anydev/anyway
0
0
Fork 0
Code Issues Pull Requests 2 Actions Packages Projects Releases Wiki Activity

Compare commits

base: anydev:v0.0.35
Branches Tags
anydev:main anydev:backend/micro-services-restructuring anydev:backend/feature/supabase anydev:renovate/configure
anydev:v0.1.3 anydev:v0.1.2 anydev:v0.1.1 anydev:v0.1.0 anydev:v0.0.37 anydev:v0.0.36 anydev:v0.0.35 anydev:v0.0.34 anydev:v0.0.32 anydev:v0.0.31 anydev:v0.0.30 anydev:v0.0.29 anydev:v0.0.28 anydev:v0.0.27 anydev:v0.0.26 anydev:v0.0.25 anydev:v0.0.24 anydev:v0.0.22 anydev:v0.0.21 anydev:v0.0.20 anydev:v0.0.19 anydev:v0.0.18 anydev:v0.0.17
..
compare: anydev:renovate/configure
Branches Tags
anydev:backend/micro-services-restructuring anydev:main anydev:backend/feature/supabase anydev:renovate/configure
anydev:v0.1.3 anydev:v0.1.2 anydev:v0.1.1 anydev:v0.1.0 anydev:v0.0.37 anydev:v0.0.36 anydev:v0.0.35 anydev:v0.0.34 anydev:v0.0.32 anydev:v0.0.31 anydev:v0.0.30 anydev:v0.0.29 anydev:v0.0.28 anydev:v0.0.27 anydev:v0.0.26 anydev:v0.0.25 anydev:v0.0.24 anydev:v0.0.22 anydev:v0.0.21 anydev:v0.0.20 anydev:v0.0.19 anydev:v0.0.18 anydev:v0.0.17

1 Commits
v0.0.35 ... renovate/c

Author SHA1 Message Date
Renovate Bot
ac99ef3930 Add renovate.json 2024-07-27 12:30:05 +00:00
181 changed files with 3162 additions and 205841 deletions
Expand all files Collapse all files

24
.gitea/workflows/backend_build-deploy-prod.yaml
View File

@@ -1,24 +0,0 @@
on:
push:
tags:
- v*
name: Build and deploy the backend to production
jobs:
build-and-push:
name: Build and push image
uses: ./.gitea/workflows/workflow_build-image.yaml
with:
tag: stable
secrets:
PACKAGE_REGISTRY_ACCESS: ${{ secrets.PACKAGE_REGISTRY_ACCESS }}
deploy-prod:
name: Deploy to production
uses: ./.gitea/workflows/workflow_deploy-container.yaml
with:
overlay: prod
secrets:
KUBE_CONFIG: ${{ secrets.KUBE_CONFIG }}
needs: build-and-push

26
.gitea/workflows/backend_build-deploy-stg.yaml
View File

@@ -1,26 +0,0 @@
on:
pull_request:
branches:
- main
paths:
- backend/**
name: Build and deploy the backend to staging
jobs:
build-and-push:
name: Build and push image
uses: ./.gitea/workflows/workflow_build-image.yaml
with:
tag: unstable
secrets:
PACKAGE_REGISTRY_ACCESS: ${{ secrets.PACKAGE_REGISTRY_ACCESS }}
deploy-prod:
name: Deploy to staging
uses: ./.gitea/workflows/workflow_deploy-container.yaml
with:
overlay: stg
secrets:
KUBE_CONFIG: ${{ secrets.KUBE_CONFIG }}
needs: build-and-push

17
.gitea/workflows/workflow_build-image.yaml → .gitea/workflows/backend_build-image.yaml
View File

@@ -1,17 +1,12 @@
on:
workflow_call:
inputs:
tag:
required: true
type: string
secrets:
PACKAGE_REGISTRY_ACCESS:
required: true
pull_request:
branches:
- main
paths:
- backend/**
name: Build and push docker image
jobs:
build:
name: Build
@@ -34,5 +29,5 @@ jobs:
uses: docker/build-push-action@v5
with:
context: backend
tags: git.kluster.moll.re/anydev/anyway-backend:${{ inputs.tag }}
tags: git.kluster.moll.re/anydev/anyway-backend:latest
push: true

34
.gitea/workflows/backend_run_lint.yaml
View File

@@ -1,34 +0,0 @@
on:
pull_request:
branches:
- main
paths:
- backend/**
name: Run linting on the backend code
jobs:
build:
name: Build
runs-on: ubuntu-latest
steps:
- uses: https://gitea.com/actions/checkout@v4
- name: Install dependencies
run: |
apt-get update && apt-get install -y python3 python3-pip
pip install pipenv
- name: Install packages
run: |
ls -la
# only install dev-packages
pipenv install --categories=dev-packages
pipenv run pip freeze
working-directory: backend
- name: Run linter
run: pipenv run pylint src --fail-under=9
working-directory: backend

40
.gitea/workflows/backend_run_test.yaml
View File

@@ -1,40 +0,0 @@
on:
pull_request:
branches:
- main
paths:
- backend/**
name: Run testing on the backend code
jobs:
build:
name: Build
runs-on: ubuntu-latest
steps:
- uses: https://gitea.com/actions/checkout@v4
- name: Install dependencies
run: |
apt-get update && apt-get install -y python3 python3-pip
pip install pipenv
- name: Install packages
run: |
ls -la
# install all packages, including dev-packages
pipenv install --dev
pipenv run pip freeze
working-directory: backend
- name: Run Tests
run: pipenv run pytest src --html=report.html --self-contained-html
working-directory: backend
- name: Upload HTML report
if: always()
uses: https://gitea.com/actions/upload-artifact@v3
with:
name: pytest-html-report
path: backend/report.html

8
.gitea/workflows/frontend_build-android.yaml
View File

@@ -6,7 +6,7 @@ on:
- frontend/**
name: Build and release debug APK
name: Build and release APK
jobs:
build:
@@ -43,10 +43,8 @@ jobs:
working-directory: ./frontend
- name: Add required secrets
env:
ANDROID_SECRETS_PROPERTIES: ${{ secrets.ANDROID_SECRETS_PROPERTIES }}
run: |
echo "$ANDROID_SECRETS_PROPERTIES" >> ./android/secrets.properties
echo ${{ secrets.ANDROID_SECRETS_PROPERTIES }} > ./android/secrets.properties
working-directory: ./frontend
- name: Sanity check
@@ -55,7 +53,7 @@ jobs:
ls -lah android
working-directory: ./frontend
- run: flutter build apk --debug --split-per-abi --build-number=${{ gitea.run_number }}
- run: flutter build apk --release --split-per-abi --build-number=${{ gitea.run_number }}
working-directory: ./frontend
- name: Upload APKs to artifacts

39
.gitea/workflows/frontend_trigger_deployment.yaml
View File

@@ -1,39 +0,0 @@
on:
push:
tags:
- v*
jobs:
push-to-remote:
# We want to use the macos runner provided by github actions. This requires to push to a remote first.
# After the push we can use the action under frontend/.github/actions/ to deploy properly using fastlane on macos.
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v3
with:
path: 'src'
- name: Checkout remote repository
uses: actions/checkout@v3
with:
path: 'dest'
ref: 'main'
github-server-url: 'https://github.com'
repository: 'moll-re/anyway-frontend-builder'
token: ${{ secrets.PUSH_GITHUB_API_TOKEN }}
fetch-depth: 0
persist-credentials: true
- name: Copy files to remote repository
run: cp -r src/frontend/. dest/
- name: Commit and push changes
run: |
cd dest
git config --global user.email "me@moll.re"
git config --global user.name "[bot]"
git add .
git commit -m "Automatic code update for tag"
git tag -a ${{ github.ref_name }} -m "mirrored tag"
git push origin main --tags

35
.gitea/workflows/workflow_deploy-container.yaml
View File

@@ -1,35 +0,0 @@
on:
workflow_call:
inputs:
overlay:
required: true
type: string
secrets:
KUBE_CONFIG:
required: true
name: Deploy the newly built container
jobs:
deploy:
name: Deploy
runs-on: ubuntu-latest
steps:
- uses: https://gitea.com/actions/checkout@v4
with:
submodules: true
- name: setup kubectl
uses: https://github.com/azure/setup-kubectl@v4
- name: Set kubeconfig
run: |
echo "${{ secrets.KUBE_CONFIG }}" > kubeconfig
- name: Deploy to k8s
run: |
kubectl apply -k backend/deployment/overlays/${{ inputs.overlay }} --kubeconfig=kubeconfig
kubectl -n anyway-backend rollout restart deployment/anyway-backend-${{ inputs.overlay }} --kubeconfig=kubeconfig

3
.gitmodules vendored
View File

@@ -1,3 +0,0 @@
[submodule "backend/deployment"]
path = backend/deployment
url = https://git.kluster.moll.re/anydev/anyway-backend-deployment

6
.vscode/launch.json vendored
View File

@@ -14,9 +14,9 @@
"DEBUG": "true"
},
"args": [
// "--app-dir",
// "src",
"src.main:app",
"--app-dir",
"src",
"main:app",
"--reload",
],
"jinja": true,

3
.vscode/settings.json vendored
View File

@@ -1,3 +0,0 @@
{
"cmake.ignoreCMakeListsMissing": true
}

6
backend/.gitignore vendored
View File

@@ -1,10 +1,6 @@
# osm-cache and wikidata cache
# osm-cache
cache/
apicache/
# wikidata throttle
*.ctrl
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]

649
backend/.pylintrc
View File

@@ -1,649 +0,0 @@
[MAIN]
# Analyse import fallback blocks. This can be used to support both Python 2 and
# 3 compatible code, which means that the block might have code that exists
# only in one or another interpreter, leading to false positives when analysed.
analyse-fallback-blocks=no
# Clear in-memory caches upon conclusion of linting. Useful if running pylint
# in a server-like mode.
clear-cache-post-run=no
# Load and enable all available extensions. Use --list-extensions to see a list
# all available extensions.
#enable-all-extensions=
# In error mode, messages with a category besides ERROR or FATAL are
# suppressed, and no reports are done by default. Error mode is compatible with
# disabling specific errors.
#errors-only=
# Always return a 0 (non-error) status code, even if lint errors are found.
# This is primarily useful in continuous integration scripts.
#exit-zero=
# A comma-separated list of package or module names from where C extensions may
# be loaded. Extensions are loading into the active Python interpreter and may
# run arbitrary code.
extension-pkg-allow-list=
# A comma-separated list of package or module names from where C extensions may
# be loaded. Extensions are loading into the active Python interpreter and may
# run arbitrary code. (This is an alternative name to extension-pkg-allow-list
# for backward compatibility.)
extension-pkg-whitelist=
# Return non-zero exit code if any of these messages/categories are detected,
# even if score is above --fail-under value. Syntax same as enable. Messages
# specified are enabled, while categories only check already-enabled messages.
fail-on=
# Specify a score threshold under which the program will exit with error.
fail-under=10
# Interpret the stdin as a python script, whose filename needs to be passed as
# the module_or_package argument.
#from-stdin=
# Files or directories to be skipped. They should be base names, not paths.
ignore=CVS
# Add files or directories matching the regular expressions patterns to the
# ignore-list. The regex matches against paths and can be in Posix or Windows
# format. Because '\\' represents the directory delimiter on Windows systems,
# it can't be used as an escape character.
ignore-paths=
# Files or directories matching the regular expression patterns are skipped.
# The regex matches against base names, not paths. The default value ignores
# Emacs file locks
ignore-patterns=^\.#
# List of module names for which member attributes should not be checked and
# will not be imported (useful for modules/projects where namespaces are
# manipulated during runtime and thus existing member attributes cannot be
# deduced by static analysis). It supports qualified module names, as well as
# Unix pattern matching.
ignored-modules=
# Python code to execute, usually for sys.path manipulation such as
# pygtk.require().
#init-hook=
# Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the
# number of processors available to use, and will cap the count on Windows to
# avoid hangs.
jobs=1
# Control the amount of potential inferred values when inferring a single
# object. This can help the performance when dealing with large functions or
# complex, nested conditions.
limit-inference-results=100
# List of plugins (as comma separated values of python module names) to load,
# usually to register additional checkers.
load-plugins=
# Pickle collected data for later comparisons.
persistent=yes
# Resolve imports to .pyi stubs if available. May reduce no-member messages and
# increase not-an-iterable messages.
prefer-stubs=no
# Minimum Python version to use for version dependent checks. Will default to
# the version used to run pylint.
py-version=3.12
# Discover python modules and packages in the file system subtree.
recursive=no
# Add paths to the list of the source roots. Supports globbing patterns. The
# source root is an absolute path or a path relative to the current working
# directory used to determine a package namespace for modules located under the
# source root.
source-roots=
# When enabled, pylint would attempt to guess common misconfiguration and emit
# user-friendly hints instead of false-positive error messages.
suggestion-mode=yes
# Allow loading of arbitrary C extensions. Extensions are imported into the
# active Python interpreter and may run arbitrary code.
unsafe-load-any-extension=no
# In verbose mode, extra non-checker-related info will be displayed.
#verbose=
[BASIC]
# Naming style matching correct argument names.
argument-naming-style=snake_case
# Regular expression matching correct argument names. Overrides argument-
# naming-style. If left empty, argument names will be checked with the set
# naming style.
#argument-rgx=
# Naming style matching correct attribute names.
attr-naming-style=snake_case
# Regular expression matching correct attribute names. Overrides attr-naming-
# style. If left empty, attribute names will be checked with the set naming
# style.
#attr-rgx=
# Bad variable names which should always be refused, separated by a comma.
bad-names=foo,
bar,
baz,
toto,
tutu,
tata
# Bad variable names regexes, separated by a comma. If names match any regex,
# they will always be refused
bad-names-rgxs=
# Naming style matching correct class attribute names.
class-attribute-naming-style=any
# Regular expression matching correct class attribute names. Overrides class-
# attribute-naming-style. If left empty, class attribute names will be checked
# with the set naming style.
#class-attribute-rgx=
# Naming style matching correct class constant names.
class-const-naming-style=UPPER_CASE
# Regular expression matching correct class constant names. Overrides class-
# const-naming-style. If left empty, class constant names will be checked with
# the set naming style.
#class-const-rgx=
# Naming style matching correct class names.
class-naming-style=PascalCase
# Regular expression matching correct class names. Overrides class-naming-
# style. If left empty, class names will be checked with the set naming style.
#class-rgx=
# Naming style matching correct constant names.
const-naming-style=UPPER_CASE
# Regular expression matching correct constant names. Overrides const-naming-
# style. If left empty, constant names will be checked with the set naming
# style.
#const-rgx=
# Minimum line length for functions/classes that require docstrings, shorter
# ones are exempt.
docstring-min-length=-1
# Naming style matching correct function names.
function-naming-style=snake_case
# Regular expression matching correct function names. Overrides function-
# naming-style. If left empty, function names will be checked with the set
# naming style.
#function-rgx=
# Good variable names which should always be accepted, separated by a comma.
good-names=i,
j,
k,
ex,
Run,
_
# Good variable names regexes, separated by a comma. If names match any regex,
# they will always be accepted
good-names-rgxs=
# Include a hint for the correct naming format with invalid-name.
include-naming-hint=no
# Naming style matching correct inline iteration names.
inlinevar-naming-style=any
# Regular expression matching correct inline iteration names. Overrides
# inlinevar-naming-style. If left empty, inline iteration names will be checked
# with the set naming style.
#inlinevar-rgx=
# Naming style matching correct method names.
method-naming-style=snake_case
# Regular expression matching correct method names. Overrides method-naming-
# style. If left empty, method names will be checked with the set naming style.
#method-rgx=
# Naming style matching correct module names.
module-naming-style=snake_case
# Regular expression matching correct module names. Overrides module-naming-
# style. If left empty, module names will be checked with the set naming style.
#module-rgx=
# Colon-delimited sets of names that determine each other's naming style when
# the name regexes allow several styles.
name-group=
# Regular expression which should only match function or class names that do
# not require a docstring.
no-docstring-rgx=^_
# List of decorators that produce properties, such as abc.abstractproperty. Add
# to this list to register other decorators that produce valid properties.
# These decorators are taken in consideration only for invalid-name.
property-classes=abc.abstractproperty
# Regular expression matching correct type alias names. If left empty, type
# alias names will be checked with the set naming style.
#typealias-rgx=
# Regular expression matching correct type variable names. If left empty, type
# variable names will be checked with the set naming style.
#typevar-rgx=
# Naming style matching correct variable names.
variable-naming-style=snake_case
# Regular expression matching correct variable names. Overrides variable-
# naming-style. If left empty, variable names will be checked with the set
# naming style.
#variable-rgx=
[CLASSES]
# Warn about protected attribute access inside special methods
check-protected-access-in-special-methods=no
# List of method names used to declare (i.e. assign) instance attributes.
defining-attr-methods=__init__,
__new__,
setUp,
asyncSetUp,
__post_init__
# List of member names, which should be excluded from the protected access
# warning.
exclude-protected=_asdict,_fields,_replace,_source,_make,os._exit
# List of valid names for the first argument in a class method.
valid-classmethod-first-arg=cls
# List of valid names for the first argument in a metaclass class method.
valid-metaclass-classmethod-first-arg=mcs
[DESIGN]
# List of regular expressions of class ancestor names to ignore when counting
# public methods (see R0903)
exclude-too-few-public-methods=
# List of qualified class names to ignore when counting class parents (see
# R0901)
ignored-parents=
# Maximum number of arguments for function / method.
max-args=5
# Maximum number of attributes for a class (see R0902).
max-attributes=7
# Maximum number of boolean expressions in an if statement (see R0916).
max-bool-expr=5
# Maximum number of branch for function / method body.
max-branches=12
# Maximum number of locals for function / method body.
max-locals=15
# Maximum number of parents for a class (see R0901).
max-parents=7
# Maximum number of positional arguments for function / method.
max-positional-arguments=5
# Maximum number of public methods for a class (see R0904).
max-public-methods=20
# Maximum number of return / yield for function / method body.
max-returns=6
# Maximum number of statements in function / method body.
max-statements=50
# Minimum number of public methods for a class (see R0903).
min-public-methods=2
[EXCEPTIONS]
# Exceptions that will emit a warning when caught.
overgeneral-exceptions=builtins.BaseException,builtins.Exception
[FORMAT]
# Expected format of line ending, e.g. empty (any line ending), LF or CRLF.
expected-line-ending-format=
# Regexp for a line that is allowed to be longer than the limit.
ignore-long-lines=^\s*(# )?<?https?://\S+>?$
# Number of spaces of indent required inside a hanging or continued line.
indent-after-paren=4
# String used as indentation unit. This is usually " " (4 spaces) or "\t" (1
# tab).
indent-string=' '
# Maximum number of characters on a single line.
max-line-length=105
# Maximum number of lines in a module.
max-module-lines=1000
# Allow the body of a class to be on the same line as the declaration if body
# contains single statement.
single-line-class-stmt=no
# Allow the body of an if to be on the same line as the test if there is no
# else.
single-line-if-stmt=no
[IMPORTS]
# List of modules that can be imported at any level, not just the top level
# one.
allow-any-import-level=
# Allow explicit reexports by alias from a package __init__.
allow-reexport-from-package=no
# Allow wildcard imports from modules that define __all__.
allow-wildcard-with-all=no
# Deprecated modules which should not be used, separated by a comma.
deprecated-modules=
# Output a graph (.gv or any supported image format) of external dependencies
# to the given file (report RP0402 must not be disabled).
ext-import-graph=
# Output a graph (.gv or any supported image format) of all (i.e. internal and
# external) dependencies to the given file (report RP0402 must not be
# disabled).
import-graph=
# Output a graph (.gv or any supported image format) of internal dependencies
# to the given file (report RP0402 must not be disabled).
int-import-graph=
# Force import order to recognize a module as part of the standard
# compatibility libraries.
known-standard-library=
# Force import order to recognize a module as part of a third party library.
known-third-party=enchant
# Couples of modules and preferred modules, separated by a comma.
preferred-modules=
[LOGGING]
# The type of string formatting that logging methods do. `old` means using %
# formatting, `new` is for `{}` formatting.
logging-format-style=old
# Logging modules to check that the string format arguments are in logging
# function parameter format.
logging-modules=logging
[MESSAGES CONTROL]
# Only show warnings with the listed confidence levels. Leave empty to show
# all. Valid levels: HIGH, CONTROL_FLOW, INFERENCE, INFERENCE_FAILURE,
# UNDEFINED.
confidence=HIGH,
CONTROL_FLOW,
INFERENCE,
INFERENCE_FAILURE,
UNDEFINED
# Disable the message, report, category or checker with the given id(s). You
# can either give multiple identifiers separated by comma (,) or put this
# option multiple times (only on the command line, not in the configuration
# file where it should appear only once). You can also use "--disable=all" to
# disable everything first and then re-enable specific checks. For example, if
# you want to run only the similarities checker, you can use "--disable=all
# --enable=similarities". If you want to run only the classes checker, but have
# no Warning level messages displayed, use "--disable=all --enable=classes
# --disable=W".
disable=raw-checker-failed,
bad-inline-option,
locally-disabled,
file-ignored,
suppressed-message,
useless-suppression,
deprecated-pragma,
use-symbolic-message-instead,
use-implicit-booleaness-not-comparison-to-string,
use-implicit-booleaness-not-comparison-to-zero,
import-error,
line-too-long
# Enable the message, report, category or checker with the given id(s). You can
# either give multiple identifier separated by comma (,) or put this option
# multiple time (only on the command line, not in the configuration file where
# it should appear only once). See also the "--disable" option for examples.
enable=
[METHOD_ARGS]
# List of qualified names (i.e., library.method) which require a timeout
# parameter e.g. 'requests.api.get,requests.api.post'
timeout-methods=requests.api.delete,requests.api.get,requests.api.head,requests.api.options,requests.api.patch,requests.api.post,requests.api.put,requests.api.request
[MISCELLANEOUS]
# List of note tags to take in consideration, separated by a comma.
notes=FIXME,
XXX,
TODO
# Regular expression of note tags to take in consideration.
notes-rgx=
[REFACTORING]
# Maximum number of nested blocks for function / method body
max-nested-blocks=5
# Complete name of functions that never returns. When checking for
# inconsistent-return-statements if a never returning function is called then
# it will be considered as an explicit return statement and no message will be
# printed.
never-returning-functions=sys.exit,argparse.parse_error
# Let 'consider-using-join' be raised when the separator to join on would be
# non-empty (resulting in expected fixes of the type: ``"- " + " -
# ".join(items)``)
suggest-join-with-non-empty-separator=yes
[REPORTS]
# Python expression which should return a score less than or equal to 10. You
# have access to the variables 'fatal', 'error', 'warning', 'refactor',
# 'convention', and 'info' which contain the number of messages in each
# category, as well as 'statement' which is the total number of statements
# analyzed. This score is used by the global evaluation report (RP0004).
evaluation=max(0, 0 if fatal else 10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10))
# Template used to display messages. This is a python new-style format string
# used to format the message information. See doc for all details.
msg-template=
# Set the output format. Available formats are: text, parseable, colorized,
# json2 (improved json format), json (old json format) and msvs (visual
# studio). You can also give a reporter class, e.g.
# mypackage.mymodule.MyReporterClass.
#output-format=
# Tells whether to display a full report or only the messages.
reports=no
# Activate the evaluation score.
score=yes
[SIMILARITIES]
# Comments are removed from the similarity computation
ignore-comments=yes
# Docstrings are removed from the similarity computation
ignore-docstrings=yes
# Imports are removed from the similarity computation
ignore-imports=yes
# Signatures are removed from the similarity computation
ignore-signatures=yes
# Minimum lines number of a similarity.
min-similarity-lines=4
[SPELLING]
# Limits count of emitted suggestions for spelling mistakes.
max-spelling-suggestions=4
# Spelling dictionary name. No available dictionaries : You need to install
# both the python package and the system dependency for enchant to work.
spelling-dict=
# List of comma separated words that should be considered directives if they
# appear at the beginning of a comment and should not be checked.
spelling-ignore-comment-directives=fmt: on,fmt: off,noqa:,noqa,nosec,isort:skip,mypy:
# List of comma separated words that should not be checked.
spelling-ignore-words=
# A path to a file that contains the private dictionary; one word per line.
spelling-private-dict-file=
# Tells whether to store unknown words to the private dictionary (see the
# --spelling-private-dict-file option) instead of raising a message.
spelling-store-unknown-words=no
[STRING]
# This flag controls whether inconsistent-quotes generates a warning when the
# character used as a quote delimiter is used inconsistently within a module.
check-quote-consistency=no
# This flag controls whether the implicit-str-concat should generate a warning
# on implicit string concatenation in sequences defined over several lines.
check-str-concat-over-line-jumps=no
[TYPECHECK]
# List of decorators that produce context managers, such as
# contextlib.contextmanager. Add to this list to register other decorators that
# produce valid context managers.
contextmanager-decorators=contextlib.contextmanager
# List of members which are set dynamically and missed by pylint inference
# system, and so shouldn't trigger E1101 when accessed. Python regular
# expressions are accepted.
generated-members=
# Tells whether to warn about missing members when the owner of the attribute
# is inferred to be None.
ignore-none=yes
# This flag controls whether pylint should warn about no-member and similar
# checks whenever an opaque object is returned when inferring. The inference
# can return multiple potential results while evaluating a Python object, but
# some branches might not be evaluated, which results in partial inference. In
# that case, it might be useful to still emit no-member and other checks for
# the rest of the inferred objects.
ignore-on-opaque-inference=yes
# List of symbolic message names to ignore for Mixin members.
ignored-checks-for-mixins=no-member,
not-async-context-manager,
not-context-manager,
attribute-defined-outside-init
# List of class names for which member attributes should not be checked (useful
# for classes with dynamically set attributes). This supports the use of
# qualified names.
ignored-classes=optparse.Values,thread._local,_thread._local,argparse.Namespace
# Show a hint with possible names when a member name was not found. The aspect
# of finding the hint is based on edit distance.
missing-member-hint=yes
# The minimum edit distance a name should have in order to be considered a
# similar match for a missing member name.
missing-member-hint-distance=1
# The total number of similar names that should be taken in consideration when
# showing a hint for a missing member.
missing-member-max-choices=1
# Regex pattern to define which classes are considered mixins.
mixin-class-rgx=.*[Mm]ixin
# List of decorators that change the signature of a decorated function.
signature-mutators=
[VARIABLES]
# List of additional names supposed to be defined in builtins. Remember that
# you should avoid defining new builtins when possible.
additional-builtins=
# Tells whether unused global variables should be treated as a violation.
allow-global-unused-variables=yes
# List of names allowed to shadow builtins
allowed-redefined-builtins=
# List of strings which can identify a callback function by name. A callback
# name must start or end with one of those strings.
callbacks=cb_,
_cb
# A regular expression matching the name of dummy variables (i.e. expected to
# not be used).
dummy-variables-rgx=_+$|(_[a-zA-Z0-9_]*[a-zA-Z0-9]+?$)|dummy|^ignored_|^unused_
# Argument names that match this expression will be ignored.
ignored-argument-names=_.*|^ignored_|^unused_
# Tells whether we should check for unused import in __init__ files.
init-import=no
# List of qualified module names which can have objects that can redefine
# builtins.
redefining-builtins-modules=six.moves,past.builtins,future.builtins,builtins,io

1
backend/Dockerfile
View File

@@ -13,6 +13,5 @@ EXPOSE 8000
# Set environment variables used by the deployment. These can be overridden by the user using this image.
ENV NUM_WORKERS=1
ENV OSM_CACHE_DIR=/cache
ENV MEMCACHED_HOST_PATH=none
CMD fastapi run src/main.py --port 8000 --workers $NUM_WORKERS

