diff --git a/.gitea/workflows/backend_build-image.yaml b/.gitea/workflows/backend_build-image.yaml
index 1552c03..0a40ccd 100644
--- a/.gitea/workflows/backend_build-image.yaml
+++ b/.gitea/workflows/backend_build-image.yaml
@@ -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:
context: backend
- tags: git.kluster.moll.re/remoll/fast_network_navigation/backend:latest
+ tags: git.kluster.moll.re/anydev/anyway-backend:latest
push: true
diff --git a/.gitea/workflows/frontend_build-android.yaml b/.gitea/workflows/frontend_build-android.yaml
index 5856576..2979c23 100644
--- a/.gitea/workflows/frontend_build-android.yaml
+++ b/.gitea/workflows/frontend_build-android.yaml
@@ -44,7 +44,7 @@ jobs:
- name: Add required secrets
run: |
- echo ${{ secrets.ANDROID_SECRETS_BASE64 }} | base64 -d > ./android/secrets.properties
+ echo ${{ secrets.ANDROID_SECRETS_PROPERTIES }} > ./android/secrets.properties
working-directory: ./frontend
- name: Sanity check
diff --git a/.gitea/workflows/frontend_build-web.yaml b/.gitea/workflows/frontend_build-web.yaml
index f5980fe..05f28d4 100644
--- a/.gitea/workflows/frontend_build-web.yaml
+++ b/.gitea/workflows/frontend_build-web.yaml
@@ -1,34 +1,34 @@
-on:
- pull_request:
- branches:
- - main
- paths:
- - frontend/**
+# on:
+# pull_request:
+# branches:
+# - main
+# paths:
+# - frontend/**
-name: Build web
+# name: Build web
-jobs:
- build:
- name: Build Web
- runs-on: ubuntu-latest
- steps:
+# jobs:
+# build:
+# name: Build Web
+# runs-on: ubuntu-latest
+# steps:
- - name: Install prerequisites
- run: |
- sudo apt-get update
- sudo apt-get install -y xz-utils
+# - name: Install prerequisites
+# run: |
+# sudo apt-get update
+# sudo apt-get install -y xz-utils
- - uses: actions/checkout@v4
+# - uses: actions/checkout@v4
- - uses: https://github.com/subosito/flutter-action@v2
- with:
- channel: stable
- flutter-version: 3.19.6
- cache: true
+# - uses: https://github.com/subosito/flutter-action@v2
+# with:
+# channel: stable
+# flutter-version: 3.19.6
+# cache: true
- - run: flutter pub get
- working-directory: ./frontend
+# - run: flutter pub get
+# working-directory: ./frontend
- - run: flutter build web
- working-directory: ./frontend
+# - run: flutter build web
+# working-directory: ./frontend
diff --git a/.vscode/launch.json b/.vscode/launch.json
new file mode 100644
index 0000000..c0cf17a
--- /dev/null
+++ b/.vscode/launch.json
@@ -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",
+ "configurations": [
+ // backend - python using fastapi
+ {
+ "name": "Backend - debug",
+ "type": "debugpy",
+ "request": "launch",
+ "module": "uvicorn",
+ "env": {
+ "DEBUG": "true"
+ },
+ "args": [
+ "--app-dir",
+ "src",
+ "main:app",
+ "--reload",
+ ],
+ "jinja": true,
+ "cwd": "${workspaceFolder}/backend"
+ },
+ {
+ "name": "Backend - tester",
+ "type": "debugpy",
+ "request": "launch",
+ "program": "src/tester.py",
+ "env": {
+ "DEBUG": "true"
+ },
+ "cwd": "${workspaceFolder}/backend"
+ },
+ // frontend - flutter app
+ {
+ "name": "Frontend - debug",
+ "type": "dart",
+ "request": "launch",
+ "program": "lib/main.dart",
+ "cwd": "${workspaceFolder}/frontend"
+ },
+ {
+ "name": "Frontend - profile",
+ "type": "dart",
+ "request": "launch",
+ "program": "lib/main.dart",
+ "flutterMode": "profile",
+ "cwd": "${workspaceFolder}/frontend"
+ }
+ ]
+}
\ No newline at end of file
diff --git a/backend/Dockerfile b/backend/Dockerfile
index 9e15e47..e399a4f 100644
--- a/backend/Dockerfile
+++ b/backend/Dockerfile
@@ -14,4 +14,4 @@ EXPOSE 8000
ENV NUM_WORKERS=1
ENV OSM_CACHE_DIR=/cache
-CMD ["pipenv", "run", "fastapi", "run", "src/main.py", '--port 8000', '--workers $NUM_WORKERS']
+CMD fastapi run src/main.py --port 8000 --workers $NUM_WORKERS
diff --git a/backend/Pipfile b/backend/Pipfile
index 4197132..c590ded 100644
--- a/backend/Pipfile
+++ b/backend/Pipfile
@@ -3,17 +3,14 @@ url = "https://pypi.org/simple"
verify_ssl = true
name = "pypi"
+[dev-packages]
+
[packages]
numpy = "*"
-scipy = "*"
fastapi = "*"
pydantic = "*"
-shapely = "*"
-networkx = "*"
geopy = "*"
-requests = ">=2.20.1"
-mwparserfromhell = ">=0.5.2"
-packaging = "*"
+shapely = "*"
+scipy = "*"
+osmpythontools = "*"
pywikibot = "*"
-
-[dev-packages]
diff --git a/backend/Pipfile.lock b/backend/Pipfile.lock
index f6188cf..387fcbd 100644
--- a/backend/Pipfile.lock
+++ b/backend/Pipfile.lock
@@ -1,7 +1,7 @@
{
"_meta": {
"hash": {
- "sha256": "6f1907b01627126d80ae688e9f2e32fc696d85f0cca3913f14fd5d26cb3ac2db"
+ "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",
@@ -142,6 +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",
@@ -175,6 +241,54 @@
"markers": "python_version >= '3.8'",
"version": "==0.0.4"
