache: true
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# - uses: https://github.com/subosito/flutter-action@v2
|
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# with:
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# channel: stable
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# flutter-version: 3.19.6
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# cache: true
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|
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- run: flutter pub get
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working-directory: ./frontend
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# - run: flutter pub get
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# working-directory: ./frontend
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|
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- run: flutter build web
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working-directory: ./frontend
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# - run: flutter build web
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# working-directory: ./frontend
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||||
52
.vscode/launch.json
vendored
Normal file
52
.vscode/launch.json
vendored
Normal file
@ -0,0 +1,52 @@
|
||||
{
|
||||
// Use IntelliSense to learn about possible attributes.
|
||||
// Hover to view descriptions of existing attributes.
|
||||
// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
|
||||
"version": "0.2.0",
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"configurations": [
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// backend - python using fastapi
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{
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"name": "Backend - debug",
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"type": "debugpy",
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"request": "launch",
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"module": "uvicorn",
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"env": {
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"DEBUG": "true"
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},
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"args": [
|
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"--app-dir",
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"src",
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"main:app",
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"--reload",
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],
|
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"jinja": true,
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"cwd": "${workspaceFolder}/backend"
|
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},
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{
|
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"name": "Backend - tester",
|
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"type": "debugpy",
|
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"request": "launch",
|
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"program": "src/tester.py",
|
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"env": {
|
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"DEBUG": "true"
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},
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||||
"cwd": "${workspaceFolder}/backend"
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},
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// frontend - flutter app
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{
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||||
"name": "Frontend - debug",
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"type": "dart",
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"request": "launch",
|
||||
"program": "lib/main.dart",
|
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"cwd": "${workspaceFolder}/frontend"
|
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},
|
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{
|
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"name": "Frontend - profile",
|
||||
"type": "dart",
|
||||
"request": "launch",
|
||||
"program": "lib/main.dart",
|
||||
"flutterMode": "profile",
|
||||
"cwd": "${workspaceFolder}/frontend"
|
||||
}
|
||||
]
|
||||
}
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||||
@ -1,4 +1,4 @@
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FROM python:3
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FROM python:3.11-slim
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|
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WORKDIR /app
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COPY Pipfile Pipfile.lock .
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@ -14,4 +14,4 @@ EXPOSE 8000
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||||
ENV NUM_WORKERS=1
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||||
ENV OSM_CACHE_DIR=/cache
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||||
|
||||
CMD ["pipenv", "run", "fastapi", "run", "src/main.py", '--port 8000', '--workers $NUM_WORKERS']
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CMD fastapi run src/main.py --port 8000 --workers $NUM_WORKERS
|
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|
||||
@ -3,17 +3,14 @@ url = "https://pypi.org/simple"
|
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verify_ssl = true
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name = "pypi"
|
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|
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[dev-packages]
|
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|
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[packages]
|
||||
numpy = "*"
|
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scipy = "*"
|
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fastapi = "*"
|
||||
pydantic = "*"
|
||||
shapely = "*"
|
||||
networkx = "*"
|
||||
geopy = "*"
|
||||
requests = ">=2.20.1"
|
||||
mwparserfromhell = ">=0.5.2"
|
||||
packaging = "*"
|
||||
shapely = "*"
|
||||
scipy = "*"
|
||||
osmpythontools = "*"
|
||||
pywikibot = "*"
|
||||
|
||||
[dev-packages]
|
||||
|
||||
631
backend/Pipfile.lock
generated
631
backend/Pipfile.lock
generated
@ -1,7 +1,7 @@
|
||||
{
|
||||
"_meta": {
|
||||
"hash": {
|
||||
"sha256": "b089081defe09c4ffad85fd22c5de336c76b31e7f6f67e5969b731bdaa58e528"
|
||||
"sha256": "f0de801038593d42d8b780d14c2c72bb4f5f5e66df02f72244917ede5d5ebce6"
|
||||
},
|
||||
"pipfile-spec": 6,
|
||||
"requires": {},
|
||||
@ -30,6 +30,14 @@
|
||||
"markers": "python_version >= '3.8'",
|
||||
"version": "==4.4.0"
|
||||
},
|
||||
"beautifulsoup4": {
|
||||
"hashes": [
|
||||
"sha256:74e3d1928edc070d21748185c46e3fb33490f22f52a3addee9aee0f4f7781051",
|
||||
"sha256:b80878c9f40111313e55da8ba20bdba06d8fa3969fc68304167741bbf9e082ed"
|
||||
],
|
||||
"markers": "python_full_version >= '3.6.0'",
|
||||
"version": "==4.12.3"
|
||||
},
|
||||
"certifi": {
|
||||
"hashes": [
|
||||
"sha256:5a1e7645bc0ec61a09e26c36f6106dd4cf40c6db3a1fb6352b0244e7fb057c7b",
|
||||
@ -134,102 +142,6 @@
|
||||
"markers": "python_full_version >= '3.7.0'",
|
||||
"version": "==3.3.2"
|
||||
},
|
||||
"charset-normalizer": {
|
||||
"hashes": [
|
||||
"sha256:06435b539f889b1f6f4ac1758871aae42dc3a8c0e24ac9e60c2384973ad73027",
|
||||
"sha256:06a81e93cd441c56a9b65d8e1d043daeb97a3d0856d177d5c90ba85acb3db087",
|
||||
"sha256:0a55554a2fa0d408816b3b5cedf0045f4b8e1a6065aec45849de2d6f3f8e9786",
|
||||
"sha256:0b2b64d2bb6d3fb9112bafa732def486049e63de9618b5843bcdd081d8144cd8",
|
||||
"sha256:10955842570876604d404661fbccbc9c7e684caf432c09c715ec38fbae45ae09",
|
||||
"sha256:122c7fa62b130ed55f8f285bfd56d5f4b4a5b503609d181f9ad85e55c89f4185",
|
||||
"sha256:1ceae2f17a9c33cb48e3263960dc5fc8005351ee19db217e9b1bb15d28c02574",
|
||||
"sha256:1d3193f4a680c64b4b6a9115943538edb896edc190f0b222e73761716519268e",
|
||||
"sha256:1f79682fbe303db92bc2b1136016a38a42e835d932bab5b3b1bfcfbf0640e519",
|
||||
"sha256:2127566c664442652f024c837091890cb1942c30937add288223dc895793f898",
|
||||
"sha256:22afcb9f253dac0696b5a4be4a1c0f8762f8239e21b99680099abd9b2b1b2269",
|
||||
"sha256:25baf083bf6f6b341f4121c2f3c548875ee6f5339300e08be3f2b2ba1721cdd3",
|
||||
"sha256:2e81c7b9c8979ce92ed306c249d46894776a909505d8f5a4ba55b14206e3222f",
|
||||
"sha256:3287761bc4ee9e33561a7e058c72ac0938c4f57fe49a09eae428fd88aafe7bb6",
|
||||
"sha256:34d1c8da1e78d2e001f363791c98a272bb734000fcef47a491c1e3b0505657a8",
|
||||
"sha256:37e55c8e51c236f95b033f6fb391d7d7970ba5fe7ff453dad675e88cf303377a",
|
||||
"sha256:3d47fa203a7bd9c5b6cee4736ee84ca03b8ef23193c0d1ca99b5089f72645c73",
|
||||
"sha256:3e4d1f6587322d2788836a99c69062fbb091331ec940e02d12d179c1d53e25fc",
|
||||
"sha256:42cb296636fcc8b0644486d15c12376cb9fa75443e00fb25de0b8602e64c1714",
|
||||
"sha256:45485e01ff4d3630ec0d9617310448a8702f70e9c01906b0d0118bdf9d124cf2",
|
||||
"sha256:4a78b2b446bd7c934f5dcedc588903fb2f5eec172f3d29e52a9096a43722adfc",
|
||||
"sha256:4ab2fe47fae9e0f9dee8c04187ce5d09f48eabe611be8259444906793ab7cbce",
|
||||
"sha256:4d0d1650369165a14e14e1e47b372cfcb31d6ab44e6e33cb2d4e57265290044d",
|
||||
"sha256:549a3a73da901d5bc3ce8d24e0600d1fa85524c10287f6004fbab87672bf3e1e",
|
||||
"sha256:55086ee1064215781fff39a1af09518bc9255b50d6333f2e4c74ca09fac6a8f6",
|
||||
"sha256:572c3763a264ba47b3cf708a44ce965d98555f618ca42c926a9c1616d8f34269",
|
||||
"sha256:573f6eac48f4769d667c4442081b1794f52919e7edada77495aaed9236d13a96",
|
||||
"sha256:5b4c145409bef602a690e7cfad0a15a55c13320ff7a3ad7ca59c13bb8ba4d45d",
|
||||
"sha256:6463effa3186ea09411d50efc7d85360b38d5f09b870c48e4600f63af490e56a",
|
||||
"sha256:65f6f63034100ead094b8744b3b97965785388f308a64cf8d7c34f2f2e5be0c4",
|
||||
"sha256:663946639d296df6a2bb2aa51b60a2454ca1cb29835324c640dafb5ff2131a77",
|
||||
"sha256:6897af51655e3691ff853668779c7bad41579facacf5fd7253b0133308cf000d",
|
||||
"sha256:68d1f8a9e9e37c1223b656399be5d6b448dea850bed7d0f87a8311f1ff3dabb0",
|
||||
"sha256:6ac7ffc7ad6d040517be39eb591cac5ff87416c2537df6ba3cba3bae290c0fed",
|
||||
"sha256:6b3251890fff30ee142c44144871185dbe13b11bab478a88887a639655be1068",
|
||||
"sha256:6c4caeef8fa63d06bd437cd4bdcf3ffefe6738fb1b25951440d80dc7df8c03ac",
|
||||
"sha256:6ef1d82a3af9d3eecdba2321dc1b3c238245d890843e040e41e470ffa64c3e25",
|
||||
"sha256:753f10e867343b4511128c6ed8c82f7bec3bd026875576dfd88483c5c73b2fd8",
|
||||
"sha256:7cd13a2e3ddeed6913a65e66e94b51d80a041145a026c27e6bb76c31a853c6ab",
|
||||
"sha256:7ed9e526742851e8d5cc9e6cf41427dfc6068d4f5a3bb03659444b4cabf6bc26",
|
||||
"sha256:7f04c839ed0b6b98b1a7501a002144b76c18fb1c1850c8b98d458ac269e26ed2",
|
||||
"sha256:802fe99cca7457642125a8a88a084cef28ff0cf9407060f7b93dca5aa25480db",
|
||||
"sha256:80402cd6ee291dcb72644d6eac93785fe2c8b9cb30893c1af5b8fdd753b9d40f",
|
||||
"sha256:8465322196c8b4d7ab6d1e049e4c5cb460d0394da4a27d23cc242fbf0034b6b5",
|
||||
"sha256:86216b5cee4b06df986d214f664305142d9c76df9b6512be2738aa72a2048f99",
|
||||
"sha256:87d1351268731db79e0f8e745d92493ee2841c974128ef629dc518b937d9194c",
|
||||
"sha256:8bdb58ff7ba23002a4c5808d608e4e6c687175724f54a5dade5fa8c67b604e4d",
|
||||
"sha256:8c622a5fe39a48f78944a87d4fb8a53ee07344641b0562c540d840748571b811",
|
||||
"sha256:8d756e44e94489e49571086ef83b2bb8ce311e730092d2c34ca8f7d925cb20aa",
|
||||
"sha256:8f4a014bc36d3c57402e2977dada34f9c12300af536839dc38c0beab8878f38a",
|
||||
"sha256:9063e24fdb1e498ab71cb7419e24622516c4a04476b17a2dab57e8baa30d6e03",
|
||||
"sha256:90d558489962fd4918143277a773316e56c72da56ec7aa3dc3dbbe20fdfed15b",
|
||||
"sha256:923c0c831b7cfcb071580d3f46c4baf50f174be571576556269530f4bbd79d04",
|
||||
"sha256:95f2a5796329323b8f0512e09dbb7a1860c46a39da62ecb2324f116fa8fdc85c",
|
||||
"sha256:96b02a3dc4381e5494fad39be677abcb5e6634bf7b4fa83a6dd3112607547001",
|
||||
"sha256:9f96df6923e21816da7e0ad3fd47dd8f94b2a5ce594e00677c0013018b813458",
|
||||
"sha256:a10af20b82360ab00827f916a6058451b723b4e65030c5a18577c8b2de5b3389",
|
||||
"sha256:a50aebfa173e157099939b17f18600f72f84eed3049e743b68ad15bd69b6bf99",
|
||||
"sha256:a981a536974bbc7a512cf44ed14938cf01030a99e9b3a06dd59578882f06f985",
|
||||