13
backend/Pipfile
View File

@@ -4,24 +4,13 @@ verify_ssl = true
name = "pypi"
[dev-packages]
pylint = "*"
pytest = "*"
tomli = "*"
httpx = "*"
exceptiongroup = "*"
pytest-html = "*"
typing-extensions = "*"
dill = "*"
[packages]
numpy = "*"
fastapi = "*"
pydantic = "*"
geopy = "*"
shapely = "*"
scipy = "*"
osmpythontools = "*"
pywikibot = "*"
pymemcache = "*"
fastapi-cli = "*"
scikit-learn = "*"
pyqt6 = "*"

3716
backend/Pipfile.lock generated
View File

File diff suppressed because it is too large Load Diff

42
backend/README.md
View File

@@ -1,44 +1,16 @@
# Backend
This repository contains the backend code for the application. It utilizes **FastAPI** to quickly create a RESTful API that exposes the endpoints of the route optimizer.
This repository contains the backend code for the application. It utilizes FastAPI that allows to quickly create a RESTful API that exposes the endpoints of the route optimizer.
## Getting Started
### Directory Structure
- The code for the Python application is located in the `src` directory.
- Package management is handled with **pipenv**, and the dependencies are listed in the `Pipfile`.
- Since the application is designed to be deployed in a container, the `Dockerfile` is provided to build the image.
### Setting Up the Development Environment
To set up your development environment using **pipenv**, follow these steps:
1. Install `pipenv` by running:
```bash
sudo apt install pipenv
```
2. Create and activate a virtual environment:
```bash
pipenv shell
```
3. Install the dependencies listed in the `Pipfile`:
```bash
pipenv install
```
4. The virtual environment will be created under:
```bash
~/.local/share/virtualenvs/...
```
- The code of the python application is located in the `src` directory.
- Package management is handled with `pipenv` and the dependencies are listed in the `Pipfile`.
- Since the application is aimed to be deployed in a container, the `Dockerfile` is provided to build the image.
### Deployment
To deploy the backend docker container, we use kubernetes. Modifications to the backend are automatically pushed to a two-stage environment through the CI pipeline. See [deployment/README](deployment/README.md] for further information.
The deployment configuration is included as a submodule in the `deployment` directory. The standalone repository is under [https://git.kluster.moll.re/anydev/anyway-backend-deployment/](https://git.kluster.moll.re/anydev/anyway-backend-deployment/).
To deploy the backend docker container, we use kubernetes. The deployment configuration is located under [https://git.kluster.moll.re/anydev/deployment-backend/](https://git.kluster.moll.re/anydev/deployment-backend/).
## Development
TBD
TBD

47
backend/conftest.py
View File

@@ -1,47 +0,0 @@
import pytest
pytest_plugins = ["pytest_html"]
def pytest_html_report_title(report):
"""modifying the title of html report"""
report.title = "Backend Testing Report"
def pytest_html_results_table_header(cells):
cells.insert(2, "<th>Detailed trip</th>")
cells.insert(3, "<th>Trip Duration</th>")
cells.insert(4, "<th>Target Duration</th>")
cells[5] = "<th>Execution time</th>" # rename the column containing execution times to avoid confusion
def pytest_html_results_table_row(report, cells):
trip_details = getattr(report, "trip_details", "N/A") # Default to "N/A" if no trip data
trip_duration = getattr(report, "trip_duration", "N/A") # Default to "N/A" if no trip data
target_duration = getattr(report, "target_duration", "N/A") # Default to "N/A" if no trip data
cells.insert(2, f"<td>{trip_details}</td>")
cells.insert(3, f"<td>{trip_duration}</td>")
cells.insert(4, f"<td>{target_duration}</td>")
@pytest.hookimpl(hookwrapper=True)
def pytest_runtest_makereport(item, call):
outcome = yield
report = outcome.get_result()
report.description = str(item.function.__doc__)
# Attach trip_details if it exists
if hasattr(item, "trip_details"):
report.trip_details = " - ".join(item.trip_details) # Convert list to string
else:
report.trip_details = "N/A" # Default if trip_string is not set
# Attach trip_duration if it exists
if hasattr(item, "trip_duration"):
report.trip_duration = item.trip_duration + " min"
else:
report.trip_duration = "N/A" # Default if duration is not set
# Attach target_duration if it exists
if hasattr(item, "target_duration"):
report.target_duration = item.target_duration + " min"
else:
report.target_duration = "N/A" # Default if duration is not set

1
backend/deployment

Submodule backend/deployment deleted from 718df09e88

1094
backend/report.html
View File

File diff suppressed because one or more lines are too long

0
backend/src/__init__.py
View File

33
backend/src/constants.py
View File

@@ -1,9 +1,6 @@
"""Module allowing to access the parameters of route generation"""
import logging
import os
import logging.config
from pathlib import Path
import os
LOCATION_PREFIX = Path('src')
PARAMETERS_DIR = LOCATION_PREFIX / 'parameters'
@@ -12,25 +9,19 @@ LANDMARK_PARAMETERS_PATH = PARAMETERS_DIR / 'landmark_parameters.yaml'
OPTIMIZER_PARAMETERS_PATH = PARAMETERS_DIR / 'optimizer_parameters.yaml'
cache_dir_string = os.getenv('OSM_CACHE_DIR', './cache')
OSM_CACHE_DIR = Path(cache_dir_string)
# if we are in a debug session, set verbose and rich logging
if os.getenv('DEBUG', "false") == "true":
from rich.logging import RichHandler
logging.basicConfig(
level=logging.DEBUG,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
handlers=[RichHandler()]
)
else:
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())
MEMCACHED_HOST_PATH = os.getenv('MEMCACHED_HOST_PATH', None)
if MEMCACHED_HOST_PATH == "none":
MEMCACHED_HOST_PATH = None
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)

34
backend/src/log_config.yaml Normal file
View 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

152
backend/src/main.py
View File

@@ -1,17 +1,12 @@
"""Main app for backend api"""
import logging
from fastapi import FastAPI, HTTPException, Query
from fastapi import FastAPI, Query, Body
from .structs.landmark import Landmark, Toilets
from .structs.preferences import Preferences
from .structs.linked_landmarks import LinkedLandmarks
from .structs.trip import Trip
from .utils.landmarks_manager import LandmarkManager
from .utils.toilets_manager import ToiletsManager
from .utils.optimizer import Optimizer
from .utils.refiner import Refiner
from .persistence import client as cache_client
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__)
@@ -22,51 +17,25 @@ optimizer = Optimizer()
refiner = Refiner(optimizer=optimizer)
@app.post("/trip/new")
def new_trip(preferences: Preferences,
start: tuple[float, float],
end: tuple[float, float] | None = None) -> Trip:
"""
@app.post("/route/new")
def get_route(preferences: Preferences, start: tuple[float, float], end: tuple[float, float] | None = None) -> str:
'''
Main function to call the optimizer.
Args:
preferences : the preferences specified by the user as the post body
start : the coordinates of the starting point
end : the coordinates of the finishing point
Returns:
(uuid) : The uuid of the first landmark in the optimized route
"""
:param preferences: the preferences specified by the user as the post body
:param start: the coordinates of the starting point as a tuple of floats (as url query parameters)
: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:
raise HTTPException(status_code=406, detail="Preferences not provided or incomplete.")
if (preferences.shopping.score == 0 and
preferences.sightseeing.score == 0 and
preferences.nature.score == 0) :
raise HTTPException(status_code=406, detail="All preferences are 0.")
raise ValueError("Please provide preferences in the form of a 'Preference' BaseModel class.")
if start is None:
raise HTTPException(status_code=406, detail="Start coordinates not provided")
if not (-90 <= start[0] <= 90 or -180 <= start[1] <= 180):
raise HTTPException(status_code=422, detail="Start coordinates not in range")
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='finish',
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='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)
# Generate the landmarks from the start location
landmarks, landmarks_short = manager.generate_landmarks_list(
@@ -77,86 +46,23 @@ def new_trip(preferences: Preferences,
# insert start and finish to the landmarks list
landmarks_short.insert(0, start_landmark)
landmarks_short.append(end_landmark)
# TODO infer these parameters from the preferences
max_walking_time = 4 # hours
detour = 30 # minutes
# First stage optimization
try:
base_tour = optimizer.solve_optimization(preferences.max_time_minute, landmarks_short)
except ArithmeticError as exc:
raise HTTPException(status_code=500, detail="No solution found") from exc
except TimeoutError as exc:
raise HTTPException(status_code=500, detail="Optimzation took too long") from exc
base_tour = optimizer.solve_optimization(max_walking_time*60, landmarks_short)
# Second stage optimization
refined_tour = refiner.refine_optimization(landmarks, base_tour,
preferences.max_time_minute,
preferences.detour_tolerance_minute)
refined_tour = refiner.refine_optimization(landmarks, base_tour, max_walking_time*60, detour)
linked_tour = LinkedLandmarks(refined_tour)
# upon creation of the trip, persistence of both the trip and its landmarks is ensured.
trip = Trip.from_linked_landmarks(linked_tour, cache_client)
return trip
return linked_tour[0].uuid
#### For already existing trips/landmarks
@app.get("/trip/{trip_uuid}")
def get_trip(trip_uuid: str) -> Trip:
"""
Look-up the cache for a trip that has been previously generated using its identifier.
Args:
trip_uuid (str) : unique identifier for a trip.
Returns:
(Trip) : the corresponding trip.
"""
try:
trip = cache_client.get(f"trip_{trip_uuid}")
return trip
except KeyError as exc:
raise HTTPException(status_code=404, detail="Trip not found") from exc
@app.get("/landmark/{landmark_uuid}")
def get_landmark(landmark_uuid: str) -> Landmark:
"""
Returns a Landmark from its unique identifier.
Args:
landmark_uuid (str) : unique identifier for a Landmark.
Returns:
(Landmark) : the corresponding Landmark.
"""
try:
landmark = cache_client.get(f"landmark_{landmark_uuid}")
return landmark
except KeyError as exc:
raise HTTPException(status_code=404, detail="Landmark not found") from exc
@app.post("/toilets/new")
def get_toilets(location: tuple[float, float] = Query(...), radius: int = 500) -> list[Toilets] :
"""
Endpoint to find toilets within a specified radius from a given location.
This endpoint expects the `location` and `radius` as **query parameters**, not in the request body.
Args:
location (tuple[float, float]): The latitude and longitude of the location to search from.
radius (int, optional): The radius (in meters) within which to search for toilets. Defaults to 500 meters.
Returns:
list[Toilets]: A list of Toilets objects that meet the criteria.
"""
if location is None:
raise HTTPException(status_code=406, detail="Coordinates not provided or invalid")
if not (-90 <= location[0] <= 90 or -180 <= location[1] <= 180):
raise HTTPException(status_code=422, detail="Start coordinates not in range")
toilets_manager = ToiletsManager(location, radius)
try :
toilets_list = toilets_manager.generate_toilet_list()
return toilets_list
except KeyError as exc:
raise HTTPException(status_code=404, detail="No toilets found") from exc
#cherche dans linked_tour et retourne le landmark correspondant
pass

61
backend/src/parameters/amenity_selectors.yaml
View File

@@ -1,6 +1,3 @@
# Tags were picked mostly arbitrarily, based on the OSM wiki and the OSM tags page.
# See https://taginfo.openstreetmap.org for more inspiration.
nature:
leisure: park
geological: ''
@@ -14,24 +11,7 @@ nature:
- alpine_hut
- viewpoint
- zoo
- resort
- picnic_site
water:
- pond
- lake
- river
- basin
- stream
- lagoon
- rapids
waterway:
- waterfall
- river
- canal
- dam
- dock
- boatyard
waterway: waterfall
shopping:
shop:
@@ -43,49 +23,10 @@ sightseeing:
- museum
- attraction
- gallery
- artwork
- aquarium
historic: ''
amenity:
- planetarium
- place_of_worship
- fountain
- townhall
water:
- reflecting_pool
bridge:
- aqueduct
- viaduct
- boardwalk
- cantilever
- abandoned
building:
- church
- chapel
- mosque
- synagogue
- ruins
- temple
- government
- cathedral
- castle
- museum
museums:
tourism:
- museum
- aquarium
# to be used later on
restauration:
shop:
- coffee
- bakery
- restaurant
- pastry
amenity:
- restaurant
- cafe
- ice_cream
- food_court
- biergarten

14
backend/src/parameters/landmark_parameters.yaml
View File

@@ -1,12 +1,6 @@
city_bbox_side: 7500 #m
city_bbox_side: 5000 #m
radius_close_to: 50
church_coeff: 0.9
nature_coeff: 1.25
overall_coeff: 10
tag_exponent: 1.15
image_bonus: 10
viewpoint_bonus: 15
wikipedia_bonus: 4
name_bonus: 3
church_coeff: 0.8
park_coeff: 1.2
tag_coeff: 10
N_important: 40
pay_bonus: -1

6
backend/src/parameters/optimizer_parameters.yaml
View File

@@ -1,6 +1,4 @@
detour_factor: 1.4
detour_corridor_width: 300
detour_corridor_width: 200
average_walking_speed: 4.8
max_landmarks: 10
max_landmarks_refiner: 30
overshoot: 1.1
max_landmarks: 7

75
backend/src/persistence.py
View File

@@ -1,75 +0,0 @@
"""Module used for handling cache"""
from pymemcache import serde
from pymemcache.client.base import Client
from .constants import MEMCACHED_HOST_PATH
class DummyClient:
"""
A dummy in-memory client that mimics the behavior of a memcached client.
This class is designed to simulate the behavior of the `pymemcache.Client`
for testing or development purposes. It stores data in a Python dictionary
and provides methods to set, get, and update key-value pairs.
Attributes:
_data (dict): A dictionary that holds the key-value pairs.
Methods:
set(key, value, **kwargs):
Stores the given key-value pair in the internal dictionary.
set_many(data, **kwargs):
Updates the internal dictionary with multiple key-value pairs.
get(key, **kwargs):
Retrieves the value associated with the given key from the internal
dictionary.
"""
_data = {}
def set(self, key, value, **kwargs): # pylint: disable=unused-argument
"""
Store a key-value pair in the internal dictionary.
Args:
key: The key for the item to be stored.
value: The value to be stored under the given key.
**kwargs: Additional keyword arguments (unused).
"""
self._data[key] = value
def set_many(self, data, **kwargs): # pylint: disable=unused-argument
"""
Update the internal dictionary with multiple key-value pairs.
Args:
data: A dictionary containing key-value pairs to be added.
**kwargs: Additional keyword arguments (unused).
"""
self._data.update(data)
def get(self, key, **kwargs): # pylint: disable=unused-argument
"""
Retrieve the value associated with the given key.
Args:
key: The key for the item to be retrieved.
**kwargs: Additional keyword arguments (unused).
Returns:
The value associated with the given key if it exists.
"""
return self._data[key]
if MEMCACHED_HOST_PATH is None:
client = DummyClient()
else:
client = Client(
MEMCACHED_HOST_PATH,
timeout=1,
allow_unicode_keys=True,
encoding='utf-8',
serde=serde.pickle_serde
)

4442
backend/src/sandbox/bandung_data.json
View File

File diff suppressed because it is too large Load Diff

698
backend/src/sandbox/colmar_data.json
View File

@@ -1,698 +0,0 @@
{
"type": "FeatureCollection",
"generator": "overpass-turbo",
"copyright": "The data included in this document is from www.openstreetmap.org. The data is made available under ODbL.",
"timestamp": "2024-12-02T21:14:59Z",
"features": [
{
"type": "Feature",
"properties": {
"@id": "node/1345741798",
"name": "Cordonnerie Saint-Joseph",
"shop": "shoes"
},
"geometry": {
"type": "Point",
"coordinates": [
7.3481705,
48.0816462
]
},
"id": "node/1345741798"
},
{
"type": "Feature",
"properties": {
"@id": "node/2659184738",
"brand": "Armand Thiery",
"brand:wikidata": "Q2861975",
"brand:wikipedia": "fr:Armand Thiery",
"name": "Armand Thiery",
"opening_hours": "Mo-Sa 09:30-19:00",
"shop": "clothes",
"wheelchair": "limited"
},
"geometry": {
"type": "Point",
"coordinates": [
7.3594454,
48.0785574
]
},
"id": "node/2659184738"
},
{
"type": "Feature",
"properties": {
"@id": "node/3618136290",
"name": "Chez Dominique",
"shop": "clothes"
},
"geometry": {
"type": "Point",
"coordinates": [
7.3362362,
48.0712174
]
},
"id": "node/3618136290"
},
{
"type": "Feature",
"properties": {
"@id": "node/3618136605",
"name": "Divamod",
"shop": "clothes"
},
"geometry": {
"type": "Point",
"coordinates": [
7.3304253,
48.0782989
]
},
"id": "node/3618136605"
},
{
"type": "Feature",
"properties": {
"@id": "node/3618284507",
"name": "Star tendances et voyages",
"shop": "clothes"
},
"geometry": {
"type": "Point",
"coordinates": [
7.3474029,
48.0830993
]
},
"id": "node/3618284507"
},
{
"type": "Feature",
"properties": {
"@id": "node/3619696125",
"brand": "Zeeman",
"brand:wikidata": "Q184399",
"name": "Zeeman",
"shop": "clothes"
},
"geometry": {
"type": "Point",
"coordinates": [
7.3413834,
48.0638444
]
},
"id": "node/3619696125"
},
{
"type": "Feature",
"properties": {
"@id": "node/4594398129",
"name": "Miss et Mister",
"shop": "clothes"
},
"geometry": {
"type": "Point",
"coordinates": [
7.3308309,
48.0779118
]
},
"id": "node/4594398129"
},
{
"type": "Feature",
"properties": {
"@id": "node/4907320441",
"brand": "Sergent Major",
"brand:wikidata": "Q62521738",
"clothes": "babies;children",
"name": "Sergent Major",
"opening_hours": "Mo-Sa 09:30-19:00",
"shop": "clothes",
"wheelchair": "no"
},
"geometry": {
"type": "Point",
"coordinates": [
7.359116,
48.0787229
]
},
"id": "node/4907320441"
},
{
"type": "Feature",
"properties": {
"@id": "node/4907364791",
"brand": "Armand Thiery",
"brand:wikidata": "Q2861975",
"brand:wikipedia": "fr:Armand Thiery",
"clothes": "women",
"name": "Armand Thiery",
"shop": "clothes"
},
"geometry": {
"type": "Point",
"coordinates": [
7.3601857,
48.0783373
]
},
"id": "node/4907364791"
},
{
"type": "Feature",
"properties": {
"@id": "node/4907385675",
"check_date": "2024-05-19",
"clothes": "children",
"name": "Du Pareil...au même",
"shop": "clothes"
},
"geometry": {
"type": "Point",
"coordinates": [
7.3604521,
48.0779726
]
},
"id": "node/4907385675"
},
{
"type": "Feature",
"properties": {
"@id": "node/4922191645",
"name": "Abilos",
"shop": "clothes"
},
"geometry": {
"type": "Point",
"coordinates": [
7.3566167,
48.0794136
]
},
"id": "node/4922191645"
},
{
"type": "Feature",
"properties": {
"@id": "node/4922191648",
"brand": "Esprit",
"brand:wikidata": "Q532746",
"brand:wikipedia": "en:Esprit Holdings",
"name": "Esprit",
"shop": "clothes"
},
"geometry": {
"type": "Point",
"coordinates": [
7.3554004,
48.0787549
]
},
"id": "node/4922191648"
},
{
"type": "Feature",
"properties": {
"@id": "node/4922191972",
"brand": "Guess",
"brand:wikidata": "Q2470307",
"brand:wikipedia": "en:Guess (clothing)",
"name": "Guess",
"shop": "clothes"
},
"geometry": {
"type": "Point",
"coordinates": [
7.355273,
48.0788003
]
},
"id": "node/4922191972"
},
{
"type": "Feature",
"properties": {
"@id": "node/4922192001",
"name": "Lingerie",
"shop": "clothes"
},
"geometry": {
"type": "Point",
"coordinates": [
7.3575453,
48.0779317
]
},
"id": "node/4922192001"
},
{
"type": "Feature",
"properties": {
"@id": "node/5359915869",
"name": "Al Assil",
"shop": "clothes"
},
"geometry": {
"type": "Point",
"coordinates": [
7.3305665,
48.0780902
]
},
"id": "node/5359915869"
},
{
"type": "Feature",
"properties": {
"@id": "node/9089360040",
"brand": "Grain de Malice",
"brand:wikidata": "Q66757157",
"clothes": "women",
"name": "Grain de Malice",
"shop": "clothes",
"short_name": "GDM"
},
"geometry": {
"type": "Point",
"coordinates": [
7.3593125,
48.0786234
]
},
"id": "node/9089360040"
},
{
"type": "Feature",
"properties": {
"@id": "node/9095193153",
"brand": "Undiz",
"brand:wikidata": "Q105306275",
"clothes": "underwear",
"name": "Undiz",
"shop": "clothes"
},
"geometry": {
"type": "Point",
"coordinates": [
7.3599579,
48.0782846
]
},
"id": "node/9095193153"
},
{
"type": "Feature",
"properties": {
"@id": "node/9095193154",
"branch": "Lingerie",
"brand": "RougeGorge",
"brand:wikidata": "Q104600739",
"clothes": "underwear",
"name": "RougeGorge",
"shop": "clothes"
},
"geometry": {
"type": "Point",
"coordinates": [
7.3604883,
48.0781607
]
},
"id": "node/9095193154"
},
{
"type": "Feature",
"properties": {
"@id": "node/9095212690",
"alt_name": "North Face",
"brand": "The North Face",
"brand:wikidata": "Q152784",
"brand:wikipedia": "en:The North Face",
"check_date": "2024-05-19",
"name": "The North Face",
"shop": "clothes"
},
"geometry": {
"type": "Point",
"coordinates": [
7.3603923,
48.0773727
]
},
"id": "node/9095212690"
},
{
"type": "Feature",
"properties": {
"@id": "node/9095270059",
"air_conditioning": "no",
"clothes": "men",
"level": "0",
"name": "Maison Aume",
"second_hand": "no",
"shop": "clothes",
"wheelchair": "no"
},
"geometry": {
"type": "Point",
"coordinates": [
7.361364,
48.0799999
]
},
"id": "node/9095270059"
},
{
"type": "Feature",
"properties": {
"@id": "node/9098624272",
"name": "Destock Place",
"shop": "clothes"
},
"geometry": {
"type": "Point",
"coordinates": [
7.3575161,
48.0793009
]
},
"id": "node/9098624272"
},
{
"type": "Feature",
"properties": {
"@id": "node/9123861652",
"name": "Weackers",
"shop": "shoes"
},
"geometry": {
"type": "Point",
"coordinates": [
7.361329,
48.0785972
]
},
"id": "node/9123861652"
},
{
"type": "Feature",
"properties": {
"@id": "node/9162179887",
"brand": "Calzedonia",
"brand:wikidata": "Q1027874",
"brand:wikipedia": "en:Calzedonia",
"name": "Calzedonia",
"shop": "clothes"
},
"geometry": {
"type": "Point",
"coordinates": [
7.3606374,
48.0780809
]
},
"id": "node/9162179887"
},
{
"type": "Feature",
"properties": {
"@id": "node/9162206449",
"clothes": "women",
"name": "Cop. Copine",
"shop": "clothes"
},
"geometry": {
"type": "Point",
"coordinates": [
7.3600947,
48.078399
]
},
"id": "node/9162206449"
},
{
"type": "Feature",
"properties": {
"@id": "node/9162226360",
"brand": "Okaïdi",
"brand:wikidata": "Q3350027",
"brand:wikipedia": "fr:Okaïdi",
"name": "Okaïdi",
"shop": "clothes"
},
"geometry": {
"type": "Point",
"coordinates": [
7.3596986,
48.078428
]
},
"id": "node/9162226360"
},
{
"type": "Feature",
"properties": {
"@id": "node/9162227010",
"brand": "Jules",
"brand:wikidata": "Q3188386",
"brand:wikipedia": "fr:Jules (enseigne)",
"clothes": "men",
"name": "Jules",
"opening_hours": "Mo-Sa 09:30-19:00",
"phone": "+33 3 89 41 03 62",
"shop": "clothes",
"website": "https://www.jules.com/fr-fr/magasins/1600133/"
},
"geometry": {
"type": "Point",
"coordinates": [
7.3600323,
48.0782229
]
},
"id": "node/9162227010"
},
{
"type": "Feature",
"properties": {
"@id": "node/10151865029",
"name": "Atelier Cinq",
"shop": "clothes"
},
"geometry": {
"type": "Point",
"coordinates": [
7.3571756,
48.0772657
]
},
"id": "node/10151865029"
},
{
"type": "Feature",
"properties": {
"@id": "node/10862176110",
"name": "L'hexagone",
"shop": "bag"
},
"geometry": {
"type": "Point",
"coordinates": [
7.3808571,
48.0814138
]
},
"id": "node/10862176110"
},
{
"type": "Feature",
"properties": {
"@id": "node/11150877331",
"brand": "Punt Roma",
"brand:wikidata": "Q101423290",
"clothes": "women",
"name": "Punt Roma",
"shop": "clothes"
},
"geometry": {
"type": "Point",
"coordinates": [
7.3571859,
48.0779406
]
},
"id": "node/11150877331"
},
{
"type": "Feature",
"properties": {
"@id": "node/11150959880",
"name": "Caroll",
"shop": "clothes"
},
"geometry": {
"type": "Point",
"coordinates": [
7.3579354,
48.0779291
]
},
"id": "node/11150959880"
},
{
"type": "Feature",
"properties": {
"@id": "node/11302242094",
"branch": "Wintzenheim",
"name": "Label Fripe",
"opening_hours": "Mo-Sa 09:00-18:45",
"phone": "+33 3 89 27 39 25",
"second_hand": "only",
"shop": "clothes",
"website": "https://labelfripe.fr/label-fripe-wintzenheim/"
},
"geometry": {
"type": "Point",
"coordinates": [
7.3109899,
48.0850362
]
},
"id": "node/11302242094"
},
{
"type": "Feature",
"properties": {
"@id": "node/11392247003",
"name": "Lingerie Sipp",
"shop": "clothes"
},
"geometry": {
"type": "Point",
"coordinates": [
7.3111507,
48.0841835
]
},
"id": "node/11392247003"
},
{
"type": "Feature",
"properties": {
"@id": "node/11778819781",
"addr:city": "Colmar",
"addr:housenumber": "10",
"addr:postcode": "68000",
"addr:street": "Rue des Têtes",
"clothes": "suits;hats;men",
"name": "Phillipe",
"phone": "0389411983",
"shop": "clothes"
},
"geometry": {
"type": "Point",
"coordinates": [
7.3559389,
48.0789064
]
},
"id": "node/11778819781"
},
{
"type": "Feature",
"properties": {
"@id": "node/11799215969",
"brand": "Petit Bateau",
"brand:wikidata": "Q3377090",
"name": "Petit Bateau",
"opening_hours": "Mo-Sa 10:00-19:00; Su 10:00-18:00",
"phone": "+33 3 89 24 97 85",
"shop": "clothes",
"website": "https://stores.petit-bateau.com/france/colmar/9-rue-des-boulangers"
},
"geometry": {
"type": "Point",
"coordinates": [
7.355149,
48.0780213
]
},
"id": "node/11799215969"
},
{
"type": "Feature",
"properties": {
"@id": "node/11816704669",
"addr:housenumber": "10",
"addr:street": "Rue des Boulangers",
"name": "des petits hauts",
"shop": "clothes"
},
"geometry": {
"type": "Point",
"coordinates": [
7.3555001,
48.0780768
]
},
"id": "node/11816704669"
},
{
"type": "Feature",
"properties": {
"@id": "node/12320343534",
"addr:city": "Colmar",
"addr:housenumber": "44",
"addr:postcode": "68000",
"addr:street": "Rue des Clefs",
"brand": "Un Jour Ailleurs",
"brand:wikidata": "Q105106211",
"clothes": "women",
"name": "Un Jour Ailleurs",
"opening_hours": "Mo-Fr 10:00-19:00; Sa 10:00-18:30",
"phone": "+33368318572",
"shop": "clothes",
"website": "https://boutique.unjourailleurs.com/fr/mode-femme/boutique-colmar-76"
},
"geometry": {
"type": "Point",
"coordinates": [
7.35897,
48.0789807
]
},
"id": "node/12320343534"
},
{
"type": "Feature",
"properties": {
"@id": "node/12320343536",
"addr:city": "Colmar",
"addr:housenumber": "38",
"addr:postcode": "68000",
"addr:street": "Rue des Clefs",
"brand": "Timberland",
"brand:wikidata": "Q1539185",
"name": "Timberland",
"opening_hours": "Mo-Sa 10:00-19:00",
"phone": "+33389298650",
"shop": "clothes"
},
"geometry": {
"type": "Point",
"coordinates": [
7.3592409,
48.0788785
]
},
"id": "node/12320343536"
}
]
}