},
+ "fonttools": {
+ "hashes": [
+ "sha256:02569e9a810f9d11f4ae82c391ebc6fb5730d95a0657d24d754ed7763fb2d122",
+ "sha256:0679a30b59d74b6242909945429dbddb08496935b82f91ea9bf6ad240ec23397",
+ "sha256:10f5e6c3510b79ea27bb1ebfcc67048cde9ec67afa87c7dd7efa5c700491ac7f",
+ "sha256:2af40ae9cdcb204fc1d8f26b190aa16534fcd4f0df756268df674a270eab575d",
+ "sha256:32f029c095ad66c425b0ee85553d0dc326d45d7059dbc227330fc29b43e8ba60",
+ "sha256:35250099b0cfb32d799fb5d6c651220a642fe2e3c7d2560490e6f1d3f9ae9169",
+ "sha256:3b3c8ebafbee8d9002bd8f1195d09ed2bd9ff134ddec37ee8f6a6375e6a4f0e8",
+ "sha256:4824c198f714ab5559c5be10fd1adf876712aa7989882a4ec887bf1ef3e00e31",
+ "sha256:5ff7e5e9bad94e3a70c5cd2fa27f20b9bb9385e10cddab567b85ce5d306ea923",
+ "sha256:651390c3b26b0c7d1f4407cad281ee7a5a85a31a110cbac5269de72a51551ba2",
+ "sha256:6e08f572625a1ee682115223eabebc4c6a2035a6917eac6f60350aba297ccadb",
+ "sha256:6ed170b5e17da0264b9f6fae86073be3db15fa1bd74061c8331022bca6d09bab",
+ "sha256:73379d3ffdeecb376640cd8ed03e9d2d0e568c9d1a4e9b16504a834ebadc2dfb",
+ "sha256:75a157d8d26c06e64ace9df037ee93a4938a4606a38cb7ffaf6635e60e253b7a",
+ "sha256:791b31ebbc05197d7aa096bbc7bd76d591f05905d2fd908bf103af4488e60670",
+ "sha256:7b6b35e52ddc8fb0db562133894e6ef5b4e54e1283dff606fda3eed938c36fc8",
+ "sha256:84ec3fb43befb54be490147b4a922b5314e16372a643004f182babee9f9c3407",
+ "sha256:8959a59de5af6d2bec27489e98ef25a397cfa1774b375d5787509c06659b3671",
+ "sha256:9dfdae43b7996af46ff9da520998a32b105c7f098aeea06b2226b30e74fbba88",
+ "sha256:9e6ceba2a01b448e36754983d376064730690401da1dd104ddb543519470a15f",
+ "sha256:9efd176f874cb6402e607e4cc9b4a9cd584d82fc34a4b0c811970b32ba62501f",
+ "sha256:a1c7c5aa18dd3b17995898b4a9b5929d69ef6ae2af5b96d585ff4005033d82f0",
+ "sha256:aae7bd54187e8bf7fd69f8ab87b2885253d3575163ad4d669a262fe97f0136cb",
+ "sha256:b21952c092ffd827504de7e66b62aba26fdb5f9d1e435c52477e6486e9d128b2",
+ "sha256:b96cd370a61f4d083c9c0053bf634279b094308d52fdc2dd9a22d8372fdd590d",
+ "sha256:becc5d7cb89c7b7afa8321b6bb3dbee0eec2b57855c90b3e9bf5fb816671fa7c",
+ "sha256:bee32ea8765e859670c4447b0817514ca79054463b6b79784b08a8df3a4d78e3",
+ "sha256:c6e7170d675d12eac12ad1a981d90f118c06cf680b42a2d74c6c931e54b50719",
+ "sha256:c818c058404eb2bba05e728d38049438afd649e3c409796723dfc17cd3f08749",
+ "sha256:c8696544c964500aa9439efb6761947393b70b17ef4e82d73277413f291260a4",
+ "sha256:c9cd19cf4fe0595ebdd1d4915882b9440c3a6d30b008f3cc7587c1da7b95be5f",
+ "sha256:d4d0096cb1ac7a77b3b41cd78c9b6bc4a400550e21dc7a92f2b5ab53ed74eb02",
+ "sha256:d92d3c2a1b39631a6131c2fa25b5406855f97969b068e7e08413325bc0afba58",
+ "sha256:da33440b1413bad53a8674393c5d29ce64d8c1a15ef8a77c642ffd900d07bfe1",
+ "sha256:e013aae589c1c12505da64a7d8d023e584987e51e62006e1bb30d72f26522c41",
+ "sha256:e128778a8e9bc11159ce5447f76766cefbd876f44bd79aff030287254e4752c4",
+ "sha256:e54f1bba2f655924c1138bbc7fa91abd61f45c68bd65ab5ed985942712864bbb",
+ "sha256:e5b708073ea3d684235648786f5f6153a48dc8762cdfe5563c57e80787c29fbb",
+ "sha256:e8bf06b94694251861ba7fdeea15c8ec0967f84c3d4143ae9daf42bbc7717fe3",
+ "sha256:f08df60fbd8d289152079a65da4e66a447efc1d5d5a4d3f299cdd39e3b2e4a7d",
+ "sha256:f1f8758a2ad110bd6432203a344269f445a2907dc24ef6bccfd0ac4e14e0d71d",
+ "sha256:f677ce218976496a587ab17140da141557beb91d2a5c1a14212c994093f2eae2"
+ ],
+ "markers": "python_version >= '3.8'",
+ "version": "==4.53.1"
+ },
"geographiclib": {
"hashes": [
"sha256:6b7225248e45ff7edcee32becc4e0a1504c606ac5ee163a5656d482e0cd38734",
@@ -183,6 +297,14 @@
"markers": "python_version >= '3.7'",
"version": "==2.0"
},
+ "geojson": {
+ "hashes": [
+ "sha256:58a7fa40727ea058efc28b0e9ff0099eadf6d0965e04690830208d3ef571adac",
+ "sha256:68a9771827237adb8c0c71f8527509c8f5bef61733aa434cefc9c9d4f0ebe8f3"
+ ],
+ "markers": "python_version >= '3.7'",
+ "version": "==3.1.0"
+ },
"geopy": {
"hashes": [
"sha256:50283d8e7ad07d89be5cb027338c6365a32044df3ae2556ad3f52f4840b3d0d1",
@@ -273,6 +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",
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@@ -347,6 +727,41 @@
"markers": "python_version >= '3.7'",
"version": "==2.1.5"
},
+ "matplotlib": {
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@@ -384,19 +799,9 @@
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@@ -449,15 +854,142 @@
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+ "sha256:ff25afb18123cea58a591ea0244b92eb1e61a1fd497bf6d6384f09bc3262ec3e",
+ "sha256:ff337c552345e95702c5fde3158acb0625111017d0e5f24bf3acdb9cc16b90d1"
+ ],
+ "markers": "python_version >= '3.8'",
+ "version": "==10.4.0"
+ },
"pydantic": {
"hashes": [
"sha256:6f62c13d067b0755ad1c21a34bdd06c0c12625a22b0fc09c6b149816604f7c2a",
@@ -570,6 +1102,22 @@