"sha256:a9a8e9031d613fd2009c182b69c7b2c1ef8239a0efb1df3f7c8da66d5dd3d537",
|
||||
"sha256:ae5f4161f18c61806f411a13b0310bea87f987c7d2ecdbdaad0e94eb2e404238",
|
||||
"sha256:aed38f6e4fb3f5d6bf81bfa990a07806be9d83cf7bacef998ab1a9bd660a581f",
|
||||
"sha256:b01b88d45a6fcb69667cd6d2f7a9aeb4bf53760d7fc536bf679ec94fe9f3ff3d",
|
||||
"sha256:b261ccdec7821281dade748d088bb6e9b69e6d15b30652b74cbbac25e280b796",
|
||||
"sha256:b2b0a0c0517616b6869869f8c581d4eb2dd83a4d79e0ebcb7d373ef9956aeb0a",
|
||||
"sha256:b4a23f61ce87adf89be746c8a8974fe1c823c891d8f86eb218bb957c924bb143",
|
||||
"sha256:bd8f7df7d12c2db9fab40bdd87a7c09b1530128315d047a086fa3ae3435cb3a8",
|
||||
"sha256:beb58fe5cdb101e3a055192ac291b7a21e3b7ef4f67fa1d74e331a7f2124341c",
|
||||
"sha256:c002b4ffc0be611f0d9da932eb0f704fe2602a9a949d1f738e4c34c75b0863d5",
|
||||
"sha256:c083af607d2515612056a31f0a8d9e0fcb5876b7bfc0abad3ecd275bc4ebc2d5",
|
||||
"sha256:c180f51afb394e165eafe4ac2936a14bee3eb10debc9d9e4db8958fe36afe711",
|
||||
"sha256:c235ebd9baae02f1b77bcea61bce332cb4331dc3617d254df3323aa01ab47bd4",
|
||||
"sha256:cd70574b12bb8a4d2aaa0094515df2463cb429d8536cfb6c7ce983246983e5a6",
|
||||
"sha256:d0eccceffcb53201b5bfebb52600a5fb483a20b61da9dbc885f8b103cbe7598c",
|
||||
"sha256:d965bba47ddeec8cd560687584e88cf699fd28f192ceb452d1d7ee807c5597b7",
|
||||
"sha256:db364eca23f876da6f9e16c9da0df51aa4f104a972735574842618b8c6d999d4",
|
||||
"sha256:ddbb2551d7e0102e7252db79ba445cdab71b26640817ab1e3e3648dad515003b",
|
||||
"sha256:deb6be0ac38ece9ba87dea880e438f25ca3eddfac8b002a2ec3d9183a454e8ae",
|
||||
"sha256:e06ed3eb3218bc64786f7db41917d4e686cc4856944f53d5bdf83a6884432e12",
|
||||
"sha256:e27ad930a842b4c5eb8ac0016b0a54f5aebbe679340c26101df33424142c143c",
|
||||
"sha256:e537484df0d8f426ce2afb2d0f8e1c3d0b114b83f8850e5f2fbea0e797bd82ae",
|
||||
"sha256:eb00ed941194665c332bf8e078baf037d6c35d7c4f3102ea2d4f16ca94a26dc8",
|
||||
"sha256:eb6904c354526e758fda7167b33005998fb68c46fbc10e013ca97f21ca5c8887",
|
||||
"sha256:eb8821e09e916165e160797a6c17edda0679379a4be5c716c260e836e122f54b",
|
||||
"sha256:efcb3f6676480691518c177e3b465bcddf57cea040302f9f4e6e191af91174d4",
|
||||
"sha256:f27273b60488abe721a075bcca6d7f3964f9f6f067c8c4c605743023d7d3944f",
|
||||
"sha256:f30c3cb33b24454a82faecaf01b19c18562b1e89558fb6c56de4d9118a032fd5",
|
||||
"sha256:fb69256e180cb6c8a894fee62b3afebae785babc1ee98b81cdf68bbca1987f33",
|
||||
"sha256:fd1abc0d89e30cc4e02e4064dc67fcc51bd941eb395c502aac3ec19fab46b519",
|
||||
"sha256:ff8fa367d09b717b2a17a052544193ad76cd49979c805768879cb63d9ca50561"
|
||||
],
|
||||
"markers": "python_full_version >= '3.7.0'",
|
||||
"version": "==3.3.2"
|
||||
},
|
||||
"click": {
|
||||
"hashes": [
|
||||
"sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28",
|
||||
@ -238,6 +150,64 @@
|
||||
"markers": "python_version >= '3.7'",
|
||||
"version": "==8.1.7"
|
||||
},
|
||||
"contourpy": {
|
||||
"hashes": [
|
||||
"sha256:00e5388f71c1a0610e6fe56b5c44ab7ba14165cdd6d695429c5cd94021e390b2",
|
||||
"sha256:10a37ae557aabf2509c79715cd20b62e4c7c28b8cd62dd7d99e5ed3ce28c3fd9",
|
||||
"sha256:11959f0ce4a6f7b76ec578576a0b61a28bdc0696194b6347ba3f1c53827178b9",
|
||||
"sha256:187fa1d4c6acc06adb0fae5544c59898ad781409e61a926ac7e84b8f276dcef4",
|
||||
"sha256:1a07fc092a4088ee952ddae19a2b2a85757b923217b7eed584fdf25f53a6e7ce",
|
||||
"sha256:1cac0a8f71a041aa587410424ad46dfa6a11f6149ceb219ce7dd48f6b02b87a7",
|
||||
"sha256:1d59e739ab0e3520e62a26c60707cc3ab0365d2f8fecea74bfe4de72dc56388f",
|
||||
"sha256:2855c8b0b55958265e8b5888d6a615ba02883b225f2227461aa9127c578a4922",
|
||||
"sha256:2e785e0f2ef0d567099b9ff92cbfb958d71c2d5b9259981cd9bee81bd194c9a4",
|
||||
"sha256:309be79c0a354afff9ff7da4aaed7c3257e77edf6c1b448a779329431ee79d7e",
|
||||
"sha256:39f3ecaf76cd98e802f094e0d4fbc6dc9c45a8d0c4d185f0f6c2234e14e5f75b",
|
||||
"sha256:457499c79fa84593f22454bbd27670227874cd2ff5d6c84e60575c8b50a69619",
|
||||
"sha256:49e70d111fee47284d9dd867c9bb9a7058a3c617274900780c43e38d90fe1205",
|
||||
"sha256:4c75507d0a55378240f781599c30e7776674dbaf883a46d1c90f37e563453480",
|
||||
"sha256:4c863140fafc615c14a4bf4efd0f4425c02230eb8ef02784c9a156461e62c965",
|
||||
"sha256:4d8908b3bee1c889e547867ca4cdc54e5ab6be6d3e078556814a22457f49423c",
|
||||
"sha256:5b9eb0ca724a241683c9685a484da9d35c872fd42756574a7cfbf58af26677fd",
|
||||
"sha256:6022cecf8f44e36af10bd9118ca71f371078b4c168b6e0fab43d4a889985dbb5",
|
||||
"sha256:6150ffa5c767bc6332df27157d95442c379b7dce3a38dff89c0f39b63275696f",
|
||||
"sha256:62828cada4a2b850dbef89c81f5a33741898b305db244904de418cc957ff05dc",
|
||||
"sha256:7b4182299f251060996af5249c286bae9361fa8c6a9cda5efc29fe8bfd6062ec",
|
||||
"sha256:94b34f32646ca0414237168d68a9157cb3889f06b096612afdd296003fdd32fd",
|
||||
"sha256:9ce6889abac9a42afd07a562c2d6d4b2b7134f83f18571d859b25624a331c90b",
|
||||
"sha256:9cffe0f850e89d7c0012a1fb8730f75edd4320a0a731ed0c183904fe6ecfc3a9",
|
||||
"sha256:a12a813949e5066148712a0626895c26b2578874e4cc63160bb007e6df3436fe",
|
||||
"sha256:a1eea9aecf761c661d096d39ed9026574de8adb2ae1c5bd7b33558af884fb2ce",
|
||||
"sha256:a31f94983fecbac95e58388210427d68cd30fe8a36927980fab9c20062645609",
|
||||
"sha256:ac58bdee53cbeba2ecad824fa8159493f0bf3b8ea4e93feb06c9a465d6c87da8",
|
||||
"sha256:af3f4485884750dddd9c25cb7e3915d83c2db92488b38ccb77dd594eac84c4a0",
|
||||
"sha256:b33d2bc4f69caedcd0a275329eb2198f560b325605810895627be5d4b876bf7f",
|
||||
"sha256:b59c0ffceff8d4d3996a45f2bb6f4c207f94684a96bf3d9728dbb77428dd8cb8",
|
||||
"sha256:bb6834cbd983b19f06908b45bfc2dad6ac9479ae04abe923a275b5f48f1a186b",
|
||||
"sha256:bd3db01f59fdcbce5b22afad19e390260d6d0222f35a1023d9adc5690a889364",
|
||||
"sha256:bd7c23df857d488f418439686d3b10ae2fbf9bc256cd045b37a8c16575ea1040",
|
||||
"sha256:c2528d60e398c7c4c799d56f907664673a807635b857df18f7ae64d3e6ce2d9f",
|
||||
"sha256:d31a63bc6e6d87f77d71e1abbd7387ab817a66733734883d1fc0021ed9bfa083",
|
||||
"sha256:d4492d82b3bc7fbb7e3610747b159869468079fe149ec5c4d771fa1f614a14df",
|
||||
"sha256:ddcb8581510311e13421b1f544403c16e901c4e8f09083c881fab2be80ee31ba",
|
||||
"sha256:e1d59258c3c67c865435d8fbeb35f8c59b8bef3d6f46c1f29f6123556af28445",
|
||||
"sha256:eb3315a8a236ee19b6df481fc5f997436e8ade24a9f03dfdc6bd490fea20c6da",
|
||||
"sha256:ef2b055471c0eb466033760a521efb9d8a32b99ab907fc8358481a1dd29e3bd3",
|
||||
"sha256:ef5adb9a3b1d0c645ff694f9bca7702ec2c70f4d734f9922ea34de02294fdf72",
|
||||
"sha256:f32c38afb74bd98ce26de7cc74a67b40afb7b05aae7b42924ea990d51e4dac02",
|
||||
"sha256:fe0ccca550bb8e5abc22f530ec0466136379c01321fd94f30a22231e8a48d985"
|
||||
],
|
||||
"markers": "python_version >= '3.9'",
|
||||
"version": "==1.2.1"
|
||||
},
|
||||
"cycler": {
|
||||
"hashes": [
|
||||
"sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30",
|
||||
"sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c"
|
||||
],
|
||||
"markers": "python_version >= '3.8'",
|
||||
"version": "==0.12.1"
|
||||
},
|
||||
"dnspython": {
|
||||
"hashes": [
|
||||
"sha256:5ef3b9680161f6fa89daf8ad451b5f1a33b18ae8a1c6778cdf4b43f08c0a6e50",
|
||||
@ -254,14 +224,6 @@
|
||||
"markers": "python_version >= '3.8'",
|
||||
"version": "==2.2.0"
|
||||
},
|
||||
"exceptiongroup": {
|
||||
"hashes": [
|
||||
"sha256:3111b9d131c238bec2f8f516e123e14ba243563fb135d3fe885990585aa7795b",
|
||||
"sha256:47c2edf7c6738fafb49fd34290706d1a1a2f4d1c6df275526b62cbb4aa5393cc"
|
||||
],
|
||||
"markers": "python_version < '3.11'",
|
||||
"version": "==1.2.2"
|
||||
},
|
||||
"fastapi": {
|
||||
"hashes": [
|
||||
"sha256:4f51cfa25d72f9fbc3280832e84b32494cf186f50158d364a8765aabf22587bf",
|
||||
@ -279,7 +241,7 @@
|
||||
"markers": "python_version >= '3.8'",
|
||||
"version": "==0.0.4"
|
||||
},
|
||||
"geopandas": {
|
||||
"fonttools": {
|
||||
"hashes": [
|
||||
"sha256:02569e9a810f9d11f4ae82c391ebc6fb5730d95a0657d24d754ed7763fb2d122",
|
||||
"sha256:0679a30b59d74b6242909945429dbddb08496935b82f91ea9bf6ad240ec23397",
|
||||
@ -433,6 +395,264 @@
|
||||
"markers": "python_version >= '3.7'",
|
||||
"version": "==3.1.4"
|
||||
},
|
||||
"kiwisolver": {
|
||||
"hashes": [
|
||||
"sha256:00bd361b903dc4bbf4eb165f24d1acbee754fce22ded24c3d56eec268658a5cf",
|
||||
"sha256:040c1aebeda72197ef477a906782b5ab0d387642e93bda547336b8957c61022e",
|
||||
"sha256:05703cf211d585109fcd72207a31bb170a0f22144d68298dc5e61b3c946518af",
|
||||
"sha256:06f54715b7737c2fecdbf140d1afb11a33d59508a47bf11bb38ecf21dc9ab79f",
|
||||
"sha256:0dc9db8e79f0036e8173c466d21ef18e1befc02de8bf8aa8dc0813a6dc8a7046",
|
||||
"sha256:0f114aa76dc1b8f636d077979c0ac22e7cd8f3493abbab152f20eb8d3cda71f3",
|
||||
"sha256:11863aa14a51fd6ec28688d76f1735f8f69ab1fabf388851a595d0721af042f5",
|
||||
"sha256:11c7de8f692fc99816e8ac50d1d1aef4f75126eefc33ac79aac02c099fd3db71",
|
||||
"sha256:11d011a7574eb3b82bcc9c1a1d35c1d7075677fdd15de527d91b46bd35e935ee",
|
||||
"sha256:146d14bebb7f1dc4d5fbf74f8a6cb15ac42baadee8912eb84ac0b3b2a3dc6ac3",
|
||||
"sha256:15568384086b6df3c65353820a4473575dbad192e35010f622c6ce3eebd57af9",
|
||||
"sha256:19df6e621f6d8b4b9c4d45f40a66839294ff2bb235e64d2178f7522d9170ac5b",
|
||||
"sha256:1b04139c4236a0f3aff534479b58f6f849a8b351e1314826c2d230849ed48985",
|
||||
"sha256:210ef2c3a1f03272649aff1ef992df2e724748918c4bc2d5a90352849eb40bea",
|
||||
"sha256:2270953c0d8cdab5d422bee7d2007f043473f9d2999631c86a223c9db56cbd16",
|
||||
"sha256:2400873bccc260b6ae184b2b8a4fec0e4082d30648eadb7c3d9a13405d861e89",
|
||||
"sha256:2a40773c71d7ccdd3798f6489aaac9eee213d566850a9533f8d26332d626b82c",
|
||||
"sha256:2c5674c4e74d939b9d91dda0fae10597ac7521768fec9e399c70a1f27e2ea2d9",
|
||||
"sha256:3195782b26fc03aa9c6913d5bad5aeb864bdc372924c093b0f1cebad603dd712",
|
||||
"sha256:31a82d498054cac9f6d0b53d02bb85811185bcb477d4b60144f915f3b3126342",
|
||||
"sha256:32d5cf40c4f7c7b3ca500f8985eb3fb3a7dfc023215e876f207956b5ea26632a",
|
||||
"sha256:346f5343b9e3f00b8db8ba359350eb124b98c99efd0b408728ac6ebf38173958",
|
||||
"sha256:378a214a1e3bbf5ac4a8708304318b4f890da88c9e6a07699c4ae7174c09a68d",
|
||||
"sha256:39b42c68602539407884cf70d6a480a469b93b81b7701378ba5e2328660c847a",
|
||||
"sha256:3a2b053a0ab7a3960c98725cfb0bf5b48ba82f64ec95fe06f1d06c99b552e130",
|
||||
"sha256:3aba7311af82e335dd1e36ffff68aaca609ca6290c2cb6d821a39aa075d8e3ff",
|
||||
"sha256:3cd32d6c13807e5c66a7cbb79f90b553642f296ae4518a60d8d76243b0ad2898",
|
||||
"sha256:3edd2fa14e68c9be82c5b16689e8d63d89fe927e56debd6e1dbce7a26a17f81b",
|
||||
"sha256:4c380469bd3f970ef677bf2bcba2b6b0b4d5c75e7a020fb863ef75084efad66f",
|
||||
"sha256:4e66e81a5779b65ac21764c295087de82235597a2293d18d943f8e9e32746265",
|
||||
"sha256:53abb58632235cd154176ced1ae8f0d29a6657aa1aa9decf50b899b755bc2b93",
|
||||
"sha256:5794cf59533bc3f1b1c821f7206a3617999db9fbefc345360aafe2e067514929",
|
||||
"sha256:59415f46a37f7f2efeec758353dd2eae1b07640d8ca0f0c42548ec4125492635",
|
||||
"sha256:59ec7b7c7e1a61061850d53aaf8e93db63dce0c936db1fda2658b70e4a1be709",
|
||||
"sha256:59edc41b24031bc25108e210c0def6f6c2191210492a972d585a06ff246bb79b",
|
||||
"sha256:5a580c91d686376f0f7c295357595c5a026e6cbc3d77b7c36e290201e7c11ecb",
|
||||
"sha256:5b94529f9b2591b7af5f3e0e730a4e0a41ea174af35a4fd067775f9bdfeee01a",
|
||||
"sha256:5c7b3b3a728dc6faf3fc372ef24f21d1e3cee2ac3e9596691d746e5a536de920",
|
||||
"sha256:5c90ae8c8d32e472be041e76f9d2f2dbff4d0b0be8bd4041770eddb18cf49a4e",
|
||||
"sha256:5e7139af55d1688f8b960ee9ad5adafc4ac17c1c473fe07133ac092310d76544",
|
||||
"sha256:5ff5cf3571589b6d13bfbfd6bcd7a3f659e42f96b5fd1c4830c4cf21d4f5ef45",
|
||||
"sha256:620ced262a86244e2be10a676b646f29c34537d0d9cc8eb26c08f53d98013390",
|
||||
"sha256:6512cb89e334e4700febbffaaa52761b65b4f5a3cf33f960213d5656cea36a77",
|
||||
"sha256:6c08e1312a9cf1074d17b17728d3dfce2a5125b2d791527f33ffbe805200a355",
|
||||
"sha256:6c3bd3cde54cafb87d74d8db50b909705c62b17c2099b8f2e25b461882e544ff",
|
||||
"sha256:6ef7afcd2d281494c0a9101d5c571970708ad911d028137cd558f02b851c08b4",
|
||||
"sha256:7269d9e5f1084a653d575c7ec012ff57f0c042258bf5db0954bf551c158466e7",
|
||||
"sha256:72d40b33e834371fd330fb1472ca19d9b8327acb79a5821d4008391db8e29f20",
|
||||
"sha256:74d1b44c6cfc897df648cc9fdaa09bc3e7679926e6f96df05775d4fb3946571c",
|
||||
"sha256:74db36e14a7d1ce0986fa104f7d5637aea5c82ca6326ed0ec5694280942d1162",
|
||||
"sha256:763773d53f07244148ccac5b084da5adb90bfaee39c197554f01b286cf869228",
|
||||
"sha256:76c6a5964640638cdeaa0c359382e5703e9293030fe730018ca06bc2010c4437",