350
backend/src/sandbox/get_streets.py
View File

@@ -1,350 +0,0 @@
# pylint: skip-file
import numpy as np
import json
import os
from typing import Optional, Literal
from sklearn.cluster import DBSCAN
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
from pydantic import BaseModel
from OSMPythonTools.overpass import Overpass, overpassQueryBuilder
from OSMPythonTools.cachingStrategy import CachingStrategy, JSON
from math import sin, cos, sqrt, atan2, radians
EARTH_RADIUS_KM = 6373
class ShoppingLocation(BaseModel):
type: Literal['street', 'area']
importance: int
centroid: tuple
start: Optional[list] = None
end: Optional[list] = None
# Output to frontend
class Landmark(BaseModel) :
# Properties of the landmark
name : str
type: Literal['sightseeing', 'nature', 'shopping', 'start', 'finish']
location : tuple
osm_type : str
osm_id : int
attractiveness : int
n_tags : int
image_url : Optional[str] = None
website_url : Optional[str] = None
description : Optional[str] = None # TODO future
duration : Optional[int] = 0
name_en : Optional[str] = None
# Additional properties depending on specific tour
must_do : Optional[bool] = False
must_avoid : Optional[bool] = False
is_secondary : Optional[bool] = False
time_to_reach_next : Optional[int] = 0
next_uuid : Optional[str] = None
def extract_points(filestr: str) :
"""
Extract points from geojson file.
Returns :
np.array containing the points
"""
points = []
with open(os.path.dirname(__file__) + '/' + filestr, 'r') as f:
geojson = json.load(f)
for feature in geojson['features']:
if feature['geometry']['type'] == 'Point':
centroid = feature['geometry']['coordinates']
points.append(centroid)
elif feature['geometry']['type'] == 'Polygon':
centroid = np.array(feature['geometry']['coordinates'][0][0])
points.append(centroid)
# Convert the list of points to a NumPy array
return np.array(points)
def get_distance(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.
"""
if p1 == p2:
return 0
else:
# Compute the distance in km along the surface of the Earth
# (assume spherical Earth)
# this is the haversine formula, stolen from stackoverflow
# in order to not use any external libraries
lat1, lon1 = radians(p1[0]), radians(p1[1])
lat2, lon2 = radians(p2[0]), radians(p2[1])
dlon = lon2 - lon1
dlat = lat2 - lat1
a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2
c = 2 * atan2(sqrt(a), sqrt(1 - a))
return EARTH_RADIUS_KM * c
def filter_clusters(cluster_points, cluster_labels):
"""
Remove clusters of less importance.
"""
label_counts = np.bincount(cluster_labels)
# Step 3: Get the indices (labels) of the 5 largest clusters
top_5_labels = np.argsort(label_counts)[-5:] # Get the largest 5 clusters
# Step 4: Filter points to keep only the points in the top 5 clusters
filtered_cluster_points = []
filtered_cluster_labels = []
for label in top_5_labels:
filtered_cluster_points.append(cluster_points[cluster_labels == label])
filtered_cluster_labels.append(np.full((label_counts[label],), label)) # Replicate the label
# Concatenate filtered clusters into a single array
return np.vstack(filtered_cluster_points), np.concatenate(filtered_cluster_labels)
def fit_lines(points, labels):
"""
Fit lines to identified clusters.
"""
all_x = []
all_y = []
lines = []
locations = []
for label in set(labels):
cluster_points = points[labels == label]
# If there's not enough points, skip
if len(cluster_points) < 2:
continue
# Apply PCA to find the principal component (i.e., the line of best fit)
pca = PCA(n_components=1)
pca.fit(cluster_points)
direction = pca.components_[0]
centroid = pca.mean_
# Project the cluster points onto the principal direction (line direction)
projections = np.dot(cluster_points - centroid, direction)
# Get the range of the projections to find the approximate length of the cluster
cluster_length = projections.max() - projections.min()
# Now adjust `t` so that it scales with the cluster length
t = np.linspace(-cluster_length / 2.75, cluster_length / 2.75, 10)
# Calculate the start and end of the line based on min/max projections
start_point = centroid[0] + t*direction[0]
end_point = centroid[1] + t*direction[1]
# Store the line
lines.append((start_point, end_point))
# For visualization, store the points
all_x.append(min(start_point))
all_x.append(max(start_point))
all_y.append(min(end_point))
all_y.append(max(end_point))
if np.linalg.norm(t) <= 0.0045 :
loc = ShoppingLocation(
type='area',
centroid=tuple((centroid[1], centroid[0])),
importance = len(cluster_points),
)
else :
loc = ShoppingLocation(
type='street',
centroid=tuple((centroid[1], centroid[0])),
importance = len(cluster_points),
start=start_point,
end=end_point
)
locations.append(loc)
xmin = min(all_x)
xmax = max(all_x)
ymin = min(all_y)
ymax = max(all_y)
corners = (xmin, xmax, ymin, ymax)
return corners, locations
def create_landmark(shopping_location: ShoppingLocation):
# Define the bounding box for a given radius around the coordinates
lat, lon = shopping_location.centroid
bbox = ("around:1000", str(lat), str(lon))
overpass = Overpass()
# CachingStrategy.use(JSON, cacheDir=OSM_CACHE_DIR)
# Query neighborhoods and shopping malls
selectors = ['"place"~"^(suburb|neighborhood|neighbourhood|quarter|city_block)$"', '"shop"="mall"']
min_dist = float('inf')
new_name = 'Shopping Area'
new_name_en = None
osm_id = 0
osm_type = 'node'
for sel in selectors :
query = overpassQueryBuilder(
bbox = bbox,
elementType = ['node', 'way', 'relation'],
selector = sel,
includeCenter = True,
out = 'center'
)
try:
result = overpass.query(query)
except Exception as e:
raise Exception("query unsuccessful")
for elem in result.elements():
location = (elem.centerLat(), elem.centerLon())
if location[0] is None :
location = (elem.lat(), elem.lon())
if location[0] is None :
# print(f"Fetching coordinates failed with {elem.type()}/{elem.id()}")
continue
# print(f"Distance : {get_distance(shopping_location.centroid, location)}")
d = get_distance(shopping_location.centroid, location)
if d < min_dist :
min_dist = d
new_name = elem.tag('name')
osm_type = elem.type() # Add type: 'way' or 'relation'
osm_id = elem.id() # Add OSM id
# add english name if it exists
try :
new_name_en = elem.tag('name:en')
except:
pass
return Landmark(
name=new_name,
type='shopping',
location=shopping_location.centroid, # TODO: use the fact the we can also recognize streets.
attractiveness=shopping_location.importance,
n_tags=0,
osm_id=osm_id,
osm_type=osm_type,
name_en=new_name_en
)
# Extract points
points = extract_points('vienna_data.json')
# print(len(points))
######## Create a figure with 1 row and 3 columns for side-by-side plots
fig, axes = plt.subplots(1, 3, figsize=(15, 5))
# Plot Raw data points
axes[0].set_title('Raw Data')
axes[0].scatter(points[:, 0], points[:, 1], color='blue', s=20)
# Apply DBSCAN to find clusters. Choose different settings for different cities.
if len(points) > 400 :
dbscan = DBSCAN(eps=0.00118, min_samples=15, algorithm='kd_tree') # for large cities
else :
dbscan = DBSCAN(eps=0.00075, min_samples=10, algorithm='kd_tree') # for small cities
labels = dbscan.fit_predict(points)
# Separate clustered points and noise points
clustered_points = points[labels != -1]
clustered_labels = labels[labels != -1]
noise_points = points[labels == -1]
######## Plot n°1: DBSCAN Clustering Results
axes[1].set_title('DBSCAN Clusters')
axes[1].scatter(clustered_points[:, 0], clustered_points[:, 1], c=clustered_labels, cmap='rainbow', s=20)
axes[1].scatter(noise_points[:, 0], noise_points[:, 1], c='blue', s=7, label='Noise')
# Keep the 5 biggest clusters
clustered_points, clustered_labels = filter_clusters(clustered_points, clustered_labels)
# Fit lines
corners, locations = fit_lines(clustered_points, clustered_labels)
(xmin, xmax, ymin, ymax) = corners
######## Plot clustered points in normal size and noise points separately
axes[2].scatter(clustered_points[:, 0], clustered_points[:, 1], c=clustered_labels, cmap='rainbow', s=30)
axes[2].set_title('PCA Fitted Lines on Clusters')
# Create a list of Landmarks for the shopping things
shopping_landmarks = []
for loc in locations :
axes[2].scatter(loc.centroid[1], loc.centroid[0], color='red', marker='x', s=200, linewidth=3)
landmark = create_landmark(loc)
shopping_landmarks.append(landmark)
axes[2].text(loc.centroid[1], loc.centroid[0], landmark.name,
ha='center', va='top', fontsize=6,
bbox=dict(facecolor='white', edgecolor='black', boxstyle='round,pad=0.2'),
zorder=3)
####### Plot the detected lines in the final plot #######
# for loc in locations:
# if loc.type == 'street' :
# line_x = loc.start
# line_y = loc.end
# axes[2].plot(line_x, line_y, color='lime', linewidth=3)
# else :
axes[0].set_xlim(xmin-0.01, xmax+0.01)
axes[0].set_ylim(ymin-0.01, ymax+0.01)
axes[1].set_xlim(xmin-0.01, xmax+0.01)
axes[1].set_ylim(ymin-0.01, ymax+0.01)
axes[2].set_xlim(xmin-0.01, xmax+0.01)
axes[2].set_ylim(ymin-0.01, ymax+0.01)
print("\n\n\n")
for landmark in shopping_landmarks :
print(f"{landmark.name} is a shopping area with a score of {landmark.attractiveness}")
plt.tight_layout()
plt.show()

17824
backend/src/sandbox/lyon_data.json
View File

File diff suppressed because it is too large Load Diff

42085
backend/src/sandbox/newyork_data.json
View File

File diff suppressed because it is too large Load Diff

83615
backend/src/sandbox/paris_data.json
View File

File diff suppressed because it is too large Load Diff

4947
backend/src/sandbox/strasbourg_data.json
View File

File diff suppressed because it is too large Load Diff

23140
backend/src/sandbox/vienna_data.json
View File

File diff suppressed because it is too large Load Diff

2844
backend/src/sandbox/winterthur_data.json
View File

File diff suppressed because it is too large Load Diff

16070
backend/src/sandbox/zurich_data.json
View File

File diff suppressed because it is too large Load Diff

134
backend/src/structs/landmark.py
View File

@@ -1,42 +1,11 @@
"""Definition of the Landmark class to handle visitable objects across the world."""
from typing import Optional, Literal
from uuid import uuid4
from pydantic import BaseModel, Field
from uuid import uuid4
# Output to frontend
class Landmark(BaseModel) :
"""
A class representing a landmark or point of interest (POI) in the context of a trip.
The Landmark class is used to model visitable locations, such as tourist attractions,
natural sites, shopping locations, and start/end points in travel itineraries. It
holds information about the landmark's attributes and supports comparisons and
calculations, such as distance between landmarks.
Attributes:
name (str): The name of the landmark.
type (Literal): The type of the landmark, which can be one of ['sightseeing', 'nature',
'shopping', 'start', 'finish'].
location (tuple): A tuple representing the (latitude, longitude) of the landmark.
osm_type (str): The OpenStreetMap (OSM) type of the landmark.
osm_id (int): The OpenStreetMap (OSM) ID of the landmark.
attractiveness (int): A score representing the attractiveness of the landmark.
n_tags (int): The number of tags associated with the landmark.
image_url (Optional[str]): A URL to an image of the landmark.
website_url (Optional[str]): A URL to the landmark's official website.
description (Optional[str]): A text description of the landmark.
duration (Optional[int]): The estimated time to visit the landmark (in minutes).
name_en (Optional[str]): The English name of the landmark.
uuid (str): A unique identifier for the landmark, generated by default using uuid4.
must_do (Optional[bool]): Whether the landmark is a "must-do" attraction.
must_avoid (Optional[bool]): Whether the landmark should be avoided.
is_secondary (Optional[bool]): Whether the landmark is secondary or less important.
time_to_reach_next (Optional[int]): Estimated time (in minutes) to reach the next landmark.
next_uuid (Optional[str]): UUID of the next landmark in sequence (if applicable).
"""
# Properties of the landmark
name : str
type: Literal['sightseeing', 'nature', 'shopping', 'start', 'finish']
@@ -45,98 +14,25 @@ class Landmark(BaseModel) :
osm_id : int
attractiveness : int
n_tags : int
image_url : Optional[str] = None
website_url : Optional[str] = None
image_url : Optional[str] = None # TODO future
description : Optional[str] = None # TODO future
duration : Optional[int] = 0
name_en : Optional[str] = None
duration : Optional[int] = 0 # TODO future
# Unique ID of a given landmark
uuid: str = Field(default_factory=uuid4)
uuid: str = Field(default_factory=uuid4) # TODO implement this ASAP
# Additional properties depending on specific tour
must_do : Optional[bool] = False
must_avoid : Optional[bool] = False
is_secondary : Optional[bool] = False
time_to_reach_next : Optional[int] = 0
next_uuid : Optional[str] = None
def __str__(self) -> str:
"""
String representation of the Landmark object.
Returns:
str: A formatted string with the landmark's type, name, location, attractiveness score,
time to the next landmark (if available), and whether the landmark is secondary.
"""
t_to_next_str = f", time_to_next={self.time_to_reach_next}" if self.time_to_reach_next else ""
is_secondary_str = ", secondary" if self.is_secondary else ""
type_str = '(' + self.type + ')'
return (f'Landmark{type_str}: [{self.name} @{self.location}, '
f'score={self.attractiveness}{t_to_next_str}{is_secondary_str}]')
def distance(self, value: 'Landmark') -> float:
"""
Calculates the squared distance between this landmark and another.
Args:
value (Landmark): Another Landmark object to calculate the distance to.
Returns:
float: The squared Euclidean distance between the two landmarks.
"""
return (self.location[0] - value.location[0])**2 + (self.location[1] - value.location[1])**2
is_secondary : Optional[bool] = False # TODO future
time_to_reach_next : Optional[int] = 0 # TODO fix this in existing code
next_uuid : Optional[str] = None # TODO implement this ASAP
def __hash__(self) -> int:
"""
Generates a hash for the Landmark based on its name.
Returns:
int: The hash of the landmark.
"""
return hash(self.name)
def __eq__(self, value: 'Landmark') -> bool:
"""
Checks equality between two Landmark objects based on UUID, OSM ID, and name.
Args:
value (Landmark): Another Landmark object to compare.
Returns:
bool: True if the landmarks are equal, False otherwise.
"""
# eq and hash must be consistent
# in particular, if two objects are equal, their hash must be equal
# uuid and osm_id are just shortcuts to avoid comparing all the properties
# if they are equal, we know that the name is also equal and in turn the hash is equal
return (self.uuid == value.uuid or
self.osm_id == value.osm_id or
(self.name == value.name and self.distance(value) < 0.001))
class Toilets(BaseModel) :
"""
Model for toilets. When false/empty the information is either false either not known.
"""
location : tuple
wheelchair : Optional[bool] = False
changing_table : Optional[bool] = False
fee : Optional[bool] = False
opening_hours : Optional[str] = ""
def __str__(self) -> str:
"""
String representation of the Toilets object.
Returns:
str: A formatted string with the toilets location.
"""
return f'Toilets @{self.location}'
return self.uuid.int
def __str__(self) -> str:
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}]'
class Config:
# This allows us to easily convert the model to and from dictionaries
orm_mode = True

73
backend/src/structs/linked_landmarks.py
View File

@@ -1,78 +1,61 @@
"""Linked and ordered list of Landmarks that represents the visiting order."""
import uuid
from .landmark import Landmark
from ..utils.get_time_separation import get_time
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).
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 = 0
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.
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.
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.
Create the links between the landmarks in the list by setting their .next_uuid and the .time_to_next attributes.
"""
# Mark secondary landmarks as such
self.update_secondary_landmarks()
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.total_time += landmark.duration
self._landmarks[-1].next_uuid = None
self._landmarks[-1].time_to_reach_next = 0
def update_secondary_landmarks(self) -> None:
"""
Mark landmarks with lower importance as secondary.
"""
# Extract the attractiveness scores and sort them in descending order
scores = sorted([landmark.attractiveness for landmark in self._landmarks], reverse=True)
# Determine the 10th highest score
if len(scores) >= 10:
threshold_score = scores[9]
else:
# If there are fewer than 10 landmarks, use the lowest score as the threshold
threshold_score = min(scores) if scores else 0
# Update 'is_secondary' for landmarks with attractiveness below the threshold score
for landmark in self._landmarks:
if (landmark.attractiveness < threshold_score and landmark.type not in ["start", "finish"]):
landmark.is_secondary = True
def __getitem__(self, index: int) -> Landmark:
return self._landmarks[index]
def __str__(self) -> str:
return f"LinkedLandmarks [{' ->'.join([str(landmark) for landmark in self._landmarks])}]"
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]
}

19
backend/src/structs/preferences.py
View File

@@ -1,26 +1,13 @@
"""Defines the Preferences used as input for trip generation."""
from typing import Optional, Literal
from pydantic import BaseModel
from typing import Optional, Literal
class Preference(BaseModel) :
"""
Type of preference.
Attributes:
type: what kind of landmark type.
score: how important that type is.
"""
name: str
type: Literal['sightseeing', 'nature', 'shopping', 'start', 'finish']
score: int # score could be from 1 to 5
# Input for optimization
class Preferences(BaseModel) :
""""
Full collection of preferences needed to generate a personalized trip.
"""
# Sightseeing / History & Culture (Musées, bâtiments historiques, opéras, églises)
sightseeing : Preference
@@ -30,5 +17,5 @@ class Preferences(BaseModel) :
# Shopping (diriger plutôt vers des zones / rues commerçantes)
shopping : Preference
max_time_minute: Optional[int] = 3*60
max_time_minute: Optional[int] = 6*60
detour_tolerance_minute: Optional[int] = 0

48
backend/src/structs/trip.py
View File

@@ -1,48 +0,0 @@
"""Definition of the Trip class."""
import uuid
from pydantic import BaseModel, Field
from pymemcache.client.base import Client
from .linked_landmarks import LinkedLandmarks
class Trip(BaseModel):
""""
A Trip represents the final guided tour that can be passed to frontend.
Attributes:
uuid: unique identifier for this particular trip.
total_time: duration of the trip (in minutes).
first_landmark_uuid: unique identifier of the first Landmark to visit.
Methods:
from_linked_landmarks: create a Trip from LinkedLandmarks object.
"""
uuid: str = Field(default_factory=uuid.uuid4)
total_time: int
first_landmark_uuid: str
@classmethod
def from_linked_landmarks(cls, landmarks: LinkedLandmarks, cache_client: Client) -> "Trip":
"""
Initialize a new Trip object and ensure it is stored in the cache.
"""
trip = Trip(
total_time = landmarks.total_time,
first_landmark_uuid = str(landmarks[0].uuid)
)
# Store the trip in the cache
cache_client.set(f"trip_{trip.uuid}", trip)
# Make sure to await the result (noreply=False).
# Otherwise the cache might not be inplace when the trip is actually requested.
cache_client.set_many({f"landmark_{landmark.uuid}": landmark for landmark in landmarks},
expire=3600, noreply=False)
# is equivalent to:
# for landmark in landmarks:
# cache_client.set(f"landmark_{landmark.uuid}", landmark, expire=3600)
return trip

85
backend/src/tester.py Normal file
View File

@@ -0,0 +1,85 @@
import logging
import yaml
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
logger = logging.getLogger(__name__)
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='sightseeing',
score = 5),
nature=Preference(
name='nature',
type='nature',
score = 5),
shopping=Preference(
name='shopping',
type='shopping',
score = 5),
max_time_minute=180,
detour_tolerance_minute=30
)
# Create start and finish
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 = 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)
# First stage optimization
base_tour = optimizer.solve_optimization(max_time=preferences.max_time_minute, landmarks=landmarks_short)
# Second stage using linear optimization
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
#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

0
backend/src/tests/__init__.py
View File

42
backend/src/tests/test_cache.py
View File

@@ -1,42 +0,0 @@
"""Collection of tests to ensure correct handling of invalid input."""
from fastapi.testclient import TestClient
import pytest
from .test_utils import load_trip_landmarks
from ..main import app
@pytest.fixture(scope="module")
def client():
"""Client used to call the app."""
return TestClient(app)
def test_cache(client, request): # pylint: disable=redefined-outer-name
"""
Test n°1 : Custom test in Turckheim to ensure small villages are also supported.
Args:
client:
request:
"""
duration_minutes = 15
response = client.post(
"/trip/new",
json={
"preferences": {"sightseeing": {"type": "sightseeing", "score": 5},
"nature": {"type": "nature", "score": 5},
"shopping": {"type": "shopping", "score": 5},
"max_time_minute": duration_minutes,
"detour_tolerance_minute": 0},
"start": [48.084588, 7.280405]
}
)
result = response.json()
landmarks = load_trip_landmarks(client, result['first_landmark_uuid'])
landmarks_cached = load_trip_landmarks(client, result['first_landmark_uuid'], True)
# checks :
assert response.status_code == 200 # check for successful planning
assert landmarks_cached == landmarks

62
backend/src/tests/test_invalid_input.py
View File

@@ -1,62 +0,0 @@
"""Collection of tests to ensure correct handling of invalid input."""
from fastapi.testclient import TestClient
import pytest
from ..main import app
@pytest.fixture(scope="module")
def invalid_client():
"""Client used to call the app."""
return TestClient(app)
@pytest.mark.parametrize(
"start,preferences,status_code",
[
# Invalid case: no preferences at all.
([48.8566, 2.3522], {}, 422),
# Invalid cases: incomplete preferences.
([48.084588, 7.280405], {"sightseeing": {"type": "nature", "score": 5}, # no shopping
"nature": {"type": "nature", "score": 5},
}, 422),
([48.084588, 7.280405], {"sightseeing": {"type": "nature", "score": 5}, # no nature
"shopping": {"type": "shopping", "score": 5},
}, 422),
([48.084588, 7.280405], {"nature": {"type": "nature", "score": 5}, # no sightseeing
"shopping": {"type": "shopping", "score": 5},
}, 422),
# Invalid cases: unexisting coords
([91, 181], {"sightseeing": {"type": "nature", "score": 5},
"nature": {"type": "nature", "score": 5},
"shopping": {"type": "shopping", "score": 5},
}, 422),
([-91, 181], {"sightseeing": {"type": "nature", "score": 5},
"nature": {"type": "nature", "score": 5},
"shopping": {"type": "shopping", "score": 5},
}, 422),
([91, -181], {"sightseeing": {"type": "nature", "score": 5},
"nature": {"type": "nature", "score": 5},
"shopping": {"type": "shopping", "score": 5},
}, 422),
([-91, -181], {"sightseeing": {"type": "nature", "score": 5},
"nature": {"type": "nature", "score": 5},
"shopping": {"type": "shopping", "score": 5},
}, 422),
]
)
def test_input(invalid_client, start, preferences, status_code): # pylint: disable=redefined-outer-name
"""
Test new trip creation with different sets of preferences and locations.
"""
response = invalid_client.post(
"/trip/new",
json={
"preferences": preferences,
"start": start
}
)
assert response.status_code == status_code

128
backend/src/tests/test_main.py
View File

@@ -1,128 +0,0 @@
"""Collection of tests to ensure correct implementation and track progress. """
from fastapi.testclient import TestClient
import pytest
from .test_utils import landmarks_to_osmid, load_trip_landmarks, log_trip_details
from ..main import app
@pytest.fixture(scope="module")
def client():
"""Client used to call the app."""
return TestClient(app)
def test_turckheim(client, request): # pylint: disable=redefined-outer-name
"""
Test n°1 : Custom test in Turckheim to ensure small villages are also supported.
Args:
client:
request:
"""
duration_minutes = 15
response = client.post(
"/trip/new",
json={
"preferences": {"sightseeing": {"type": "sightseeing", "score": 5},
"nature": {"type": "nature", "score": 5},
"shopping": {"type": "shopping", "score": 5},
"max_time_minute": duration_minutes,
"detour_tolerance_minute": 0},
"start": [48.084588, 7.280405]
}
)
result = response.json()
landmarks = load_trip_landmarks(client, result['first_landmark_uuid'])
# Add details to report
log_trip_details(request, landmarks, result['total_time'], duration_minutes)
# checks :
assert response.status_code == 200 # check for successful planning
assert isinstance(landmarks, list) # check that the return type is a list
assert duration_minutes*0.8 < int(result['total_time']) < duration_minutes*1.2
assert len(landmarks) > 2 # check that there is something to visit
def test_bellecour(client, request) : # pylint: disable=redefined-outer-name
"""
Test n°2 : Custom test in Lyon centre to ensure proper decision making in crowded area.
Args:
client:
request:
"""
duration_minutes = 30
response = client.post(
"/trip/new",
json={
"preferences": {"sightseeing": {"type": "sightseeing", "score": 5},
"nature": {"type": "nature", "score": 5},
"shopping": {"type": "shopping", "score": 5},
"max_time_minute": duration_minutes,
"detour_tolerance_minute": 0},
"start": [45.7576485, 4.8330241]
}
)
result = response.json()
landmarks = load_trip_landmarks(client, result['first_landmark_uuid'])
osm_ids = landmarks_to_osmid(landmarks)
# Add details to report
log_trip_details(request, landmarks, result['total_time'], duration_minutes)
# checks :
assert response.status_code == 200 # check for successful planning
assert duration_minutes*0.8 < int(result['total_time']) < duration_minutes*1.2
assert 136200148 in osm_ids # check for Cathédrale St. Jean in trip
def test_shopping(client, request) : # pylint: disable=redefined-outer-name
"""
Test n°3 : Custom test in Lyon centre to ensure shopping clusters are found.
Args:
client:
request:
"""
duration_minutes = 600
response = client.post(
"/trip/new",
json={
"preferences": {"sightseeing": {"type": "sightseeing", "score": 0},
"nature": {"type": "nature", "score": 0},
"shopping": {"type": "shopping", "score": 5},
"max_time_minute": duration_minutes,
"detour_tolerance_minute": 0},
"start": [45.7576485, 4.8330241]
}
)
result = response.json()
landmarks = load_trip_landmarks(client, result['first_landmark_uuid'])
# osm_ids = landmarks_to_osmid(landmarks)
# Add details to report
log_trip_details(request, landmarks, result['total_time'], duration_minutes)
# checks :
assert response.status_code == 200 # check for successful planning
assert duration_minutes*0.8 < int(result['total_time']) < duration_minutes*1.2
# def test_new_trip_single_prefs(client):
# response = client.post(
# "/trip/new",
# json={
# "preferences": {"sightseeing": {"type": "sightseeing", "score": 1},
# "nature": {"type": "nature", "score": 1},
# "shopping": {"type": "shopping", "score": 1},
# "max_time_minute": 360,
# "detour_tolerance_minute": 0},
# "start": [48.8566, 2.3522]
# }
# )
# assert response.status_code == 200
# def test_new_trip_matches_prefs(client):
# pass