"markers": "python_version >= '3.8'",
"version": "==2.18.0"
},
+ "pyparsing": {
+ "hashes": [
+ "sha256:a1bac0ce561155ecc3ed78ca94d3c9378656ad4c94c1270de543f621420f94ad",
+ "sha256:f9db75911801ed778fe61bb643079ff86601aca99fcae6345aa67292038fb742"
+ ],
+ "markers": "python_full_version >= '3.6.8'",
+ "version": "==3.1.2"
+ },
+ "python-dateutil": {
+ "hashes": [
+ "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3",
+ "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427"
+ ],
+ "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
+ "version": "==2.9.0.post0"
+ },
"python-dotenv": {
"hashes": [
"sha256:e324ee90a023d808f1959c46bcbc04446a10ced277783dc6ee09987c37ec10ca",
@@ -585,6 +1133,13 @@
"markers": "python_version >= '3.8'",
"version": "==0.0.9"
},
+ "pytz": {
+ "hashes": [
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+ "sha256:328171f4e3623139da4983451950b28e95ac706e13f3f2630a879749e7a8b319"
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+ "version": "==2024.1"
+ },
"pywikibot": {
"hashes": [
"sha256:3f4fbc57f1765aa0fa1ccf84125bcfa475cae95b9cc0291867b751f3d4ac8fa2",
@@ -655,7 +1210,6 @@
"sha256:55365417734eb18255590a9ff9eb97e9e1da868d4ccd6402399eaf68af20a760",
"sha256:70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6"
],
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"markers": "python_version >= '3.8'",
"version": "==2.32.3"
},
@@ -750,6 +1304,14 @@
"markers": "python_version >= '3.7'",
"version": "==1.5.4"
},
+ "six": {
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+ "version": "==1.16.0"
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"sniffio": {
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@@ -758,6 +1320,14 @@
"markers": "python_version >= '3.7'",
"version": "==1.3.1"
},
+ "soupsieve": {
+ "hashes": [
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+ "sha256:eaa337ff55a1579b6549dc679565eac1e3d000563bcb1c8ab0d0fefbc0c2cdc7"
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+ "markers": "python_version >= '3.8'",
+ "version": "==2.5"
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"starlette": {
"hashes": [
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@@ -782,6 +1352,98 @@
"markers": "python_version >= '3.8'",
"version": "==4.12.2"
},
+ "tzdata": {
+ "hashes": [
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+ "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"
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+ "markers": "python_version >= '2'",
+ "version": "==2024.1"
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+ },
"urllib3": {
"hashes": [
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@@ -993,6 +1655,14 @@
"sha256:ffefa1374cd508d633646d51a8e9277763a9b78ae71324183693959cf94635a7"
],
"version": "==12.0"
+ },
+ "xarray": {
+ "hashes": [
+ "sha256:0b91e0bc4dc0296947947640fe31ec6e867ce258d2f7cbc10bedf4a6d68340c7",
+ "sha256:721a7394e8ec3d592b2d8ebe21eed074ac077dc1bb1bd777ce00e41700b4866c"
+ ],
+ "markers": "python_version >= '3.9'",
+ "version": "==2024.6.0"
}
},
"develop": {}
diff --git a/backend/landmarks_Strasbourg.txt b/backend/landmarks_Strasbourg.txt
deleted file mode 100644
index d97fb8e..0000000
--- a/backend/landmarks_Strasbourg.txt
+++ /dev/null
@@ -1,10082 +0,0 @@
-{
- "0":{
- "name":"Musée de Sismologie et de Magnétisme Terrestre",
- "type":"sightseeing",
- "location":[
- 48.5839194,
- 7.7656424
- ],
- "osm_type":"way",
- "osm_id":19794129,
- "attractiveness":377,
- "n_tags":19,
- "image_url":null,
- "description":null,
- "duration":0,
- "uuid":"9f0ed68d-f891-4e0a-8909-90a68e735721",
- "must_do":false,
- "must_avoid":false,
- "is_secondary":false,
- "time_to_reach_next":0,
- "next_uuid":null
- },
- "1":{
- "name":"Jardin Botanique",
- "type":"sightseeing",
- "location":[
- 48.583626,
- 7.7674616
- ],
- "osm_type":"way",
- "osm_id":19794224,
- "attractiveness":569,
- "n_tags":28,
- "image_url":null,
- "description":null,
- "duration":0,
- "uuid":"bed47635-ec34-4528-bc21-235c4baf704d",
- "must_do":false,
- "must_avoid":false,
- "is_secondary":false,
- "time_to_reach_next":0,
- "next_uuid":null
- },
- "2":{
- "name":"Musée Historique",
- "type":"sightseeing",
- "location":[
- 48.5799461,
- 7.7509819
- ],
- "osm_type":"way",
- "osm_id":22290183,
- "attractiveness":478,
- "n_tags":22,
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- "image_url":null,
- "description":null,
- "duration":0,
- "uuid":"96035ac3-07f2-47ba-a6e6-2d89b64f35fe",
- "must_do":false,
- "must_avoid":false,
- "is_secondary":false,
- "time_to_reach_next":0,
- "next_uuid":null
- },
- "456":{
- "name":"Faculté de Chirurgie Dentaire",
- "type":"sightseeing",
- "location":[
- 48.5773439,
- 7.7449516
