typo

remove deleted library from API
Merge remote-tracking branch 'origin/main'
2025-01-19 14:23:02 +01:00 · 2025-01-19 14:17:57 +01:00 · 2025-01-19 14:12:59 +01:00 · 2025-01-19 14:12:36 +01:00 · 2025-01-19 13:14:35 +01:00 · 2025-01-19 12:44:52 +01:00
1450 changed files with 5902 additions and 165101 deletions
--- a/etl/.gitattributes
+++ b/etl/.gitattributes
--- a/.gitignore
+++ b/.gitignore
@ -23,6 +23,7 @@
 *.ipr
 .idea/

+
 # eclipse project file
 .settings/
 .classpath
@ -65,3 +66,8 @@ env3.*/
 # duckdb
 *.duckdb

+# cache
+*.obj
+
+/src/mauro/dok/
+
--- a/README.md
+++ b/README.md
@ -0,0 +1,6 @@
+# Consultancy 2
+
+## Projektstruktur
+- etl: Enthält den Programmcode, welcher die Daten aufbereitet und via REST-API zur Verfügung stellt.
+- dashboard: Webapplikation zur Exploration und Visualisierung der Daten.
+
--- a/dashboard/.editorconfig
+++ b/dashboard/.editorconfig
--- a/dashboard/.env.example
+++ b/dashboard/.env.example
@ -21,14 +21,14 @@ LOG_STACK=single
 LOG_DEPRECATIONS_CHANNEL=null
 LOG_LEVEL=debug

-DB_CONNECTION=sqlite
+# DB_CONNECTION=sqlite
 # DB_HOST=127.0.0.1
 # DB_PORT=3306
 # DB_DATABASE=laravel
 # DB_USERNAME=root
 # DB_PASSWORD=

-SESSION_DRIVER=database
+SESSION_DRIVER=file
 SESSION_LIFETIME=120
 SESSION_ENCRYPT=false
 SESSION_PATH=/
@ -38,7 +38,7 @@ BROADCAST_CONNECTION=log
 FILESYSTEM_DISK=local
 QUEUE_CONNECTION=database

-CACHE_STORE=database
+CACHE_STORE=file
 CACHE_PREFIX=

 MEMCACHED_HOST=127.0.0.1
@ -49,11 +49,11 @@ REDIS_PASSWORD=null
 REDIS_PORT=6379

 MAIL_MAILER=log
+MAIL_SCHEME=null
 MAIL_HOST=127.0.0.1
 MAIL_PORT=2525
 MAIL_USERNAME=null
 MAIL_PASSWORD=null
-MAIL_ENCRYPTION=null
 MAIL_FROM_ADDRESS="hello@example.com"
 MAIL_FROM_NAME="${APP_NAME}"