102
backend/src/tests/test_toilets.py
View File

@@ -1,102 +0,0 @@
"""Collection of tests to ensure correct implementation and track progress. """
from fastapi.testclient import TestClient
import pytest
from ..structs.landmark import Toilets
from ..main import app
@pytest.fixture(scope="module")
def client():
"""Client used to call the app."""
return TestClient(app)
@pytest.mark.parametrize(
"location,radius,status_code",
[
({}, None, 422), # Invalid case: no location at all.
([443], None, 422), # Invalid cases: invalid location.
([443, 433], None, 422), # Invalid cases: invalid location.
]
)
def test_invalid_input(client, location, radius, status_code): # pylint: disable=redefined-outer-name
"""
Test n°1 : Verify handling of invalid input.
Args:
client:
request:
"""
response = client.post(
"/toilets/new",
params={
"location": location,
"radius": radius
}
)
# checks :
assert response.status_code == status_code
@pytest.mark.parametrize(
"location,status_code",
[
([48.2270, 7.4370], 200), # Orschwiller.
([10.2012, 10.123], 200), # Nigerian desert.
([63.989, -19.677], 200), # Hekla volcano, Iceland
]
)
def test_no_toilets(client, location, status_code): # pylint: disable=redefined-outer-name
"""
Test n°3 : Verify the code finds some toilets in big cities.
Args:
client:
request:
"""
response = client.post(
"/toilets/new",
params={
"location": location
}
)
toilets_list = [Toilets.model_validate(toilet) for toilet in response.json()]
# checks :
assert response.status_code == 200 # check for successful planning
assert isinstance(toilets_list, list) # check that the return type is a list
@pytest.mark.parametrize(
"location,status_code",
[
([45.7576485, 4.8330241], 200), # Lyon, Bellecour.
([-6.913795, 107.60278], 200), # Bandung, train station
([-22.970140, -43.18181], 200), # Rio de Janeiro, Copacabana
]
)
def test_toilets(client, location, status_code): # pylint: disable=redefined-outer-name
"""
Test n°3 : Verify the code finds some toilets in big cities.
Args:
client:
request:
"""
response = client.post(
"/toilets/new",
params={
"location": location,
"radius" : 600
}
)
toilets_list = [Toilets.model_validate(toilet) for toilet in response.json()]
# checks :
assert response.status_code == 200 # check for successful planning
assert isinstance(toilets_list, list) # check that the return type is a list
assert len(toilets_list) > 0

137
backend/src/tests/test_utils.py
View File

@@ -1,137 +0,0 @@
"""Helper methods for testing."""
import logging
from fastapi import HTTPException
from pydantic import ValidationError
from ..structs.landmark import Landmark
from ..persistence import client as cache_client
def landmarks_to_osmid(landmarks: list[Landmark]) -> list[int] :
"""
Convert the list of landmarks into a list containing their osm ids for quick landmark checking.
Args :
landmarks (list): the list of landmarks
Returns :
ids (list) : the list of corresponding OSM ids
"""
ids = []
for landmark in landmarks :
ids.append(landmark.osm_id)
return ids
def fetch_landmark(client, landmark_uuid: str):
"""
Fetch landmark data from the API based on the landmark UUID.
Args:
landmark_uuid (str): The UUID of the landmark.
Returns:
dict: Landmark data fetched from the API.
"""
logger = logging.getLogger(__name__)
response = client.get(f"/landmark/{landmark_uuid}")
if response.status_code != 200:
raise HTTPException(status_code=500,
detail=f"Failed to fetch landmark with UUID {landmark_uuid}: {response.status_code}")
try:
json_data = response.json()
logger.info(f"API Response: {json_data}")
except ValueError as e:
logger.error(f"Failed to parse response as JSON: {response.text}")
raise HTTPException(status_code=500, detail="Invalid response format from API")
# Try validating against the Landmark model here to ensure consistency
try:
landmark = Landmark(**json_data)
except ValidationError as ve:
logging.error(f"Validation error: {ve}")
raise HTTPException(status_code=500, detail="Invalid data format received from API")
if "detail" in json_data:
raise HTTPException(status_code=500, detail=json_data["detail"])
return Landmark(**json_data)
def fetch_landmark_cache(landmark_uuid: str):
"""
Fetch landmark data from the cache based on the landmark UUID.
Args:
landmark_uuid (str): The UUID of the landmark.
Returns:
dict: Landmark data fetched from the cache or raises an HTTP exception.
"""
logger = logging.getLogger(__name__)
# Try to fetch the landmark data from the cache
try:
landmark = cache_client.get(f"landmark_{landmark_uuid}")
if not landmark :
logger.warning(f"Cache miss for landmark UUID: {landmark_uuid}")
raise HTTPException(status_code=404, detail=f"Landmark with UUID {landmark_uuid} not found in cache.")
# Validate that the fetched data is a dictionary
if not isinstance(landmark, Landmark):
logger.error(f"Invalid cache data format for landmark UUID: {landmark_uuid}. Expected dict, got {type(landmark).__name__}.")
raise HTTPException(status_code=500, detail="Invalid cache data format.")
return landmark
except Exception as exc:
logger.error(f"Unexpected error occurred while fetching landmark UUID {landmark_uuid}: {exc}")
raise HTTPException(status_code=500, detail="An unexpected error occurred while fetching the landmark from the cache") from exc
def load_trip_landmarks(client, first_uuid: str, from_cache=None) -> list[Landmark]:
"""
Load all landmarks for a trip using the response from the API.
Args:
first_uuid (str) : The first UUID of the landmark.
Returns:
landmarks (list) : An list containing all landmarks for the trip.
"""
landmarks = []
next_uuid = first_uuid
while next_uuid is not None:
if from_cache :
landmark = fetch_landmark_cache(next_uuid)
else :
landmark = fetch_landmark(client, next_uuid)
landmarks.append(landmark)
next_uuid = landmark.next_uuid # Prepare for the next iteration
return landmarks
def log_trip_details(request, landmarks: list[Landmark], duration: int, target_duration: int) :
"""
Allows to show the detailed trip in the html test report.
Args:
request:
landmarks (list): the ordered list of visited landmarks
duration (int): the total duration of this trip
target_duration(int): the target duration of this trip
"""
trip_string = [f"{landmark.name} ({landmark.attractiveness} | {landmark.duration}) - {landmark.time_to_reach_next}" for landmark in landmarks]
# Pass additional info to pytest for reporting
request.node.trip_details = trip_string
request.node.trip_duration = str(duration) # result['total_time']
request.node.target_duration = str(target_duration)

283
backend/src/utils/cluster_processing.py
View File

@@ -1,283 +0,0 @@
import logging
from typing import Literal
import numpy as np
from sklearn.cluster import DBSCAN
from pydantic import BaseModel
from OSMPythonTools.overpass import Overpass, overpassQueryBuilder
from OSMPythonTools.cachingStrategy import CachingStrategy, JSON
from ..structs.landmark import Landmark
from ..utils.get_time_separation import get_distance
from ..constants import AMENITY_SELECTORS_PATH, LANDMARK_PARAMETERS_PATH, OPTIMIZER_PARAMETERS_PATH, OSM_CACHE_DIR
class ShoppingLocation(BaseModel):
""""
A classe representing an interesting area for shopping.
It can represent either a general area or a specifc route with start and end point.
The importance represents the number of shops found in this cluster.
Attributes:
type : either a 'street' or 'area' (representing a denser field of shops).
importance : size of the cluster (number of points).
centroid : center of the cluster.
start : if the type is a street it goes from here...
end : ...to here
"""
type: Literal['street', 'area']
importance: int
centroid: tuple
# start: Optional[list] = None # for later use if we want to have streets as well
# end: Optional[list] = None
class ShoppingManager:
logger = logging.getLogger(__name__)
# NOTE: all points are in (lat, lon) format
valid: bool # Ensure the manager is valid (ie there are some clusters to be found)
all_points: list
cluster_points: list
cluster_labels: list
shopping_locations: list[ShoppingLocation]
def __init__(self, bbox: tuple) -> None:
"""
Upon intialization, generate the point cloud used for cluster detection.
The points represent bag/clothes shops and general boutiques.
Args:
bbox: The bounding box coordinates (around:radius, center_lat, center_lon).
"""
# Initialize overpass and cache
self.overpass = Overpass()
CachingStrategy.use(JSON, cacheDir=OSM_CACHE_DIR)
# Initialize the points for cluster detection
query = overpassQueryBuilder(
bbox = bbox,
elementType = ['node'],
selector = ['"shop"~"^(bag|boutique|clothes)$"'],
includeCenter = True,
out = 'skel'
)
try:
result = self.overpass.query(query)
except Exception as e:
self.logger.error(f"Error fetching landmarks: {e}")
if len(result.elements()) == 0 :
self.valid = False
else :
points = []
for elem in result.elements() :
points.append(tuple((elem.lat(), elem.lon())))
self.all_points = np.array(points)
self.valid = True
def generate_shopping_landmarks(self) -> list[Landmark]:
"""
Generate shopping landmarks based on clustered locations.
This method first generates clusters of locations and then extracts shopping-related
locations from these clusters. It transforms each shopping location into a `Landmark` object.
Returns:
list[Landmark]: A list of `Landmark` objects representing shopping locations.
Returns an empty list if no clusters are found.
"""
self.generate_clusters()
if len(set(self.cluster_labels)) == 0 :
return [] # Return empty list if no clusters were found
# Then generate the shopping locations
self.generate_shopping_locations()
# Transform the locations in landmarks and return the list
shopping_landmarks = []
for location in self.shopping_locations :
shopping_landmarks.append(self.create_landmark(location))
return shopping_landmarks
def generate_clusters(self) :
"""
Generate clusters of points using DBSCAN.
This method applies the DBSCAN clustering algorithm with different
parameters depending on the size of the city (number of points).
It filters out noise points and keeps only the largest clusters.
The method updates:
- `self.cluster_points`: The points belonging to clusters.
- `self.cluster_labels`: The labels for the points in clusters.
The method also calls `filter_clusters()` to retain only the largest clusters.
"""
# Apply DBSCAN to find clusters. Choose different settings for different cities.
if len(self.all_points) > 200 :
dbscan = DBSCAN(eps=0.00118, min_samples=15, algorithm='kd_tree') # for large cities
else :
dbscan = DBSCAN(eps=0.00075, min_samples=10, algorithm='kd_tree') # for small cities
labels = dbscan.fit_predict(self.all_points)
# Separate clustered points and noise points
self.cluster_points = self.all_points[labels != -1]
self.cluster_labels = labels[labels != -1]
# filter the clusters to keep only the largest ones
self.filter_clusters()
def generate_shopping_locations(self) :
"""
Generate shopping locations based on clustered points.
This method iterates over the different clusters, calculates the centroid
(as the mean of the points within each cluster), and assigns an importance
based on the size of the cluster.
The generated shopping locations are stored in `self.shopping_locations`
as a list of `ShoppingLocation` objects, each with:
- `type`: Set to 'area'.
- `centroid`: The calculated centroid of the cluster.
- `importance`: The number of points in the cluster.
"""
locations = []
# loop through the different clusters
for label in set(self.cluster_labels):
# Extract points belonging to the current cluster
current_cluster = self.cluster_points[self.cluster_labels == label]
# Calculate the centroid as the mean of the points
centroid = np.mean(current_cluster, axis=0)
locations.append(ShoppingLocation(
type='area',
centroid=centroid,
importance = len(current_cluster)
))
self.shopping_locations = locations
def create_landmark(self, shopping_location: ShoppingLocation) -> Landmark:
"""
Create a Landmark object based on the given shopping location.
This method queries the Overpass API for nearby neighborhoods and shopping malls
within a 1000m radius around the shopping location centroid. It selects the closest
result and creates a landmark with the associated details such as name, type, and OSM ID.
Parameters:
shopping_location (ShoppingLocation): A ShoppingLocation object containing
the centroid and importance of the area.
Returns:
Landmark: A Landmark object containing details such as the name, type,
location, attractiveness, and OSM details.
"""
# Define the bounding box for a given radius around the coordinates
lat, lon = shopping_location.centroid
bbox = ("around:1000", str(lat), str(lon))
# Query neighborhoods and shopping malls
selectors = ['"place"~"^(suburb|neighborhood|neighbourhood|quarter|city_block)$"', '"shop"="mall"']
min_dist = float('inf')
new_name = 'Shopping Area'
new_name_en = None
osm_id = 0
osm_type = 'node'
for sel in selectors :
query = overpassQueryBuilder(
bbox = bbox,
elementType = ['node', 'way', 'relation'],
selector = sel,
includeCenter = True,
out = 'center'
)
try:
result = self.overpass.query(query)
except Exception as e:
self.logger.error(f"Error fetching landmarks: {e}")
continue
for elem in result.elements():
location = (elem.centerLat(), elem.centerLon())
if location[0] is None :
location = (elem.lat(), elem.lon())
if location[0] is None :
continue
d = get_distance(shopping_location.centroid, location)
if d < min_dist :
min_dist = d
new_name = elem.tag('name')
osm_type = elem.type() # Add type: 'way' or 'relation'
osm_id = elem.id() # Add OSM id
# Add english name if it exists
try :
new_name_en = elem.tag('name:en')
except:
pass
return Landmark(
name=new_name,
type='shopping',
location=shopping_location.centroid, # TODO: use the fact the we can also recognize streets.
attractiveness=shopping_location.importance,
n_tags=0,
osm_id=osm_id,
osm_type=osm_type,
name_en=new_name_en
)
def filter_clusters(self):
"""
Filter clusters to retain only the 5 largest clusters by point count.
This method calculates the size of each cluster and filters out all but the
5 largest clusters. It then updates the cluster points and labels to reflect
only those from the top 5 clusters.
"""
label_counts = np.bincount(self.cluster_labels)
# Step 3: Get the indices (labels) of the 5 largest clusters
top_5_labels = np.argsort(label_counts)[-5:] # Get the largest 5 clusters
# Step 4: Filter points to keep only the points in the top 5 clusters
filtered_cluster_points = []
filtered_cluster_labels = []
for label in top_5_labels:
filtered_cluster_points.append(self.cluster_points[self.cluster_labels == label])
filtered_cluster_labels.append(np.full((label_counts[label],), label)) # Replicate the label
# update the cluster points and labels with the filtered data
self.cluster_points = np.vstack(filtered_cluster_points)
self.cluster_labels = np.concatenate(filtered_cluster_labels)

73
backend/src/utils/get_time_separation.py
View File

@@ -1,82 +1,39 @@
import yaml
from math import sin, cos, sqrt, atan2, radians
from geopy.distance import geodesic
from ..constants import OPTIMIZER_PARAMETERS_PATH
import constants
with OPTIMIZER_PARAMETERS_PATH.open('r') as f:
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']
EARTH_RADIUS_KM = 6373
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.
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:
Returns:
int: Time to travel from p1 to p2 in minutes.
"""
if p1 == p2:
# Compute the straight-line distance in km
if p1 == p2 :
return 0
else:
# Compute the distance in km along the surface of the Earth
# (assume spherical Earth)
# this is the haversine formula, stolen from stackoverflow
# in order to not use any external libraries
lat1, lon1 = radians(p1[0]), radians(p1[1])
lat2, lon2 = radians(p2[0]), radians(p2[1])
else:
dist = geodesic(p1, p2).kilometers
dlon = lon2 - lon1
dlat = lat2 - lat1
a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2
c = 2 * atan2(sqrt(a), sqrt(1 - a))
distance = EARTH_RADIUS_KM * c
# Consider the detour factor for average an average city
walk_distance = distance * DETOUR_FACTOR
# 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_distance / AVERAGE_WALKING_SPEED * 60
walk_time = walk_dist/AVERAGE_WALKING_SPEED*60
return round(walk_time)
def get_distance(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.
"""
if p1 == p2:
return 0
else:
# Compute the distance in km along the surface of the Earth
# (assume spherical Earth)
# this is the haversine formula, stolen from stackoverflow
# in order to not use any external libraries
lat1, lon1 = radians(p1[0]), radians(p1[1])
lat2, lon2 = radians(p2[0]), radians(p2[1])
dlon = lon2 - lon1
dlat = lat2 - lat1
a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2
c = 2 * atan2(sqrt(a), sqrt(1 - a))
return EARTH_RADIUS_KM * c

357
backend/src/utils/landmarks_manager.py
View File

@@ -1,56 +1,54 @@
import math, yaml, logging
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
from ..structs.landmark import Landmark
from structs.preferences import Preferences, Preference
from structs.landmark import Landmark
from .take_most_important import take_most_important
from .cluster_processing import ShoppingManager
import constants
from ..constants import AMENITY_SELECTORS_PATH, LANDMARK_PARAMETERS_PATH, OPTIMIZER_PARAMETERS_PATH, OSM_CACHE_DIR
# silence the overpass logger
logging.getLogger('OSMPythonTools').setLevel(level=logging.CRITICAL)
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
nature_coeff: float # coeff to adjust score of parks
overall_coeff: float # coeff to adjust weight of tags
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 AMENITY_SELECTORS_PATH.open('r') as f:
with constants.AMENITY_SELECTORS_PATH.open('r') as f:
self.amenity_selectors = yaml.safe_load(f)
with LANDMARK_PARAMETERS_PATH.open('r') as f:
with constants.LANDMARK_PARAMETERS_PATH.open('r') as f:
parameters = yaml.safe_load(f)
self.max_bbox_side = parameters['city_bbox_side']
self.city_bbox_side = parameters['city_bbox_side']
self.radius_close_to = parameters['radius_close_to']
self.church_coeff = parameters['church_coeff']
self.nature_coeff = parameters['nature_coeff']
self.overall_coeff = parameters['overall_coeff']
self.tag_exponent = parameters['tag_exponent']
self.image_bonus = parameters['image_bonus']
self.name_bonus = parameters['name_bonus']
self.wikipedia_bonus = parameters['wikipedia_bonus']
self.viewpoint_bonus = parameters['viewpoint_bonus']
self.pay_bonus = parameters['pay_bonus']
self.park_coeff = parameters['park_coeff']
self.tag_coeff = parameters['tag_coeff']
self.N_important = parameters['N_important']
with OPTIMIZER_PARAMETERS_PATH.open('r') as f:
parameters = yaml.safe_load(f)
self.walking_speed = parameters['average_walking_speed']
self.detour_factor = parameters['detour_factor']
self.overpass = Overpass()
CachingStrategy.use(JSON, cacheDir=OSM_CACHE_DIR)
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]]:
@@ -61,60 +59,93 @@ class LandmarkManager:
and current location. It scores and corrects these landmarks, removes duplicates, and then selects the most important
landmarks based on a predefined criterion.
Args:
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.
tuple[list[Landmark], list[Landmark]]:
- A list of all existing landmarks.
- A list of the most important landmarks based on the user's preferences.
"""
max_walk_dist = (preferences.max_time_minute/2)/60*self.walking_speed*1000/self.detour_factor
reachable_bbox_side = min(max_walk_dist, self.max_bbox_side)
# use set to avoid duplicates, this requires some __methods__ to be set in Landmark
all_landmarks = set()
# Create a bbox using the around technique
bbox = tuple((f"around:{reachable_bbox_side/2}", str(center_coordinates[0]), str(center_coordinates[1])))
L = []
bbox = self.create_bbox(center_coordinates)
# list for sightseeing
if preferences.sightseeing.score != 0:
score_function = lambda score: score * 10 * preferences.sightseeing.score / 5
current_landmarks = self.fetch_landmarks(bbox, self.amenity_selectors['sightseeing'], preferences.sightseeing.type, score_function)
all_landmarks.update(current_landmarks)
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 score: score * 10 * self.nature_coeff * preferences.nature.score / 5
current_landmarks = self.fetch_landmarks(bbox, self.amenity_selectors['nature'], preferences.nature.type, score_function)
all_landmarks.update(current_landmarks)
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 score: score * 10 * preferences.shopping.score / 5
current_landmarks = self.fetch_landmarks(bbox, self.amenity_selectors['shopping'], preferences.shopping.type, score_function)
# set time for all shopping activites :
for landmark in current_landmarks : landmark.duration = 30
all_landmarks.update(current_landmarks)
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
# special pipeline for shopping malls
shopping_manager = ShoppingManager(bbox)
if shopping_manager.valid :
shopping_clusters = shopping_manager.generate_shopping_landmarks()
for landmark in shopping_clusters : landmark.duration = 45
all_landmarks.update(shopping_clusters)
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
landmarks_constrained = take_most_important(all_landmarks, self.N_important)
self.logger.info(f'Generated {len(all_landmarks)} landmarks around {center_coordinates}, and constrained to {len(landmarks_constrained)} most important ones.')
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
return all_landmarks, landmarks_constrained
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:
@@ -137,7 +168,7 @@ class LandmarkManager:
radius = self.radius_close_to
alpha = (180 * radius) / (6371000 * math.pi)
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
@@ -160,24 +191,35 @@ class LandmarkManager:
return 0
# def create_bbox(self, coordinates: tuple[float, float], reachable_bbox_side: int) -> tuple[float, float, float, float]:
# """
# Create a bounding box around the given coordinates.
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.
# reachable_bbox_side (int): The side length of the bounding box in meters.
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.
# """
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 m (since it's a square bbox)
# half_side_length_m = reachable_bbox_side / 2
# Half the side length in km (since it's a square bbox)
half_side_length_km = self.city_bbox_side / 2 / 1000
# return tuple((f"around:{half_side_length_m}", str(coordinates[0]), str(coordinates[1])))
# 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]:
@@ -185,7 +227,7 @@ class LandmarkManager:
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 (around:radius, center_lat, center_lon).
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.
@@ -201,169 +243,106 @@ class LandmarkManager:
"""
return_list = []
if landmarktype == 'nature' : query_conditions = []
else : query_conditions = ['count_tags()>5']
# 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_conditions = ['count_tags()>5']
# if landmarktype == 'shopping' : # use this later for shopping clusters
# element_types = ['node']
element_types = ['way', 'relation']
if 'viewpoint' in sel :
query_conditions = []
element_types.append('node')
query = overpassQueryBuilder(
bbox = bbox,
elementType = element_types,
# selector can in principle be a list already,
# but it generates the intersection of the queries
# we want the union
elementType = ['way', 'relation'],
selector = sel,
conditions = query_conditions, # except for nature....
# conditions = [],
includeCenter = True,
out = 'center'
out = 'body'
)
self.logger.debug(f"Query: {query}")
try:
result = self.overpass.query(query)
except Exception as e:
self.logger.error(f"Error fetching landmarks: {e}")
continue
return
for elem in result.elements():
name = elem.tag('name')
location = (elem.centerLat(), elem.centerLon())
osm_type = elem.type() # Add type: 'way' or 'relation'
osm_id = elem.id() # Add OSM id
name = elem.tag('name') # Add name
location = (elem.centerLat(), elem.centerLon()) # Add coordinates (lat, lon)
# TODO: exclude these from the get go
# handle unprecise and no-name locations
# skip if unprecise location
if name is None or location[0] is None:
if osm_type == 'node' and 'viewpoint' in elem.tags().values():
name = 'Viewpoint'
name_en = 'Viewpoint'
location = (elem.lat(), elem.lon())
else :
continue
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
score = n_tags**self.tag_exponent # Add score
website_url = None
image_url = None
name_en = None
# Adjust scoring, browse through tag keys
# remove specific tags
skip = False
for tag_key in elem.tags().keys():
if "pay" in tag_key:
# payment options are misleading and should not count for the scoring.
score += self.pay_bonus
for tag in elem.tags().keys():
if "pay" in tag:
n_tags -= 1 # discard payment options for tags
if "disused" in tag_key:
# skip disused amenities
skip = True
if "disused" in tag:
skip = True # skip disused amenities
break
if "boundary" in tag_key:
# skip "areas" like administrative boundaries and stuff
skip = True
break
if "wikipedia" in tag:
n_tags += 3 # wikipedia entries count more
if "historic" in tag_key and elem.tag('historic') in ['manor', 'optical_telegraph', 'pound', 'shieling', 'wayside_cross']:
# skip useless amenities
skip = True
break
if "name" in tag_key :
score += self.name_bonus
if "wiki" in tag_key:
# wikipedia entries count more
score += self.wikipedia_bonus
if "image" in tag_key:
# images must count more
score += self.image_bonus
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_key and elem.tag('leisure') == "park":
if "leisure" in tag and elem.tag('leisure') == "park":
elem_type = "nature"
if landmarktype != "shopping":
if "shop" in tag_key:
if landmarktype != SHOPPING:
if "shop" in tag:
skip = True
break
if tag_key == "building" and elem.tag('building') in ['retail', 'supermarket', 'parking']:
if tag == "building" and elem.tag('building') in ['retail', 'supermarket', 'parking']:
skip = True
break
# Extract image, website and english name
if tag_key in ['website', 'contact:website']:
website_url = elem.tag(tag_key)
if tag_key == 'image':
image_url = elem.tag('image')
if tag_key =='name:en':
name_en = elem.tag('name:en')
if skip:
continue
# Don't visit random apartments
if 'apartments' in elem.tags().values():
continue
score = score_function(score)
if "place_of_worship" in elem.tags().values():
score = score * self.church_coeff
duration = 10
if 'viewpoint' in elem.tags().values() :
# viewpoints must count more
score += self.viewpoint_bonus
duration = 10
elif "museum" in elem.tags().values() or "aquarium" in elem.tags().values() or "planetarium" in elem.tags().values():
duration = 60
else:
duration = 5
# finally create our own landmark object
landmark = Landmark(
name = name,
type = elem_type,
location = location,
osm_type = osm_type,
osm_id = osm_id,
attractiveness = int(score),
must_do = False,
n_tags = int(n_tags),
duration = int(duration),
name_en = name_en,
image_url = image_url,
website_url = website_url
)
return_list.append(landmark)
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.
@@ -378,7 +357,7 @@ def dict_to_selector_list(d: dict) -> list:
for key, value in d.items():
if type(value) == list:
val = '|'.join(value)
return_list.append(f'{key}~"^({val})$"')
return_list.append(f'{key}~"{val}"')
elif type(value) == str and len(value) == 0:
return_list.append(f'{key}')
else:

63
backend/src/utils/optimizer.py
View File

@@ -3,10 +3,11 @@ 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 structs.landmark import Landmark
from .get_time_separation import get_time
from ..constants import OPTIMIZER_PARAMETERS_PATH
import constants
@@ -16,22 +17,20 @@ 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
overshoot: float # overshoot to allow maxtime to overflow. Optimizer is a bit restrictive
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 OPTIMIZER_PARAMETERS_PATH.open('r') as f:
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']
self.overshoot = parameters['overshoot']
@@ -44,7 +43,7 @@ class Optimizer:
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):
@@ -79,7 +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
@@ -107,7 +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
@@ -168,7 +167,7 @@ class Optimizer:
def init_ub_dist(self, landmarks: list[Landmark], max_time: int):
def init_ub_dist(self, landmarks: list[Landmark], max_steps: int):
"""
Initialize the objective function coefficients and inequality constraints for the optimization problem.
@@ -177,10 +176,10 @@ class Optimizer:
Args:
landmarks (list[Landmark]): List of landmarks.
max_time (int): Maximum time of visit allowed.
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 + ...
@@ -192,19 +191,19 @@ class Optimizer:
dist_table = [0]*len(landmarks)
c.append(-spot1.attractiveness)
for j, spot2 in enumerate(landmarks) :
t = get_time(spot1.location, spot2.location) + spot1.duration
t = get_time(spot1.location, spot2.location)
dist_table[j] = t
closest = sorted(dist_table)[:25]
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_time*self.overshoot]
return c, A_ub, [max_steps]
def respect_number(self, L, max_landmarks: int):
def respect_number(self, L: int):
"""
Generate constraints to ensure each landmark is visited only once and cap the total number of visited landmarks.
@@ -212,7 +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
@@ -225,7 +224,7 @@ class Optimizer:
b.append(1)
A = np.vstack((A, ones*L))
b.append(max_landmarks+1)
b.append(self.max_landmarks+1)
return A, b
@@ -239,7 +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)
@@ -270,7 +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
@@ -293,7 +292,7 @@ class Optimizer:
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)
@@ -319,7 +318,7 @@ class Optimizer:
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)
@@ -346,7 +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)
@@ -374,7 +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
@@ -434,7 +433,6 @@ class Optimizer:
self,
max_time: int,
landmarks: list[Landmark],
max_landmarks: int = None
) -> list[Landmark]:
"""
Main optimization pipeline to solve the landmark visiting problem.
@@ -445,18 +443,15 @@ class Optimizer:
Args:
max_time (int): Maximum time allowed for the tour in minutes.
landmarks (list[Landmark]): List of landmarks to visit.
max_landmarks (int): Maximum number of landmarks visited
Returns:
list[Landmark]: The optimized tour of landmarks with updated travel times, or None if no valid solution is found.
"""
if max_landmarks is None :
max_landmarks = self.max_landmarks
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, max_landmarks) # Respect max number of visits (no more possible stops than landmarks).
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
@@ -475,7 +470,7 @@ class Optimizer:
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_time is limiting factor)
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
@@ -487,7 +482,7 @@ class Optimizer:
# Raise error if no solution is found
if not res.success :
raise ArithmeticError("No solution could be found, the problem is overconstrained. Try with a longer trip (>30 minutes).")
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)