- ],
- "osm_type":"relation",
- "osm_id":230046,
- "attractiveness":110,
- "n_tags":6,
- "image_url":null,
- "description":null,
- "duration":0,
- "uuid":"68b58da5-61ab-47f8-bb3e-ec9eca19a00b",
- "must_do":false,
- "must_avoid":false,
- "is_secondary":false,
- "time_to_reach_next":0,
- "next_uuid":null
- },
- "457":{
- "name":"Bibliothèque Nationale Universitaire de Strasbourg",
- "type":"sightseeing",
- "location":[
- 48.587169,
- 7.7558213
- ],
- "osm_type":"relation",
- "osm_id":236931,
- "attractiveness":671,
- "n_tags":31,
- "image_url":null,
- "description":null,
- "duration":0,
- "uuid":"31488c2a-cf7e-4bd4-8354-7f0b2c00e04e",
- "must_do":false,
- "must_avoid":false,
- "is_secondary":false,
- "time_to_reach_next":0,
- "next_uuid":null
- },
- "458":{
- "name":"Restaurant Universitaire Gallia",
- "type":"sightseeing",
- "location":[
- 48.5843491,
- 7.7595883
- ],
- "osm_type":"relation",
- "osm_id":238063,
- "attractiveness":234,
- "n_tags":11,
- "image_url":null,
- "description":null,
- "duration":0,
- "uuid":"6c399982-2f67-47cc-805d-aa1ccbdda7b0",
- "must_do":false,
- "must_avoid":false,
- "is_secondary":false,
- "time_to_reach_next":0,
- "next_uuid":null
- },
- "459":{
- "name":"Faculté des Langues",
- "type":"sightseeing",
- "location":[
- 48.5781106,
- 7.7654182
- ],
- "osm_type":"relation",
- "osm_id":240864,
- "attractiveness":251,
- "n_tags":14,
- "image_url":null,
- "description":null,
- "duration":0,
- "uuid":"1cc31db2-a5f6-416d-80a6-7f0e7a10c141",
- "must_do":false,
- "must_avoid":false,
- "is_secondary":false,
- "time_to_reach_next":0,
- "next_uuid":null
- },
- "460":{
- "name":"Institut de Géologie",
- "type":"sightseeing",
- "location":[
- 48.5834376,
- 7.7629399
- ],
- "osm_type":"relation",
- "osm_id":241594,
- "attractiveness":179,
- "n_tags":9,
- "image_url":null,
- "description":null,
- "duration":0,
- "uuid":"a93acdfd-af2a-4c73-aadc-968fd0e15f88",
- "must_do":false,
- "must_avoid":false,
- "is_secondary":false,
- "time_to_reach_next":0,
- "next_uuid":null
- },
- "461":{
- "name":"Institut Universitaire de Technologie Louis Pasteur",
- "type":"sightseeing",
- "location":[
- 48.6074836,
- 7.7079621
- ],
- "osm_type":"relation",
- "osm_id":252831,
- "attractiveness":111,
- "n_tags":7,
- "image_url":null,
- "description":null,
- "duration":0,
- "uuid":"6ed37369-5728-46d2-a743-42543effa536",
- "must_do":false,
- "must_avoid":false,
- "is_secondary":false,
- "time_to_reach_next":0,
- "next_uuid":null
- },
- "462":{
- "name":"Parking Gare Wodli",
- "type":"sightseeing",
- "location":[
- 48.5873726,
- 7.7367546
- ],
- "osm_type":"relation",
- "osm_id":259307,
- "attractiveness":282,
- "n_tags":13,
- "image_url":null,
- "description":null,
- "duration":0,
- "uuid":"9a5e9bd9-052e-49c1-ad33-2258da3afaf7",
- "must_do":false,
- "must_avoid":false,
- "is_secondary":false,
- "time_to_reach_next":0,
- "next_uuid":null
- },
- "463":{
- "name":"Direction Régionale de la SNCF",
- "type":"sightseeing",
- "location":[
- 48.5862571,
- 7.7361786
- ],
- "osm_type":"relation",
- "osm_id":261658,
- "attractiveness":113,
- "n_tags":5,
- "image_url":null,
- "description":null,
- "duration":0,
- "uuid":"af58b049-3dfa-45dc-8fa7-63d6286f9d39",
- "must_do":false,
- "must_avoid":false,
- "is_secondary":false,
- "time_to_reach_next":0,
- "next_uuid":null
- },
- "464":{
- "name":"Mathématique, Informatique - Physique du Globe",
- "type":"sightseeing",
- "location":[
- 48.5798654,
- 7.7632636
- ],
- "osm_type":"relation",
- "osm_id":366500,
- "attractiveness":90,
- "n_tags":5,
- "image_url":null,
- "description":null,
- "duration":0,
- "uuid":"2109f1ba-006b-4a0e-b6ae-5a72fc1f21bb",
- "must_do":false,
- "must_avoid":false,
- "is_secondary":false,
- "time_to_reach_next":0,
- "next_uuid":null
- },
- "465":{
- "name":"Palais Universitaire",
- "type":"sightseeing",
- "location":[
- 48.5848129,
- 7.7624991
- ],
- "osm_type":"relation",
- "osm_id":368604,
- "attractiveness":399,
- "n_tags":19,
- "image_url":null,
- "description":null,
- "duration":0,
- "uuid":"d0845932-207f-46d4-85dc-0f2a823c5906",
- "must_do":false,
- "must_avoid":false,
- "is_secondary":false,
- "time_to_reach_next":0,
- "next_uuid":null
- },
- "466":{
- "name":"Collège Louis Pasteur",
- "type":"sightseeing",
- "location":[
- 48.5733055,
- 7.7387487
- ],
- "osm_type":"relation",
- "osm_id":4460379,
- "attractiveness":117,
- "n_tags":7,
- "image_url":null,
- "description":null,
- "duration":0,
- "uuid":"b8657678-a0e5-41a1-a5a4-45d6e7d00e9d",
- "must_do":false,
- "must_avoid":false,
- "is_secondary":false,
- "time_to_reach_next":0,
- "next_uuid":null
- },
- "467":{
- "name":"École primaire Exen Pire",
- "type":"sightseeing",