|
||||
"sha256:76d9289ed3f7501012e05abb8358bbb129149dbd173f1f57a1bf1c22d19ab7cc",
|
||||
"sha256:7931d8f1f67c4be9ba1dd9c451fb0eeca1a25b89e4d3f89e828fe12a519b782a",
|
||||
"sha256:7b8b454bac16428b22560d0a1cf0a09875339cab69df61d7805bf48919415901",
|
||||
"sha256:7e5bab140c309cb3a6ce373a9e71eb7e4873c70c2dda01df6820474f9889d6d4",
|
||||
"sha256:83d78376d0d4fd884e2c114d0621624b73d2aba4e2788182d286309ebdeed770",
|
||||
"sha256:852542f9481f4a62dbb5dd99e8ab7aedfeb8fb6342349a181d4036877410f525",
|
||||
"sha256:85267bd1aa8880a9c88a8cb71e18d3d64d2751a790e6ca6c27b8ccc724bcd5ad",
|
||||
"sha256:88a2df29d4724b9237fc0c6eaf2a1adae0cdc0b3e9f4d8e7dc54b16812d2d81a",
|
||||
"sha256:88b9f257ca61b838b6f8094a62418421f87ac2a1069f7e896c36a7d86b5d4c29",
|
||||
"sha256:8ab3919a9997ab7ef2fbbed0cc99bb28d3c13e6d4b1ad36e97e482558a91be90",
|
||||
"sha256:92dea1ffe3714fa8eb6a314d2b3c773208d865a0e0d35e713ec54eea08a66250",
|
||||
"sha256:9407b6a5f0d675e8a827ad8742e1d6b49d9c1a1da5d952a67d50ef5f4170b18d",
|
||||
"sha256:9408acf3270c4b6baad483865191e3e582b638b1654a007c62e3efe96f09a9a3",
|
||||
"sha256:955e8513d07a283056b1396e9a57ceddbd272d9252c14f154d450d227606eb54",
|
||||
"sha256:9db8ea4c388fdb0f780fe91346fd438657ea602d58348753d9fb265ce1bca67f",
|
||||
"sha256:9eaa8b117dc8337728e834b9c6e2611f10c79e38f65157c4c38e9400286f5cb1",
|
||||
"sha256:a51a263952b1429e429ff236d2f5a21c5125437861baeed77f5e1cc2d2c7c6da",
|
||||
"sha256:a6aa6315319a052b4ee378aa171959c898a6183f15c1e541821c5c59beaa0238",
|
||||
"sha256:aa12042de0171fad672b6c59df69106d20d5596e4f87b5e8f76df757a7c399aa",
|
||||
"sha256:aaf7be1207676ac608a50cd08f102f6742dbfc70e8d60c4db1c6897f62f71523",
|
||||
"sha256:b0157420efcb803e71d1b28e2c287518b8808b7cf1ab8af36718fd0a2c453eb0",
|
||||
"sha256:b3f7e75f3015df442238cca659f8baa5f42ce2a8582727981cbfa15fee0ee205",
|
||||
"sha256:b9098e0049e88c6a24ff64545cdfc50807818ba6c1b739cae221bbbcbc58aad3",
|
||||
"sha256:ba55dce0a9b8ff59495ddd050a0225d58bd0983d09f87cfe2b6aec4f2c1234e4",
|
||||
"sha256:bb86433b1cfe686da83ce32a9d3a8dd308e85c76b60896d58f082136f10bffac",
|
||||
"sha256:bbea0db94288e29afcc4c28afbf3a7ccaf2d7e027489c449cf7e8f83c6346eb9",
|
||||
"sha256:bbf1d63eef84b2e8c89011b7f2235b1e0bf7dacc11cac9431fc6468e99ac77fb",
|
||||
"sha256:c7940c1dc63eb37a67721b10d703247552416f719c4188c54e04334321351ced",
|
||||
"sha256:c9bf3325c47b11b2e51bca0824ea217c7cd84491d8ac4eefd1e409705ef092bd",
|
||||
"sha256:cdc8a402aaee9a798b50d8b827d7ecf75edc5fb35ea0f91f213ff927c15f4ff0",
|
||||
"sha256:ceec1a6bc6cab1d6ff5d06592a91a692f90ec7505d6463a88a52cc0eb58545da",
|
||||
"sha256:cfe6ab8da05c01ba6fbea630377b5da2cd9bcbc6338510116b01c1bc939a2c18",
|
||||
"sha256:d099e745a512f7e3bbe7249ca835f4d357c586d78d79ae8f1dcd4d8adeb9bda9",
|
||||
"sha256:d0ef46024e6a3d79c01ff13801cb19d0cad7fd859b15037aec74315540acc276",
|
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||||
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||||
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||||
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|
||||
},
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||||
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||||
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||||
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||||
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"sha256:355216845c60bd96232cd8d8c40e8f9765cc86f46880e43a8fd22dc1a1a8cab1",
|
||||
@ -579,86 +799,73 @@
|
||||
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|
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|
||||
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|
||||
@ -895,70 +1102,13 @@
|
||||
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|
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|
||||
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|
||||
"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"
|
||||
"markers": "python_full_version >= '3.6.8'",
|
||||
"version": "==3.1.2"
|
||||
},
|
||||
"python-dateutil": {
|
||||
"hashes": [
|
||||
@ -1060,7 +1210,6 @@
|
||||
"sha256:55365417734eb18255590a9ff9eb97e9e1da868d4ccd6402399eaf68af20a760",
|
||||
"sha256:70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6"
|
||||
],
|
||||
"index": "pypi",
|
||||
"markers": "python_version >= '3.8'",
|
||||
"version": "==2.32.3"
|
||||
},
|
||||
@ -1171,6 +1320,14 @@
|
||||
"markers": "python_version >= '3.7'",
|
||||
"version": "==1.3.1"
|
||||
},
|
||||
"soupsieve": {
|
||||
"hashes": [
|
||||
"sha256:5663d5a7b3bfaeee0bc4372e7fc48f9cff4940b3eec54a6451cc5299f1097690",
|
||||
"sha256:eaa337ff55a1579b6549dc679565eac1e3d000563bcb1c8ab0d0fefbc0c2cdc7"
|
||||
],
|
||||
"markers": "python_version >= '3.8'",
|
||||
"version": "==2.5"
|
||||
},
|
||||
"starlette": {
|
||||
"hashes": [
|
||||
"sha256:6fe59f29268538e5d0d182f2791a479a0c64638e6935d1c6989e63fb2699c6ee",
|
||||
@ -1300,11 +1457,11 @@
|
||||
"standard"
|
||||
],
|
||||
"hashes": [
|
||||
"sha256:cd17daa7f3b9d7a24de3617820e634d0933b69eed8e33a516071174427238c81",
|
||||
"sha256:d46cd8e0fd80240baffbcd9ec1012a712938754afcf81bce56c024c1656aece8"
|
||||
"sha256:0d114d0831ff1adbf231d358cbf42f17333413042552a624ea6a9b4c33dcfd81",
|
||||
"sha256:94a3608da0e530cea8f69683aa4126364ac18e3826b6630d1a65f4638aade503"
|
||||
],
|
||||
"markers": "python_version >= '3.8'",
|
||||
"version": "==0.30.1"
|
||||
"version": "==0.30.3"
|
||||
},
|
||||
"uvloop": {
|
||||
"hashes": [
|
||||
@ -1498,6 +1655,14 @@
|
||||
"sha256:ffefa1374cd508d633646d51a8e9277763a9b78ae71324183693959cf94635a7"
|
||||
],
|
||||
"version": "==12.0"
|
||||
},
|
||||
"xarray": {
|
||||
"hashes": [
|
||||
"sha256:0b91e0bc4dc0296947947640fe31ec6e867ce258d2f7cbc10bedf4a6d68340c7",
|
||||
"sha256:721a7394e8ec3d592b2d8ebe21eed074ac077dc1bb1bd777ce00e41700b4866c"
|
||||
],
|
||||
"markers": "python_version >= '3.9'",
|
||||
"version": "==2024.6.0"
|
||||
}
|
||||
},
|
||||
"develop": {}
|
||||
|
||||
@ -1,12 +0,0 @@
|
||||
'leisure'='park'
|
||||
geological
|
||||
'natural'='geyser'
|
||||
'natural'='hot_spring'
|
||||
'natural'='arch'
|
||||
'natural'='volcano'
|
||||
'natural'='stone'
|
||||
'tourism'='alpine_hut'
|
||||
'tourism'='viewpoint'
|
||||
'tourism'='zoo'
|
||||
'tourism'='artwork'
|
||||
'waterway'='waterfall'
|
||||
@ -1,11 +0,0 @@
|
||||
'tourism'='museum'
|
||||
'tourism'='attraction'
|
||||
'tourism'='gallery'
|
||||
'tourism'='artwork'
|
||||
historic
|
||||
'amenity'='planetarium'
|
||||
'amenity'='place_of_worship'
|
||||
'amenity'='fountain'
|
||||
'water'='reflecting_pool'
|
||||
# 'tourism'='attraction' might be a bit too broad
|
||||
# historic as well
|
||||
@ -1,8 +1,9 @@
|
||||
import logging.config
|
||||
from pathlib import Path
|
||||
import os
|
||||
import logging
|
||||
|
||||
PARAMETERS_DIR = Path('src/parameters')
|
||||
LOCATION_PREFIX = Path('src')
|
||||
PARAMETERS_DIR = LOCATION_PREFIX / 'parameters'
|
||||
AMENITY_SELECTORS_PATH = PARAMETERS_DIR / 'amenity_selectors.yaml'
|
||||
LANDMARK_PARAMETERS_PATH = PARAMETERS_DIR / 'landmark_parameters.yaml'
|
||||
OPTIMIZER_PARAMETERS_PATH = PARAMETERS_DIR / 'optimizer_parameters.yaml'
|
||||
@ -12,8 +13,15 @@ OPTIMIZER_PARAMETERS_PATH = PARAMETERS_DIR / 'optimizer_parameters.yaml'
|
||||
cache_dir_string = os.getenv('OSM_CACHE_DIR', './cache')
|
||||
OSM_CACHE_DIR = Path(cache_dir_string)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
logging.basicConfig(
|
||||
level = logging.INFO,
|
||||
format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
|
||||
)
|
||||
|
||||
import logging
|
||||
import yaml
|
||||
|
||||
LOGGING_CONFIG = LOCATION_PREFIX / 'log_config.yaml'
|
||||
config = yaml.safe_load(LOGGING_CONFIG.read_text())
|
||||
|
||||
logging.config.dictConfig(config)
|
||||
|
||||
# if we are in a debug session, set the log level to debug
|
||||
if os.getenv('DEBUG', False):
|
||||
logging.getLogger().setLevel(logging.DEBUG)
|
||||
|
||||
@ -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)
|
||||
@ -1,308 +0,0 @@
|
||||
import math as m
|
||||
import json, os
|
||||
|
||||
from typing import List, Tuple, Optional
|
||||
from OSMPythonTools.overpass import Overpass, overpassQueryBuilder
|
||||
from pywikibot import ItemPage, Site
|
||||
from pywikibot import config
|
||||
config.put_throttle = 0
|
||||
config.maxlag = 0
|
||||
|
||||
from structs.landmarks import Landmark, LandmarkType
|
||||
from structs.preferences import Preferences, Preference
|
||||
|
||||
|
||||
SIGHTSEEING = LandmarkType(landmark_type='sightseeing')
|
||||
NATURE = LandmarkType(landmark_type='nature')
|
||||
SHOPPING = LandmarkType(landmark_type='shopping')
|
||||
|
||||
|
||||
# Include the json here
|
||||
# Create a list of all things to visit given some preferences and a city. Ready for the optimizer
|
||||
def generate_landmarks(preferences: Preferences, coordinates: Tuple[float, float]) :
|
||||
|
||||
l_sights, l_nature, l_shop = get_amenities()
|
||||
L = []
|
||||
|
||||
# List for sightseeing
|
||||
if preferences.sightseeing.score != 0 :
|
||||
L1 = get_landmarks(l_sights, SIGHTSEEING, coordinates=coordinates)
|
||||
correct_score(L1, preferences.sightseeing)
|
||||
L += L1
|
||||
|
||||
# List for nature
|
||||
if preferences.nature.score != 0 :
|
||||
L2 = get_landmarks(l_nature, NATURE, coordinates=coordinates)
|
||||
correct_score(L2, preferences.nature)
|
||||
L += L2
|
||||
|
||||
# List for shopping
|
||||
if preferences.shopping.score != 0 :
|
||||
L3 = get_landmarks(l_shop, SHOPPING, coordinates=coordinates)
|
||||
correct_score(L3, preferences.shopping)
|
||||
L += L3
|
||||
|
||||
L = remove_duplicates(L)
|
||||
|
||||
return L, take_most_important(L)
|
||||
|
||||
|
||||
# Helper function to gather the amenities list
|
||||
def get_amenities() -> List[List[str]] :
|
||||
|
||||
# Get the list of amenities from the files
|
||||
sightseeing = get_list('/amenities/sightseeing.am')
|
||||
nature = get_list('/amenities/nature.am')
|
||||
shopping = get_list('/amenities/shopping.am')
|
||||
|
||||
return sightseeing, nature, shopping
|
||||
|
||||
|
||||
# Helper function to read a .am file and generate the corresponding list
|
||||
def get_list(path: str) -> List[str] :
|
||||
|
||||
with open(os.path.dirname(os.path.abspath(__file__)) + path) as f :
|
||||
content = f.readlines()
|
||||
|
||||
amenities = []
|
||||
for line in content :
|
||||
if not line.startswith('#') :
|
||||
amenities.append(line.strip('\n'))
|
||||
|
||||
return amenities
|
||||
|
||||
|
||||
# Take the most important landmarks from the list
|
||||
def take_most_important(L: List[Landmark], N = 0) -> List[Landmark] :
|
||||
|
||||
# Read the parameters from the file
|
||||
with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/landmarks_manager.params', "r") as f :
|
||||
parameters = json.loads(f.read())
|
||||
N_important = parameters['N important']
|
||||
|
||||
L_copy = []
|
||||
L_clean = []
|
||||
scores = [0]*len(L)
|
||||
names = []
|
||||
name_id = {}
|
||||
|
||||
for i, elem in enumerate(L) :
|
||||
if elem.name not in names :
|
||||
names.append(elem.name)
|
||||
name_id[elem.name] = [i]
|
||||
L_copy.append(elem)
|
||||
else :
|
||||
name_id[elem.name] += [i]
|
||||
scores = []
|
||||
for j in name_id[elem.name] :
|
||||
scores.append(L[j].attractiveness)
|
||||
best_id = max(range(len(scores)), key=scores.__getitem__)
|
||||
t = name_id[elem.name][best_id]
|
||||
if t == i :
|
||||
for old in L_copy :
|
||||
if old.name == elem.name :
|
||||
old.attractiveness = L[t].attractiveness
|
||||
|
||||
scores = [0]*len(L_copy)
|
||||
for i, elem in enumerate(L_copy) :
|
||||
scores[i] = elem.attractiveness
|
||||
|
||||
res = sorted(range(len(scores)), key = lambda sub: scores[sub])[-(N_important-N):]
|
||||
|
||||
for i, elem in enumerate(L_copy) :
|
||||
if i in res :
|
||||
L_clean.append(elem)
|
||||
|
||||
return L_clean
|
||||
|
||||
|
||||
# Remove duplicate elements and elements with low score
|
||||
def remove_duplicates(L: List[Landmark]) -> List[Landmark] :
|
||||
"""
|
||||
Removes duplicate landmarks based on their names from the given list.
|
||||
|
||||
Parameters:
|
||||
L (List[Landmark]): A list of Landmark objects.
|
||||
|
||||
Returns:
|
||||
List[Landmark]: A list of unique Landmark objects based on their names.