61
backend/src/utils/refiner.py
View File

@@ -3,10 +3,10 @@ import yaml, logging
from shapely import buffer, LineString, Point, Polygon, MultiPoint, concave_hull
from math import pi
from ..structs.landmark import Landmark
from structs.landmark import Landmark
from . import take_most_important, get_time_separation
from .optimizer import Optimizer
from ..constants import OPTIMIZER_PARAMETERS_PATH
import constants
@@ -17,19 +17,19 @@ class Refiner :
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_refiner: int # max number of landmarks to visit
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 OPTIMIZER_PARAMETERS_PATH.open('r') as f:
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_refiner = parameters['max_landmarks_refiner']
self.max_landmarks = parameters['max_landmarks'] + 4
def create_corridor(self, landmarks: list[Landmark], width: float) :
@@ -37,11 +37,11 @@ class Refiner :
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.
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.
Geometry: A buffered geometry object representing the corridor around the path.
"""
corrected_width = (180*width)/(6371000*pi)
@@ -133,21 +133,6 @@ class Refiner :
i += 1
return tour
def integrate_landmarks(self, sub_list: list[Landmark], main_list: list[Landmark]) :
"""
Inserts 'sub_list' of Landmarks inside the 'main_list' by leaving the ends untouched.
Args:
sub_list : the list of Landmarks to be inserted inside of the 'main_list'.
main_list : the original list with start and finish.
Returns:
the full list.
"""
sub_list.append(main_list[-1]) # add finish back
return main_list[:-1] + sub_list # create full set of possible landmarks
def find_shortest_path_through_all_landmarks(self, landmarks: list[Landmark]) -> tuple[list[Landmark], Polygon]:
@@ -229,7 +214,7 @@ 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.take_most_important(second_order_landmarks, int(self.max_landmarks_refiner*0.75))
return take_most_important.take_most_important(second_order_landmarks, len(visited_landmarks))
# Try fix the shortest path using shapely
@@ -268,11 +253,6 @@ class Refiner :
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
"""
ERROR HERE :
Exception has occurred: AttributeError
'LineString' object has no attribute 'exterior'
"""
# reverse the xs and ys
@@ -328,37 +308,32 @@ class Refiner :
"""
# No need to refine if no detour is taken
# if detour == 0:
# return base_tour
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 = self.integrate_landmarks(minor_landmarks, base_tour) # could probably be optimized with less overhead
# 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
# Generate a new tour with the optimizer.
# get a new tour
new_tour = self.optimizer.solve_optimization(
max_time = max_time + detour,
landmarks = full_set,
max_landmarks = self.max_landmarks_refiner
landmarks = full_set
)
# If unsuccessful optimization, use the base_tour.
if new_tour is None:
self.logger.warning("No solution found for the refined tour. Returning the initial tour.")
new_tour = base_tour
# If only one landmark, return it.
if len(new_tour) < 4 :
return new_tour
# Find shortest path using the nearest neighbor heuristic.
# 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.
# Conditions : circular trip and invalid polygon.
# 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)

48
backend/src/utils/take_most_important.py
View File

@@ -1,16 +1,38 @@
from ..structs.landmark import Landmark
from structs.landmark import Landmark
def take_most_important(landmarks: list[Landmark], n_important) -> list[Landmark]:
"""
Given a list of landmarks, return the n_important most important landmarks
Args:
landmarks: list[Landmark] - list of landmarks
n_important: int - number of most important landmarks to return
Returns:
list[Landmark] - list of the n_important most important landmarks
"""
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 = {}
# Sort landmarks by attractiveness (descending)
sorted_landmarks = sorted(landmarks, key=lambda x: x.attractiveness, reverse=True)
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
return sorted_landmarks[:n_important]
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

78
backend/src/utils/toilets_manager.py
View File

@@ -1,78 +0,0 @@
import logging, yaml
from OSMPythonTools.overpass import Overpass, overpassQueryBuilder
from OSMPythonTools.cachingStrategy import CachingStrategy, JSON
from ..structs.landmark import Toilets
from ..constants import LANDMARK_PARAMETERS_PATH, OSM_CACHE_DIR
# silence the overpass logger
logging.getLogger('OSMPythonTools').setLevel(level=logging.CRITICAL)
class ToiletsManager:
logger = logging.getLogger(__name__)
location: tuple[float, float]
radius: int # radius in meters
def __init__(self, location: tuple[float, float], radius : int) -> None:
self.radius = radius
self.location = location
self.overpass = Overpass()
CachingStrategy.use(JSON, cacheDir=OSM_CACHE_DIR)
def generate_toilet_list(self) -> list[Toilets] :
# Create a bbox using the around technique
bbox = tuple((f"around:{self.radius}", str(self.location[0]), str(self.location[1])))
toilets_list = []
query = overpassQueryBuilder(
bbox = bbox,
elementType = ['node', 'way', 'relation'],
# selector can in principle be a list already,
# but it generates the intersection of the queries
# we want the union
selector = ['"amenity"="toilets"'],
includeCenter = True,
out = 'center'
)
self.logger.debug(f"Query: {query}")
try:
result = self.overpass.query(query)
except Exception as e:
self.logger.error(f"Error fetching landmarks: {e}")
return None
for elem in result.elements():
location = (elem.centerLat(), elem.centerLon())
# handle unprecise and no-name locations
if location[0] is None:
location = (elem.lat(), elem.lon())
else :
continue
toilets = Toilets(location=location)
if 'wheelchair' in elem.tags().keys() and elem.tag('wheelchair') == 'yes':
toilets.wheelchair = True
if 'changing_table' in elem.tags().keys() and elem.tag('changing_table') == 'yes':
toilets.changing_table = True
if 'fee' in elem.tags().keys() and elem.tag('fee') == 'yes':
toilets.fee = True
if 'opening_hours' in elem.tags().keys() :
toilets.opening_hours = elem.tag('opening_hours')
toilets_list.append(toilets)
return toilets_list

58
frontend/.github/workflows/build_app_android.yaml vendored
View File

@@ -1,58 +0,0 @@
on:
push:
tags:
- 'v*'
jobs:
build:
runs-on: macos-latest
steps:
- uses: actions/checkout@v4
- name: Set up ruby env
uses: ruby/setup-ruby@v1
with:
ruby-version: 3.2.1
bundler-cache: true
- name: Setup java for android build
uses: actions/setup-java@v4
with:
java-version: '17'
distribution: 'zulu'
- name: Setup android SDK
uses: android-actions/setup-android@v3
- name: Install Flutter
uses: subosito/flutter-action@v2
with:
channel: stable
flutter-version: 3.22.0
cache: true
- name: Infer version number from git tag
id: version
env:
REF_NAME: ${{ github.ref_name }}
run:
# remove the 'v' prefix from the tag name
echo "BUILD_NAME=${REF_NAME//v}" >> $GITHUB_ENV
- name: Load secrets from github
run: |
echo "${{ secrets.ANDROID_SECRET_PROPERTIES_BASE64 }}" | base64 -d > secrets.properties
echo "${{ secrets.ANDROID_GOOGLE_PLAY_JSON_BASE64 }}" | base64 -d > google-key.json
echo "${{ secrets.ANDROID_KEYSTORE_BASE64 }}" | base64 -d > release.keystore
working-directory: android
- name: Install fastlane
run: bundle install
working-directory: android
- name: Run fastlane lane
run: bundle exec fastlane deploy_testing
working-directory: android
env:
BUILD_NUMBER: ${{ github.run_number }}
# BUILD_NAME is implicitly available

42
frontend/README.md
View File

@@ -1,6 +1,6 @@
# Frontend
The frontend of this project is a Flutter application designed to run on both Android and iOS devices (and possibly as a PWA). The frontend is responsible for displaying the user interface and handling user input. It communicates with the backend via a REST-api to retrieve and send data.
This is the frontend of the project. It is a Flutter application that is designed to run on both Android and iOS devices. The frontend is responsible for displaying the user interface and handling user input. It communicates with the backend to retrieve and send data.
## Getting Started
@@ -15,43 +15,3 @@ Once you have the Flutter SDK installed, you can locally install the dependencie
```bash
flutter pub get
```
## Development
### ...
### Icons and logos
The application uses a custom launcher icon and splash screen. These are managed platform-independently using the `flutter_launcher_icons` package.
To update the icons, change the `flutter_launcher_icons.yaml` configuration file. Especially the `image_path` is relevant. Then run
```bash
dart run flutter_launcher_icons
```
### Deploying a new version
To truly deploy a new version of the application, i.e. to the official app stores, a special CI step is required. This listens for new tags. To create a new tag position yourself on the main branch and run
```bash
git tag -a v<name> -m "Release <name>"
git push origin v<name>
```
We adhere to the [Semantic Versioning](https://semver.org/) standard, so the tag should be of the form `v0.1.8` for example.
## Fastlane - in depth
The application is deployed to the Google Play Store and the Apple App Store using fastlane: [https://docs.fastlane.tools/](https://docs.fastlane.tools/)
Fastlane is installed as a Ruby gem. Since the bundler-gemfile is scoped to a single directory, a `Gemfile` is included in both the `android` and `ios` directories. Once installed, the usage is
```bash
cd frontend/android # or ios
bundle install
bundle exec fastlane <lane>
```
This is reused in the CI/CD pipeline to automate the deployment process.
Fastlane assumes mutliple secrets to be present as files in the platform directories. These are:
- for android:
- `secrets.properties` used by gradle to load secrets needed at execution time
- `release.keystore` used by gradle to sign the apk
- `google-key.json` used by fastlane to authenticate with the Google Play Store
- for ios:
- TODO
These files are stored as secrets in the GitHub repository so that the CI pipeline can access them.

9
frontend/android/.gitignore vendored
View File

@@ -1,8 +1,8 @@
gradlew
gradlew.bat
gradle/
gradle-wrapper.jar
/.gradle
/captures/
/gradlew
/gradlew.bat
/local.properties
/secrets.properties
GeneratedPluginRegistrant.java
@@ -12,6 +12,3 @@ GeneratedPluginRegistrant.java
key.properties
**/*.keystore
**/*.jks
# Fastlane google cloud access
google-key.json

3
frontend/android/Gemfile
View File

@@ -1,3 +0,0 @@
source "https://rubygems.org"
gem "fastlane"

220
frontend/android/Gemfile.lock
View File

@@ -1,220 +0,0 @@
GEM
remote: https://rubygems.org/
specs:
CFPropertyList (3.0.7)
base64
nkf
rexml
addressable (2.8.7)
public_suffix (>= 2.0.2, < 7.0)
artifactory (3.0.17)
atomos (0.1.3)
aws-eventstream (1.3.0)
aws-partitions (1.970.0)
aws-sdk-core (3.202.2)
aws-eventstream (~> 1, >= 1.3.0)
aws-partitions (~> 1, >= 1.651.0)
aws-sigv4 (~> 1.9)
jmespath (~> 1, >= 1.6.1)
aws-sdk-kms (1.88.0)
aws-sdk-core (~> 3, >= 3.201.0)
aws-sigv4 (~> 1.5)
aws-sdk-s3 (1.159.0)
aws-sdk-core (~> 3, >= 3.201.0)
aws-sdk-kms (~> 1)
aws-sigv4 (~> 1.5)
aws-sigv4 (1.9.1)
aws-eventstream (~> 1, >= 1.0.2)
babosa (1.0.4)
base64 (0.2.0)
claide (1.1.0)
colored (1.2)
colored2 (3.1.2)
commander (4.6.0)
highline (~> 2.0.0)
declarative (0.0.20)
digest-crc (0.6.5)
rake (>= 12.0.0, < 14.0.0)
domain_name (0.6.20240107)
dotenv (2.8.1)
emoji_regex (3.2.3)
excon (0.111.0)
faraday (1.10.3)
faraday-em_http (~> 1.0)
faraday-em_synchrony (~> 1.0)
faraday-excon (~> 1.1)
faraday-httpclient (~> 1.0)
faraday-multipart (~> 1.0)
faraday-net_http (~> 1.0)
faraday-net_http_persistent (~> 1.0)
faraday-patron (~> 1.0)
faraday-rack (~> 1.0)
faraday-retry (~> 1.0)
ruby2_keywords (>= 0.0.4)
faraday-cookie_jar (0.0.7)
faraday (>= 0.8.0)
http-cookie (~> 1.0.0)
faraday-em_http (1.0.0)
faraday-em_synchrony (1.0.0)
faraday-excon (1.1.0)
faraday-httpclient (1.0.1)
faraday-multipart (1.0.4)
multipart-post (~> 2)
faraday-net_http (1.0.2)
faraday-net_http_persistent (1.2.0)
faraday-patron (1.0.0)
faraday-rack (1.0.0)
faraday-retry (1.0.3)
faraday_middleware (1.2.0)
faraday (~> 1.0)
fastimage (2.3.1)
fastlane (2.222.0)
CFPropertyList (>= 2.3, < 4.0.0)
addressable (>= 2.8, < 3.0.0)
artifactory (~> 3.0)
aws-sdk-s3 (~> 1.0)
babosa (>= 1.0.3, < 2.0.0)
bundler (>= 1.12.0, < 3.0.0)
colored (~> 1.2)
commander (~> 4.6)
dotenv (>= 2.1.1, < 3.0.0)
emoji_regex (>= 0.1, < 4.0)
excon (>= 0.71.0, < 1.0.0)
faraday (~> 1.0)
faraday-cookie_jar (~> 0.0.6)
faraday_middleware (~> 1.0)
fastimage (>= 2.1.0, < 3.0.0)
gh_inspector (>= 1.1.2, < 2.0.0)
google-apis-androidpublisher_v3 (~> 0.3)
google-apis-playcustomapp_v1 (~> 0.1)
google-cloud-env (>= 1.6.0, < 2.0.0)
google-cloud-storage (~> 1.31)
highline (~> 2.0)
http-cookie (~> 1.0.5)
json (< 3.0.0)
jwt (>= 2.1.0, < 3)
mini_magick (>= 4.9.4, < 5.0.0)
multipart-post (>= 2.0.0, < 3.0.0)
naturally (~> 2.2)
optparse (>= 0.1.1, < 1.0.0)
plist (>= 3.1.0, < 4.0.0)
rubyzip (>= 2.0.0, < 3.0.0)
security (= 0.1.5)
simctl (~> 1.6.3)
terminal-notifier (>= 2.0.0, < 3.0.0)
terminal-table (~> 3)
tty-screen (>= 0.6.3, < 1.0.0)
tty-spinner (>= 0.8.0, < 1.0.0)
word_wrap (~> 1.0.0)
xcodeproj (>= 1.13.0, < 2.0.0)
xcpretty (~> 0.3.0)
xcpretty-travis-formatter (>= 0.0.3, < 2.0.0)
gh_inspector (1.1.3)
google-apis-androidpublisher_v3 (0.54.0)
google-apis-core (>= 0.11.0, < 2.a)
google-apis-core (0.11.3)
addressable (~> 2.5, >= 2.5.1)
googleauth (>= 0.16.2, < 2.a)
httpclient (>= 2.8.1, < 3.a)
mini_mime (~> 1.0)
representable (~> 3.0)
retriable (>= 2.0, < 4.a)
rexml
google-apis-iamcredentials_v1 (0.17.0)
google-apis-core (>= 0.11.0, < 2.a)
google-apis-playcustomapp_v1 (0.13.0)
google-apis-core (>= 0.11.0, < 2.a)
google-apis-storage_v1 (0.31.0)
google-apis-core (>= 0.11.0, < 2.a)
google-cloud-core (1.7.1)
google-cloud-env (>= 1.0, < 3.a)
google-cloud-errors (~> 1.0)
google-cloud-env (1.6.0)
faraday (>= 0.17.3, < 3.0)
google-cloud-errors (1.4.0)
google-cloud-storage (1.47.0)
addressable (~> 2.8)
digest-crc (~> 0.4)
google-apis-iamcredentials_v1 (~> 0.1)
google-apis-storage_v1 (~> 0.31.0)
google-cloud-core (~> 1.6)
googleauth (>= 0.16.2, < 2.a)
mini_mime (~> 1.0)
googleauth (1.8.1)
faraday (>= 0.17.3, < 3.a)
jwt (>= 1.4, < 3.0)
multi_json (~> 1.11)
os (>= 0.9, < 2.0)
signet (>= 0.16, < 2.a)
highline (2.0.3)
http-cookie (1.0.7)
domain_name (~> 0.5)
httpclient (2.8.3)
jmespath (1.6.2)
json (2.7.2)
jwt (2.8.2)
base64
mini_magick (4.13.2)
mini_mime (1.1.5)
multi_json (1.15.0)
multipart-post (2.4.1)
nanaimo (0.3.0)
naturally (2.2.1)
nkf (0.2.0)
optparse (0.5.0)
os (1.1.4)
plist (3.7.1)
public_suffix (6.0.1)
rake (13.2.1)
representable (3.2.0)
declarative (< 0.1.0)
trailblazer-option (>= 0.1.1, < 0.2.0)
uber (< 0.2.0)
retriable (3.1.2)
rexml (3.3.6)
strscan
rouge (2.0.7)
ruby2_keywords (0.0.5)
rubyzip (2.3.2)
security (0.1.5)
signet (0.19.0)
addressable (~> 2.8)
faraday (>= 0.17.5, < 3.a)
jwt (>= 1.5, < 3.0)
multi_json (~> 1.10)
simctl (1.6.10)
CFPropertyList
naturally
strscan (3.1.0)
terminal-notifier (2.0.0)
terminal-table (3.0.2)
unicode-display_width (>= 1.1.1, < 3)
trailblazer-option (0.1.2)
tty-cursor (0.7.1)
tty-screen (0.8.2)
tty-spinner (0.9.3)
tty-cursor (~> 0.7)
uber (0.1.0)
unicode-display_width (2.5.0)
word_wrap (1.0.0)
xcodeproj (1.25.0)
CFPropertyList (>= 2.3.3, < 4.0)
atomos (~> 0.1.3)
claide (>= 1.0.2, < 2.0)
colored2 (~> 3.1)
nanaimo (~> 0.3.0)
rexml (>= 3.3.2, < 4.0)
xcpretty (0.3.0)
rouge (~> 2.0.7)
xcpretty-travis-formatter (1.0.1)
xcpretty (~> 0.2, >= 0.0.7)
PLATFORMS
ruby
x86_64-linux
DEPENDENCIES
fastlane
BUNDLED WITH
2.5.18

45
frontend/android/README.md
View File

@@ -2,12 +2,13 @@
### Keystore setup
```bash
keytool -genkey -v -keystore release.keystore -keyalg RSA -keysize 2048 -validity 10000 -alias upload
keytool -genkey -v -keystore release.keystore -keyalg RSA -keysize 2048 -validity 10000 -alias release
```
- This is required to store local credentials securely and more importantly to sign the app for google play store distribution.
- This is required to store local credentials securely (not used for now).
- But necesseary in order to restrict the particular api key to a particular app (through the sha1 of the associated keystore).
### Using secret credentials during build
### Building and secret credentials
Following the guide under [https://developers.google.com/maps/flutter-package/config#android_1](https://developers.google.com/maps/flutter-package/config#android_1).
- Add the following to `android/build.gradle`:
```gradle
@@ -35,39 +36,13 @@ Following the guide under [https://developers.google.com/maps/flutter-package/co
android:value="${MAPS_API_KEY}" />
```
### Signing the app
Compared to the flutter template application, a few changes have to be made:
- Added to `android/app/build.gradle`:
```gradle
signingConfigs {
release {
keyAlias = secretProperties['keyAlias']
keyPassword = secretProperties['keyPassword']
storeFile = secretProperties['storeFile'] ? file(secretProperties['storeFile']) : null
storePassword = secretProperties['storePassword']
}
}
```
- Changed the `buildTypes` to use the `release` signing config:
```gradle
buildTypes {
release {
signingConfig signingConfigs.release
}
}
```
This makes use of the `secretProperties` defined previously:
```gradle
secretPropertiesFile.withReader('UTF-8') { reader ->
secretProperties.load(reader)
}
```
### Using the credentials in CI
- Add the secret files to the repository secrets (e.g. `ANDROID_SECRETS_PROPERTIES`).
- temporarily write them back to files during the CI execution:
- Add the base64 encoded credentials to the repository secrets (e.g. `ANDROID_SECRETS`).
```bash
echo {{ secrets.ANDROID_SECRETS }} >> android/secrets.properties
base64 -i android/secrets.properties
```
- Use the following in the CI script:
```bash
echo {{ secrets.ANDROID_SECRETS }} | base64 -d > android/secrets.properties
```

24
frontend/android/app/build.gradle
View File

@@ -30,24 +30,19 @@ if (flutterVersionName == null) {
def secretPropertiesFile = rootProject.file('secrets.properties')
def fallbackPropertiesFile = rootProject.file('fallback.properties')
def secretProperties = new Properties()
if (secretPropertiesFile.exists()) {
secretPropertiesFile.withReader('UTF-8') { reader ->
secretProperties.load(reader)
}
} else if (fallbackPropertiesFile.exists()) {
fallbackPropertiesFile.withReader('UTF-8') { reader ->
secretProperties.load(reader)
}
} else {
throw new GradleException("Secrets file (secrets.properties, fallback.properties) not found")
throw new GradleException("Secrets file secrets.properties not found")
}
android {
namespace "com.anydev.anyway"
namespace "com.example.fast_network_navigation"
compileSdk flutter.compileSdkVersion
ndkVersion flutter.ndkVersion
@@ -66,7 +61,7 @@ android {
defaultConfig {
// TODO: Specify your own unique Application ID (https://developer.android.com/studio/build/application-id.html).
applicationId "com.anydev.anyway"
applicationId "com.example.fast_network_navigation"
// You can update the following values to match your application needs.
// For more information, see: https://docs.flutter.dev/deployment/android#reviewing-the-gradle-build-configuration.
// Minimum Android version for Google Maps SDK
@@ -81,18 +76,11 @@ android {
}
signingConfigs {
release {
keyAlias = secretProperties['keyAlias']
keyPassword = secretProperties['keyPassword']
storeFile = secretProperties['storeFile'] ? file(secretProperties['storeFile']) : null
storePassword = secretProperties['storePassword']
}
}
buildTypes {
release {
signingConfig = signingConfigs.release
// TODO: Add your own signing config for the release build.
// Signing with the debug keys for now, so `flutter run --release` works.
signingConfig signingConfigs.debug
}
}
}

5
frontend/android/app/src/main/AndroidManifest.xml
View File

@@ -1,12 +1,9 @@
<manifest xmlns:android="http://schemas.android.com/apk/res/android">
<!-- Required to fetch data from the internet. -->
<uses-permission android:name="android.permission.INTERNET"/>
<!-- Required to show user location -->
<uses-permission android:name="android.permission.ACCESS_FINE_LOCATION"/>
<uses-permission android:name="android.permission.ACCESS_COARSE_LOCATION" />
<application
android:label="anyway"
android:label="fast_network_navigation"
android:name="${applicationName}"
android:icon="@mipmap/ic_launcher">
<activity

2
frontend/android/app/src/main/kotlin/com/example/fast_network_navigation/MainActivity.kt
View File

@@ -1,4 +1,4 @@
package com.anydev.anyway
package com.example.fast_network_navigation
import io.flutter.embedding.android.FlutterActivity

BIN
frontend/android/app/src/main/res/mipmap-hdpi/ic_launcher.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 3.6 KiB

After

Width:  |  Height:  |  Size: 544 B

BIN
frontend/android/app/src/main/res/mipmap-mdpi/ic_launcher.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 2.3 KiB

After

Width:  |  Height:  |  Size: 442 B

BIN
frontend/android/app/src/main/res/mipmap-xhdpi/ic_launcher.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 5.2 KiB

After

Width:  |  Height:  |  Size: 721 B

BIN
frontend/android/app/src/main/res/mipmap-xxhdpi/ic_launcher.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 9.4 KiB

After

Width:  |  Height:  |  Size: 1.0 KiB

BIN
frontend/android/app/src/main/res/mipmap-xxxhdpi/ic_launcher.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 13 KiB

After

Width:  |  Height:  |  Size: 1.4 KiB

2
frontend/android/fallback.properties
View File

@@ -1,3 +1 @@
# This file mirrors the state of secrets.properties as a reference for the developer.
# And as a fallback for build.gradle
MAPS_API_KEY=Key

2
frontend/android/fastlane/Appfile
View File

@@ -1,2 +0,0 @@
json_key_file("google-key.json") # Path to the json secret file - Follow https://docs.fastlane.tools/actions/supply/#setup to get one
package_name("com.anydev.anyway") # e.g. com.krausefx.app

53
frontend/android/fastlane/Fastfile
View File

@@ -1,53 +0,0 @@
# Uncomment the line if you want fastlane to automatically update itself
# update_fastlane
default_platform(:android)
platform :android do
desc "Deploy a new version to closed testing"
lane :deploy_testing do
build_name = ENV["BUILD_NAME"]
build_number = ENV["BUILD_NUMBER"]
sh(
"flutter",
"build",
"appbundle",
"--release",
"--build-name=#{build_name}",
"--build-number=#{build_number}",
)
upload_to_play_store(
track: 'alpha',
skip_upload_apk: true,
skip_upload_changelogs: true,
aab: "../build/app/outputs/bundle/release/app-release.aab",
# this is the default output of flutter build ... --release
# in particular this the build folder lies in the flutter root folder
# this is the parent folder for the android folder
)
end
desc "Deploy a new version as a full release"
lane :deploy_release do
gradle(
task: "clean assembleRelease",
# todo update to a flutter call
properties: {
# loaded from environment
"android.injected.version.name" => ENV["VERSION_NAME"],
}
)
upload_to_play_store(
track: "production",
skip_upload_apk: true,
skip_upload_changelogs: true,
aab: "../build/app/outputs/bundle/release/app-release.aab",
# this is the default output of flutter build ... --release
# in particular this the build folder lies in the flutter root folder
# this is the parent folder for the android folder
)
end
end

40
frontend/android/fastlane/README.md
View File

@@ -1,40 +0,0 @@
fastlane documentation
----
# Installation
Make sure you have the latest version of the Xcode command line tools installed:
```sh
xcode-select --install
```
For _fastlane_ installation instructions, see [Installing _fastlane_](https://docs.fastlane.tools/#installing-fastlane)
# Available Actions
## Android
### android deploy_testing
```sh
[bundle exec] fastlane android deploy_testing
```
Deploy a new version as a preview version
### android deploy_release
```sh
[bundle exec] fastlane android deploy_release
```
Deploy a new version as a full release
----
This README.md is auto-generated and will be re-generated every time [_fastlane_](https://fastlane.tools) is run.
More information about _fastlane_ can be found on [fastlane.tools](https://fastlane.tools).
The documentation of _fastlane_ can be found on [docs.fastlane.tools](https://docs.fastlane.tools).