- "location":[
- 48.6052315,
- 7.7495768
- ],
- "osm_type":"relation",
- "osm_id":6327072,
- "attractiveness":158,
- "n_tags":8,
- "image_url":null,
- "description":null,
- "duration":0,
- "uuid":"36841ce3-7fef-40af-b14e-bb31068bd839",
- "must_do":false,
- "must_avoid":false,
- "is_secondary":false,
- "time_to_reach_next":0,
- "next_uuid":null
- },
- "468":{
- "name":"École primaire Exen Schweitzer",
- "type":"sightseeing",
- "location":[
- 48.6057675,
- 7.7499638
- ],
- "osm_type":"relation",
- "osm_id":6327073,
- "attractiveness":130,
- "n_tags":7,
- "image_url":null,
- "description":null,
- "duration":0,
- "uuid":"3c046f26-6007-4928-9956-75fb24879d77",
- "must_do":false,
- "must_avoid":false,
- "is_secondary":false,
- "time_to_reach_next":0,
- "next_uuid":null
- },
- "469":{
- "name":"Restaurant Universitaire Paul Appell",
- "type":"sightseeing",
- "location":[
- 48.5759619,
- 7.762996
- ],
- "osm_type":"relation",
- "osm_id":8240757,
- "attractiveness":190,
- "n_tags":10,
- "image_url":null,
- "description":null,
- "duration":0,
- "uuid":"c7cee4a8-b74c-428f-838f-8dc69f3703d7",
- "must_do":false,
- "must_avoid":false,
- "is_secondary":false,
- "time_to_reach_next":0,
- "next_uuid":null
- },
- "470":{
- "name":"Parking relais-tram P+R Parc des Romains",
- "type":"sightseeing",
- "location":[
- 48.5817024,
- 7.7241421
- ],
- "osm_type":"relation",
- "osm_id":11668557,
- "attractiveness":418,
- "n_tags":20,
- "image_url":null,
- "description":null,
- "duration":0,
- "uuid":"a2195bec-4190-474f-96cd-8c735d6a21c2",
- "must_do":false,
- "must_avoid":false,
- "is_secondary":false,
- "time_to_reach_next":0,
- "next_uuid":null
- },
- "471":{
- "name":"Groupe scolaire Sainte-Madeleine",
- "type":"sightseeing",
- "location":[
- 48.579786,
- 7.7542586
- ],
- "osm_type":"relation",
- "osm_id":14103600,
- "attractiveness":247,
- "n_tags":12,
- "image_url":null,
- "description":null,
- "duration":0,
- "uuid":"0877f1d4-83e9-40bb-bdd7-996062d4ceb0",
- "must_do":false,
- "must_avoid":false,
- "is_secondary":false,
- "time_to_reach_next":0,
- "next_uuid":null
- },
- "472":{
- "name":"La Grenze",
- "type":"sightseeing",
- "location":[
- 48.5889655,
- 7.7348998
- ],
- "osm_type":"relation",
- "osm_id":14211218,
- "attractiveness":317,
- "n_tags":16,
- "image_url":null,
- "description":null,
- "duration":0,
- "uuid":"0cddda65-3cd3-4eac-944f-bdc8bec91fd5",
- "must_do":false,
- "must_avoid":false,
- "is_secondary":false,
- "time_to_reach_next":0,
- "next_uuid":null
- },
- "473":{
- "name":"École maternelle-élémentaire Rosa Parks",
- "type":"sightseeing",
- "location":[
- 48.6074934,
- 7.7664371
- ],
- "osm_type":"relation",
- "osm_id":14254313,
- "attractiveness":250,
- "n_tags":10,
- "image_url":null,
- "description":null,
- "duration":0,
- "uuid":"45ef7d92-e3b9-4340-9f20-c5033211521b",
- "must_do":false,
- "must_avoid":false,
- "is_secondary":false,
- "time_to_reach_next":0,
- "next_uuid":null
- },
- "474":{
- "name":"Toilettes publiques",
- "type":"sightseeing",
- "location":[
- 48.5846113,
- 7.7336135
- ],
- "osm_type":"relation",
- "osm_id":16354356,
- "attractiveness":197,
- "n_tags":6,
- "image_url":null,
- "description":null,
- "duration":0,
- "uuid":"5b8a3a59-9468-46bc-a30e-0a480daeebe1",
- "must_do":false,
- "must_avoid":false,
- "is_secondary":false,
- "time_to_reach_next":0,
- "next_uuid":null
- },
- "475":{
- "name":"Canal de la Marne au Rhin",
- "type":"sightseeing",
- "location":[
- 48.5963838,
- 7.7782303
- ],
- "osm_type":"way",
- "osm_id":10424914,
- "attractiveness":100,
- "n_tags":6,
- "image_url":null,
- "description":null,
- "duration":0,
- "uuid":"144c0f13-e60a-4970-a9b6-a401075b7a54",
- "must_do":false,
- "must_avoid":false,
- "is_secondary":false,
- "time_to_reach_next":0,
- "next_uuid":null
- },
- "476":{
- "name":"Bassin de la Porte de l’Hôpital",
- "type":"sightseeing",
- "location":[
- 48.573628,
- 7.7456353
- ],
- "osm_type":"way",
- "osm_id":43520883,
- "attractiveness":136,
- "n_tags":7,
- "image_url":null,
- "description":null,
- "duration":0,
- "uuid":"fb1a7576-2231-4083-a23a-4aa0052c93ca",
- "must_do":false,
- "must_avoid":false,
- "is_secondary":false,
- "time_to_reach_next":0,
- "next_uuid":null
- },
- "477":{
- "name":"Le Rhin Tortu",
- "type":"sightseeing",
- "location":[
- 48.5656635,
- 7.7460474
- ],
- "osm_type":"way",
- "osm_id":43570553,
- "attractiveness":140,
- "n_tags":9,
- "image_url":null,
- "description":null,
- "duration":0,
- "uuid":"43da5dc5-5f34-417c-a908-893a104a8e22",
- "must_do":false,
- "must_avoid":false,
- "is_secondary":false,
- "time_to_reach_next":0,
- "next_uuid":null
- },
- "478":{
- "name":"Bassin d'Austerlitz",
- "type":"sightseeing",