|
||||
"""
|
||||
|
||||
L_clean = []
|
||||
names = []
|
||||
|
||||
for landmark in L :
|
||||
if landmark.name in names:
|
||||
continue
|
||||
|
||||
|
||||
else :
|
||||
names.append(landmark.name)
|
||||
L_clean.append(landmark)
|
||||
|
||||
return L_clean
|
||||
|
||||
|
||||
# Correct the score of a list of landmarks by taking into account preference settings
|
||||
def correct_score(L: List[Landmark], preference: Preference) :
|
||||
|
||||
if len(L) == 0 :
|
||||
return
|
||||
|
||||
if L[0].type != preference.type :
|
||||
raise TypeError(f"LandmarkType {preference.type} does not match the type of Landmark {L[0].name}")
|
||||
|
||||
for elem in L :
|
||||
elem.attractiveness = int(elem.attractiveness*preference.score/5) # arbitrary computation
|
||||
|
||||
|
||||
# Function to count elements within a certain radius of a location
|
||||
def count_elements_within_radius(coordinates: Tuple[float, float], radius: int) -> int:
|
||||
|
||||
lat = coordinates[0]
|
||||
lon = coordinates[1]
|
||||
|
||||
alpha = (180*radius)/(6371000*m.pi)
|
||||
bbox = {'latLower':lat-alpha,'lonLower':lon-alpha,'latHigher':lat+alpha,'lonHigher': lon+alpha}
|
||||
|
||||
# Build the query to find elements within the radius
|
||||
radius_query = overpassQueryBuilder(bbox=[bbox['latLower'],bbox['lonLower'],bbox['latHigher'],bbox['lonHigher']],
|
||||
elementType=['node', 'way', 'relation'])
|
||||
|
||||
try :
|
||||
overpass = Overpass()
|
||||
radius_result = overpass.query(radius_query)
|
||||
N_elem = radius_result.countWays() + radius_result.countRelations()
|
||||
#print(f"There are {N_elem} ways/relations within 50m")
|
||||
if N_elem is None :
|
||||
return 0
|
||||
return N_elem
|
||||
|
||||
except :
|
||||
return 0
|
||||
|
||||
|
||||
# Creates a bounding box around given coordinates, side_length in meters
|
||||
def create_bbox(coordinates: Tuple[float, float], side_length: int) -> Tuple[float, float, float, float]:
|
||||
|
||||
lat = coordinates[0]
|
||||
lon = coordinates[1]
|
||||
|
||||
# Half the side length in km (since it's a square bbox)
|
||||
half_side_length_km = side_length / 2 / 1000
|
||||
|
||||
# Convert distance to degrees
|
||||
lat_diff = half_side_length_km / 111 # 1 degree latitude is approximately 111 km
|
||||
lon_diff = half_side_length_km / (111 * m.cos(m.radians(lat))) # Adjust for longitude based on latitude
|
||||
|
||||
# Calculate bbox
|
||||
min_lat = lat - lat_diff
|
||||
max_lat = lat + lat_diff
|
||||
min_lon = lon - lon_diff
|
||||
max_lon = lon + lon_diff
|
||||
|
||||
return min_lat, min_lon, max_lat, max_lon
|
||||
|
||||
|
||||
def get_landmarks(list_amenity: list, landmarktype: LandmarkType, coordinates: Tuple[float, float]) -> List[Landmark] :
|
||||
|
||||
# Read the parameters from the file
|
||||
with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/landmarks_manager.params', "r") as f :
|
||||
parameters = json.loads(f.read())
|
||||
tag_coeff = parameters['tag coeff']
|
||||
park_coeff = parameters['park coeff']
|
||||
church_coeff = parameters['church coeff']
|
||||
radius = parameters['radius close to']
|
||||
bbox_side = parameters['city bbox side']
|
||||
|
||||
# Create bbox around start location
|
||||
bbox = create_bbox(coordinates, bbox_side)
|
||||
|
||||
# Initialize some variables
|
||||
N = 0
|
||||
L = []
|
||||
overpass = Overpass()
|
||||
|
||||
for amenity in list_amenity :
|
||||
query = overpassQueryBuilder(bbox=bbox, elementType=['way', 'relation'], selector=amenity, includeCenter=True, out='body')
|
||||
result = overpass.query(query)
|
||||
N += result.countElements()
|
||||
|
||||
for elem in result.elements():
|
||||
|
||||
name = elem.tag('name') # Add name
|
||||
location = (elem.centerLat(), elem.centerLon()) # Add coordinates (lat, lon)
|
||||
|
||||
# skip if unprecise location
|
||||
if name is None or location[0] is None:
|
||||
continue
|
||||
|
||||
# skip if unused
|
||||
if 'disused:leisure' in elem.tags().keys():
|
||||
continue
|
||||
|
||||
# skip if part of another building
|
||||
if 'building:part' in elem.tags().keys() and elem.tag('building:part') == 'yes':
|
||||
continue
|
||||
|
||||
else :
|
||||
osm_type = elem.type() # Add type : 'way' or 'relation'
|
||||
osm_id = elem.id() # Add OSM id
|
||||
elem_type = landmarktype # Add the landmark type as 'sightseeing,
|
||||
n_tags = len(elem.tags().keys()) # Add number of tags
|
||||
|
||||
# remove specific tags
|
||||
skip = False
|
||||
for tag in elem.tags().keys() :
|
||||
if "pay" in tag :
|
||||
n_tags -= 1 # discard payment options for tags
|
||||
|
||||
if "disused" in tag :
|
||||
skip = True # skip disused amenities
|
||||
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 != LandmarkType(landmark_type="nature") :
|
||||
if "leisure" in tag and elem.tag('leisure') == "park":
|
||||
elem_type = LandmarkType(landmark_type="nature")
|
||||
|
||||
if amenity not in ["'shop'='department_store'", "'shop'='mall'"] :
|
||||
if "shop" in tag :
|
||||
skip = True
|
||||
break
|
||||
|
||||
if tag == "building" and elem.tag('building') in ['retail', 'supermarket', 'parking']:
|
||||
skip = True
|
||||
break
|
||||
|
||||
if skip:
|
||||
continue
|
||||
|
||||
# Add score of given landmark based on the number of surrounding elements. Penalty for churches as there are A LOT
|
||||
if amenity == "'amenity'='place_of_worship'" :
|
||||
#score = int((count_elements_within_radius(location, radius) + (n_tags*tag_coeff) )*church_coeff)
|
||||
score = int((count_elements_within_radius(location, radius) + ((n_tags**1.2)*tag_coeff) )*church_coeff)
|
||||
elif amenity == "'leisure'='park'" :
|
||||
score = int((count_elements_within_radius(location, radius) + ((n_tags**1.2)*tag_coeff) )*park_coeff)
|
||||
else :
|
||||
score = int(count_elements_within_radius(location, radius) + ((n_tags**1.2)*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
|
||||
|
||||
|
||||
34
backend/src/log_config.yaml
Normal file
34
backend/src/log_config.yaml
Normal file
@ -0,0 +1,34 @@
|
||||
version: 1
|
||||
disable_existing_loggers: False
|
||||
formatters:
|
||||
simple:
|
||||
format: '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
|
||||
handlers:
|
||||
console:
|
||||
class: rich.logging.RichHandler
|
||||
formatter: simple
|
||||
# access:
|
||||
# class: logging.FileHandler
|
||||
# filename: logs/access.log
|
||||
# level: INFO
|
||||
# formatter: simple
|
||||
|
||||
|
||||
|
||||
|
||||
loggers:
|
||||
uvicorn.error:
|
||||
level: INFO
|
||||
handlers:
|
||||
- console
|
||||
propagate: no
|
||||
# uvicorn.access:
|
||||
# level: INFO
|
||||
# handlers:
|
||||
# - access
|
||||
# propagate: no
|
||||
root:
|
||||
level: INFO
|
||||
handlers:
|
||||
- console
|
||||
propagate: yes
|
||||
@ -1,20 +1,24 @@
|
||||
from backend.src.example_optimizer import solve_optimization
|
||||
# from refiner import refine_optimization
|
||||
from landmarks_manager import LandmarkManager
|
||||
from structs.landmarks import Landmark
|
||||
from structs.landmarktype import LandmarkType
|
||||
from structs.preferences import Preferences
|
||||
import logging
|
||||
from fastapi import FastAPI, Query, Body
|
||||
|
||||
from structs.landmark import Landmark
|
||||
from structs.preferences import Preferences
|
||||
from structs.linked_landmarks import LinkedLandmarks
|
||||
from utils.landmarks_manager import LandmarkManager
|
||||
from utils.optimizer import Optimizer
|
||||
from utils.refiner import Refiner
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
app = FastAPI()
|
||||
manager = LandmarkManager()
|
||||
|
||||
# TODO: needs a global variable to store the landmarks accross function calls
|
||||
# linked_tour = []
|
||||
optimizer = Optimizer()
|
||||
refiner = Refiner(optimizer=optimizer)
|
||||
|
||||
|
||||
@app.post("/route/new")
|
||||
def main1(preferences: Preferences, start: tuple[float, float], end: tuple[float, float] = None) -> str:
|
||||
def get_route(preferences: Preferences, start: tuple[float, float], end: tuple[float, float] | None = None) -> str:
|
||||
'''
|
||||
Main function to call the optimizer.
|
||||
:param preferences: the preferences specified by the user as the post body
|
||||
@ -22,19 +26,23 @@ def main1(preferences: Preferences, start: tuple[float, float], end: tuple[float
|
||||
:param end: the coordinates of the finishing point as a tuple of floats (as url query parameters)
|
||||
:return: the uuid of the first landmark in the optimized route
|
||||
'''
|
||||
if preferences is None :
|
||||
if preferences is None:
|
||||
raise ValueError("Please provide preferences in the form of a 'Preference' BaseModel class.")
|
||||
if start is None:
|
||||
raise ValueError("Please provide the starting coordinates as a tuple of floats.")
|
||||
if end is None:
|
||||
end = start
|
||||
logger.info("No end coordinates provided. Using start=end.")
|
||||
|
||||
start_landmark = Landmark(name='start', type='start', location=(start[0], start[1]), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
|
||||
end_landmark = Landmark(name='end', type='finish', location=(end[0], end[1]), osm_type='end', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
|
||||
|
||||
start_landmark = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(start[0], start[1]), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
|
||||
end_landmark = Landmark(name='end', type=LandmarkType(landmark_type='end'), location=(end[0], end[1]), osm_type='end', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
|
||||
|
||||
# Generate the landmarks from the start location
|
||||
landmarks, landmarks_short = LandmarkManager.get_landmark_lists(preferences=preferences, coordinates=start.location)
|
||||
print([l.name for l in landmarks_short])
|
||||
landmarks, landmarks_short = manager.generate_landmarks_list(
|
||||
center_coordinates = start,
|
||||
preferences = preferences
|
||||
)
|
||||
|
||||
# insert start and finish to the landmarks list
|
||||
landmarks_short.insert(0, start_landmark)
|
||||
landmarks_short.append(end_landmark)
|
||||
@ -44,14 +52,13 @@ def main1(preferences: Preferences, start: tuple[float, float], end: tuple[float
|
||||
detour = 30 # minutes
|
||||
|
||||
# First stage optimization
|
||||
base_tour = solve_optimization(landmarks_short, max_walking_time*60, True)
|
||||
base_tour = optimizer.solve_optimization(max_walking_time*60, landmarks_short)
|
||||
|
||||
# Second stage optimization
|
||||
# refined_tour = refine_optimization(landmarks, base_tour, max_walking_time*60+detour, True)
|
||||
refined_tour = refiner.refine_optimization(landmarks, base_tour, max_walking_time*60, detour)
|
||||
|
||||
# linked_tour = ...
|
||||
# return linked_tour[0].uuid
|
||||
return base_tour[0].uuid
|
||||
linked_tour = LinkedLandmarks(refined_tour)
|
||||
return linked_tour[0].uuid
|
||||
|
||||
|
||||
|
||||
|
||||
@ -1,446 +0,0 @@
|
||||
import numpy as np
|
||||
import json, os
|
||||
|
||||
from typing import List, Tuple
|
||||
from scipy.optimize import linprog
|
||||
from collections import defaultdict, deque
|
||||
from geopy.distance import geodesic
|
||||
|
||||
from structs.landmarks import Landmark
|
||||
|
||||
|
||||
# Function to print the result
|
||||
def print_res(L: List[Landmark]):
|
||||
|
||||
print('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} landmarks")
|
||||
|
||||
|
||||
# Prevent the use of a particular solution
|
||||
def prevent_config(resx):
|
||||
|
||||
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
|
||||
|
||||
return h, [len(vertices_visited)-1]
|
||||
|
||||
|
||||
# Prevents the creation of the same circle (both directions)
|
||||
def prevent_circle(circle_vertices: list, L: int) :
|
||||
|
||||
l1 = [0]*L*L
|
||||
l2 = [0]*L*L
|
||||
for i, node in enumerate(circle_vertices[:-1]) :
|
||||
next = circle_vertices[i+1]
|
||||
|
||||
l1[node*L + next] = 1
|
||||
l2[next*L + node] = 1
|
||||
|
||||
s = circle_vertices[0]
|
||||
g = circle_vertices[-1]
|
||||
|
||||
l1[g*L + s] = 1
|
||||
l2[s*L + g] = 1
|
||||
|
||||
return np.vstack((l1, l2)), [0, 0]
|
||||
|
||||
|
||||
# Returns the order of visit as well as any circles if there are some
|
||||
def is_connected(resx) :
|
||||
|
||||
# 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()
|
||||
|
||||
# Step 1: Create a graph representation
|
||||
graph = defaultdict(list)
|
||||
for a, b in zip(ind_a, ind_b):
|
||||
graph[a].append(b)
|
||||
|
||||
# Step 2: Function to perform BFS/DFS to extract journeys
|
||||
def get_journey(start):
|
||||
journey_nodes = []
|
||||
visited = set()
|
||||
stack = deque([start])
|
||||
|
||||
while stack:
|
||||
node = stack.pop()
|
||||
if node not in visited:
|
||||
visited.add(node)
|
||||
journey_nodes.append(node)
|
||||
for neighbor in graph[node]:
|
||||
if neighbor not in visited:
|
||||
stack.append(neighbor)
|
||||
|
||||
return journey_nodes
|
||||
|
||||
# Step 3: Extract all journeys
|
||||
all_journeys_nodes = []
|
||||
visited_nodes = set()
|
||||
|
||||
for node in ind_a:
|
||||
if node not in visited_nodes:
|
||||
journey_nodes = get_journey(node)
|
||||
all_journeys_nodes.append(journey_nodes)
|
||||
visited_nodes.update(journey_nodes)
|
||||
|
||||
for l in all_journeys_nodes :
|
||||
if 0 in l :
|
||||
order = l
|
||||
all_journeys_nodes.remove(l)
|
||||
break
|
||||
|
||||
if len(all_journeys_nodes) == 0 :
|
||||
return order, None
|
||||
|
||||
return order, all_journeys_nodes
|
||||
|
||||
|
||||
# Function that returns the time in minutes from one location to another
|
||||
def get_time(p1: Tuple[float, float], p2: Tuple[float, float], detour: float, speed: float) :
|
||||
|
||||
# Compute the straight-line distance in km
|
||||
if p1 == p2 :
|
||||
return 0
|
||||
else:
|
||||
dist = geodesic(p1, p2).kilometers
|
||||
|
||||
# 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
|
||||
|
||||
return round(walk_time)
|
||||
|
||||
|
||||
# Initialize A and c. Compute the distances from all landmarks to each other and store attractiveness
|
||||
# We want to maximize the sightseeing : max(c) st. A*x < b and A_eq*x = b_eq
|
||||
def init_ub_dist(landmarks: List[Landmark], max_steps: int):
|
||||
|
||||
with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
|
||||
parameters = json.loads(f.read())
|
||||
detour = parameters['detour factor']
|
||||
speed = parameters['average walking speed']