7
frontend/android/fastlane/metadata/android/en-US/full_description.txt
View File

@@ -1,7 +0,0 @@
AnyWay - plan city trips your way
AnyWay is a mobile application that helps users plan city trips. The app allows users to specify their preferences and constraints, and then generates a personalized itinerary for them. The planning follows some guiding principles:
- **Personalization**:The user's preferences should be reflected in the choice of destinations.
- **Efficiency**:The itinerary should be optimized for the user's constraints.
- **Flexibility**: We aknowledge that tourism is a dynamic activity, and that users may want to change their plans on the go.
- **Discoverability**: Tourism is an inherently exploratory activity. Once a rough itinerary is generated, detours and spontaneous decisions should be encouraged.

BIN
frontend/android/fastlane/metadata/android/en-US/images/phoneScreenshots/Screenshot_20240925-161254_anyway.png
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 106 KiB

BIN
frontend/android/fastlane/metadata/android/en-US/images/phoneScreenshots/Screenshot_20240925-161325_anyway.png
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 1.3 MiB

BIN
frontend/android/fastlane/metadata/android/en-US/images/phoneScreenshots/Screenshot_20240925-161337_anyway.png
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 637 KiB

BIN
frontend/android/fastlane/metadata/android/en-US/images/phoneScreenshots/Screenshot_20240925-161641_anyway.png
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 573 KiB

BIN
frontend/android/fastlane/metadata/android/en-US/images/phoneScreenshots/Screenshot_20240925-161653_anyway.png
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 175 KiB

BIN
frontend/android/fastlane/metadata/android/en-US/images/phoneScreenshots/Screenshot_20240925-161848_anyway.png
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 360 KiB

1
frontend/android/fastlane/metadata/android/en-US/short_description.txt
View File

@@ -1 +0,0 @@
AnyWay - plan city trips your way!

1
frontend/android/fastlane/metadata/android/en-US/title.txt
View File

@@ -1 +0,0 @@
AnyWay

0
frontend/android/fastlane/metadata/android/en-US/video.txt
View File

5
frontend/android/gradle/wrapper/gradle-wrapper.properties vendored Normal file
View File

@@ -0,0 +1,5 @@
distributionBase=GRADLE_USER_HOME
distributionPath=wrapper/dists
zipStoreBase=GRADLE_USER_HOME
zipStorePath=wrapper/dists
distributionUrl=https\://services.gradle.org/distributions/gradle-7.6.3-all.zip

2
frontend/android/settings.gradle
View File

@@ -20,7 +20,7 @@ pluginManagement {
plugins {
id "dev.flutter.flutter-plugin-loader" version "1.0.0"
id "com.android.application" version "7.3.0" apply false
id "org.jetbrains.kotlin.android" version "2.0.20" apply false
id "org.jetbrains.kotlin.android" version "1.7.10" apply false
}
include ":app"

107
frontend/assets/cat.svg
View File

@@ -1,107 +0,0 @@
<?xml version="1.0" encoding="utf-8"?>
<!-- Generator: Adobe Illustrator 27.5.0, SVG Export Plug-In . SVG Version: 6.00 Build 0) -->
<svg version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
viewBox="0 0 500 500" style="enable-background:new 0 0 500 500;" xml:space="preserve">
<g id="OBJECTS">
<g>
<path style="fill:#F2DBDE;" d="M381.005,363.01c-53.963,8.445-84.441,11.1-138.832,6.101
c-54.388-4.998-109.48-25.844-144.743-67.555c-23.468-27.759-36.728-62.943-43.732-98.613c-3.745-19.07-5.754-39.21,0.433-57.635
c7.513-22.378,26.565-39.569,48.136-49.156c21.572-9.589,45.552-12.365,69.151-12.944c47.753-1.172,95.706,6.26,140.863,21.831
c35.603,12.277,69.954,29.937,96.972,56.171c27.019,26.233,46.213,61.723,47.963,99.341
C458.967,298.17,438.434,354.022,381.005,363.01z"/>
<g>
<path style="fill:#F2BFC6;" d="M314.479,248.209c-22.398,36.41-29.246,81.831-19.597,123.401
c27.302-0.242,52.026-3.263,86.124-8.6c57.429-8.989,77.961-64.84,76.211-102.458c-1.503-32.308-15.881-63.041-37.024-87.694
C375.546,184.337,337.241,211.21,314.479,248.209z"/>
<path style="fill:#F2BFC6;" d="M60.074,229.111c2.232,7.566,4.802,15.029,7.749,22.32c40.138-5.931,78.066-26.379,104.834-56.907
c26.459-30.176,41.716-69.876,42.677-109.969c-14.6-1.246-29.267-1.705-43.916-1.345c-11.908,0.292-23.911,1.147-35.655,3.151
C136.569,142.478,107.155,198.423,60.074,229.111z"/>
<path style="fill:#F2BFC6;" d="M365.131,128.557c-16.748-9.529-34.631-17.233-52.85-23.516
c-6.45-2.224-12.962-4.262-19.517-6.153c-1.712,23.304-4.543,46.555-11.914,68.659c-9.236,27.692-26.464,53.808-52.01,67.931
c-22.973,12.7-50.376,14.689-74.443,25.169c-21.624,9.417-39.587,25.305-54.36,43.893c8.346,9.381,17.714,17.663,27.862,24.902
c16.736-21.461,41.874-37.166,67.161-48.559c35.578-16.03,74.129-26.682,105.739-49.566
C334.357,207.023,357.577,169.22,365.131,128.557z"/>
</g>
</g>
<ellipse style="opacity:0.15;fill:#2D3038;" cx="250.223" cy="394.224" rx="109.236" ry="18.917"/>
<g>
<path style="fill:#2D3038;" d="M305.132,388.442c-0.168,1.158-0.626,2.243-1.458,3.061c-1.863,1.832-4.823,1.724-7.427,1.538
c-17.939-1.285-36.017-0.625-53.815,1.965c-7.053,3.155-16.423,3.233-25.275,2.004c-8.853-1.231-17.514-3.684-26.397-4.661
c-8.885-0.976-21.867-0.33-26.499,2.758c0,0-7.266,3.996-12.907,12.021c-3.367,4.789-4.105,11.306-2.377,16.899
c2.452,7.945,10.312,13.334,18.475,14.912c8.163,1.579,16.603-0.053,24.6-2.327c22.82-6.49,43.805-18.134,66.018-26.468
c22.213-8.334,47.017-13.282,69.546-5.844c3.96,1.306,7.879,3.033,10.941,5.866c3.062,2.832,5.173,6.927,4.813,11.081
c-0.464,5.356-4.97,9.719-10.061,11.444c-5.092,1.726-10.658,1.275-15.953,0.346c-5.296-0.93-10.554-2.17-15.926-2.414
c-20.08-0.909-38.455,4.247-56.124,10.857c-17.669,6.608-35.096,14.21-53.56,18.085c-18.463,3.874-35.807,8.106-51.682-4.186
c-20.345-15.753-19.603-41.137-8.091-63.296c5.521-10.629,12.589-18.637,19.416-27.732c-1.72-12.542-6.898-24.945-9.467-37.525
c-4.135-20.25-1.309-41.854,7.666-61.314c5.614-15.439,11.257-30.942,19.093-45.38c7.835-14.438,18.007-27.88,31.297-37.536
c13.289-9.656,29.927-15.279,46.993-13.222c7.787-8.403,16.038-16.377,24.703-23.871c-1.319-7.29-1.183-14.637,0.584-20.961
c-4.077-8.872-8.2-17.907-9.54-27.579c-0.835-6.027-0.441-12.408,1.577-17.991c1.878-5.198,8.452-6.799,12.542-3.08
c6.673,6.07,12.683,12.869,17.891,20.235c18.398-4.802,38.164-4.231,56.264,1.583c6.473-8.017,14.398-14.861,23.286-20.075
c2.366-1.388,5.533-2.613,7.657-0.875c1.683,1.377,1.736,3.89,1.592,6.059c-0.815,12.217-3.418,24.313-8.016,36.577
c4.862,15.779,0.82,33.862-9.812,46.412c-2.168,11.956,1.193,24.438,2.504,36.665c2.294,21.385-1.98,43.411-12.271,62.744
c-2.4,4.508-5.754,8.444-9.863,11.477c-1.71,1.263-3.38,2.581-5.006,3.951c-5.172,20.881-10.139,41.311-15.351,62.281
c2.061,7.78,4.487,15.496,7.272,23.126c3.209-0.899,6.478-1.696,9.816-1.809c3.896-0.132,7.942,0.744,11.024,3.131
c2.308,1.785,3.979,4.375,4.658,7.212c0.484,2.028,0.445,4.26-0.563,6.086c-0.663,1.203-1.81,2.171-3.102,2.583
c0.454,1.78,0.565,3.616,0.106,5.385c-0.778,3.004-3.622,5.6-6.675,5.375c-0.047,0.112-0.097,0.223-0.151,0.333
c-0.979,1.985-3.08,3.228-5.239,3.714c-2.063,0.464-4.207,0.333-6.319,0.174c-0.138,0.225-0.3,0.437-0.489,0.633
c-1.556,1.603-4.16,1.338-6.346,0.87c-3.015-0.645-6.04-1.471-8.688-3.051c-2.647-1.583-4.906-4.013-5.707-6.991
c-1.237-4.607,2.111-10.097,0.151-14.313c-3.538-7.609-7.733-14.893-12.004-22.126c-8.712,7.077-18.162,13.242-28.147,18.367
c6.95-0.974,14.248-1.345,21.476-0.293c3.273,0.475,6.596,1.283,9.285,3.208c2.689,1.924,4.631,5.173,4.214,8.453
c-0.34,2.664-2.596,5.054-5.156,5.449"/>
<path style="fill:none;stroke:#FFFFFF;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:10;" d="M151.465,379.089
c0.578-3.877,0.614-7.729,0.28-11.566"/>
<path style="fill:none;stroke:#FFFFFF;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:10;" d="M296.431,98.602
c1.739,2.591,3.381,5.247,4.918,7.962"/>
<path style="fill:none;stroke:#FFFFFF;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:10;" d="M273.736,153.553
c-0.645-1.929-1.188-3.891-1.625-5.865"/>
<path style="fill:none;stroke:#FFFFFF;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:10;" d="M295.23,106.811
c-4.87-7.904-10.55-15.309-16.923-22.061c-1.834-1.943-4.156-3.987-6.799-3.598c-2.928,0.431-4.574,3.626-5.147,6.53
c-1.629,8.254,1.474,16.627,4.521,24.47"/>
<path style="fill:none;stroke:#FFFFFF;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:10;" d="M352.846,98.327
c1.084,0.372,2.162,0.763,3.232,1.174"/>
<path style="fill:none;stroke:#FFFFFF;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:10;" d="M363.545,168.179
c-1.077,1.107-2.211,2.161-3.399,3.155"/>
<path style="fill:none;stroke:#FFFFFF;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:10;" d="M295.583,130.136
c3.86-4.907,10.772-7.181,16.791-5.521c6.019,1.659,10.791,7.151,11.446,13.054c-4.594,3.601-11.6,3.717-16.311,0.268
c-3.162-2.315-5.105-6.101-5.423-9.993"/>
<path style="fill:none;stroke:#FFFFFF;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:10;" d="M363.109,126.785
c-1.79-2.631-5.159-4.002-8.321-3.646c-3.162,0.356-6.042,2.317-7.787,4.979c-1.743,2.662-2.395,5.96-1.828,9.854
c4.738,1.952,10.727,0.164,13.621-4.066c1.462-2.137,2.057-4.785,1.832-7.36"/>
<path style="fill:none;stroke:#FFFFFF;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:10;" d="M350.957,171.048
c-4.278,4.378-10.749,6.497-16.787,5.499"/>
<path style="fill:none;stroke:#FFFFFF;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:10;" d="M338.68,282.717
c-5.42,4.867-10.31,10.327-14.541,16.258"/>
<path style="fill:none;stroke:#FFFFFF;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:10;" d="M333.834,368.351
c0.757,2.017,1.54,4.028,2.348,6.032c2.26-0.589,4.541-1.183,6.876-1.268c2.333-0.084,4.757,0.381,6.656,1.74
c1.559,1.116,2.664,2.753,3.552,4.452c0.261,0.499,0.505,1.013,0.727,1.536"/>
<path style="fill:none;stroke:#FFFFFF;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:10;" d="M317.138,283.315
c0.476,18.805,3.038,37.553,7.633,55.961"/>
<path style="fill:none;stroke:#FFFFFF;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:10;" d="M337.823,376.837
c2.877-0.595,5.878,0.99,7.67,3.316c1.791,2.327,2.567,5.273,3.025,8.174c0.191,1.214,0.327,2.48,0.209,3.695"/>
<path style="fill:none;stroke:#FFFFFF;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:10;" d="M327.236,380.633
c3.086-0.38,6.102,1.606,7.733,4.252c1.632,2.645,2.112,5.835,2.285,8.939c0.04,0.721,0.054,1.476-0.027,2.204"/>
<path style="fill:none;stroke:#FFFFFF;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:10;" d="M305.059,385.808
c-0.036-0.193-0.079-0.385-0.128-0.573c-1.058-4.111-4.728-7.422-8.927-8.052"/>
<g>
<path style="fill:none;stroke:#FFFFFF;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:10;" d="M250.442,264.812
c-1.67-3.125-3.183-6.325-4.488-9.622c-5.098-12.883-6.92-27.047-5.248-40.801"/>
<path style="fill:none;stroke:#FFFFFF;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:10;" d="M302.266,351.248
c-7.667-12.944-15.022-25.405-19.496-39.762"/>
<path style="fill:none;stroke:#FFFFFF;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:10;" d="M272.435,372.065
c-3.368,0.554-6.637,1.226-9.757,1.918c10.852-22.715,21.971-46.794,19.913-71.883c-0.826-10.055-4.036-20.316-11.156-27.463
c-8.522-8.553-21.576-11.406-33.547-9.827c-22.022,2.903-41.327,20.57-46.167,42.248"/>
</g>
<g>
<path style="fill:none;stroke:#FFFFFF;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:10;" d="M328.579,152.076
c1.379-0.341,2.796,0.501,3.736,1.565c0.942,1.065,1.588,2.366,2.551,3.41c0.963,1.044,2.43,1.826,3.784,1.398
c1.002-0.317,1.702-1.217,2.207-2.139c0.504-0.921,0.888-1.923,1.572-2.721c1.237-1.447,3.432-1.978,5.192-1.258"/>
<path style="fill:none;stroke:#FFFFFF;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:10;" d="M360.735,158.173
c-2.16,5.007-7.325,8.57-12.773,8.812c-1.946,0.086-3.967-0.245-5.593-1.317c-1.872-1.234-2.979-3.253-3.85-5.361
c-0.089,1.146-0.496,2.29-1.133,3.25c-1.229,1.854-3.175,3.116-5.189,4.059c-3.3,1.546-7.007,2.373-10.616,1.879
c-3.611-0.495-7.099-2.413-9.07-5.477"/>
<path style="fill:none;stroke:#FFFFFF;stroke-linecap:round;stroke-linejoin:round;stroke-miterlimit:10;" d="M338.276,158.534
c0,0,0.176,1.073,0.244,1.773"/>
</g>
</g>
</g>
</svg>
Side by Side