- "location":[
- 48.5740949,
- 7.7605279
- ],
- "osm_type":"way",
- "osm_id":43799886,
- "attractiveness":68,
- "n_tags":4,
- "image_url":null,
- "description":null,
- "duration":0,
- "uuid":"756463c9-6c39-41b6-9324-a0b812b892cd",
- "must_do":false,
- "must_avoid":false,
- "is_secondary":false,
- "time_to_reach_next":0,
- "next_uuid":null
- },
- "479":{
- "name":"Bras de l'Ill",
- "type":"sightseeing",
- "location":[
- 48.6034214,
- 7.7660788
- ],
- "osm_type":"way",
- "osm_id":931979292,
- "attractiveness":60,
- "n_tags":4,
- "image_url":null,
- "description":null,
- "duration":0,
- "uuid":"b062cda1-9d52-4e66-b5bb-ed98de20d999",
- "must_do":false,
- "must_avoid":false,
- "is_secondary":false,
- "time_to_reach_next":0,
- "next_uuid":null
- }
-}
\ No newline at end of file
diff --git a/backend/src/constants.py b/backend/src/constants.py
index 951a51d..1b57a3a 100644
--- a/backend/src/constants.py
+++ b/backend/src/constants.py
@@ -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.DEBUG,
- format = '%(asctime)s - %(name)s\t- %(levelname)s\t- %(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)
diff --git a/backend/src/log_config.yaml b/backend/src/log_config.yaml
new file mode 100644
index 0000000..3e9c977
--- /dev/null
+++ b/backend/src/log_config.yaml
@@ -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
diff --git a/backend/src/main.py b/backend/src/main.py
index 8a76288..cb245cb 100644
--- a/backend/src/main.py
+++ b/backend/src/main.py
@@ -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
diff --git a/backend/src/parameters/optimizer_parameters.yaml b/backend/src/parameters/optimizer_parameters.yaml
index efd2547..2bf4f32 100644
--- a/backend/src/parameters/optimizer_parameters.yaml
+++ b/backend/src/parameters/optimizer_parameters.yaml
@@ -1,3 +1,4 @@
detour_factor: 1.4
+detour_corridor_width: 200
average_walking_speed: 4.8
max_landmarks: 7
diff --git a/backend/src/structs/landmarks.py b/backend/src/structs/landmark.py
similarity index 84%
rename from backend/src/structs/landmarks.py
rename to backend/src/structs/landmark.py
index 6f449a8..2f22d42 100644
--- a/backend/src/structs/landmarks.py
+++ b/backend/src/structs/landmark.py
@@ -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}]'
diff --git a/backend/src/structs/linked_landmarks.py b/backend/src/structs/linked_landmarks.py
new file mode 100644
index 0000000..a0799f3
--- /dev/null
+++ b/backend/src/structs/linked_landmarks.py
@@ -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]
+ }
diff --git a/backend/src/tester.py b/backend/src/tester.py
index 3c1cd94..8a39967 100644
--- a/backend/src/tester.py
+++ b/backend/src/tester.py
@@ -1,30 +1,22 @@
-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 import Optimizer
-from refiner import Refiner
-from structs.landmarks import Landmark
+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 test(start_coords: tuple[float, float], finish_coords: tuple[float, float] = None) -> List[Landmark]:
+
+def test(start_coords: tuple[float, float], finish_coords: tuple[float, float] = None) -> list[Landmark]:
manager = LandmarkManager()
+ optimizer = Optimizer()
+ refiner = Refiner(optimizer=optimizer)
preferences = Preferences(
@@ -42,7 +34,7 @@ def test(start_coords: tuple[float, float], finish_coords: tuple[float, float] =
score = 5),
max_time_minute=180,
- detour_tolerance_minute=0
+ detour_tolerance_minute=30
)
# Create start and finish
@@ -71,15 +63,18 @@ def test(start_coords: tuple[float, float], finish_coords: tuple[float, float] =
landmarks_short.append(finish)
# First stage optimization
- optimizer = Optimizer(max_time=preferences.max_time_minute, landmarks=landmarks_short)
- base_tour = optimizer.solve_optimization()
+ base_tour = optimizer.solve_optimization(max_time=preferences.max_time_minute, landmarks=landmarks_short)
# Second stage using linear optimization
- refiner = Refiner(max_time = preferences.max_time_minute, detour = preferences.detour_tolerance_minute)
- refined_tour = refiner.refine_optimization(all_landmarks=landmarks, base_tour=base_tour)
+ 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}")
- return refined_tour
+ # with open('linked_tour.yaml', 'w') as f:
+ # yaml.dump(linked_tour.asdict(), f)
+
+ return linked_tour
#test(tuple((48.8344400, 2.3220540))) # Café Chez César
diff --git a/backend/src/utils.py b/backend/src/utils.py
deleted file mode 100644
index d145c6f..0000000
--- a/backend/src/utils.py
+++ /dev/null
@@ -1,106 +0,0 @@
-import yaml
-from typing import List, Tuple
-from geopy.distance import geodesic
-
-from structs.landmarks import Landmark
-import constants
-
-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.