|
||||
|
||||
# Objective function coefficients. a*x1 + b*x2 + c*x3 + ...
|
||||
c = []
|
||||
# Coefficients of inequality constraints (left-hand side)
|
||||
A_ub = []
|
||||
|
||||
for spot1 in landmarks :
|
||||
dist_table = [0]*len(landmarks)
|
||||
c.append(-spot1.attractiveness)
|
||||
for j, spot2 in enumerate(landmarks) :
|
||||
t = get_time(spot1.location, spot2.location, detour, speed)
|
||||
dist_table[j] = t
|
||||
closest = sorted(dist_table)[:22]
|
||||
for i, dist in enumerate(dist_table) :
|
||||
if dist not in closest :
|
||||
dist_table[i] = 32700
|
||||
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, max_landmarks: int):
|
||||
|
||||
ones = [1]*L
|
||||
zeros = [0]*L
|
||||
A = ones + zeros*(L-1)
|
||||
b = [1]
|
||||
for i in range(L-1) :
|
||||
h_new = zeros*i + ones + zeros*(L-1-i)
|
||||
A = np.vstack((A, h_new))
|
||||
b.append(1)
|
||||
|
||||
if max_landmarks is None :
|
||||
# Read the parameters from the file
|
||||
with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
|
||||
parameters = json.loads(f.read())
|
||||
max_landmarks = parameters['max landmarks']
|
||||
|
||||
A = np.vstack((A, ones*L))
|
||||
b.append(max_landmarks+1)
|
||||
|
||||
return A, b
|
||||
|
||||
|
||||
# Constraint to not have d14 and d41 simultaneously. Does not prevent cyclic paths with more elements
|
||||
def break_sym(L):
|
||||
|
||||
upper_ind = np.triu_indices(L,0,L)
|
||||
|
||||
up_ind_x = upper_ind[0]
|
||||
up_ind_y = upper_ind[1]
|
||||
|
||||
A = [0]*L*L # useless row to prevent overhead ? better solution welcomed
|
||||
# A[up_ind_x[0]*L + up_ind_y[0]] = 1
|
||||
# A[up_ind_y[0]*L + up_ind_x[0]] = 1
|
||||
b = [1]
|
||||
|
||||
for i, _ in enumerate(up_ind_x[1:]) :
|
||||
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 = np.vstack((A,l))
|
||||
b.append(1)
|
||||
|
||||
return A, b
|
||||
|
||||
|
||||
# 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), dtype=np.int8)
|
||||
|
||||
return [l], [0]
|
||||
|
||||
|
||||
# Go through the landmarks and force the optimizer to use landmarks marked as must_do
|
||||
def respect_user_must_do(landmarks: List[Landmark]) :
|
||||
L = len(landmarks)
|
||||
|
||||
A = [0]*L*L
|
||||
b = [0]
|
||||
for i, elem in enumerate(landmarks[1:]) :
|
||||
if elem.must_do is True and elem.name not in ['finish', 'start']:
|
||||
l = [0]*L*L
|
||||
l[i*L:i*L+L] = [1]*L # set mandatory departures from landmarks tagged as 'must_do'
|
||||
|
||||
A = np.vstack((A,l))
|
||||
b.append(1)
|
||||
|
||||
return A, b
|
||||
|
||||
|
||||
# Constraint to ensure start at start and finish at goal
|
||||
def respect_start_finish(L: int):
|
||||
l_start = [1]*L + [0]*L*(L-1) # sets departures only for start (horizontal ones)
|
||||
l_start[L-1] = 0 # prevents the jump from start to finish
|
||||
l_goal = [0]*L*L # sets arrivals only for finish (vertical ones)
|
||||
l_L = [0]*L*(L-1) + [1]*L # prevents arrivals at start and departures from goal
|
||||
for k in range(L-1) : # sets only vertical ones for goal (go to)
|
||||
l_L[k*L] = 1
|
||||
if k != 0 :
|
||||
l_goal[k*L+L-1] = 1
|
||||
|
||||
|
||||
A = np.vstack((l_start, l_goal))
|
||||
b = [1, 1]
|
||||
A = np.vstack((A,l_L))
|
||||
b.append(0)
|
||||
|
||||
return A, b
|
||||
|
||||
|
||||
# Constraint to tie the problem together. Necessary but not sufficient to avoid circles
|
||||
def respect_order(L: int):
|
||||
|
||||
A = [0]*L*L # useless row to reduce overhead ? better solution is welcome
|
||||
b = [0]
|
||||
for i in range(L-1) : # Prevent stacked ones
|
||||
if i == 0 or i == L-1: # Don't touch start or finish
|
||||
continue
|
||||
else :
|
||||
l = [0]*L
|
||||
l[i] = -1
|
||||
l = l*L
|
||||
for j in range(L) :
|
||||
l[i*L + j] = 1
|
||||
|
||||
A = np.vstack((A,l))
|
||||
b.append(0)
|
||||
|
||||
return A, b
|
||||
|
||||
|
||||
# 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 open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
|
||||
parameters = json.loads(f.read())
|
||||
detour_factor = parameters['detour factor']
|
||||
speed = parameters['average walking speed']
|
||||
|
||||
L = []
|
||||
j = 0
|
||||
total_dist = 0
|
||||
while j < len(order)-1 :
|
||||
elem = landmarks[order[j]]
|
||||
next = landmarks[order[j+1]]
|
||||
|
||||
d = get_time(elem.location, next.location, detour_factor, speed)
|
||||
elem.time_to_reach_next = d
|
||||
elem.must_do = True
|
||||
elem.location = (round(elem.location[0], 5), round(elem.location[1], 5))
|
||||
L.append(elem)
|
||||
j += 1
|
||||
total_dist += d
|
||||
|
||||
next.location = (round(next.location[0], 5), round(next.location[1], 5))
|
||||
L.append(next)
|
||||
|
||||
return L, total_dist
|
||||
|
||||
|
||||
# Same as link_list but does it on a already ordered list
|
||||
def link_list_simple(ordered_visit: List[Landmark])-> List[Landmark] :
|
||||
|
||||
# Read the parameters from the file
|
||||
with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
|
||||
parameters = json.loads(f.read())
|
||||
detour_factor = parameters['detour factor']
|
||||
speed = parameters['average walking speed']
|
||||
|
||||
L = []
|
||||
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_time(elem.location, next.location, detour_factor, speed)
|
||||
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, max_landmarks = None) :
|
||||
|
||||
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, b = respect_number(L, max_landmarks) # Respect max number of visits (no more possible stops than landmarks).
|
||||
A_ub = np.vstack((A_ub, A), dtype=np.int16)
|
||||
b_ub += b
|
||||
A, b = break_sym(L) # break the 'zig-zag' symmetry
|
||||
A_ub = np.vstack((A_ub, A), dtype=np.int16)
|
||||
b_ub += b
|
||||
|
||||
|
||||
# SET CONSTRAINTS FOR EQUALITY
|
||||
A_eq, b_eq = init_eq_not_stay(L) # Force solution not to stay in same place
|
||||
A, b = respect_user_must_do(landmarks) # Check if there are user_defined must_see. Also takes care of start/goal
|
||||
A_eq = np.vstack((A_eq, A), dtype=np.int8)
|
||||
b_eq += b
|
||||
A, b = respect_start_finish(L) # Force start and finish positions
|
||||
A_eq = np.vstack((A_eq, A), dtype=np.int8)
|
||||
b_eq += b
|
||||
A, b = respect_order(L) # Respect order of visit (only works when max_steps is limiting factor)
|
||||
A_eq = np.vstack((A_eq, A), dtype=np.int8)
|
||||
b_eq += b
|
||||
|
||||
# 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, circles = is_connected(res.x)
|
||||
#nodes, edges = is_connected(res.x)
|
||||
i = 0
|
||||
timeout = 80
|
||||
while circles is not None and i < timeout:
|
||||
A, b = prevent_config(res.x)
|
||||
A_ub = np.vstack((A_ub, A))
|
||||
b_ub += b
|
||||
#A_ub, b_ub = prevent_circle(order, len(landmarks), A_ub, b_ub)
|
||||
for circle in circles :
|
||||
A, b = prevent_circle(circle, len(landmarks))
|
||||
A_eq = np.vstack((A_eq, A))
|
||||
b_eq += b
|
||||
res = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq = b_eq, bounds=x_bounds, method='highs', integrality=3)
|
||||
if not res.success :
|
||||
print("Solving failed because of overconstrained problem")
|
||||
return None
|
||||
order, circles = is_connected(res.x)
|
||||
#nodes, edges = is_connected(res.x)
|
||||
if circles is None :
|
||||
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, _ = 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)
|
||||
print("\nTotal score : " + str(int(-res.fun)))
|
||||
|
||||
return L
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
city_bbox_side: 5000 #m
|
||||
radius_close_to: 30
|
||||
church_coeff: 0.6
|
||||
park_coeff: 1.5
|
||||
tag_coeff: 100
|
||||
radius_close_to: 50
|
||||
church_coeff: 0.8
|
||||
park_coeff: 1.2
|
||||
tag_coeff: 10
|
||||
N_important: 40
|
||||
|
||||
@ -1,8 +0,0 @@
|
||||
{
|
||||
"city bbox side" : 5000,
|
||||
"radius close to" : 50,
|
||||
"church coeff" : 0.8,
|
||||
"park coeff" : 1.2,
|
||||
"tag coeff" : 10,
|
||||
"N important" : 40
|
||||
}
|
||||
@ -1,5 +0,0 @@
|
||||
{
|
||||
"detour factor" : 1.4,
|
||||
"average walking speed" : 4.8,
|
||||
"max landmarks" : 7
|
||||
}
|
||||
@ -1,3 +1,4 @@
|
||||
detour_factor: 1.4
|
||||
detour_corridor_width: 200
|
||||
average_walking_speed: 4.8
|
||||
max_landmarks: 40
|
||||
max_landmarks: 7
|
||||
|
||||
@ -1,244 +0,0 @@
|
||||
import os, json
|
||||
|
||||
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 optimizer_v4 import solve_optimization, link_list_simple, print_res, get_time
|
||||
|
||||
|
||||
# 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
|
||||
|
||||
|
||||
# Simple nearest neighbour planner to try to fix the path
|
||||
def find_shortest_path_through_all_landmarks(landmarks: List[Landmark]) -> Tuple[List[Landmark], Polygon]:
|
||||
|
||||
# Read from data
|
||||
with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
|
||||
parameters = json.loads(f.read())
|
||||
detour = parameters['detour factor']
|
||||
speed = parameters['average walking speed']
|
||||
|
||||
# Step 1: Find 'start' and 'finish' landmarks
|
||||
start_idx = next(i for i, lm in enumerate(landmarks) if lm.name == 'start')
|
||||
finish_idx = next(i for i, lm in enumerate(landmarks) if lm.name == 'finish')
|
||||
|
||||
start_landmark = landmarks[start_idx]
|
||||
finish_landmark = landmarks[finish_idx]
|
||||
|
||||
|
||||
# Step 2: Create a list of unvisited landmarks excluding 'start' and 'finish'
|
||||
unvisited_landmarks = [lm for i, lm in enumerate(landmarks) if i not in [start_idx, finish_idx]]
|
||||
|
||||
# Step 3: Initialize the path with the 'start' landmark
|
||||
path = [start_landmark]
|
||||
coordinates = [landmarks[start_idx].location]
|
||||
|
||||
current_landmark = start_landmark
|
||||
|
||||
# Step 4: Use nearest neighbor heuristic to visit all landmarks
|
||||
while unvisited_landmarks:
|
||||
nearest_landmark = min(unvisited_landmarks, key=lambda lm: get_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)
|
||||
|
||||
return take_most_important(second_order_landmarks, 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 from the file
|
||||
with open (os.path.dirname(os.path.abspath(__file__)) + '/parameters/optimizer.params', "r") as f :
|
||||
parameters = json.loads(f.read())
|
||||
max_landmarks = parameters['max landmarks'] + 4
|
||||
|
||||
# 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
|
||||
|
||||
|
||||
@ -8,7 +8,7 @@ class Landmark(BaseModel) :
|
||||
|
||||
# Properties of the landmark
|
||||
name : str
|
||||
type: Literal['sightseeing', 'nature', 'shopping']
|
||||
type: Literal['sightseeing', 'nature', 'shopping', 'start', 'finish']
|
||||
location : tuple
|
||||
osm_type : str
|
||||
osm_id : int
|
||||
@ -22,8 +22,8 @@ class Landmark(BaseModel) :
|
||||
uuid: str = Field(default_factory=uuid4) # TODO implement this ASAP
|
||||
|
||||
# Additional properties depending on specific tour
|
||||
must_do : bool
|
||||
must_avoid : bool
|
||||
must_do : Optional[bool] = False
|
||||
must_avoid : Optional[bool] = False
|
||||
is_secondary : Optional[bool] = False # TODO future
|
||||
|
||||
time_to_reach_next : Optional[int] = 0 # TODO fix this in existing code
|
||||
@ -33,5 +33,6 @@ class Landmark(BaseModel) :
|
||||
return self.uuid.int
|
||||
|
||||
def __str__(self) -> str:
|
||||
return f'Landmark: [{self.name}, {self.location}, {self.attractiveness}]'
|
||||
time_to_next_str = f", time_to_next={self.time_to_reach_next}" if self.time_to_reach_next else ""
|
||||
return f'Landmark({self.type}): [{self.name} @{self.location}, score={self.attractiveness}{time_to_next_str}]'
|
||||
|
||||
61
backend/src/structs/linked_landmarks.py
Normal file
61
backend/src/structs/linked_landmarks.py
Normal file
@ -0,0 +1,61 @@
|
||||
import uuid
|
||||
from .landmark import Landmark
|
||||
from utils.get_time_separation import get_time
|
||||
|
||||
class LinkedLandmarks:
|
||||
"""
|
||||
A list of landmarks that are linked together, e.g. in a route.
|
||||
Each landmark serves as a node in the linked list, but since we expect these to be consumed through the rest API, a pythonic reference to the next landmark is not well suited. Instead we use the uuid of the next landmark to reference the next landmark in the list. This is not very efficient, but appropriate for the expected use case ("short" trips with onyl few landmarks).
|
||||
"""
|
||||
|
||||
_landmarks = list[Landmark]
|
||||
total_time = int
|
||||
uuid = str
|
||||
|
||||
def __init__(self, data: list[Landmark] = None) -> None:
|
||||
"""
|
||||
Initialize a new LinkedLandmarks object. This expects an ORDERED list of landmarks, where the first landmark is the starting point and the last landmark is the end point.
|
||||
|
||||
Args:
|
||||
data (list[Landmark], optional): The list of landmarks that are linked together. Defaults to None.
|
||||
"""
|
||||
self.uuid = uuid.uuid4()
|
||||
self._landmarks = data if data else []
|
||||
self._link_landmarks()
|
||||
|
||||
|
||||
def _link_landmarks(self) -> None:
|
||||
"""
|
||||
Create the links between the landmarks in the list by setting their .next_uuid and the .time_to_next attributes.
|
||||
"""
|
||||
self.total_time = 0
|
||||
for i, landmark in enumerate(self._landmarks[:-1]):
|
||||
landmark.next_uuid = self._landmarks[i + 1].uuid
|
||||
time_to_next = get_time(landmark.location, self._landmarks[i + 1].location)
|
||||
landmark.time_to_reach_next = time_to_next
|
||||
self.total_time += time_to_next
|
||||
|
||||
self._landmarks[-1].next_uuid = None
|
||||
self._landmarks[-1].time_to_reach_next = 0
|
||||
|
||||
|
||||
def __getitem__(self, index: int) -> Landmark:
|
||||
return self._landmarks[index]
|
||||
|
||||
|
||||
def __str__(self) -> str:
|
||||
return f"LinkedLandmarks, total time: {self.total_time} minutes, {len(self._landmarks)} stops: [{','.join([str(landmark) for landmark in self._landmarks])}]"
|
||||
|
||||
|
||||
def asdict(self) -> dict:
|
||||
"""
|
||||
Convert the linked landmarks to a json serializable dictionary.
|
||||
|
||||
Returns:
|
||||
dict: A dictionary representation of the linked landmarks.
|
||||
"""
|
||||
return {
|
||||
'uuid': self.uuid,
|
||||
'total_time': self.total_time,
|
||||
'landmarks': [landmark.dict() for landmark in self._landmarks]
|
||||
}
|
||||
@ -3,7 +3,7 @@ from typing import Optional, Literal
|
||||
|
||||
class Preference(BaseModel) :
|
||||
name: str
|
||||
type: Literal['sightseeing', 'nature', 'shopping']
|
||||
type: Literal['sightseeing', 'nature', 'shopping', 'start', 'finish']
|
||||
score: int # score could be from 1 to 5
|
||||
|
||||
# Input for optimization
|
||||
|
||||
@ -1,81 +1,85 @@
|
||||
import pandas as pd
|
||||
import logging
|
||||
import yaml
|
||||
|
||||
from typing import List
|
||||
from landmarks_manager import LandmarkManager
|
||||
from fastapi.encoders import jsonable_encoder
|
||||
|
||||
from optimizer_v4 import solve_optimization
|
||||
from refiner import refine_optimization
|
||||
from structs.landmarks import Landmark
|
||||
from structs.landmarktype import LandmarkType
|
||||
from utils.landmarks_manager import LandmarkManager
|
||||
from utils.optimizer import Optimizer
|
||||
from utils.refiner import Refiner
|
||||
from structs.landmark import Landmark
|
||||
from structs.linked_landmarks import LinkedLandmarks
|
||||
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)
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def test4(coordinates: tuple[float, float]) -> List[Landmark]:
|
||||
|
||||
def test(start_coords: tuple[float, float], finish_coords: tuple[float, float] = None) -> list[Landmark]:
|
||||
manager = LandmarkManager()
|
||||
optimizer = Optimizer()
|
||||
refiner = Refiner(optimizer=optimizer)
|
||||
|
||||
|
||||
preferences = Preferences(
|
||||
sightseeing=Preference(
|
||||
name='sightseeing',
|
||||
type=LandmarkType(landmark_type='sightseeing'),
|
||||
type='sightseeing',
|
||||
score = 5),
|
||||
nature=Preference(
|
||||
name='nature',
|
||||
type=LandmarkType(landmark_type='nature'),
|
||||
score = 0),
|
||||
type='nature',
|
||||
score = 5),
|
||||
shopping=Preference(
|
||||
name='shopping',
|
||||
type=LandmarkType(landmark_type='shopping'),
|
||||
score = 0))
|
||||
type='shopping',
|
||||
score = 5),
|
||||
|
||||
max_time_minute=180,
|
||||
detour_tolerance_minute=30
|
||||
)
|
||||
|
||||
# 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=False, n_tags = 0)
|
||||
finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=coordinates, osm_type='finish', osm_id=0, attractiveness=0, must_do=False, n_tags = 0)
|
||||
if finish_coords is None :
|
||||
finish_coords = start_coords
|
||||
start = Landmark(name='start', type='start', location=start_coords, osm_type='', osm_id=0, attractiveness=0, n_tags = 0)
|
||||
finish = Landmark(name='finish', type='finish', location=finish_coords, osm_type='', osm_id=0, attractiveness=0, n_tags = 0)
|
||||
#finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.8777055, 2.3640967), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
|
||||
#start = Landmark(name='start', type=LandmarkType(landmark_type='start'), location=(48.847132, 2.312359), osm_type='start', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
|
||||
#finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.843185, 2.344533), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
|
||||
#finish = Landmark(name='finish', type=LandmarkType(landmark_type='finish'), location=(48.847132, 2.312359), osm_type='finish', osm_id=0, attractiveness=0, must_do=True, n_tags = 0)
|
||||
|
||||
|
||||
|
||||
# Generate the landmarks from the start location
|
||||
landmarks, landmarks_short = generate_landmarks(preferences=preferences, coordinates=start.location)
|
||||
#write_data(landmarks, "landmarks_Wien.txt")
|
||||
landmarks, landmarks_short = manager.generate_landmarks_list(
|
||||
center_coordinates = start_coords,
|
||||
preferences = preferences
|
||||
)
|
||||
|
||||
# Store data to file for debug purposes
|
||||
# write_data(landmarks, "landmarks_Strasbourg.txt")
|
||||
|
||||
# Insert start and finish to the landmarks list
|
||||
landmarks_short.insert(0, start)
|
||||
landmarks_short.append(finish)
|
||||
|
||||
max_walking_time = 180 # minutes
|
||||
detour = 0 # minutes
|
||||
|
||||
# First stage optimization
|
||||
base_tour = solve_optimization(landmarks_short, max_walking_time, True)
|
||||
base_tour = optimizer.solve_optimization(max_time=preferences.max_time_minute, landmarks=landmarks_short)
|
||||
|
||||
# Second stage using linear optimization
|
||||
|
||||
refined_tour = refine_optimization(landmarks, base_tour, max_walking_time, detour, True)
|
||||
refined_tour = refiner.refine_optimization(all_landmarks=landmarks, base_tour=base_tour, max_time = preferences.max_time_minute, detour = preferences.detour_tolerance_minute)
|
||||
|
||||
linked_tour = LinkedLandmarks(refined_tour)
|
||||
logger.info(f"Optimized route: {linked_tour}")
|
||||
|
||||
# with open('linked_tour.yaml', 'w') as f:
|
||||
# yaml.dump(linked_tour.asdict(), f)
|
||||
|
||||
return linked_tour
|
||||
|
||||
|
||||
return refined_tour
|
||||
|
||||
|
||||
#test4(tuple((48.8344400, 2.3220540))) # Café Chez César
|
||||
#test4(tuple((48.8375946, 2.2949904))) # Point random
|
||||
#test4(tuple((47.377859, 8.540585))) # Zurich HB
|
||||
#test4(tuple((45.7576485, 4.8330241))) # Lyon Bellecour
|
||||
test4(tuple((48.5848435, 7.7332974))) # Strasbourg Gare
|
||||
#test4(tuple((48.2067858, 16.3692340))) # Vienne
|
||||
#test(tuple((48.8344400, 2.3220540))) # Café Chez César
|
||||
#test(tuple((48.8375946, 2.2949904))) # Point random
|
||||
#test(tuple((47.377859, 8.540585))) # Zurich HB
|
||||
#test(tuple((45.7576485, 4.8330241))) # Lyon Bellecour
|
||||
test(tuple((48.5848435, 7.7332974))) # Strasbourg Gare
|
||||
#test(tuple((48.2067858, 16.3692340))) # Vienne
|
||||
|
||||
39
backend/src/utils/get_time_separation.py
Normal file
39
backend/src/utils/get_time_separation.py
Normal file
@ -0,0 +1,39 @@
|
||||
import yaml
|
||||
from geopy.distance import geodesic
|
||||
|
||||
import constants
|
||||
|
||||
with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
|
||||
parameters = yaml.safe_load(f)
|
||||
DETOUR_FACTOR = parameters['detour_factor']
|
||||
AVERAGE_WALKING_SPEED = parameters['average_walking_speed']
|
||||
|
||||
|
||||
def get_time(p1: tuple[float, float], p2: tuple[float, float]) -> int:
|
||||
"""
|
||||
Calculate the time in minutes to travel from one location to another.