Before

Width:  |  Height:  |  Size: 9.5 KiB

273
frontend/assets/city.svg
View File

@@ -1,273 +0,0 @@
<?xml version="1.0" encoding="utf-8"?>
<!-- Generator: Adobe Illustrator 27.5.0, SVG Export Plug-In . SVG Version: 6.00 Build 0) -->
<svg version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
viewBox="0 0 1000 700" style="enable-background:new 0 0 1000 700;" xml:space="preserve">
<g id="Shadow">
<g style="opacity:0.1;">
<path style="fill:#38415C;" d="M186.919,556.734c0,0.331,0.541,0.599,1.208,0.599c0.667,0,1.208-0.268,1.208-0.599
c0-0.331-0.541-0.599-1.208-0.599C187.46,556.135,186.919,556.403,186.919,556.734z"/>
<path style="fill:#38415C;" d="M957.699,446.328h-12.196h-37.267h-8.131h-4.525h-22.106h-29.729h-8.01h-8.921h-7.462H777.69h-5.38
h-8.517h-13.921h-24.898h-0.367h-35.201h-33.29h-13.405h-8.642h-18.387h-20.084H584.19h-2.542h-5.421h-37.944h-7.453h-2.757
h-2.757h-1.428h-8.748h-5.514h-10.175h-0.905h-4.609h-10.175h-5.514h-10.175h-2.757h-2.757h-27.938h-8.05h-29.96h-6.713h-18.964
h-11.234h-48.644h-12.099h-10.229h-20.764h-12.382h-3.512h-23.242h-5.943h-13.266h-10.795h-35.413h-16.467h-4.656h-8.696h-25.877
H89.054h-4.763H72.026h-7.508H53.821H42.302v9.376h11.519v6.41h10.696v6.835h19.774v2.177h20.658v9.405h17.084v-5.919h11.557
v10.475h3.789v11.69h11.18v9.823h-4.017v1.763h7.066v4.785h23.433v28.254h-1.845v1.897h4.028v2.429h4.636v0.913v2.777v2.41h5.594
v4.306h0.673l0,0h0.673v-4.306h3.015l1.823-2.41h12.206v-3.69h31.948V543.3h4.028v-1.897h-1.845V484.37h15.302v40.617h1.509v3.023
h2.811v10.012h1.016v-10.012h2.287v7.997h1.017v-7.997h4.828v-3.023h6.098v-10.569h11.445v-0.743h-1.492v-2.116h7.56v32.974
h-1.078v0.849h7.678v5.101h-0.992v0.817h7.047v2.933h-1.099v0.627h3.502v2.77h3.348v5.513h0.402h0.398h0.314v-5.513h1.354v9.889
h0.402h0.399h0.314v-9.889h0.451h2.897v-2.77h3.034v-0.627h-0.632v-2.933h7.047v-0.817h-0.992v-5.101h7.678v-0.849h-1.078v-43.096
h23.505v-5.982h8.399v31.88h-4.954v0.806h4.954v1.443h6.279v2.37h30.344v27.318h7.165v21.803h13.871V593h1.952v-11.927h4.298
v9.528h1.952v-9.528h21.941v-12.964h3.982v-8.839h11.148v-32.999h4.318v-7.629l6.342,0.769v1.742h5.514v-1.073l10.175,1.234v1.969
h5.514v-1.3l9.332,1.131v9.523h2.491v16.539h11.982v6.297h6.46v5.29h2.267v9.068h0.586v-9.068h2.267v-5.29h6.46v-6.297h12.075
v-16.539h2.399v-45.67l5.467,13.925h5.729v12.219h-2.645v0.527h31.278v6.75h-3.52v0.763h-1.791v2.08h8.284v2.313h-1.087v0.668
h18.198v-0.668h-1.087v-2.313h23.966c0.802,1.935,2.023,3.811,3.668,5.596l-3.992,0.913c-0.688-0.732-2.184-1.239-3.92-1.239
c-2.388,0-4.324,0.96-4.324,2.143c0,1.183,1.936,2.143,4.324,2.143c2.388,0,4.324-0.96,4.324-2.143
c0-0.239-0.08-0.468-0.225-0.683l4.015-0.919c2.595,2.749,6.165,5.281,10.623,7.491c0.352,0.174,0.709,0.346,1.069,0.515
l-3.154,1.668c-0.76-0.329-1.753-0.528-2.841-0.528c-2.388,0-4.324,0.959-4.324,2.143s1.936,2.143,4.324,2.143
c2.388,0,4.324-0.959,4.324-2.143c0-0.559-0.432-1.068-1.139-1.449l3.16-1.671c5.36,2.471,11.576,4.337,18.308,5.527l-1.744,2.453
c-0.378-0.054-0.777-0.083-1.19-0.083c-2.388,0-4.324,0.96-4.324,2.143c0,1.183,1.936,2.143,4.324,2.143
c2.388,0,4.324-0.96,4.324-2.143c0-0.895-1.107-1.662-2.68-1.982l1.743-2.451c5.551,0.953,11.445,1.449,17.493,1.449
c0.498,0,0.995-0.003,1.491-0.01l0.198,3.017c-2.096,0.148-3.707,1.041-3.707,2.121c0,1.183,1.936,2.143,4.324,2.143
c2.388,0,4.324-0.96,4.324-2.143c0-1.184-1.936-2.143-4.324-2.143c-0.046,0-0.091,0.001-0.137,0.002l-0.197-3.004
c2.456-0.044,4.881-0.173,7.265-0.378l-2.223,24.735l79.948-8.225v-43.336h13.883v22.309h24.985v8.902h1.355v-8.902h2.795v16.446
h1.355v-16.446h3.219v11.855h1.355v-11.855h4.235v-54.059h12.874V506.6h2.033v3.715h2.033v2.582h14.483v-2.582h2.033V506.6h7.369
v1.594h3.557V506.6h1.259v4.262h5.082V506.6h1.452l3.161-11.593h11.528v5.526h0.762v-5.526h4.32v6.746h0.762v-6.746h6.25v-1.567
h-1.592v-17.317h12.874l1.507-4.997h10.931v-9.012h11.954v-6.86h12.196V446.328z M653.829,518.335l-11.117,0.179v-7.937h2.055
v-0.76h-2.055v-0.593l13.295,2.426c-1.417,1.94-2.19,4.031-2.19,6.21C653.816,518.019,653.821,518.177,653.829,518.335z
M689.289,499.58c-4.354,0.083-8.516,0.542-12.36,1.312l-5.314-6.414v-0.42h5.082v4.786h5.082v-4.786h7.148L689.289,499.58z
M702.329,517.554l-8.713,0.14c-0.026-0.114-0.079-0.224-0.155-0.328l9.073-2.076L702.329,517.554z M666.025,494.058v0.401
c-0.325,0.085-0.657,0.163-0.979,0.251l-0.713-0.651H666.025z M666.025,495.263v0.341l-0.291-0.266
C665.83,495.311,665.929,495.289,666.025,495.263z M666.025,496.603v2.241h2.454l3.554,3.247c-2.98,0.871-5.693,1.943-8.062,3.179
l-10.904-5.064c0.33-0.173,0.666-0.344,1.007-0.513c3.276-1.624,6.914-3.003,10.823-4.12L666.025,496.603z M672.377,502.405
l15.07,13.768l-22.95-10.659C666.813,504.306,669.465,503.258,672.377,502.405z M669.572,498.844h2.043v-3.739l4.87,5.877
c-1.242,0.259-2.449,0.55-3.618,0.872L669.572,498.844z M691.664,494.058v4.786h12.332l-1.224,1.721
c-3.776-0.648-7.828-1.001-12.044-1.001c-0.32,0-0.64,0.002-0.959,0.006l-0.361-5.512H691.664z M703.939,499.641l-0.101,1.127
c-0.206-0.039-0.404-0.087-0.612-0.124L703.939,499.641z M702.896,511.25l-8.307,4.394l8.619-7.874L702.896,511.25z
M702.863,511.616l-0.306,3.407l-9.299,2.127c-0.053-0.046-0.11-0.09-0.172-0.133l0.598-0.547L702.863,511.616z M693.364,518.468
l8.74,1.595l-0.252,2.801l-8.846-4.108C693.147,518.667,693.268,518.571,693.364,518.468z M693.53,518.245
c0.056-0.1,0.091-0.205,0.102-0.312l8.676-0.14l-0.182,2.021L693.53,518.245z M656.116,486.551l-11.415-10.428h11.415V486.551z
M656.116,487.55v4.746h-1.779v1.763h8.903l0.969,0.885c-4.029,1.15-7.778,2.571-11.154,4.243
c-0.352,0.175-0.698,0.352-1.039,0.53l-3.311-1.538c0.63-0.372,1.01-0.852,1.01-1.376c0-1.184-1.936-2.143-4.324-2.143
c-1.018,0-1.941,0.182-2.68,0.473v-5.035h2.055v-0.76h-2.055v-9.479h2.055v-0.76h-2.055v-2.977h0.897L656.116,487.55z
M642.711,500.338h2.055v-0.76h-2.055v-1.104c0.739,0.292,1.662,0.473,2.68,0.473c1.158,0,2.209-0.226,2.985-0.593l3.31,1.537
c-3.677,1.959-6.684,4.15-8.975,6.503V500.338z M642.711,508.163c2.337-2.844,5.703-5.479,10.027-7.784l10.906,5.065
c-3.215,1.722-5.771,3.749-7.47,5.983l-13.463-2.457V508.163z M664.17,505.688l24.004,11.148l0.198,0.181
c-0.107,0.074-0.2,0.152-0.279,0.235l-31.242-5.701C658.515,509.362,661.02,507.375,664.17,505.688z M673.21,502.168
c1.146-0.316,2.33-0.602,3.548-0.855l12.647,15.264c-0.111,0.028-0.218,0.06-0.32,0.094L673.21,502.168z M677.203,501.222
c3.766-0.755,7.844-1.204,12.11-1.286l1.082,16.492c-0.187,0.011-0.369,0.03-0.544,0.057L677.203,501.222z M689.793,499.928
c0.311-0.004,0.623-0.006,0.935-0.006c4.131,0,8.103,0.346,11.804,0.981l-11.067,15.563c-0.19-0.025-0.388-0.04-0.591-0.045
L689.793,499.928z M702.985,500.982c0.278,0.05,0.543,0.112,0.818,0.165l-0.497,5.535l-10.935,9.99
c-0.142-0.048-0.294-0.09-0.453-0.126L702.985,500.982z M692.678,518.929l9.146,4.247l-0.629,7.002l-9.143-11.035
C692.279,519.085,692.49,519.013,692.678,518.929z M656.683,511.774l31.244,5.702c-0.056,0.1-0.091,0.204-0.102,0.312
l-33.276,0.536c-0.008-0.154-0.012-0.309-0.012-0.464C654.537,515.724,655.295,513.675,656.683,511.774z M687.841,518.026
c0.026,0.114,0.079,0.224,0.155,0.328l-30.225,6.916c-1.892-2.059-3.018-4.325-3.204-6.708L687.841,518.026z M688.199,518.57
c0.106,0.092,0.231,0.178,0.373,0.257l-22.753,12.035c-3.243-1.527-5.915-3.348-7.845-5.376L688.199,518.57z M688.923,518.989
c0.188,0.074,0.394,0.137,0.616,0.186l-11.067,15.563c-4.604-0.824-8.776-2.098-12.301-3.714L688.923,518.989z M689.992,519.254
c0.19,0.024,0.388,0.04,0.591,0.045l1.082,16.493c-0.311,0.004-0.623,0.006-0.936,0.006c-4.131,0-8.102-0.346-11.804-0.98
L689.992,519.254z M691.063,519.291c0.187-0.011,0.369-0.03,0.544-0.057l9.537,11.51l-0.39,4.342
c-2.753,0.394-5.632,0.641-8.61,0.697L691.063,519.291z M640.035,523.229h7.987v-2.08h-1.791v-0.763h-3.52v-1.635l11.136-0.179
c0.189,2.432,1.339,4.745,3.27,6.846l-13.578,3.106C641.982,526.835,640.816,525.059,640.035,523.229z M654.074,536.027
c-4.336-2.149-7.809-4.612-10.333-7.285l13.579-3.107c1.969,2.07,4.697,3.929,8.007,5.487l-10.218,5.404
C654.761,536.362,654.415,536.196,654.074,536.027z M655.459,536.689l10.219-5.405c3.597,1.65,7.855,2.95,12.553,3.791
l-4.97,6.989C666.716,540.907,660.672,539.092,655.459,536.689z M690.728,543.552c-5.882,0-11.614-0.483-17.013-1.409l4.97-6.989
c3.776,0.648,7.828,1.001,12.043,1.001c0.321,0,0.64-0.002,0.959-0.006l0.485,7.393
C691.692,543.549,691.211,543.552,690.728,543.552z M692.653,543.535l-0.485-7.395c2.956-0.057,5.813-0.299,8.553-0.681
l-0.69,7.679C697.611,543.354,695.148,543.49,692.653,543.535z"/>
</g>
</g>
<g id="Object">
<g style="opacity:0.3;">
<linearGradient id="SVGID_1_" gradientUnits="userSpaceOnUse" x1="207.5072" y1="393.376" x2="207.5072" y2="229.7061">
<stop offset="0" style="stop-color:#403E40"/>
<stop offset="0.1275" style="stop-color:#4E4D4E"/>
<stop offset="0.3124" style="stop-color:#5A5A5A"/>
<stop offset="0.5479" style="stop-color:#626262"/>
<stop offset="1" style="stop-color:#646464"/>
</linearGradient>
<polygon style="fill:url(#SVGID_1_);" points="175.04,393.376 239.974,393.376 239.974,259.43 241.819,259.43 241.819,255.601
237.792,255.601 237.792,250.701 205.844,250.701 205.844,243.255 193.638,243.255 191.815,238.393 188.799,238.393
188.799,229.706 188.126,229.706 187.454,229.706 187.454,238.393 181.859,238.393 181.859,243.255 181.859,248.859
181.859,250.701 177.223,250.701 177.223,255.601 173.195,255.601 173.195,259.43 175.04,259.43 "/>
<linearGradient id="SVGID_00000000931258187104496080000017865145222397382034_" gradientUnits="userSpaceOnUse" x1="188.1266" y1="229.7061" x2="188.1266" y2="227.2891">
<stop offset="0" style="stop-color:#403E40"/>
<stop offset="0.1275" style="stop-color:#4E4D4E"/>
<stop offset="0.3124" style="stop-color:#5A5A5A"/>
<stop offset="0.5479" style="stop-color:#626262"/>
<stop offset="1" style="stop-color:#646464"/>
</linearGradient>
<path style="fill:url(#SVGID_00000000931258187104496080000017865145222397382034_);" d="M189.335,228.498
c0-0.668-0.541-1.209-1.209-1.209c-0.667,0-1.208,0.541-1.208,1.209c0,0.667,0.541,1.208,1.208,1.208
C188.794,229.706,189.335,229.165,189.335,228.498z"/>
<linearGradient id="SVGID_00000036247364810958532620000001993945857249512106_" gradientUnits="userSpaceOnUse" x1="508.9194" y1="421.4165" x2="508.9194" y2="155.3276">
<stop offset="0" style="stop-color:#403E40"/>
<stop offset="0.1275" style="stop-color:#4E4D4E"/>
<stop offset="0.3124" style="stop-color:#5A5A5A"/>
<stop offset="0.5479" style="stop-color:#626262"/>
<stop offset="1" style="stop-color:#646464"/>
</linearGradient>
<polygon style="fill:url(#SVGID_00000036247364810958532620000001993945857249512106_);" points="777.689,401.218 777.689,221.14
697.741,204.545 706.501,401.218 471.904,401.218 471.904,289.958 467.586,289.958 467.586,223.38 456.438,223.38
456.438,205.547 452.456,205.547 452.456,179.391 430.514,179.391 430.514,160.168 428.562,160.168 428.562,179.391
424.264,179.391 424.264,155.328 422.311,155.328 422.311,179.391 408.44,179.391 408.44,223.38 401.275,223.38 401.275,289.958
395.133,289.958 395.133,401.218 332.748,401.218 332.748,253.114 333.825,253.114 333.825,251.402 326.147,251.402
326.147,241.111 327.14,241.111 327.14,239.463 320.092,239.463 320.092,233.547 320.725,233.547 320.725,232.282 317.69,232.282
317.69,226.693 314.794,226.693 314.343,226.693 314.343,206.742 314.029,206.742 313.63,206.742 313.228,206.742
313.228,226.693 311.874,226.693 311.874,215.571 311.56,215.571 311.161,215.571 310.759,215.571 310.759,226.693
307.411,226.693 307.411,232.282 303.909,232.282 303.909,233.547 305.009,233.547 305.009,239.463 297.962,239.463
297.962,241.111 298.954,241.111 298.954,251.402 291.276,251.402 291.276,253.114 292.354,253.114 292.354,401.218
84.29,401.218 84.29,421.417 933.548,421.417 933.548,401.218 "/>
</g>
<linearGradient id="SVGID_00000121963338060960119620000016097684000583641491_" gradientUnits="userSpaceOnUse" x1="499.6613" y1="451.2495" x2="499.6613" y2="202.0752">
<stop offset="0.0815" style="stop-color:#403E40"/>
<stop offset="0.4715" style="stop-color:#444244"/>
<stop offset="0.8768" style="stop-color:#504F50"/>
<stop offset="1" style="stop-color:#555455"/>
</linearGradient>
<path style="fill:url(#SVGID_00000121963338060960119620000016097684000583641491_);" d="M918.278,419.4v-18.183h-25.56
l-9.674-71.571h-1.452v-8.598h-5.082v8.598h-1.259v-3.216h-3.557v3.216h-7.369v-7.496h-2.033v-5.209h-14.483v5.209h-2.033v7.496
h-2.033v42.986h-12.874V263.564h-4.235v-23.92h-1.355v23.92h-3.219v-33.181h-1.355v33.181h-2.795v-17.961h-1.355v17.961h-24.985
v77.418h-27.78v50.154h-25.944l-20.601-37.972c3.473-2,6.738-4.405,9.735-7.193l4.225,4.508c-0.907,0.793-1.481,1.957-1.481,3.256
c0,2.388,1.936,4.324,4.324,4.324c2.388,0,4.324-1.936,4.324-4.324c0-2.388-1.936-4.324-4.324-4.324
c-0.916,0-1.764,0.285-2.463,0.771l-4.255-4.54c0.36-0.341,0.717-0.687,1.069-1.039c4.458-4.459,8.028-9.568,10.623-15.114
l4.705,2.172c-0.086,0.339-0.131,0.693-0.131,1.059c0,2.388,1.936,4.324,4.324,4.324c2.388,0,4.324-1.936,4.324-4.324
c0-2.388-1.936-4.324-4.324-4.324c-1.852,0-3.432,1.165-4.048,2.803l-4.648-2.146c2.866-6.273,4.489-13.092,4.744-20.153
l5.065,0.165c0.062,2.334,1.972,4.207,4.321,4.207c2.388,0,4.324-1.936,4.324-4.324c0-2.388-1.936-4.324-4.324-4.324
c-2.266,0-4.123,1.743-4.308,3.961l-5.064-0.165c0.013-0.496,0.021-0.993,0.021-1.491c0-6.303-1.088-12.438-3.173-18.192
l4.231-1.558c0.664,1.533,2.191,2.606,3.968,2.606c2.388,0,4.324-1.936,4.324-4.324c0-2.388-1.936-4.324-4.324-4.324
c-2.388,0-4.324,1.936-4.324,4.324c0,0.44,0.066,0.866,0.189,1.267l-4.229,1.557c-2.41-6.464-6.085-12.437-10.898-17.611
l3.31-3.102c0.776,0.741,1.827,1.197,2.985,1.197c2.388,0,4.324-1.935,4.324-4.324c0-2.388-1.936-4.324-4.324-4.324
c-2.388,0-4.324,1.936-4.324,4.324c0,1.057,0.38,2.025,1.01,2.776l-3.31,3.102c-0.341-0.36-0.687-0.717-1.039-1.069
c-5.012-5.013-10.848-8.903-17.201-11.546l1.821-4.434c0.413,0.131,0.853,0.203,1.309,0.203c2.388,0,4.324-1.936,4.324-4.324
c0-2.388-1.936-4.324-4.324-4.324c-2.388,0-4.324,1.936-4.324,4.324c0,1.762,1.054,3.276,2.566,3.95l-1.816,4.423
c-5.685-2.304-11.775-3.615-18.057-3.842l0.197-6.06c0.046,0.001,0.091,0.003,0.137,0.003c2.388,0,4.324-1.936,4.324-4.324
c0-2.388-1.936-4.324-4.324-4.324c-2.388,0-4.324,1.936-4.324,4.324c0,2.179,1.611,3.98,3.707,4.279l-0.198,6.086
c-0.496-0.014-0.993-0.021-1.491-0.021c-6.048,0-11.942,1.001-17.493,2.925l-1.743-4.946c1.573-0.647,2.68-2.193,2.68-4
c0-2.388-1.936-4.324-4.324-4.324c-2.388,0-4.324,1.936-4.324,4.324c0,2.388,1.936,4.324,4.324,4.324
c0.413,0,0.812-0.059,1.19-0.167l1.744,4.948c-6.732,2.401-12.948,6.166-18.308,11.152l-3.16-3.372
c0.707-0.77,1.139-1.796,1.139-2.923c0-2.388-1.936-4.324-4.324-4.324c-2.388,0-4.324,1.935-4.324,4.324
c0,2.388,1.936,4.324,4.324,4.324c1.088,0,2.081-0.402,2.841-1.065l3.154,3.366c-0.36,0.341-0.717,0.688-1.069,1.04
c-4.458,4.458-8.028,9.568-10.623,15.114l-4.015-1.854c0.146-0.433,0.225-0.896,0.225-1.377c0-2.388-1.936-4.324-4.324-4.324
c-2.388,0-4.324,1.936-4.324,4.324c0,2.388,1.936,4.324,4.324,4.324c1.736,0,3.232-1.023,3.92-2.5l3.992,1.843
c-2.865,6.273-4.489,13.093-4.744,20.153l-5.154-0.167c-0.064-2.333-1.973-4.204-4.321-4.204c-2.388,0-4.324,1.936-4.324,4.324
c0,2.388,1.936,4.324,4.324,4.324c2.267,0,4.125-1.744,4.308-3.963l5.153,0.167c-0.013,0.496-0.021,0.993-0.021,1.491
c0,6.302,1.088,12.438,3.173,18.191l-4.231,1.558c-0.664-1.533-2.191-2.606-3.969-2.606c-2.388,0-4.324,1.936-4.324,4.324
c0,2.388,1.936,4.324,4.324,4.324c2.388,0,4.324-1.936,4.324-4.324c0-0.44-0.066-0.866-0.189-1.266l4.229-1.557
c2.409,6.464,6.084,12.436,10.898,17.611l-3.31,3.102c-0.776-0.741-1.827-1.197-2.985-1.197c-2.388,0-4.324,1.935-4.324,4.324
c0,2.388,1.936,4.324,4.324,4.324c2.388,0,4.324-1.936,4.324-4.324c0-1.057-0.38-2.025-1.01-2.776l3.311-3.102
c0.341,0.36,0.687,0.717,1.039,1.069c3.376,3.375,7.125,6.242,11.154,8.561l-20.601,37.972h-16.685v-25.743h-6.801v-48.882h2.645
v-1.063H564.32v1.063h2.645v24.652h-5.729l-5.467,28.096v-92.143h-2.399v-33.37h-12.075v-12.705h-6.46V220.37h-2.267v-18.294
h-0.586v18.294h-2.267v10.672h-6.46v12.705h-11.982v33.37h-2.491V337.7h-18.579v53.436h-61.639V287.814h4.954v-1.626h-4.954v-2.911
h-6.279v-4.781h-51.354v4.781h-6.279v2.911h-4.954v1.626h4.954v64.319h-8.4v-12.069h-51.819v14.737h-11.298v27.442h-18.294V292.55
h-6.098v-6.099h-4.828v-16.134h-1.017v16.134h-2.287v-20.2h-1.016v20.2h-2.811v6.099h-1.509v89.693h-37.859v-52.597h1.779v-3.557
h-11.688v-9.655h-5.59v9.655h-5.082v-9.655h-5.082v9.655h-9.885v-9.655h-30.261v9.655h-7.066v3.557h4.017v19.819h-11.18v44.72
h-15.346v-11.942h-17.084v26.044H84.29V419.4H53.82v31.849h891.682V419.4H918.278z M716.559,351.896l-7.135-13.152
c2.287-1.349,4.417-2.938,6.354-4.731l10.219,10.906C723.091,347.622,719.925,349.955,716.559,351.896z M689.85,309.7
c0.175,0.055,0.357,0.094,0.544,0.116l-1.082,33.274c-4.266-0.165-8.344-1.071-12.11-2.594L689.85,309.7z M676.758,340.314
c-1.218-0.512-2.402-1.089-3.548-1.726l15.875-29.261c0.102,0.07,0.209,0.133,0.32,0.19L676.758,340.314z M690.874,309.832
c0.203-0.01,0.401-0.041,0.591-0.091l11.067,31.4c-3.701,1.281-7.673,1.978-11.804,1.978c-0.313,0-0.625-0.004-0.936-0.012
L690.874,309.832z M691.917,309.581c0.159-0.072,0.311-0.157,0.453-0.254l15.875,29.261c-1.677,0.932-3.435,1.734-5.261,2.394
L691.917,309.581z M694.589,311.399l20.697,22.088c-1.893,1.751-3.973,3.303-6.206,4.623L694.589,311.399z M693.683,309.731
l-0.598-1.103c0.062-0.086,0.119-0.176,0.172-0.268l30.224,13.952c-1.93,4.091-4.602,7.766-7.844,10.847L693.683,309.731z
M693.46,307.925c0.077-0.21,0.129-0.432,0.156-0.662l33.274,1.082c-0.186,4.809-1.313,9.38-3.204,13.534L693.46,307.925z
M693.631,306.783c-0.011-0.217-0.046-0.428-0.102-0.63l31.244-11.503c1.388,3.836,2.146,7.971,2.146,12.279
c0,0.313-0.004,0.624-0.012,0.936L693.631,306.783z M693.364,305.702c-0.097-0.207-0.217-0.401-0.358-0.579l24.281-22.752
c3.15,3.404,5.654,7.411,7.319,11.828L693.364,305.702z M692.678,304.772c-0.188-0.17-0.399-0.315-0.627-0.433l12.647-30.797
c4.661,1.958,8.83,4.864,12.261,8.477L692.678,304.772z M691.606,304.157c-0.175-0.055-0.357-0.094-0.544-0.116l1.082-33.273
c4.265,0.164,8.344,1.071,12.11,2.594L691.606,304.157z M690.582,304.025c-0.203,0.01-0.401,0.041-0.591,0.09l-11.067-31.4
c3.701-1.281,7.672-1.978,11.804-1.978c0.313,0,0.625,0.004,0.936,0.012L690.582,304.025z M689.539,304.276
c-0.221,0.099-0.428,0.226-0.616,0.375L666.17,280.37c3.525-3.261,7.697-5.832,12.301-7.494L689.539,304.276z M688.572,304.978
c-0.143,0.158-0.268,0.332-0.373,0.518l-30.224-13.953c1.929-4.091,4.602-7.766,7.845-10.847L688.572,304.978z M687.996,305.932
c-0.077,0.211-0.129,0.432-0.156,0.662l-33.274-1.082c0.186-4.809,1.313-9.38,3.204-13.534L687.996,305.932z M687.825,307.075
c0.011,0.217,0.046,0.427,0.102,0.629l-31.244,11.503c-1.388-3.836-2.146-7.97-2.146-12.279c0-0.313,0.004-0.625,0.012-0.936
L687.825,307.075z M688.092,308.155c0.078,0.168,0.171,0.326,0.279,0.474l-0.198,0.365l-24.004,22.492
c-3.15-3.404-5.654-7.411-7.319-11.828L688.092,308.155z M687.446,310.332l-15.07,27.777c-2.912-1.72-5.564-3.835-7.88-6.272
L687.446,310.332z M672.866,339.221c1.169,0.649,2.376,1.238,3.618,1.759l-5.681,13.833c-1.732-0.721-3.424-1.537-5.07-2.445
L672.866,339.221z M676.929,341.163c3.843,1.555,8.006,2.479,12.36,2.647l-0.485,14.92c-6.107-0.221-12.028-1.496-17.555-3.735
L676.929,341.163z M689.769,343.828c0.319,0.008,0.638,0.013,0.959,0.013c4.215,0,8.267-0.712,12.043-2.02l4.97,14.101
c-5.399,1.87-11.131,2.844-17.013,2.844c-0.482,0-0.964-0.007-1.444-0.021L689.769,343.828z M703.225,341.661
c1.862-0.672,3.655-1.49,5.365-2.44l7.133,13.148c-2.417,1.333-4.933,2.468-7.528,3.394L703.225,341.661z M727.382,343.582
c-0.341,0.341-0.686,0.676-1.035,1.007l-10.217-10.904c3.31-3.144,6.038-6.895,8.007-11.07l13.579,6.269
C735.191,334.277,731.718,339.247,727.382,343.582z M737.917,328.448l-13.578-6.268c1.931-4.239,3.081-8.904,3.27-13.813
l14.921,0.485C742.281,315.718,740.702,322.349,737.917,328.448z M742.565,306.929c0,0.482-0.007,0.963-0.02,1.444l-14.917-0.485
c0.008-0.319,0.012-0.639,0.012-0.959c0-4.397-0.774-8.615-2.19-12.528l14.03-5.165
C741.507,294.832,742.565,300.799,742.565,306.929z M728.718,271.66c4.68,5.032,8.253,10.839,10.596,17.124l-14.032,5.167
c-1.699-4.508-4.255-8.598-7.47-12.072L728.718,271.66z M727.382,270.274c0.341,0.341,0.676,0.686,1.007,1.035l-10.904,10.217
c-3.502-3.687-7.756-6.653-12.513-8.65l5.681-13.833C716.831,261.615,722.507,265.399,727.382,270.274z M692.652,255.126
c6.107,0.221,12.028,1.496,17.555,3.735l-5.681,13.833c-3.843-1.555-8.006-2.479-12.36-2.647L692.652,255.126z M690.728,255.092
c0.482,0,0.964,0.007,1.444,0.02l-0.485,14.917c-0.319-0.008-0.638-0.012-0.959-0.012c-4.215,0-8.267,0.712-12.043,2.019
l-4.97-14.101C679.114,256.065,684.846,255.092,690.728,255.092z M673.261,258.094l4.97,14.102
c-4.698,1.696-8.956,4.319-12.553,7.648l-10.219-10.906C660.671,264.091,666.716,260.43,673.261,258.094z M654.074,270.274
c0.341-0.341,0.687-0.676,1.035-1.007l10.218,10.904c-3.31,3.144-6.038,6.895-8.007,11.071l-13.579-6.269
C646.265,279.58,649.738,274.61,654.074,270.274z M643.539,285.409l13.578,6.268c-1.931,4.239-3.081,8.905-3.27,13.813
l-14.921-0.485C639.175,298.14,640.754,291.509,643.539,285.409z M638.891,306.929c0-0.482,0.007-0.964,0.02-1.444l14.917,0.485
c-0.008,0.318-0.012,0.638-0.012,0.959c0,4.396,0.774,8.614,2.19,12.528l-14.03,5.166
C639.949,319.025,638.891,313.058,638.891,306.929z M652.738,342.197c-4.68-5.032-8.253-10.839-10.597-17.124l14.032-5.167
c1.699,4.508,4.255,8.598,7.47,12.072L652.738,342.197z M654.074,343.582c-0.341-0.341-0.676-0.686-1.007-1.035l10.904-10.217
c2.368,2.494,5.082,4.656,8.062,6.414l-7.135,13.152C660.988,349.642,657.35,346.859,654.074,343.582z M665.046,353.636
c1.692,0.934,3.431,1.771,5.21,2.512l-1.821,4.434c-0.413-0.131-0.853-0.202-1.309-0.202c-2.388,0-4.324,1.936-4.324,4.324
c0,2.388,1.936,4.324,4.324,4.324c2.388,0,4.324-1.936,4.324-4.324c0-1.762-1.054-3.276-2.566-3.95l1.816-4.423
c5.685,2.304,11.775,3.615,18.057,3.842l-0.148,4.534c-2.341,0.053-4.224,1.967-4.224,4.321c0,2.388,1.936,4.324,4.324,4.324
c2.388,0,4.324-1.936,4.324-4.324c0-2.26-1.734-4.113-3.944-4.306l0.148-4.535c0.496,0.014,0.993,0.021,1.491,0.021
c6.048,0,11.942-1.001,17.493-2.925l1.554,4.408c-1.471,0.69-2.491,2.184-2.491,3.916c0,2.388,1.936,4.324,4.324,4.324
c2.388,0,4.324-1.936,4.324-4.324c0-2.388-1.936-4.324-4.324-4.324c-0.485,0-0.951,0.081-1.387,0.229l-1.547-4.388
c2.667-0.952,5.252-2.117,7.736-3.487l20.345,37.5h-92.055L665.046,353.636z"/>
<g>
<linearGradient id="SVGID_00000121273610027325662480000007068999652675512506_" gradientUnits="userSpaceOnUse" x1="815.83" y1="285.1626" x2="815.83" y2="287.5796">
<stop offset="0" style="stop-color:#403E40"/>
<stop offset="1" style="stop-color:#161F21"/>
</linearGradient>
<path style="fill:url(#SVGID_00000121273610027325662480000007068999652675512506_);" d="M816.844,286.371
c0-0.667-0.454-1.208-1.014-1.208c-0.56,0-1.014,0.541-1.014,1.208c0,0.668,0.454,1.208,1.014,1.208
C816.39,287.58,816.844,287.039,816.844,286.371z"/>
<linearGradient id="SVGID_00000011738580747612097720000010840228285618223286_" gradientUnits="userSpaceOnUse" x1="500" y1="287.5796" x2="500" y2="451.2495">
<stop offset="0" style="stop-color:#403E40"/>
<stop offset="1" style="stop-color:#161F21"/>
</linearGradient>
<polygon style="fill:url(#SVGID_00000011738580747612097720000010840228285618223286_);" points="927.404,433.241 921.11,391.136
908.236,391.136 908.236,356.197 909.828,356.197 909.828,353.036 903.578,353.036 903.578,339.427 902.815,339.427
902.815,353.036 898.496,353.036 898.496,341.887 897.734,341.887 897.734,353.036 871.695,353.036 871.695,356.197
873.474,356.197 873.474,427.088 843.745,427.088 843.745,395.334 826.815,395.334 826.815,317.303 828.363,317.303
828.363,313.475 824.982,313.475 824.982,308.574 821.091,308.574 821.091,306.732 821.091,301.129 821.091,296.267
816.395,296.267 816.395,287.58 815.83,287.58 815.265,287.58 815.265,296.267 812.734,296.267 811.204,301.129 800.958,301.129
800.958,308.574 774.141,308.574 774.141,313.475 770.76,313.475 770.76,317.303 772.309,317.303 772.309,368.073
749.871,368.073 749.871,417.957 724.973,406.925 724.973,358.507 728.991,358.507 728.991,354.95 721.924,354.95
721.924,345.294 691.664,345.294 691.664,354.95 681.778,354.95 681.778,345.294 676.696,345.294 676.696,354.95 671.615,354.95
671.615,345.294 666.025,345.294 666.025,354.95 654.337,354.95 654.337,358.507 656.115,358.507 656.115,425.617
644.765,425.617 644.765,424.913 642.711,424.913 642.711,405.789 644.765,405.789 644.765,404.255 642.711,404.255
642.711,385.13 644.765,385.13 644.765,383.597 642.711,383.597 642.711,364.472 644.765,364.472 644.765,362.939
642.711,362.939 642.711,343.814 644.765,343.814 644.765,342.28 642.711,342.28 642.711,323.156 644.765,323.156
644.765,321.622 642.711,321.622 642.711,301.83 646.231,301.83 646.231,300.291 648.021,300.291 648.021,296.095
614.595,296.095 614.595,291.429 615.682,291.429 615.682,290.081 597.484,290.081 597.484,291.429 598.571,291.429
598.571,296.095 590.287,296.095 590.287,300.291 592.078,300.291 592.078,301.83 595.598,301.83 595.598,321.622
593.543,321.622 593.543,323.156 595.598,323.156 595.598,342.28 593.543,342.28 593.543,343.814 595.598,343.814
595.598,362.939 593.543,362.939 593.543,364.472 595.598,364.472 595.598,383.597 593.543,383.597 593.543,385.13
595.598,385.13 595.598,404.255 593.543,404.255 593.543,405.789 595.598,405.789 595.598,424.913 593.543,424.913
593.543,426.447 595.598,426.447 595.598,442.075 584.189,442.075 584.189,381.685 538.283,381.685 538.283,425.617
530.83,425.617 530.83,288.763 525.315,288.763 525.315,292.286 515.14,294.775 515.14,292.487 509.625,292.487 509.625,296.124
499.45,298.612 499.45,295.989 493.936,295.989 493.936,299.961 483.76,302.45 483.76,300.286 478.246,300.286 478.246,303.799
468.071,306.288 468.071,304.423 462.557,304.423 462.557,438.816 454.799,438.816 454.799,367.168 426.065,367.168
426.065,411.227 396.608,411.227 396.608,373.655 392.979,373.655 392.979,361.316 395.133,361.316 395.133,360.077
385.601,360.077 385.601,356.197 384.584,356.197 384.584,360.077 381.535,360.077 381.535,340.162 380.773,340.162
380.773,360.077 376.802,360.077 376.802,361.316 379.138,361.316 379.138,373.655 359.697,373.655 359.697,402.316
340.771,402.316 330.886,438.816 311.053,438.816 311.053,319.642 284.794,319.642 284.794,315.373 286.286,315.373
286.286,313.875 266.397,313.875 266.397,315.373 267.961,315.373 267.961,319.642 267.961,326.508 267.961,408.127
255.579,408.127 255.579,377.289 259.98,377.289 259.98,374.497 228.825,374.497 228.825,355.867 222.882,355.867
222.882,352.039 209.616,352.039 209.616,355.867 198.821,355.867 198.821,422.389 163.408,422.389 163.408,373.052
133.589,373.052 133.589,412.021 107.712,412.021 107.712,436.954 89.054,436.954 89.054,405.609 64.517,405.609 64.517,432.333
42.301,432.333 42.301,451.25 64.517,451.25 72.025,451.25 84.29,451.25 89.054,451.25 107.712,451.25 133.589,451.25
142.285,451.25 146.941,451.25 163.408,451.25 198.821,451.25 209.616,451.25 222.882,451.25 228.825,451.25 252.067,451.25
255.579,451.25 267.961,451.25 288.725,451.25 298.954,451.25 311.053,451.25 359.697,451.25 370.931,451.25 389.895,451.25
396.608,451.25 426.568,451.25 434.618,451.25 462.557,451.25 465.314,451.25 468.071,451.25 478.246,451.25 483.76,451.25
493.936,451.25 498.545,451.25 499.45,451.25 509.625,451.25 515.14,451.25 523.887,451.25 525.315,451.25 528.072,451.25
530.83,451.25 538.283,451.25 576.227,451.25 581.647,451.25 584.189,451.25 595.598,451.25 615.682,451.25 634.069,451.25
642.711,451.25 656.115,451.25 689.405,451.25 724.606,451.25 724.973,451.25 749.871,451.25 763.792,451.25 772.309,451.25
777.689,451.25 819.353,451.25 826.815,451.25 835.736,451.25 843.745,451.25 873.474,451.25 895.58,451.25 900.105,451.25
908.236,451.25 957.698,451.25 957.698,433.241 "/>
</g>
</g>
</svg>
Side by Side

Before

Width:  |  Height:  |  Size: 28 KiB

BIN
frontend/assets/launcher/icon.png
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 24 KiB