- """
-
- 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']
-
- # 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)
-
-
-
-
-# Same as link_list but does it on a already ordered list
-def link_list_simple(ordered_visit: List[Landmark])-> List[Landmark] :
-
- 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)
- 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
-
-
-
-# Take the most important landmarks from the list
-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
\ No newline at end of file
diff --git a/backend/src/utils/get_time_separation.py b/backend/src/utils/get_time_separation.py
new file mode 100644
index 0000000..5d7f5cb
--- /dev/null
+++ b/backend/src/utils/get_time_separation.py
@@ -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)
diff --git a/backend/src/landmarks_manager.py b/backend/src/utils/landmarks_manager.py
similarity index 97%
rename from backend/src/landmarks_manager.py
rename to backend/src/utils/landmarks_manager.py
index 403648f..2ba9100 100644
--- a/backend/src/landmarks_manager.py
+++ b/backend/src/utils/landmarks_manager.py
@@ -10,8 +10,8 @@ config.put_throttle = 0
config.maxlag = 0
from structs.preferences import Preferences, Preference
-from structs.landmarks import Landmark
-from utils import take_most_important
+from structs.landmark import Landmark
+from .take_most_important import take_most_important
import constants
diff --git a/backend/src/optimizer.py b/backend/src/utils/optimizer.py
similarity index 71%
rename from backend/src/optimizer.py
rename to backend/src/utils/optimizer.py
index 4c070f6..931a3d2 100644
--- a/backend/src/optimizer.py
+++ b/backend/src/utils/optimizer.py
@@ -1,12 +1,12 @@
import yaml, logging
import numpy as np
-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
+from structs.landmark import Landmark
+from .get_time_separation import get_time
import constants
@@ -17,17 +17,13 @@ class Optimizer:
logger = logging.getLogger(__name__)
- landmarks: List[Landmark] # list of landmarks
- max_time: int = None # max visit time (in minutes)
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, max_time: int, landmarks: List[Landmark]) :
- self.max_time = max_time
- self.landmarks = landmarks
+ def __init__(self) :
# load parameters from file
with constants.OPTIMIZER_PARAMETERS_PATH.open('r') as f:
@@ -37,25 +33,6 @@ class Optimizer:
self.max_landmarks = parameters['max_landmarks']
- def print_res(self, L: List[Landmark]):
- """
- Print the suggested order of landmarks to visit and log the total time walked.
-
- Args:
- L (List[Landmark]): List of landmarks in the suggested visit order.
- """
-
- self.logger.info(f'The following order is suggested : ')
- dist = 0
- for elem in L :
- if elem.time_to_reach_next is not None :
- self.logger.info(f" {elem.name}, time to next = {elem.time_to_reach_next}")
- dist += elem.time_to_reach_next
- else :
- self.logger.info(f" {elem.name}")
- self.logger.info(f'Minutes walked : {dist}')
- self.logger.info(f'Visited {len(L)-2} landmarks')
-
# Prevent the use of a particular solution
def prevent_config(self, resx):
@@ -63,10 +40,10 @@ class Optimizer:
Prevent the use of a particular solution by adding constraints to the optimization.
Args:
- resx (List[float]): List of edge weights.
+ 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.
+ 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):
@@ -101,7 +78,7 @@ class Optimizer:
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.
+ 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
@@ -129,7 +106,7 @@ class Optimizer:
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.
+ 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
@@ -189,34 +166,8 @@ class Optimizer:
return order, all_journeys_nodes
- def get_time(self, 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.
-
- 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*self.detour_factor
-
- # Time to walk this distance (in minutes)
- walk_time = walk_dist/self.average_walking_speed*60
-
- return round(walk_time)
-
-
- def init_ub_dist(self, landmarks: List[Landmark], max_steps: int):
+ def init_ub_dist(self, landmarks: list[Landmark], max_steps: int):
"""
Initialize the objective function coefficients and inequality constraints for the optimization problem.
@@ -224,11 +175,11 @@ class Optimizer:
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.
+ 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.
+ 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 + ...
@@ -240,7 +191,7 @@ class Optimizer:
dist_table = [0]*len(landmarks)
c.append(-spot1.attractiveness)
for j, spot2 in enumerate(landmarks) :
- t = self.get_time(spot1.location, spot2.location)
+ t = get_time(spot1.location, spot2.location)
dist_table[j] = t
closest = sorted(dist_table)[:22]
for i, dist in enumerate(dist_table) :
@@ -260,7 +211,7 @@ class Optimizer:
L (int): Number of landmarks.
Returns:
- Tuple[np.ndarray, List[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
+ Tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
"""
ones = [1]*L
@@ -287,7 +238,7 @@ class Optimizer:
L (int): Number of landmarks.
Returns:
- Tuple[np.ndarray, List[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
+ 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)
@@ -318,7 +269,7 @@ class Optimizer:
L (int): Number of landmarks.
Returns:
- Tuple[List[np.ndarray], List[int]]: Equality constraint coefficients and the right-hand side of the equality constraints.
+ Tuple[list[np.ndarray], list[int]]: Equality constraint coefficients and the right-hand side of the equality constraints.
"""
l = [0]*L*L
@@ -333,15 +284,15 @@ class Optimizer:
return [l], [0]
- def respect_user_must_do(self, landmarks: List[Landmark]) :
+ 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'.
+ 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.
+ Tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
"""
L = len(landmarks)
@@ -359,15 +310,15 @@ class Optimizer:
return A, b
- def respect_user_must_avoid(self, landmarks: List[Landmark]) :
+ 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'.
+ 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.
+ Tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
"""
L = len(landmarks)
@@ -394,7 +345,7 @@ class Optimizer:
L (int): Number of landmarks.
Returns:
- Tuple[np.ndarray, List[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
+ 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)
@@ -422,7 +373,7 @@ class Optimizer:
L (int): Number of landmarks.
Returns:
- Tuple[np.ndarray, List[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
+ Tuple[np.ndarray, list[int]]: Inequality constraint coefficients and the right-hand side of the inequality constraints.
"""
A = [0]*L*L
@@ -443,16 +394,16 @@ class Optimizer:
return A, b
- def link_list(self, order: List[int], landmarks: List[Landmark])->List[Landmark] :
+ 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.
+ 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
+ list[Landmark]]: The updated linked list of landmarks with travel times
"""
L = []
@@ -463,7 +414,7 @@ class Optimizer:
next = landmarks[order[j+1]]
# get attributes
- elem.time_to_reach_next = self.get_time(elem.location, next.location)
+ 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
@@ -478,21 +429,28 @@ class Optimizer:
# Main optimization pipeline
- def solve_optimization (self, hide_log=False) :
+ 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.
+ list[Landmark]: The optimized tour of landmarks with updated travel times, or None if no valid solution is found.