|
||||
|
||||
Args:
|
||||
p1 (Tuple[float, float]): Coordinates of the starting location.
|
||||
p2 (Tuple[float, float]): Coordinates of the destination.
|
||||
detour (float): Detour factor affecting the distance.
|
||||
speed (float): Walking speed in kilometers per hour.
|
||||
|
||||
Returns:
|
||||
int: Time to travel from p1 to p2 in minutes.
|
||||
"""
|
||||
|
||||
|
||||
# Compute the straight-line distance in km
|
||||
if p1 == p2 :
|
||||
return 0
|
||||
else:
|
||||
dist = geodesic(p1, p2).kilometers
|
||||
|
||||
# Consider the detour factor for average cityto deterline walking distance (in km)
|
||||
walk_dist = dist*DETOUR_FACTOR
|
||||
|
||||
# Time to walk this distance (in minutes)
|
||||
walk_time = walk_dist/AVERAGE_WALKING_SPEED*60
|
||||
|
||||
return round(walk_time)
|
||||
365
backend/src/utils/landmarks_manager.py
Normal file
365
backend/src/utils/landmarks_manager.py
Normal file
@ -0,0 +1,365 @@
|
||||
import math as m
|
||||
import yaml
|
||||
import logging
|
||||
|
||||
from OSMPythonTools.overpass import Overpass, overpassQueryBuilder
|
||||
from OSMPythonTools.cachingStrategy import CachingStrategy, JSON
|
||||
from pywikibot import ItemPage, Site
|
||||
from pywikibot import config
|
||||
config.put_throttle = 0
|
||||
config.maxlag = 0
|
||||
|
||||
from structs.preferences import Preferences, Preference
|
||||
from structs.landmark import Landmark
|
||||
from .take_most_important import take_most_important
|
||||
import constants
|
||||
|
||||
|
||||
SIGHTSEEING = 'sightseeing'
|
||||
NATURE = 'nature'
|
||||
SHOPPING = 'shopping'
|
||||
|
||||
|
||||
|
||||
class LandmarkManager:
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
city_bbox_side: int # bbox side in meters
|
||||
radius_close_to: int # radius in meters
|
||||
church_coeff: float # coeff to adjsut score of churches
|
||||
park_coeff: float # coeff to adjust score of parks
|
||||
tag_coeff: float # coeff to adjust weight of tags
|
||||
N_important: int # number of important landmarks to consider
|
||||
|
||||
|
||||
def __init__(self) -> None:
|
||||
|
||||
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:
|
||||
parameters = yaml.safe_load(f)
|
||||
self.city_bbox_side = parameters['city_bbox_side']
|
||||
self.radius_close_to = parameters['radius_close_to']
|
||||
self.church_coeff = parameters['church_coeff']
|
||||
self.park_coeff = parameters['park_coeff']
|
||||
self.tag_coeff = parameters['tag_coeff']
|
||||
self.N_important = parameters['N_important']
|
||||
|
||||
self.overpass = Overpass()
|
||||
CachingStrategy.use(JSON, cacheDir=constants.OSM_CACHE_DIR)
|
||||
|
||||
|
||||
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.
|
||||
|
||||
This method fetches landmarks from various categories (sightseeing, nature, shopping) based on the user's preferences
|
||||
and current location. It scores and corrects these landmarks, removes duplicates, and then selects the most important
|
||||
landmarks based on a predefined criterion.
|
||||
|
||||
Parameters:
|
||||
center_coordinates (tuple[float, float]): The latitude and longitude of the center location around which to search.
|
||||
preferences (Preferences): The user's preference settings that influence the landmark selection.
|
||||
|
||||
Returns:
|
||||
tuple[list[Landmark], list[Landmark]]:
|
||||
- A list of all existing landmarks.
|
||||
- A list of the most important landmarks based on the user's preferences.
|
||||
"""
|
||||
|
||||
L = []
|
||||
bbox = self.create_bbox(center_coordinates)
|
||||
# list for sightseeing
|
||||
if preferences.sightseeing.score != 0:
|
||||
score_function = lambda loc, n_tags: int((self.count_elements_close_to(loc) + ((n_tags**1.2)*self.tag_coeff) )*self.church_coeff)
|
||||
L1 = self.fetch_landmarks(bbox, self.amenity_selectors['sightseeing'], SIGHTSEEING, score_function)
|
||||
self.correct_score(L1, preferences.sightseeing)
|
||||
L += L1
|
||||
|
||||
# list for nature
|
||||
if preferences.nature.score != 0:
|
||||
score_function = lambda loc, n_tags: int((self.count_elements_close_to(loc) + ((n_tags**1.2)*self.tag_coeff) )*self.park_coeff)
|
||||
L2 = self.fetch_landmarks(bbox, self.amenity_selectors['nature'], NATURE, score_function)
|
||||
self.correct_score(L2, preferences.nature)
|
||||
L += L2
|
||||
|
||||
# list for shopping
|
||||
if preferences.shopping.score != 0:
|
||||
score_function = lambda loc, n_tags: int(self.count_elements_close_to(loc) + ((n_tags**1.2)*self.tag_coeff))
|
||||
L3 = self.fetch_landmarks(bbox, self.amenity_selectors['shopping'], SHOPPING, score_function)
|
||||
self.correct_score(L3, preferences.shopping)
|
||||
L += L3
|
||||
|
||||
L = self.remove_duplicates(L)
|
||||
L_constrained = take_most_important(L, self.N_important)
|
||||
self.logger.info(f'Generated {len(L)} landmarks around {center_coordinates}, and constrained to {len(L_constrained)} most important ones.')
|
||||
|
||||
return L, L_constrained
|
||||
|
||||
|
||||
def remove_duplicates(self, landmarks: list[Landmark]) -> list[Landmark]:
|
||||
"""
|
||||
Removes duplicate landmarks based on their names from the given list. Only retains the landmark with highest score
|
||||
|
||||
Parameters:
|
||||
landmarks (list[Landmark]): A list of Landmark objects.
|
||||
|
||||
Returns:
|
||||
list[Landmark]: A list of unique Landmark objects based on their names.
|
||||
"""
|
||||
|
||||
L_clean = []
|
||||
names = []
|
||||
|
||||
for landmark in landmarks:
|
||||
if landmark.name in names:
|
||||
continue
|
||||
else:
|
||||
names.append(landmark.name)
|
||||
L_clean.append(landmark)
|
||||
|
||||
return L_clean
|
||||
|
||||
|
||||
def correct_score(self, landmarks: list[Landmark], preference: Preference):
|
||||
"""
|
||||
Adjust the attractiveness score of each landmark in the list based on user preferences.
|
||||
|
||||
This method updates the attractiveness of each landmark by scaling it according to the user's preference score.
|
||||
The score adjustment is computed using a simple linear transformation based on the preference score.
|
||||
|
||||
Args:
|
||||
landmarks (list[Landmark]): A list of landmarks whose scores need to be corrected.
|
||||
preference (Preference): The user's preference settings that influence the attractiveness score adjustment.
|
||||
|
||||
Raises:
|
||||
TypeError: If the type of any landmark in the list does not match the expected type in the preference.
|
||||
"""
|
||||
|
||||
if len(landmarks) == 0:
|
||||
return
|
||||
|
||||
if landmarks[0].type != preference.type:
|
||||
raise TypeError(f"LandmarkType {preference.type} does not match the type of Landmark {landmarks[0].name}")
|
||||
|
||||
for elem in landmarks:
|
||||
elem.attractiveness = int(elem.attractiveness*preference.score/5) # arbitrary computation
|
||||
|
||||
|
||||
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.
|
||||
|
||||
This function constructs a bounding box around the specified coordinates based on the radius. It then queries
|
||||
OpenStreetMap data to count the number of elements within that bounding box.
|
||||
|
||||
Args:
|
||||
coordinates (tuple[float, float]): The latitude and longitude of the location to search around.
|
||||
|
||||
Returns:
|
||||
int: The number of elements (nodes, ways, relations) within the specified radius. Returns 0 if no elements
|
||||
are found or if an error occurs during the query.
|
||||
"""
|
||||
|
||||
lat = coordinates[0]
|
||||
lon = coordinates[1]
|
||||
|
||||
radius = self.radius_close_to
|
||||
|
||||
alpha = (180*radius) / (6371000*m.pi)
|
||||
bbox = {'latLower':lat-alpha,'lonLower':lon-alpha,'latHigher':lat+alpha,'lonHigher': lon+alpha}
|
||||
|
||||
# Build the query to find elements within the radius
|
||||
radius_query = overpassQueryBuilder(
|
||||
bbox=[bbox['latLower'],
|
||||
bbox['lonLower'],
|
||||
bbox['latHigher'],
|
||||
bbox['lonHigher']],
|
||||
elementType=['node', 'way', 'relation']
|
||||
)
|
||||
|
||||
try:
|
||||
radius_result = self.overpass.query(radius_query)
|
||||
N_elem = radius_result.countWays() + radius_result.countRelations()
|
||||
self.logger.debug(f"There are {N_elem} ways/relations within 50m")
|
||||
if N_elem is None:
|
||||
return 0
|
||||
return N_elem
|
||||
except:
|
||||
return 0
|
||||
|
||||
|
||||
def create_bbox(self, coordinates: tuple[float, float]) -> tuple[float, float, float, float]:
|
||||
"""
|
||||
Create a bounding box around the given coordinates.
|
||||
|
||||
Args:
|
||||
coordinates (tuple[float, float]): The latitude and longitude of the center of the bounding box.
|
||||
|
||||
Returns:
|
||||
tuple[float, float, float, float]: The minimum latitude, minimum longitude, maximum latitude, and maximum longitude
|
||||
defining the bounding box.
|
||||
"""
|
||||
|
||||
lat = coordinates[0]
|
||||
lon = coordinates[1]
|
||||
|
||||
# Half the side length in km (since it's a square bbox)
|
||||
half_side_length_km = self.city_bbox_side / 2 / 1000
|
||||
|
||||
# Convert distance to degrees
|
||||
lat_diff = half_side_length_km / 111 # 1 degree latitude is approximately 111 km
|
||||
lon_diff = half_side_length_km / (111 * m.cos(m.radians(lat))) # Adjust for longitude based on latitude
|
||||
|
||||
# Calculate bbox
|
||||
min_lat = lat - lat_diff
|
||||
max_lat = lat + lat_diff
|
||||
min_lon = lon - lon_diff
|
||||
max_lon = lon + lon_diff
|
||||
|
||||
return min_lat, min_lon, max_lat, max_lon
|
||||
|
||||
|
||||
def fetch_landmarks(self, bbox: tuple, amenity_selector: dict, landmarktype: str, score_function: callable) -> list[Landmark]:
|
||||
"""
|
||||
Fetches landmarks of a specified type from OpenStreetMap (OSM) within a bounding box centered on given coordinates.
|
||||
|
||||
Args:
|
||||
bbox (tuple[float, float, float, float]): The bounding box coordinates (min_lat, min_lon, max_lat, max_lon).
|
||||
amenity_selector (dict): The Overpass API query selector for the desired landmark type.
|
||||
landmarktype (str): The type of the landmark (e.g., 'sightseeing', 'nature', 'shopping').
|
||||
score_function (callable): The function to compute the score of the landmark based on its attributes.
|
||||
|
||||
Returns:
|
||||
list[Landmark]: A list of Landmark objects that were fetched and filtered based on the provided criteria.
|
||||
|
||||
Notes:
|
||||
- Landmarks are fetched using Overpass API queries.
|
||||
- Selectors are translated from the dictionary to the Overpass query format. (e.g., 'amenity'='place_of_worship')
|
||||
- Landmarks are filtered based on various conditions including tags and type.
|
||||
- Scores are assigned to landmarks based on their attributes and surrounding elements.