161
frontend/assets/plan.svg
View File

@@ -1,161 +0,0 @@
<?xml version="1.0" encoding="utf-8"?>
<!-- Generator: Adobe Illustrator 27.5.0, SVG Export Plug-In . SVG Version: 6.00 Build 0) -->
<svg version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
viewBox="0 0 2200 2200" style="enable-background:new 0 0 2200 2200;" xml:space="preserve">
<g id="Objects">
<g>
<path style="fill:#788D8E;" d="M1202.178,2002.073c-5.328,0-9.648-4.319-9.649-9.647c-0.001-5.328,4.319-9.649,9.647-9.649
c9.63-0.001,19.271-0.006,28.918-0.014c0.003,0,0.006,0,0.009,0c5.325,0,9.643,4.314,9.647,9.639
c0.005,5.328-4.311,9.651-9.639,9.656C1221.458,2002.068,1211.813,2002.072,1202.178,2002.073z M1144.298,2002.03
c-0.006,0-0.01,0-0.016,0c-9.658-0.015-19.305-0.036-28.94-0.061c-5.328-0.014-9.636-4.345-9.622-9.673
c0.014-5.319,4.331-9.622,9.648-9.622c0.008,0,0.017,0,0.025,0c9.628,0.025,19.269,0.046,28.919,0.061
c5.328,0.009,9.641,4.335,9.632,9.663C1153.937,1997.721,1149.619,2002.03,1144.298,2002.03z M1288.979,2001.966
c-5.317,0-9.634-4.306-9.647-9.626c-0.012-5.328,4.298-9.657,9.626-9.669c9.634-0.022,19.274-0.047,28.923-0.075
c5.297,0.018,9.66,4.292,9.676,9.619c0.015,5.328-4.291,9.66-9.619,9.676c-9.652,0.028-19.299,0.054-28.936,0.075
C1288.994,2001.966,1288.986,2001.966,1288.979,2001.966z M1057.498,2001.759c-0.015,0-0.03,0-0.045,0
c-9.659-0.044-19.306-0.095-28.939-0.152c-5.328-0.031-9.622-4.376-9.591-9.704c0.031-5.309,4.344-9.591,9.646-9.591
c0.02,0,0.04,0,0.058,0c9.625,0.057,19.263,0.107,28.914,0.152c5.328,0.025,9.628,4.364,9.603,9.692
C1067.12,1997.468,1062.805,2001.759,1057.498,2001.759z M1375.787,2001.691c-5.31,0-9.625-4.293-9.647-9.609
c-0.022-5.328,4.281-9.665,9.609-9.686c9.636-0.039,19.28-0.079,28.928-0.123c0.015,0,0.029,0,0.044,0
c5.308,0,9.623,4.291,9.647,9.604c0.024,5.328-4.276,9.666-9.604,9.691c-9.651,0.043-19.297,0.084-28.937,0.122
C1375.813,2001.691,1375.8,2001.691,1375.787,2001.691z M1462.601,2001.29c-5.305,0-9.619-4.287-9.647-9.598
c-0.027-5.328,4.27-9.67,9.598-9.698c9.641-0.05,19.285-0.102,28.932-0.156c0.018,0,0.037,0,0.056,0
c5.302,0,9.616,4.284,9.646,9.594c0.029,5.328-4.266,9.671-9.594,9.701c-9.649,0.054-19.297,0.105-28.94,0.156
C1462.635,2001.29,1462.618,2001.29,1462.601,2001.29z M970.707,2001.198c-0.027,0-0.054,0-0.081,0
c-9.662-0.079-19.308-0.166-28.939-0.259c-5.328-0.052-9.605-4.413-9.554-9.741c0.052-5.296,4.361-9.554,9.646-9.554
c0.032,0,0.063,0,0.095,0.001c9.621,0.093,19.258,0.179,28.911,0.258c5.328,0.044,9.612,4.399,9.568,9.727
C980.31,1996.93,975.998,2001.198,970.707,2001.198z M1549.42,2000.802c-5.301,0-9.614-4.281-9.646-9.59
c-0.032-5.328,4.262-9.673,9.59-9.705l28.936-0.176c0.021,0,0.042,0,0.061,0c5.301,0,9.614,4.279,9.647,9.587
c0.033,5.328-4.259,9.674-9.587,9.708l-28.942,0.176C1549.459,2000.802,1549.439,2000.802,1549.42,2000.802z M883.923,2000.297
c-0.041,0-0.083,0-0.124-0.001c-9.663-0.122-19.308-0.25-28.935-0.385c-5.328-0.075-9.586-4.454-9.511-9.782
c0.075-5.328,4.464-9.604,9.782-9.511c9.617,0.136,19.254,0.264,28.907,0.385c5.328,0.067,9.592,4.44,9.525,9.768
C893.501,1996.057,889.195,2000.297,883.923,2000.297z M1636.244,2000.264c-5.3,0-9.612-4.279-9.646-9.586
c-0.034-5.328,4.258-9.675,9.586-9.709l28.943-0.184c0.021,0,0.042,0,0.062,0c5.3,0,9.613,4.279,9.647,9.586
c0.034,5.328-4.259,9.675-9.586,9.709l-28.943,0.184C1636.286,2000.264,1636.265,2000.264,1636.244,2000.264z M1723.075,1999.719
c-5.3,0-9.614-4.28-9.647-9.588c-0.033-5.328,4.26-9.674,9.588-9.707l28.946-0.177c0.02,0,0.04,0,0.059,0
c5.301,0,9.615,4.28,9.647,9.589c0.032,5.328-4.261,9.674-9.589,9.706l-28.945,0.177
C1723.115,1999.719,1723.095,1999.719,1723.075,1999.719z M1809.912,1999.201c-5.303,0-9.616-4.283-9.647-9.593
c-0.03-5.328,4.265-9.672,9.593-9.702l28.952-0.16c0.018,0,0.036,0,0.053,0c5.304,0,9.618,4.285,9.647,9.596
c0.028,5.328-4.268,9.67-9.596,9.699l-28.947,0.16C1809.949,1999.201,1809.931,1999.201,1809.912,1999.201z M797.15,1998.997
c-0.057,0-0.115-0.001-0.172-0.002c-9.665-0.169-19.31-0.346-28.935-0.531c-5.327-0.103-9.562-4.504-9.459-9.832
c0.104-5.327,4.48-9.533,9.832-9.459c9.612,0.186,19.245,0.363,28.899,0.531c5.327,0.092,9.57,4.487,9.477,9.814
C806.701,1994.788,802.399,1998.997,797.15,1998.997z M1896.756,1998.75c-5.307,0-9.621-4.29-9.647-9.602
c-0.025-5.328,4.274-9.667,9.602-9.693c9.658-0.045,19.31-0.089,28.958-0.129c0.014,0,0.027,0,0.041,0
c5.309,0,9.624,4.292,9.647,9.607c0.023,5.328-4.279,9.666-9.607,9.688c-9.644,0.041-19.294,0.084-28.948,0.129
C1896.787,1998.75,1896.771,1998.75,1896.756,1998.75z M710.388,1997.237c-0.076,0-0.152-0.001-0.228-0.003
c-9.667-0.223-19.311-0.457-28.932-0.7c-5.326-0.135-9.535-4.562-9.401-9.889c0.133-5.243,4.425-9.404,9.64-9.404
c0.083,0,0.166,0.001,0.249,0.003c9.607,0.243,19.237,0.477,28.891,0.7c5.327,0.122,9.545,4.541,9.421,9.868
C719.907,1993.064,715.612,1997.237,710.388,1997.237z M623.642,1994.948c-0.096,0-0.193-0.001-0.29-0.004
c-9.67-0.287-19.314-0.585-28.929-0.894c-5.326-0.172-9.503-4.628-9.333-9.953c0.171-5.325,4.65-9.465,9.953-9.333
c9.6,0.309,19.227,0.607,28.88,0.892c5.326,0.158,9.516,4.604,9.357,9.93C633.126,1990.815,628.838,1994.948,623.642,1994.948z
M536.937,1991.683c-0.226,0-0.453-0.007-0.682-0.024c-10.955-0.765-20.624-1.8-29.559-3.167
c-5.267-0.806-8.883-5.728-8.078-10.995c0.805-5.268,5.725-8.888,10.995-8.078c8.408,1.286,17.563,2.264,27.985,2.992
c5.316,0.371,9.323,4.981,8.952,10.296C546.196,1987.794,541.959,1991.683,536.937,1991.683z M452.936,1972.044
c-1.444,0-2.91-0.325-4.291-1.012c-5.129-2.553-10.134-5.398-14.875-8.458c-3.821-2.466-7.597-5.17-11.224-8.036
c-4.179-3.305-4.889-9.372-1.585-13.552c3.305-4.18,9.371-4.889,13.552-1.585c3.144,2.486,6.415,4.828,9.719,6.961
c4.142,2.673,8.519,5.162,13.01,7.397c4.77,2.373,6.713,8.165,4.34,12.935C459.893,1970.083,456.481,1972.044,452.936,1972.044z
M390.843,1913.094c-3.257,0-6.436-1.65-8.252-4.636c-5.088-8.366-9.753-17.406-13.866-26.869
c-2.125-4.886,0.115-10.57,5.002-12.693c4.884-2.125,10.569,0.114,12.694,5.002c3.765,8.661,8.023,16.915,12.657,24.534
c2.768,4.552,1.323,10.487-3.23,13.256C394.28,1912.639,392.55,1913.094,390.843,1913.094z M360.798,1832.103
c-4.545,0-8.593-3.227-9.469-7.857c-1.832-9.681-3.208-19.642-4.092-29.605c-0.471-5.307,3.45-9.991,8.757-10.463
c5.305-0.466,9.991,3.449,10.462,8.757c0.828,9.335,2.117,18.663,3.831,27.724c0.99,5.236-2.451,10.282-7.686,11.272
C361.996,1832.047,361.393,1832.103,360.798,1832.103z M357.3,1745.636c-0.337,0-0.677-0.018-1.02-0.054
c-5.299-0.557-9.143-5.304-8.586-10.603c1.051-10.004,2.612-19.95,4.636-29.561c1.098-5.214,6.217-8.55,11.429-7.451
c5.214,1.099,8.55,6.215,7.451,11.429c-1.889,8.965-3.345,18.252-4.327,27.6C366.362,1741.952,362.175,1745.636,357.3,1745.636z
M379.592,1662.099c-1.292,0-2.604-0.261-3.863-0.812c-4.881-2.136-7.107-7.824-4.97-12.706
c3.967-9.067,8.429-18.064,13.26-26.742c2.591-4.655,8.466-6.329,13.122-3.737c4.656,2.592,6.329,8.466,3.737,13.122
c-4.533,8.143-8.72,16.585-12.442,25.091C386.85,1659.939,383.308,1662.099,379.592,1662.099z M425.149,1588.527
c-2.174,0-4.36-0.73-6.162-2.228c-4.097-3.407-4.658-9.489-1.252-13.587c6.291-7.567,13.033-14.977,20.039-22.024
c3.756-3.78,9.865-3.796,13.644-0.039c3.778,3.756,3.796,9.865,0.039,13.643c-6.604,6.642-12.958,13.624-18.884,20.754
C430.666,1587.342,427.917,1588.527,425.149,1588.527z M488.757,1529.81c-3.021,0-5.994-1.414-7.875-4.063
c-3.085-4.345-2.062-10.367,2.282-13.452c8.036-5.705,16.424-11.148,24.935-16.182c4.587-2.711,10.502-1.194,13.215,3.393
c2.712,4.586,1.194,10.503-3.393,13.215c-8.051,4.762-15.987,9.912-23.588,15.308
C492.639,1529.232,490.688,1529.81,488.757,1529.81z M565.026,1488.849c-3.858,0-7.5-2.33-8.989-6.14
c-1.939-4.962,0.513-10.558,5.476-12.497c8.996-3.515,18.386-6.814,27.91-9.805c5.085-1.593,10.499,1.23,12.094,6.314
c1.597,5.083-1.229,10.498-6.313,12.095c-9.107,2.86-18.081,6.012-26.67,9.368
C567.381,1488.636,566.194,1488.849,565.026,1488.849z M648.419,1465.236c-4.529,0-8.569-3.204-9.462-7.816
c-1.012-5.232,2.408-10.293,7.639-11.306c9.243-1.788,18.981-3.446,28.945-4.926c5.268-0.786,10.177,2.855,10.961,8.125
c0.783,5.27-2.855,10.178-8.125,10.961c-9.686,1.439-19.145,3.049-28.114,4.784
C649.644,1465.178,649.027,1465.236,648.419,1465.236z M734.448,1453.776c-4.949,0-9.161-3.786-9.599-8.809
c-0.464-5.308,3.463-9.987,8.771-10.45c8.927-0.779,18.419-1.521,29.019-2.265c5.285-0.369,9.926,3.633,10.3,8.948
c0.373,5.315-3.633,9.926-8.948,10.299c-10.489,0.737-19.875,1.469-28.691,2.239
C735.013,1453.764,734.729,1453.776,734.448,1453.776z M821.074,1447.866c-5.059,0-9.308-3.941-9.621-9.059
c-0.325-5.318,3.722-9.893,9.041-10.219l28.889-1.767c5.341-0.318,9.893,3.723,10.219,9.041c0.325,5.318-3.722,9.893-9.041,10.219
l-28.889,1.766C821.471,1447.86,821.272,1447.866,821.074,1447.866z M907.74,1442.568c-5.059,0-9.308-3.941-9.621-9.059
c-0.325-5.318,3.722-9.893,9.041-10.219l28.889-1.766c5.344-0.326,9.893,3.723,10.219,9.041c0.325,5.318-3.722,9.893-9.041,10.218
l-28.889,1.767C908.137,1442.562,907.938,1442.568,907.74,1442.568z M994.406,1437.269c-5.059,0-9.307-3.941-9.62-9.059
c-0.325-5.318,3.722-9.893,9.041-10.219l28.889-1.766c5.345-0.309,9.893,3.722,10.219,9.041c0.325,5.318-3.722,9.893-9.041,10.219
l-28.889,1.767C994.805,1437.263,994.604,1437.269,994.406,1437.269z M1081.072,1431.969c-5.059,0-9.307-3.941-9.62-9.059
c-0.325-5.318,3.722-9.893,9.041-10.219l28.889-1.766c5.322-0.318,9.894,3.723,10.219,9.041c0.325,5.318-3.722,9.893-9.041,10.219
l-28.889,1.767C1081.47,1431.964,1081.27,1431.969,1081.072,1431.969z M1167.738,1426.671c-5.059,0-9.307-3.941-9.62-9.059
c-0.325-5.318,3.722-9.893,9.041-10.219l28.889-1.767c5.349-0.326,9.894,3.723,10.219,9.041c0.325,5.318-3.722,9.893-9.041,10.219
l-28.889,1.766C1168.135,1426.665,1167.936,1426.671,1167.738,1426.671z M1254.358,1420.693c-4.914,0-9.115-3.738-9.592-8.73
c-0.508-5.304,3.38-10.015,8.685-10.523c10.205-0.975,19.481-2.043,28.357-3.264c5.276-0.72,10.146,2.966,10.872,8.244
c0.725,5.279-2.966,10.146-8.244,10.872c-9.142,1.257-18.676,2.354-29.148,3.356
C1254.976,1420.678,1254.665,1420.693,1254.358,1420.693z M1339.835,1406.05c-4.233,0-8.115-2.807-9.295-7.086
c-1.416-5.136,1.6-10.448,6.735-11.864c9.242-2.549,18.223-5.456,26.694-8.639c4.994-1.875,10.551,0.65,12.425,5.636
c1.875,4.988-0.649,10.55-5.636,12.425c-9.014,3.388-18.553,6.476-28.353,9.178
C1341.548,1405.937,1340.684,1406.05,1339.835,1406.05z M1418.99,1371.212c-3.131,0-6.201-1.522-8.057-4.329
c-2.938-4.445-1.716-10.43,2.729-13.368c7.542-4.985,14.948-10.622,22.014-16.754c4.022-3.493,10.115-3.063,13.609,0.962
c3.493,4.024,3.062,10.117-0.962,13.609c-7.698,6.683-15.781,12.832-24.022,18.28
C1422.663,1370.694,1420.817,1371.212,1418.99,1371.212z M1480.743,1310.868c-1.931,0-3.882-0.578-5.577-1.782
c-4.344-3.086-5.366-9.108-2.281-13.452c5.366-7.558,10.318-15.487,14.721-23.567c2.549-4.679,8.41-6.406,13.087-3.856
c4.679,2.549,6.406,8.409,3.856,13.087c-4.765,8.747-10.126,17.328-15.932,25.506
C1486.737,1309.454,1483.762,1310.868,1480.743,1310.868z M1517.216,1232.602c-0.778,0-1.568-0.094-2.356-0.292
c-5.169-1.297-8.306-6.538-7.009-11.707c2.256-8.984,3.898-18.121,4.878-27.155c0.575-5.299,5.342-9.13,10.632-8.55
c5.298,0.575,9.126,5.335,8.55,10.633c-1.076,9.911-2.875,19.928-5.346,29.771
C1525.467,1229.681,1521.535,1232.602,1517.216,1232.602z M1520.169,1146.498c-4.554,0-8.606-3.239-9.472-7.879
c-1.672-8.957-4.032-17.905-7.015-26.593c-1.731-5.04,0.953-10.527,5.992-12.257c5.036-1.729,10.528,0.953,12.257,5.992
c3.287,9.575,5.889,19.438,7.733,29.318c0.978,5.237-2.475,10.276-7.713,11.254
C1521.352,1146.444,1520.757,1146.498,1520.169,1146.498z M1486.631,1067.128c-3.053,0-6.055-1.445-7.93-4.142
c-5.223-7.514-11.025-14.791-17.243-21.63c-3.585-3.942-3.295-10.044,0.647-13.629c3.942-3.584,10.044-3.295,13.628,0.648
c6.781,7.457,13.11,15.396,18.811,23.597c3.041,4.375,1.961,10.387-2.415,13.428
C1490.452,1066.568,1488.532,1067.128,1486.631,1067.128z M1424.593,1007.152c-1.794,0-3.608-0.499-5.227-1.546
c-7.728-4.995-15.75-9.441-23.845-13.217l-0.314-0.146c-4.827-2.257-6.911-7.999-4.653-12.826c2.257-4.826,8-6.91,12.825-4.653
l0.276,0.129c8.915,4.158,17.716,9.036,26.183,14.508c4.475,2.892,5.758,8.864,2.866,13.339
C1430.859,1005.596,1427.759,1007.152,1424.593,1007.152z M1344.533,974.803c-0.7,0-1.41-0.076-2.123-0.236
c-8.759-1.966-18.077-3.643-27.693-4.985c-5.277-0.736-8.958-5.611-8.222-10.888c0.737-5.277,5.615-8.957,10.888-8.222
c10.137,1.415,19.98,3.187,29.254,5.268c5.199,1.167,8.467,6.327,7.3,11.527C1352.93,971.754,1348.947,974.803,1344.533,974.803z
M274.324,966.773c-5.318,0-9.634-4.305-9.647-9.625c-0.012-5.328,4.297-9.657,9.625-9.67l28.943-0.066c0.008,0,0.016,0,0.023,0
c5.317,0,9.634,4.305,9.647,9.625c0.012,5.328-4.297,9.657-9.625,9.67l-28.943,0.066
C274.339,966.773,274.331,966.773,274.324,966.773z M361.152,966.573c-5.318,0-9.635-4.305-9.647-9.625
c-0.012-5.328,4.297-9.657,9.625-9.67l28.943-0.066c5.338-0.002,9.657,4.297,9.67,9.625s-4.297,9.657-9.625,9.67l-28.943,0.066
C361.167,966.573,361.16,966.573,361.152,966.573z M447.979,966.373c-5.318,0-9.634-4.305-9.647-9.625
c-0.012-5.328,4.297-9.657,9.625-9.67l28.942-0.066c0.008,0,0.015,0,0.023,0c5.318,0,9.635,4.305,9.648,9.625
c0.012,5.328-4.297,9.657-9.626,9.67l-28.942,0.066C447.995,966.373,447.987,966.373,447.979,966.373z M534.808,966.174
c-5.318,0-9.635-4.305-9.648-9.625c-0.012-5.328,4.297-9.657,9.625-9.67l28.943-0.066c0.008,0,0.015,0,0.023,0
c5.318,0,9.635,4.305,9.648,9.625c0.012,5.328-4.297,9.657-9.626,9.67l-28.943,0.066
C534.823,966.174,534.815,966.174,534.808,966.174z M621.636,965.973c-5.318,0-9.635-4.305-9.648-9.625
c-0.012-5.328,4.297-9.657,9.625-9.67l28.943-0.066c0.008,0,0.015,0,0.023,0c5.318,0,9.635,4.305,9.648,9.625
c0.012,5.328-4.297,9.657-9.626,9.67l-28.943,0.066C621.651,965.973,621.643,965.973,621.636,965.973z M708.463,965.774
c-5.318,0-9.635-4.305-9.648-9.625c-0.012-5.328,4.297-9.657,9.625-9.67l28.943-0.066c0.008,0,0.015,0,0.023,0
c5.318,0,9.635,4.305,9.648,9.625c0.012,5.328-4.297,9.657-9.626,9.67l-28.943,0.066
C708.478,965.774,708.471,965.774,708.463,965.774z M795.291,965.574c-5.318,0-9.635-4.305-9.648-9.625
c-0.012-5.328,4.297-9.657,9.625-9.67l28.943-0.066c5.292,0.018,9.658,4.297,9.67,9.625c0.012,5.328-4.297,9.657-9.626,9.67
l-28.943,0.066C795.306,965.574,795.299,965.574,795.291,965.574z M882.12,965.374c-5.318,0-9.635-4.305-9.648-9.625
c-0.012-5.328,4.297-9.657,9.625-9.67l28.943-0.066c0.008,0,0.016,0,0.024,0c5.317,0,9.634,4.305,9.647,9.625
c0.012,5.328-4.297,9.657-9.626,9.67l-28.943,0.066C882.135,965.374,882.127,965.374,882.12,965.374z M968.947,965.174
c-5.318,0-9.635-4.305-9.648-9.625c-0.012-5.328,4.297-9.657,9.625-9.67l28.943-0.066c0.008,0,0.015,0,0.023,0
c5.318,0,9.635,4.305,9.648,9.625c0.012,5.328-4.297,9.657-9.626,9.67l-28.943,0.066
C968.962,965.174,968.955,965.174,968.947,965.174z M1258.376,965.13c-0.103,0-0.204-0.001-0.307-0.005
c-8.629-0.27-17.745-0.424-28.69-0.483c-5.328-0.029-9.623-4.372-9.595-9.7c0.029-5.31,4.342-9.595,9.647-9.595
c0.018,0,0.036,0,0.054,0c11.114,0.06,20.389,0.216,29.188,0.492c5.326,0.167,9.508,4.619,9.341,9.945
C1267.849,961.007,1263.564,965.13,1258.376,965.13z M1055.775,964.974c-5.318,0-9.635-4.305-9.648-9.625
c-0.012-5.328,4.297-9.657,9.625-9.67l28.943-0.066c0.008,0,0.016,0,0.024,0c5.318,0,9.634,4.305,9.647,9.625
c0.012,5.328-4.297,9.657-9.626,9.67l-28.943,0.066C1055.79,964.974,1055.783,964.974,1055.775,964.974z M1142.603,964.775
c-5.318,0-9.635-4.305-9.648-9.625c-0.012-5.328,4.297-9.657,9.625-9.67l28.943-0.066c0.007,0,0.015,0,0.023,0
c5.318,0,9.635,4.305,9.648,9.625c0.012,5.328-4.297,9.657-9.626,9.67l-28.943,0.066
C1142.619,964.775,1142.611,964.775,1142.603,964.775z"/>
<path style="fill:#D13737;" d="M462.452,197.927c-156.647,0-283.638,126.991-283.638,283.638
c0,251.801,283.638,464.048,283.638,464.048S746.09,733.366,746.09,481.565C746.09,324.918,619.099,197.927,462.452,197.927z
M462.316,679.485c-109.374,0-198.045-88.671-198.045-198.055c0-109.374,88.671-198.045,198.045-198.045
c109.384,0,198.055,88.671,198.055,198.045C660.371,590.814,571.701,679.485,462.316,679.485z"/>
<path style="fill:#18ACB7;" d="M1737.548,1228.212c-156.647,0-283.638,126.991-283.638,283.638
c0,251.801,283.638,464.048,283.638,464.048s283.638-212.246,283.638-464.048
C2021.187,1355.203,1894.196,1228.212,1737.548,1228.212z M1737.413,1709.77c-109.374,0-198.045-88.671-198.045-198.055
c0-109.374,88.671-198.045,198.045-198.045c109.384,0,198.055,88.671,198.055,198.045
C1935.468,1621.1,1846.797,1709.77,1737.413,1709.77z"/>
</g>
</g>
</svg>
Side by Side

Before

Width:  |  Height:  |  Size: 16 KiB

3
frontend/devtools_options.yaml
View File

@@ -1,3 +0,0 @@
description: This file stores settings for Dart & Flutter DevTools.
documentation: https://docs.flutter.dev/tools/devtools/extensions#configure-extension-enablement-states
extensions:

17
frontend/flutter_launcher_icons.yaml
View File

@@ -1,17 +0,0 @@
flutter_launcher_icons:
image_path: "assets/launcher/icon.png"
# Android section
android: true
min_sdk_android: 21 # android min sdk min:16, default 21
# iOS section
ios: true
remove_alpha_ios: true # app store rejecs icons with alpha
# Web section
web:
generate: true
image_path: "assets/launcher/icon.png"
# background_color: "#hexcode"
# theme_color: "#hexcode"

4
frontend/ios/Runner.xcodeproj/project.pbxproj
View File

@@ -427,7 +427,7 @@
isa = XCBuildConfiguration;
buildSettings = {
ALWAYS_SEARCH_USER_PATHS = NO;
ASSETCATALOG_COMPILER_GENERATE_SWIFT_ASSET_SYMBOL_EXTENSIONS = AppIcon;
ASSETCATALOG_COMPILER_GENERATE_SWIFT_ASSET_SYMBOL_EXTENSIONS = YES;
CLANG_ANALYZER_NONNULL = YES;
CLANG_CXX_LANGUAGE_STANDARD = "gnu++0x";
CLANG_CXX_LIBRARY = "libc++";
@@ -484,7 +484,7 @@
isa = XCBuildConfiguration;
buildSettings = {
ALWAYS_SEARCH_USER_PATHS = NO;
ASSETCATALOG_COMPILER_GENERATE_SWIFT_ASSET_SYMBOL_EXTENSIONS = AppIcon;
ASSETCATALOG_COMPILER_GENERATE_SWIFT_ASSET_SYMBOL_EXTENSIONS = YES;
CLANG_ANALYZER_NONNULL = YES;
CLANG_CXX_LANGUAGE_STANDARD = "gnu++0x";
CLANG_CXX_LIBRARY = "libc++";

BIN
frontend/ios/Runner/Assets.xcassets/AppIcon.appiconset/Icon-App-1024x1024@1x.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 97 KiB

After

Width:  |  Height:  |  Size: 11 KiB

BIN
frontend/ios/Runner/Assets.xcassets/AppIcon.appiconset/Icon-App-20x20@1x.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 738 B

After

Width:  |  Height:  |  Size: 295 B

BIN
frontend/ios/Runner/Assets.xcassets/AppIcon.appiconset/Icon-App-20x20@2x.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 1.6 KiB

After

Width:  |  Height:  |  Size: 406 B

BIN
frontend/ios/Runner/Assets.xcassets/AppIcon.appiconset/Icon-App-20x20@3x.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 2.5 KiB

After

Width:  |  Height:  |  Size: 450 B

BIN
frontend/ios/Runner/Assets.xcassets/AppIcon.appiconset/Icon-App-29x29@1x.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 1.1 KiB

After

Width:  |  Height:  |  Size: 282 B

BIN
frontend/ios/Runner/Assets.xcassets/AppIcon.appiconset/Icon-App-29x29@2x.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 2.3 KiB

After

Width:  |  Height:  |  Size: 462 B

BIN
frontend/ios/Runner/Assets.xcassets/AppIcon.appiconset/Icon-App-29x29@3x.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 3.8 KiB

After

Width:  |  Height:  |  Size: 704 B

Some files were not shown because too many files have changed in this diff Show More