"""
- L = len(self.landmarks)
+ L = len(landmarks)
# SET CONSTRAINTS FOR INEQUALITY
- c, A_ub, b_ub = self.init_ub_dist(self.landmarks, self.max_time) # Add the distances from each landmark to the other
+ 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
@@ -503,10 +461,10 @@ class Optimizer:
# 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(self.landmarks) # Check if there are user_defined must_see. Also takes care of start/goal
+ 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(self.landmarks) # Check if there are user_defined must_see. Also takes care of start/goal
+ 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
@@ -524,51 +482,38 @@ class Optimizer:
# 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")
+ 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 = 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)
- 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.")
-
- # Add the times to reach and stop optimizing
- tour = self.link_list(order, self.landmarks)
-
- # logging
- if not hide_log :
- if i != 0 :
- self.logger.info(f"Neded to recompute paths {i} times")
- self.print_res(tour) # how to do better ?
- self.logger.info(f"Total score : {int(-res.fun)}")
-
- return tour
-
-
-
-
-
+ 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
diff --git a/backend/src/refiner.py b/backend/src/utils/refiner.py
similarity index 71%
rename from backend/src/refiner.py
rename to backend/src/utils/refiner.py
index 85aa7c5..c8f0aa5 100644
--- a/backend/src/refiner.py
+++ b/backend/src/utils/refiner.py
@@ -1,12 +1,11 @@
import yaml, logging
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 optimizer import Optimizer
-from utils import get_time, link_list_simple, take_most_important
+from structs.landmark import Landmark
+from . import take_most_important, get_time_separation
+from .optimizer import Optimizer
import constants
@@ -15,31 +14,30 @@ class Refiner :
logger = logging.getLogger(__name__)
- max_time: int = None # max visit time (in minutes)
- detour: int = None # accepted max detour time (in minutes)
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, max_time: int, detour: int) :
- self.max_time = max_time
- self.detour = detour
+ 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) :
+ 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
+ landmarks (list[Landmark]): the landmark path around which to create the corridor
width (float): Width of the corridor in meters.
Returns:
@@ -54,12 +52,12 @@ class Refiner :
return obj
- def create_linestring(self, tour: List[Landmark])->LineString :
+ 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.
+ tour (list[Landmark]): An ordered sequence of landmarks that represents the visiting order.
Returns:
LineString: A `LineString` object representing the path through the landmarks.
@@ -79,7 +77,7 @@ class Refiner :
Args:
area (Polygon): The polygon defining the area.
- coordinates (Tuple[float, float]): The coordinates of the point to check.
+ coordinates (tuple[float, float]): The coordinates of the point to check.
Returns:
bool: True if the point is within the area, otherwise False.
@@ -89,13 +87,13 @@ class Refiner :
# Function to determine if two landmarks are close to each other
- def is_close_to(self, location1: Tuple[float], location2: Tuple[float]):
+ 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.
+ 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.
@@ -108,7 +106,7 @@ class Refiner :
#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]:
+ def rearrange(self, tour: list[Landmark]) -> list[Landmark]:
"""
Rearrange landmarks to group nearby visits together.
@@ -116,10 +114,10 @@ class Refiner :
while keeping 'start' and 'finish' landmarks in their original positions.
Args:
- tour (List[Landmark]): Ordered list of landmarks to be rearranged.
+ tour (list[Landmark]): Ordered list of landmarks to be rearranged.
Returns:
- List[Landmark]: The rearranged list of landmarks with grouped nearby visits.
+ list[Landmark]: The rearranged list of landmarks with grouped nearby visits.
"""
i = 1
@@ -137,7 +135,7 @@ class Refiner :
return tour
- def find_shortest_path_through_all_landmarks(self, landmarks: List[Landmark]) -> Tuple[List[Landmark], Polygon]:
+ 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.
@@ -146,10 +144,10 @@ class Refiner :
polygon representing the path.
Args:
- landmarks (List[Landmark]): List of all landmarks including 'start' and 'finish'.
+ 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
+ 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.
"""
@@ -173,7 +171,7 @@ class Refiner :
# 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))
+ 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
@@ -189,7 +187,7 @@ class Refiner :
# 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] :
+ 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.
@@ -197,12 +195,12 @@ class Refiner :
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.
+ 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.
+ list[Landmark]: list of important landmarks within the corridor that have not been visited yet.
"""
second_order_landmarks = []
@@ -216,11 +214,11 @@ class Refiner :
if self.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))
+ 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] :
+ 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.
@@ -229,10 +227,10 @@ class Refiner :
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.
+ 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.
+ list[Landmark]: Refined list of landmarks in the order of visit to produce a better tour path.
"""
coords = []
@@ -261,7 +259,7 @@ class Refiner :
xs.reverse()
ys.reverse()
- better_tour = [] # List of ordered visit
+ 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
@@ -285,67 +283,58 @@ class Refiner :
return better_tour
- # Second stage of the optimization. Use linear programming again to refine the path
- def refine_optimization(self, all_landmarks: List[Landmark], base_tour: List[Landmark]) -> List[Landmark] :
+ def refine_optimization(
+ self,
+ all_landmarks: list[Landmark],
+ base_tour: list[Landmark],
+ max_time: int,
+ detour: int
+ ) -> list[Landmark]:
"""
- Refine the initial tour path by considering additional minor landmarks and optimizing the path.
+ 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
+ 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.
+ list[Landmark]: The refined list of landmarks representing the optimized tour path.
"""
# No need to refine if no detour is taken
- # if detour == 0 :
- if False :
+ 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
-
- else :
- minor_landmarks = self.get_minor_landmarks(all_landmarks, base_tour, 200)
- 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
- optimizer = Optimizer(self.max_time + self.detour, full_set)
- new_tour = optimizer.solve_optimization(hide_log=True)
-
- 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 = 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)
-
- # 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
-
- self.logger.info("Refined tour (result of second stage optimization): ")
- optimizer.print_res(final_tour)
-
- return final_tour
-
+ return better_tour
diff --git a/backend/src/utils/take_most_important.py b/backend/src/utils/take_most_important.py
new file mode 100644
index 0000000..b363add
--- /dev/null
+++ b/backend/src/utils/take_most_important.py
@@ -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
diff --git a/backend/throttle.ctrl b/backend/throttle.ctrl
deleted file mode 100644
index e69de29..0000000