|
||||
"""
|
||||
return_list = []
|
||||
|
||||
# caution, when applying a list of selectors, overpass will search for elements that match ALL selectors simultaneously
|
||||
# we need to split the selectors into separate queries and merge the results
|
||||
for sel in dict_to_selector_list(amenity_selector):
|
||||
self.logger.debug(f"Current selector: {sel}")
|
||||
query = overpassQueryBuilder(
|
||||
bbox = bbox,
|
||||
elementType = ['way', 'relation'],
|
||||
selector = sel,
|
||||
# conditions = [],
|
||||
includeCenter = True,
|
||||
out = 'body'
|
||||
)
|
||||
|
||||
try:
|
||||
result = self.overpass.query(query)
|
||||
except Exception as e:
|
||||
self.logger.error(f"Error fetching landmarks: {e}")
|
||||
return
|
||||
|
||||
for elem in result.elements():
|
||||
|
||||
name = elem.tag('name') # Add name
|
||||
location = (elem.centerLat(), elem.centerLon()) # Add coordinates (lat, lon)
|
||||
|
||||
# TODO: exclude these from the get go
|
||||
# skip if unprecise location
|
||||
if name is None or location[0] is None:
|
||||
continue
|
||||
|
||||
# skip if unused
|
||||
if 'disused:leisure' in elem.tags().keys():
|
||||
continue
|
||||
|
||||
# skip if part of another building
|
||||
if 'building:part' in elem.tags().keys() and elem.tag('building:part') == 'yes':
|
||||
continue
|
||||
|
||||
osm_type = elem.type() # Add type: 'way' or 'relation'
|
||||
osm_id = elem.id() # Add OSM id
|
||||
elem_type = landmarktype # Add the landmark type as 'sightseeing,
|
||||
n_tags = len(elem.tags().keys()) # Add number of tags
|
||||
|
||||
# remove specific tags
|
||||
skip = False
|
||||
for tag in elem.tags().keys():
|
||||
if "pay" in tag:
|
||||
n_tags -= 1 # discard payment options for tags
|
||||
|
||||
if "disused" in tag:
|
||||
skip = True # skip disused amenities
|
||||
break
|
||||
|
||||
if "wikipedia" in tag:
|
||||
n_tags += 3 # wikipedia entries count more
|
||||
|
||||
if tag == "wikidata":
|
||||
Q = elem.tag('wikidata')
|
||||
site = Site("wikidata", "wikidata")
|
||||
item = ItemPage(site, Q)
|
||||
item.get()
|
||||
n_languages = len(item.labels)
|
||||
n_tags += n_languages/10
|
||||
|
||||
if elem_type != "nature":
|
||||
if "leisure" in tag and elem.tag('leisure') == "park":
|
||||
elem_type = "nature"
|
||||
|
||||
if landmarktype != SHOPPING:
|
||||
if "shop" in tag:
|
||||
skip = True
|
||||
break
|
||||
|
||||
if tag == "building" and elem.tag('building') in ['retail', 'supermarket', 'parking']:
|
||||
skip = True
|
||||
break
|
||||
|
||||
if skip:
|
||||
continue
|
||||
|
||||
score = score_function(location, n_tags)
|
||||
if score != 0:
|
||||
# Generate the landmark and append it to the list
|
||||
landmark = Landmark(
|
||||
name=name,
|
||||
type=elem_type,
|
||||
location=location,
|
||||
osm_type=osm_type,
|
||||
osm_id=osm_id,
|
||||
attractiveness=score,
|
||||
must_do=False,
|
||||
n_tags=int(n_tags)
|
||||
)
|
||||
return_list.append(landmark)
|
||||
|
||||
self.logger.debug(f"Fetched {len(return_list)} landmarks of type {landmarktype} in {bbox}")
|
||||
|
||||
return return_list
|
||||
|
||||
|
||||
|
||||
def dict_to_selector_list(d: dict) -> list:
|
||||
"""
|
||||
Convert a dictionary of key-value pairs to a list of Overpass query strings.
|
||||
|
||||
Args:
|
||||
d (dict): A dictionary of key-value pairs representing the selector.
|
||||
|
||||
Returns:
|
||||
list: A list of strings representing the Overpass query selectors.
|
||||
"""
|
||||
return_list = []
|
||||
for key, value in d.items():
|
||||
if type(value) == list:
|
||||
val = '|'.join(value)
|
||||
return_list.append(f'{key}~"{val}"')
|
||||
elif type(value) == str and len(value) == 0:
|
||||
return_list.append(f'{key}')
|
||||
else:
|
||||
return_list.append(f'{key}={value}')
|
||||
return return_list
|
||||
519
backend/src/utils/optimizer.py
Normal file
519
backend/src/utils/optimizer.py
Normal file
@ -0,0 +1,519 @@
|
||||
import yaml, logging
|
||||
import numpy as np
|
||||
|
||||
from scipy.optimize import linprog
|
||||
from collections import defaultdict, deque
|
||||
from geopy.distance import geodesic
|
||||
|
||||
from structs.landmark import Landmark
|
||||
from .get_time_separation import get_time
|
||||
import constants
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
class Optimizer:
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
detour: int = None # accepted max detour time (in minutes)
|
||||
detour_factor: float # detour factor of straight line vs real distance in cities
|
||||
average_walking_speed: float # average walking speed of adult
|
||||
max_landmarks: int # max number of landmarks to visit
|
||||
|
||||
|
||||
def __init__(self) :
|
||||
|
||||
# load parameters from file
|
||||
with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
|
||||
parameters = yaml.safe_load(f)
|
||||
self.detour_factor = parameters['detour_factor']
|
||||
self.average_walking_speed = parameters['average_walking_speed']
|
||||
self.max_landmarks = parameters['max_landmarks']
|
||||
|
||||
|
||||
|
||||
# Prevent the use of a particular solution
|
||||
def prevent_config(self, resx):
|
||||
"""
|
||||
Prevent the use of a particular solution by adding constraints to the optimization.
|
||||
|
||||
Args:
|
||||
resx (list[float]): List of edge weights.
|
||||
|
||||
Returns:
|
||||
Tuple[list[int], list[int]]: A tuple containing a new row for constraint matrix and new value for upper bound vector.
|
||||
"""
|
||||
|
||||
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
|
||||
|
||||
return h, [len(vertices_visited)-1]
|
||||
|
||||
|
||||
# Prevents the creation of the same circle (both directions)
|
||||
def prevent_circle(self, circle_vertices: list, L: int) :
|
||||
"""
|
||||
Prevent circular paths by by adding constraints to the optimization.
|
||||
|
||||
Args:
|
||||
circle_vertices (list): List of vertices forming a circle.
|
||||
L (int): Number of landmarks.
|
||||
|
||||
Returns:
|
||||
Tuple[np.ndarray, list[int]]: A tuple containing a new row for constraint matrix and new value for upper bound vector.
|
||||
"""
|
||||
|
||||
l1 = [0]*L*L
|
||||
l2 = [0]*L*L
|
||||
for i, node in enumerate(circle_vertices[:-1]) :
|
||||
next = circle_vertices[i+1]
|
||||
|
||||
l1[node*L + next] = 1
|
||||
l2[next*L + node] = 1
|
||||
|
||||
s = circle_vertices[0]
|
||||
g = circle_vertices[-1]
|
||||
|
||||
l1[g*L + s] = 1
|
||||
l2[s*L + g] = 1
|
||||
|
||||
return np.vstack((l1, l2)), [0, 0]
|
||||
|
||||
|
||||
def is_connected(self, resx) :
|
||||
"""
|
||||
Determine the order of visits and detect any circular paths in the given configuration.
|
||||
|
||||
Args:
|
||||
resx (list): List of edge weights.
|
||||
|
||||
Returns:
|
||||
Tuple[list[int], Optional[list[list[int]]]]: A tuple containing the visit order and a list of any detected circles.
|
||||
"""
|
||||
|
||||
# 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.
|
||||
|
||||
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()
|
||||
|
||||
# Step 1: Create a graph representation
|
||||
graph = defaultdict(list)
|
||||
for a, b in zip(ind_a, ind_b):
|
||||
graph[a].append(b)
|
||||
|
||||
# Step 2: Function to perform BFS/DFS to extract journeys
|
||||
def get_journey(start):
|
||||
journey_nodes = []
|
||||
visited = set()
|
||||
stack = deque([start])
|
||||
|
||||
while stack:
|
||||
node = stack.pop()
|
||||
if node not in visited:
|
||||
visited.add(node)
|
||||
journey_nodes.append(node)
|
||||
for neighbor in graph[node]:
|
||||
if neighbor not in visited:
|
||||
stack.append(neighbor)
|
||||
|
||||
return journey_nodes
|
||||
|
||||
# Step 3: Extract all journeys
|
||||
all_journeys_nodes = []
|
||||
visited_nodes = set()
|
||||
|
||||
for node in ind_a:
|
||||
if node not in visited_nodes:
|
||||
journey_nodes = get_journey(node)
|
||||
all_journeys_nodes.append(journey_nodes)
|
||||
visited_nodes.update(journey_nodes)
|
||||
|
||||
for l in all_journeys_nodes :
|
||||
if 0 in l :
|
||||
order = l
|
||||
all_journeys_nodes.remove(l)
|
||||
break
|
||||
|
||||
if len(all_journeys_nodes) == 0 :
|
||||
return order, None
|
||||
|
||||
return order, all_journeys_nodes
|
||||
|
||||
|
||||
|
||||
def init_ub_dist(self, landmarks: list[Landmark], max_steps: int):
|
||||
"""
|
||||
Initialize the objective function coefficients and inequality constraints for the optimization problem.
|
||||
|
||||
This function computes the distances between all landmarks and stores their attractiveness to maximize sightseeing.
|
||||
The goal is to maximize the objective function subject to the constraints A*x < b and A_eq*x = b_eq.
|
||||
|
||||
Args:
|
||||
landmarks (list[Landmark]): List of landmarks.
|
||||
max_steps (int): Maximum number of steps allowed.
|
||||
|
||||
Returns:
|
||||
Tuple[list[float], list[float], list[int]]: Objective function coefficients, inequality constraint coefficients, and the right-hand side of the inequality constraint.
|
||||
"""
|
||||
|
||||
# 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_time(spot1.location, spot2.location)
|
||||
dist_table[j] = t
|
||||
closest = sorted(dist_table)[:22]
|
||||
for i, dist in enumerate(dist_table) :
|
||||
if dist not in closest :
|
||||
dist_table[i] = 32700
|
||||
A_ub += dist_table
|
||||
c = c*len(landmarks)
|
||||
|
||||
return c, A_ub, [max_steps]
|
||||
|
||||
|
||||
def respect_number(self, L: int):
|
||||
"""
|
||||
Generate constraints to ensure each landmark is visited only once and cap the total number of visited landmarks.
|
||||
|
||||
Args:
|
||||
L (int): Number of landmarks.
|
||||
|
||||
Returns:
|
||||
Tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
|
||||
"""
|
||||
|
||||
ones = [1]*L
|
||||
zeros = [0]*L
|
||||
A = ones + zeros*(L-1)
|
||||
b = [1]
|
||||
for i in range(L-1) :
|
||||
h_new = zeros*i + ones + zeros*(L-1-i)
|
||||
A = np.vstack((A, h_new))
|
||||
b.append(1)
|
||||
|
||||
A = np.vstack((A, ones*L))
|
||||
b.append(self.max_landmarks+1)
|
||||
|
||||
return A, b
|
||||
|
||||
|
||||
# Constraint to not have d14 and d41 simultaneously. Does not prevent cyclic paths with more elements
|
||||
def break_sym(self, L):
|
||||
"""
|
||||
Generate constraints to prevent simultaneous travel between two landmarks in both directions.
|
||||
|
||||
Args:
|
||||
L (int): Number of landmarks.
|
||||
|
||||
Returns:
|
||||
Tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
|
||||
"""
|
||||
|
||||
upper_ind = np.triu_indices(L,0,L)
|
||||
|
||||
up_ind_x = upper_ind[0]
|
||||
up_ind_y = upper_ind[1]
|
||||
|
||||
A = [0]*L*L
|
||||
b = [1]
|
||||
|
||||
for i, _ in enumerate(up_ind_x[1:]) :
|
||||
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 = np.vstack((A,l))
|
||||
b.append(1)
|
||||
|
||||
return A, b
|
||||
|
||||
|
||||
def init_eq_not_stay(self, L: int):
|
||||
"""
|
||||
Generate constraints to prevent staying in the same position (e.g., removing d11, d22, d33, etc.).
|
||||
|
||||
Args:
|
||||
L (int): Number of landmarks.
|
||||
|
||||
Returns:
|
||||
Tuple[list[np.ndarray], list[int]]: Equality constraint coefficients and the right-hand side of the equality constraints.
|
||||
"""
|
||||
|
||||
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), dtype=np.int8)
|
||||
|
||||
return [l], [0]
|
||||
|
||||
|
||||
def respect_user_must_do(self, landmarks: list[Landmark]) :
|
||||
"""
|
||||
Generate constraints to ensure that landmarks marked as 'must_do' are included in the optimization.
|
||||
|
||||
Args:
|
||||
landmarks (list[Landmark]): List of landmarks, where some are marked as 'must_do'.
|
||||
|
||||
Returns:
|
||||
Tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
|
||||
"""
|
||||
|
||||
L = len(landmarks)
|
||||
A = [0]*L*L
|
||||
b = [0]
|
||||
|
||||
for i, elem in enumerate(landmarks[1:]) :
|
||||
if elem.must_do is True and elem.name not in ['finish', 'start']:
|
||||
l = [0]*L*L
|
||||
l[i*L:i*L+L] = [1]*L # set mandatory departures from landmarks tagged as 'must_do'
|
||||
|
||||
A = np.vstack((A,l))
|
||||
b.append(1)
|
||||
|
||||
return A, b
|
||||
|
||||
|
||||
def respect_user_must_avoid(self, landmarks: list[Landmark]) :
|
||||
"""
|
||||
Generate constraints to ensure that landmarks marked as 'must_avoid' are skipped in the optimization.
|
||||
|
||||
Args:
|
||||
landmarks (list[Landmark]): List of landmarks, where some are marked as 'must_avoid'.
|
||||
|
||||
Returns:
|
||||
Tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
|
||||
"""
|
||||
|
||||
L = len(landmarks)
|
||||
A = [0]*L*L
|
||||
b = [0]
|
||||
|
||||
for i, elem in enumerate(landmarks[1:]) :
|
||||
if elem.must_avoid is True and elem.name not in ['finish', 'start']:
|
||||
l = [0]*L*L
|
||||
l[i*L:i*L+L] = [1]*L
|
||||
|
||||
A = np.vstack((A,l))
|
||||
b.append(0) # prevent departures from landmarks tagged as 'must_do'
|
||||
|
||||
return A, b
|
||||
|
||||
|
||||
# Constraint to ensure start at start and finish at goal
|
||||
def respect_start_finish(self, L: int):
|
||||
"""
|
||||
Generate constraints to ensure that the optimization starts at the designated start landmark and finishes at the goal landmark.
|
||||
|
||||
Args:
|
||||
L (int): Number of landmarks.
|
||||
|
||||
Returns:
|
||||
Tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
|
||||
"""
|
||||
|
||||
l_start = [1]*L + [0]*L*(L-1) # sets departures only for start (horizontal ones)
|
||||
l_start[L-1] = 0 # prevents the jump from start to finish
|
||||
l_goal = [0]*L*L # sets arrivals only for finish (vertical ones)
|
||||
l_L = [0]*L*(L-1) + [1]*L # prevents arrivals at start and departures from goal
|
||||
for k in range(L-1) : # sets only vertical ones for goal (go to)
|
||||
l_L[k*L] = 1
|
||||
if k != 0 :
|
||||
l_goal[k*L+L-1] = 1
|
||||
|
||||
A = np.vstack((l_start, l_goal))
|
||||
b = [1, 1]
|
||||
A = np.vstack((A,l_L))
|
||||
b.append(0)
|
||||
|
||||
return A, b
|
||||
|
||||
|
||||
def respect_order(self, L: int):
|
||||
"""
|
||||
Generate constraints to tie the optimization problem together and prevent stacked ones, although this does not fully prevent circles.
|
||||
|
||||
Args:
|
||||
L (int): Number of landmarks.
|
||||
|
||||
Returns:
|
||||
Tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
|
||||
"""
|
||||
|
||||
A = [0]*L*L
|
||||
b = [0]
|
||||
for i in range(L-1) : # Prevent stacked ones
|
||||
if i == 0 or i == L-1: # Don't touch start or finish
|
||||
continue
|
||||
else :
|
||||
l = [0]*L
|
||||
l[i] = -1
|
||||
l = l*L
|
||||
for j in range(L) :
|
||||
l[i*L + j] = 1
|
||||
|
||||
A = np.vstack((A,l))
|
||||
b.append(0)
|
||||
|
||||
return A, b
|
||||
|
||||
|
||||
def link_list(self, order: list[int], landmarks: list[Landmark])->list[Landmark] :
|
||||
"""
|
||||
Compute the time to reach from each landmark to the next and create a list of landmarks with updated travel times.
|
||||
|
||||
Args:
|
||||
order (list[int]): List of indices representing the order of landmarks to visit.
|
||||
landmarks (list[Landmark]): List of all landmarks.
|
||||
|
||||
Returns:
|
||||
list[Landmark]]: The updated linked list of landmarks with travel times
|
||||
"""
|
||||
|
||||
L = []
|
||||
j = 0
|
||||
while j < len(order)-1 :
|
||||
# get landmarks involved
|
||||
elem = landmarks[order[j]]
|
||||
next = landmarks[order[j+1]]
|
||||
|
||||
# get attributes
|
||||
elem.time_to_reach_next = get_time(elem.location, next.location)
|
||||
elem.must_do = True
|
||||
elem.location = (round(elem.location[0], 5), round(elem.location[1], 5))
|
||||
elem.next_uuid = next.uuid
|
||||
L.append(elem)
|
||||
j += 1
|
||||
|
||||
next.location = (round(next.location[0], 5), round(next.location[1], 5))
|
||||
next.must_do = True
|
||||
L.append(next)
|
||||
|
||||
return L
|
||||
|
||||
|
||||
# Main optimization pipeline
|
||||
def solve_optimization(
|
||||
self,
|
||||
max_time: int,
|
||||
landmarks: list[Landmark],
|
||||
) -> list[Landmark]:
|
||||
"""
|
||||
Main optimization pipeline to solve the landmark visiting problem.
|
||||
|
||||
This method sets up and solves a linear programming problem with constraints to find an optimal tour of landmarks,
|
||||
considering user-defined must-visit landmarks, start and finish points, and ensuring no cycles are present.
|
||||
|
||||
Args:
|
||||
max_time (int): Maximum time allowed for the tour in minutes.
|
||||
landmarks (list[Landmark]): List of landmarks to visit.
|
||||
Returns:
|
||||
list[Landmark]: The optimized tour of landmarks with updated travel times, or None if no valid solution is found.
|
||||
"""
|
||||
|
||||
L = len(landmarks)
|
||||
|
||||
# SET CONSTRAINTS FOR INEQUALITY
|
||||
c, A_ub, b_ub = self.init_ub_dist(landmarks, max_time) # Add the distances from each landmark to the other
|
||||
A, b = self.respect_number(L) # Respect max number of visits (no more possible stops than landmarks).
|
||||
A_ub = np.vstack((A_ub, A), dtype=np.int16)
|
||||
b_ub += b
|
||||
A, b = self.break_sym(L) # break the 'zig-zag' symmetry
|
||||
A_ub = np.vstack((A_ub, A), dtype=np.int16)
|
||||
b_ub += b
|
||||
|
||||
|
||||
# SET CONSTRAINTS FOR EQUALITY
|
||||
A_eq, b_eq = self.init_eq_not_stay(L) # Force solution not to stay in same place
|
||||
A, b = self.respect_user_must_do(landmarks) # Check if there are user_defined must_see. Also takes care of start/goal
|
||||
A_eq = np.vstack((A_eq, A), dtype=np.int8)
|
||||
b_eq += b
|
||||
A, b = self.respect_user_must_avoid(landmarks) # Check if there are user_defined must_see. Also takes care of start/goal
|
||||
A_eq = np.vstack((A_eq, A), dtype=np.int8)
|
||||
b_eq += b
|
||||
A, b = self.respect_start_finish(L) # Force start and finish positions
|
||||
A_eq = np.vstack((A_eq, A), dtype=np.int8)
|
||||
b_eq += b
|
||||
A, b = self.respect_order(L) # Respect order of visit (only works when max_steps is limiting factor)
|
||||
A_eq = np.vstack((A_eq, A), dtype=np.int8)
|
||||
b_eq += b
|
||||
|
||||
# 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
|
||||
order, circles = self.is_connected(res.x)
|
||||
#nodes, edges = is_connected(res.x)
|
||||
i = 0
|
||||
timeout = 80
|
||||
while circles is not None and i < timeout:
|
||||
A, b = self.prevent_config(res.x)
|
||||
A_ub = np.vstack((A_ub, A))
|
||||
b_ub += b
|
||||
#A_ub, b_ub = prevent_circle(order, len(landmarks), A_ub, b_ub)
|
||||
for circle in circles :
|
||||
A, b = self.prevent_circle(circle, L)
|
||||
A_eq = np.vstack((A_eq, A))
|
||||
b_eq += b
|
||||
res = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq = b_eq, bounds=x_bounds, method='highs', integrality=3)
|
||||
if not res.success :
|
||||
raise ArithmeticError("Solving failed because of overconstrained problem")
|
||||
return None
|
||||
order, circles = self.is_connected(res.x)
|
||||
#nodes, edges = is_connected(res.x)
|
||||
if circles is None :
|
||||
break
|
||||
# print(i)
|
||||
i += 1
|
||||
|
||||
if i == timeout :
|
||||
raise TimeoutError(f"Optimization took too long. No solution found after {timeout} iterations.")
|
||||
|
||||
#sort the landmarks in the order of the solution
|
||||
tour = [landmarks[i] for i in order]
|
||||
|
||||
self.logger.debug(f"Re-optimized {i} times, score: {int(-res.fun)}")
|
||||
return tour
|
||||
340
backend/src/utils/refiner.py
Normal file
340
backend/src/utils/refiner.py
Normal file
@ -0,0 +1,340 @@
|
||||
import yaml, logging
|
||||
|
||||
from shapely import buffer, LineString, Point, Polygon, MultiPoint, concave_hull
|
||||
from math import pi
|
||||
|
||||
from structs.landmark import Landmark
|
||||
from . import take_most_important, get_time_separation
|
||||
from .optimizer import Optimizer
|
||||
import constants
|
||||
|
||||
|
||||
|
||||
class Refiner :
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
detour_factor: float # detour factor of straight line vs real distance in cities
|
||||
detour_corridor_width: float # width of the corridor around the path
|
||||
average_walking_speed: float # average walking speed of adult
|
||||
max_landmarks: int # max number of landmarks to visit
|
||||
optimizer: Optimizer # optimizer object
|
||||
|
||||
def __init__(self, optimizer: Optimizer) :
|
||||
self.optimizer = optimizer
|
||||
|
||||
# load parameters from file
|
||||
with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
|
||||
parameters = yaml.safe_load(f)
|
||||
self.detour_factor = parameters['detour_factor']
|
||||
self.detour_corridor_width = parameters['detour_corridor_width']
|
||||
self.average_walking_speed = parameters['average_walking_speed']
|
||||
self.max_landmarks = parameters['max_landmarks'] + 4
|
||||
|
||||
|
||||
def create_corridor(self, landmarks: list[Landmark], width: float) :
|
||||
"""
|
||||
Create a corridor around the path connecting the landmarks.
|
||||
|
||||
Args:
|
||||
landmarks (list[Landmark]): the landmark path around which to create the corridor
|
||||
width (float): Width of the corridor in meters.
|
||||
|
||||
Returns:
|
||||
Geometry: A buffered geometry object representing the corridor around the path.
|
||||
"""
|
||||
|
||||
corrected_width = (180*width)/(6371000*pi)
|
||||
|
||||
path = self.create_linestring(landmarks)
|
||||
obj = buffer(path, corrected_width, join_style="mitre", cap_style="square", mitre_limit=2)
|
||||
|
||||
return obj
|
||||
|
||||
|
||||
def create_linestring(self, tour: list[Landmark]) -> LineString :
|
||||
"""
|
||||
Create a `LineString` object from a tour.
|
||||
|
||||
Args:
|
||||
tour (list[Landmark]): An ordered sequence of landmarks that represents the visiting order.
|
||||
|
||||
Returns:
|
||||
LineString: A `LineString` object representing the path through the landmarks.
|
||||
"""
|
||||
|
||||
points = []
|
||||
for landmark in tour :
|
||||
points.append(Point(landmark.location))
|
||||
|
||||
return LineString(points)
|
||||
|
||||
|
||||
# Check if some coordinates are in area. Used for the corridor
|
||||
def is_in_area(self, area: Polygon, coordinates) -> bool :
|
||||
"""
|
||||
Check if a given point is within a specified area.
|
||||
|
||||
Args:
|
||||
area (Polygon): The polygon defining the area.
|
||||
coordinates (tuple[float, float]): The coordinates of the point to check.
|
||||
|
||||
Returns:
|
||||
bool: True if the point is within the area, otherwise False.
|
||||
"""
|
||||
point = Point(coordinates)
|
||||
return point.within(area)
|
||||
|
||||
|
||||
# Function to determine if two landmarks are close to each other
|
||||
def is_close_to(self, location1: tuple[float], location2: tuple[float]):
|
||||
"""
|
||||
Determine if two locations are close to each other by rounding their coordinates to 3 decimal places.
|
||||
|
||||
Args:
|
||||
location1 (tuple[float, float]): The coordinates of the first location.
|
||||
location2 (tuple[float, float]): The coordinates of the second location.
|
||||
|
||||
Returns:
|
||||
bool: True if the locations are within 0.001 degrees of each other, otherwise False.
|
||||
"""
|
||||
|
||||
absx = abs(location1[0] - location2[0])
|
||||
absy = abs(location1[1] - location2[1])
|
||||
|
||||
return absx < 0.001 and absy < 0.001
|
||||
#return (round(location1[0], 3), round(location1[1], 3)) == (round(location2[0], 3), round(location2[1], 3))
|
||||
|
||||
|
||||
def rearrange(self, tour: list[Landmark]) -> list[Landmark]:
|
||||
"""
|
||||
Rearrange landmarks to group nearby visits together.
|
||||
|
||||
This function reorders landmarks so that nearby landmarks are adjacent to each other in the list,
|
||||
while keeping 'start' and 'finish' landmarks in their original positions.
|
||||
|
||||
Args:
|
||||
tour (list[Landmark]): Ordered list of landmarks to be rearranged.
|
||||
|
||||
Returns:
|
||||
list[Landmark]: The rearranged list of landmarks with grouped nearby visits.
|
||||
"""
|
||||
|
||||
i = 1
|
||||
while i < len(tour):
|
||||
j = i+1
|
||||
while j < len(tour):
|
||||
if self.is_close_to(tour[i].location, tour[j].location) and tour[i].name not in ['start', 'finish'] and tour[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:
|
||||
tour.insert(i + 1, tour.pop(j))
|
||||
break # Move to the next i-th element after rearrangement
|
||||
j += 1
|
||||
i += 1
|
||||
|
||||
return tour
|
||||
|
||||
|
||||
def find_shortest_path_through_all_landmarks(self, landmarks: list[Landmark]) -> tuple[list[Landmark], Polygon]:
|
||||
"""
|
||||
Find the shortest path through all landmarks using a nearest neighbor heuristic.
|
||||
|
||||
This function constructs a path that starts from the 'start' landmark, visits all other landmarks in the order
|
||||
of their proximity, and ends at the 'finish' landmark. It returns both the ordered list of landmarks and a
|
||||
polygon representing the path.
|
||||
|
||||
Args:
|
||||
landmarks (list[Landmark]): list of all landmarks including 'start' and 'finish'.
|
||||
|
||||
Returns:
|
||||
tuple[list[Landmark], Polygon]: A tuple where the first element is the list of landmarks in the order they
|
||||
should be visited, and the second element is a `Polygon` representing
|
||||
the path connecting all landmarks.
|
||||
"""
|
||||
|
||||
# Step 1: Find 'start' and 'finish' landmarks
|
||||
start_idx = next(i for i, lm in enumerate(landmarks) if lm.type == 'start')
|
||||
finish_idx = next(i for i, lm in enumerate(landmarks) if lm.type == '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_separation.get_time(current_landmark.location, lm.location))
|
||||
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(self, all_landmarks: list[Landmark], visited_landmarks: list[Landmark], width: float) -> list[Landmark] :
|
||||
"""
|
||||
Identify landmarks within a specified corridor that have not been visited yet.
|
||||
|
||||
This function creates a corridor around the path defined by visited landmarks and then finds landmarks that fall
|
||||
within this corridor. It returns a list of these landmarks, excluding those already visited, sorted by their importance.
|
||||
|
||||
Args:
|
||||
all_landmarks (list[Landmark]): list of all available landmarks.
|
||||
visited_landmarks (list[Landmark]): list of landmarks that have already been visited.
|
||||
width (float): Width of the corridor around the visited landmarks.
|
||||
|
||||
Returns:
|
||||
list[Landmark]: list of important landmarks within the corridor that have not been visited yet.
|
||||
"""
|
||||
|
||||
second_order_landmarks = []
|
||||
visited_names = []
|
||||
area = self.create_corridor(visited_landmarks, width)
|
||||
|
||||
for visited in visited_landmarks :
|
||||
visited_names.append(visited.name)
|
||||
|
||||
for landmark in all_landmarks :
|
||||
if self.is_in_area(area, landmark.location) and landmark.name not in visited_names:
|
||||
second_order_landmarks.append(landmark)
|
||||
|
||||
return take_most_important.take_most_important(second_order_landmarks, len(visited_landmarks))
|
||||
|
||||
|
||||
# Try fix the shortest path using shapely
|
||||
def fix_using_polygon(self, tour: list[Landmark])-> list[Landmark] :
|
||||
"""
|
||||
Improve the tour path using geometric methods to ensure it follows a more optimal shape.
|
||||
|
||||
This function creates a polygon from the given tour and attempts to refine it using a concave hull. It reorders
|
||||
the landmarks to fit within this refined polygon and adjusts the tour to ensure the 'start' landmark is at the
|
||||
beginning. It also checks if the final polygon is simple and rearranges the tour if necessary.
|
||||
|
||||
Args:
|
||||
tour (list[Landmark]): list of landmarks representing the current tour path.
|
||||
|
||||
Returns:
|
||||
list[Landmark]: Refined list of landmarks in the order of visit to produce a better tour path.
|
||||
"""
|
||||
|
||||
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 = self.rearrange(better_tour)
|
||||
|
||||
return better_tour
|
||||
|
||||
|
||||
def refine_optimization(
|
||||
self,
|
||||
all_landmarks: list[Landmark],
|
||||
base_tour: list[Landmark],
|
||||
max_time: int,
|
||||
detour: int
|
||||
) -> list[Landmark]:
|
||||
"""
|
||||
This is the second stage of the optimization. It refines the initial tour path by considering additional minor landmarks and optimizes the path.
|
||||
|
||||
This method evaluates the need for further optimization based on the initial tour. If a detour is required
|
||||
it adds minor landmarks around the initial predicted path and solves a new optimization problem to find a potentially better
|
||||
tour. It then links the new tour and adjusts it using a nearest neighbor heuristic and polygon-based methods to
|
||||
ensure a valid path. The final tour is chosen based on the shortest distance.
|
||||
|
||||
Args:
|
||||
all_landmarks (list[Landmark]): The full list of landmarks available for the optimization.
|
||||
base_tour (list[Landmark]): The initial tour path to be refined.
|
||||
max_time (int): The maximum time available for the tour in minutes.
|
||||
detour (int): The maximum detour time allowed for the tour in minutes.
|
||||
Returns:
|
||||
list[Landmark]: The refined list of landmarks representing the optimized tour path.
|
||||
"""
|
||||
|
||||
# No need to refine if no detour is taken
|
||||
if detour == 0:
|
||||
return base_tour
|
||||
|
||||
minor_landmarks = self.get_minor_landmarks(all_landmarks, base_tour, self.detour_corridor_width)
|
||||
|
||||
self.logger.info(f"Using {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 = self.optimizer.solve_optimization(
|
||||
max_time = max_time + detour,
|
||||
landmarks = full_set
|
||||
)
|
||||
|
||||
if new_tour is None:
|
||||
self.logger.warning("No solution found for the refined tour. Returning the initial tour.")
|
||||
new_tour = base_tour
|
||||
|
||||
|
||||
# Find shortest path using the nearest neighbor heuristic
|
||||
better_tour, better_poly = self.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 = self.fix_using_polygon(better_tour)
|
||||
|
||||
return better_tour
|
||||
38
backend/src/utils/take_most_important.py
Normal file
38
backend/src/utils/take_most_important.py
Normal file
@ -0,0 +1,38 @@
|
||||
from structs.landmark import Landmark
|
||||
|
||||
def take_most_important(landmarks: list[Landmark], N_important) -> list[Landmark] :
|
||||
L = len(landmarks)
|
||||
L_copy = []
|
||||
L_clean = []
|
||||
scores = [0]*len(landmarks)
|
||||
names = []
|
||||
name_id = {}
|
||||
|
||||
for i, elem in enumerate(landmarks) :
|
||||
if elem.name not in names :
|
||||
names.append(elem.name)
|
||||
name_id[elem.name] = [i]
|
||||
L_copy.append(elem)
|
||||
else :
|
||||
name_id[elem.name] += [i]
|
||||
scores = []
|
||||
for j in name_id[elem.name] :
|
||||
scores.append(L[j].attractiveness)
|
||||
best_id = max(range(len(scores)), key=scores.__getitem__)
|
||||
t = name_id[elem.name][best_id]
|
||||
if t == i :
|
||||
for old in L_copy :
|
||||
if old.name == elem.name :
|
||||
old.attractiveness = L[t].attractiveness
|
||||
|
||||
scores = [0]*len(L_copy)
|
||||
for i, elem in enumerate(L_copy) :
|
||||
scores[i] = elem.attractiveness
|
||||
|
||||
res = sorted(range(len(scores)), key = lambda sub: scores[sub])[-(N_important-L):]
|
||||
|
||||
for i, elem in enumerate(L_copy) :
|
||||
if i in res :
|
||||
L_clean.append(elem)
|
||||
|
||||
return L_clean
|
||||
@ -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
|
||||
Loading…
x
Reference in New Issue
Block a user