@ -64,3 +64,5 @@ AWS_BUCKET=
 AWS_USE_PATH_STYLE_ENDPOINT=false

 VITE_APP_NAME="${APP_NAME}"
+
+FASTAPI_URI=http://localhost:8080
--- a/dashboard/.gitattributes
+++ b/dashboard/.gitattributes
--- a/dashboard/.gitignore
+++ b/dashboard/.gitignore
--- a/dashboard/README.md
+++ b/dashboard/README.md
@ -0,0 +1,16 @@
+# Install
+## Prerequisites
+- In order to run this project please install all required software according to the laravel documentation: https://laravel.com/docs/11.x#installing-php
+
+## Configuration & installation
+- Make a copy of the .env.example to .env
+- Run the following commands:
+```bash
+composer install && php artisan key:generate && npm i
+```
+
+# Run server
+```bash
+composer run dev
+```
+
--- a/dashboard/app/Api.php
+++ b/dashboard/app/Api.php
@ -0,0 +1,115 @@
+<?php
+
+namespace App;
+
+use Illuminate\Support\Facades\Cache;
+use Illuminate\Support\Facades\Http;
+
+/*
+ * Class contains methods which make calls to the API.
+ * Successfull calls get cached.
+ */
+
+class Api
+{
+
+    public static function get(string $path, string $query = ''): ?array
+    {
+
+        $endpoint = env('FASTAPI_URI');
+        $request = $endpoint.$path;
+
+        // load from cache if available
+        if (Cache::has($request)) {
+            return Cache::get($request);
+        }
+
+        // Set timeout to .5h
+        $get = Http::timeout(1800)->get($request);
+
+        // return result and cache it
+        if($get->successful()){
+            $result = $get->json();
+            Cache::put($request, $result);
+            return $result;
+        }
+
+        return null;
+    }
+
+    public static function propertiesGrowth(): mixed
+    {
+        return self::get('/properties/growth');
+    }
+
+     public static function propertiesGeo(): mixed
+    {
+        return self::get('/properties/geo');
+    }
+
+    public static function propertyExtractions(int $id): mixed
+    {
+        return self::get("/properties/{$id}/extractions");
+    }
+
+    public static function propertyCapacities(int $id): mixed
+    {
+        return self::get("/properties/{$id}/capacities");
+    }
+
+    public static function propertyBase(int $id): mixed
+    {
+        return self::get("/properties/{$id}/base");
+    }
+
+    public static function propertyCapacitiesMonthly(int $id, string $date): mixed
+    {
+        return self::get("/properties/{$id}/capacities/monthly/{$date}");
+    }
+
+    public static function propertyCapacitiesDaily(int $id, string $date): mixed
+    {
+        return self::get("/properties/{$id}/capacities/daily/{$date}");
+    }
+
+    public static function propertyNeighbours(int $id): mixed
+    {
+        return self::get("/properties/{$id}/neighbours");
+    }
+
+    public static function regions(): mixed
+    {
+        return self::get('/regions');
+    }
+
+    public static function regionBase(int $id): mixed
+    {
+        return self::get("/regions/{$id}/base");
+    }
+
+    public static function regionPropertiesCapacities(int $id): mixed
+    {
+        return self::get("/regions/{$id}/properties/capacities");
+    }
+
+    public static function regionCapacitiesMonthly(int $id, string $date): mixed
+    {
+        return self::get("/regions/{$id}/capacities/monthly/{$date}");
+    }
+
+    public static function regionCapacitiesDaily(int $id, string $date): mixed
+    {
+        return self::get("/regions/{$id}/capacities/daily/{$date}");
+    }
+
+    public static function regionCapacities(int $id): mixed
+    {
+        return self::get("/regions/{$id}/capacities");
+    }
+
+    public static function regionMovingAverage(int $id, string $date): mixed
+    {
+        return self::get("/regions/{$id}/moving-average/{$date}");
+    }
+    
+}
--- a/dashboard/app/Chart.php
+++ b/dashboard/app/Chart.php
@ -0,0 +1,12 @@
+<?php
+
+namespace App;
+
+class Chart
+{
+    public static function colors(int $count = 5){
+        $colors = ['#9ebcda','#8c96c6','#88419d','#810f7c','#4d004b'];
+        return json_encode($colors);
+    }
+
+}
--- a/dashboard/app/Http/Controllers/Controller.php
+++ b/dashboard/app/Http/Controllers/Controller.php
--- a/dashboard/app/Models/User.php
+++ b/dashboard/app/Models/User.php
@ -15,7 +15,7 @@ class User extends Authenticatable
    /**
     * The attributes that are mass assignable.
     *
-     * @var array<int, string>
+     * @var list<string>
     */
    protected $fillable = [
        'name',
@ -26,7 +26,7 @@ class User extends Authenticatable
    /**
     * The attributes that should be hidden for serialization.
     *
-     * @var array<int, string>
+     * @var list<string>
     */
    protected $hidden = [
        'password',
--- a/dashboard/app/Providers/AppServiceProvider.php
+++ b/dashboard/app/Providers/AppServiceProvider.php
--- a/dashboard/artisan
+++ b/dashboard/artisan
--- a/dashboard/bootstrap/app.php
+++ b/dashboard/bootstrap/app.php
--- a/dashboard/bootstrap/cache/.gitignore
+++ b/dashboard/bootstrap/cache/.gitignore
--- a/dashboard/bootstrap/providers.php
+++ b/dashboard/bootstrap/providers.php
--- a/dashboard/composer.json
+++ b/dashboard/composer.json
@ -3,7 +3,10 @@
    "name": "laravel/laravel",
    "type": "project",
    "description": "The skeleton application for the Laravel framework.",
-    "keywords": ["laravel", "framework"],
+    "keywords": [
+        "laravel",
+        "framework"
+    ],
    "license": "MIT",
    "require": {
        "php": "^8.2",
@ -68,4 +71,4 @@
    },
    "minimum-stability": "stable",
    "prefer-stable": true
-}
+}
--- a/dashboard/composer.lock
+++ b/dashboard/composer.lock
--- a/dashboard/config/app.php
+++ b/dashboard/config/app.php
--- a/dashboard/config/auth.php
+++ b/dashboard/config/auth.php
--- a/dashboard/config/cache.php
+++ b/dashboard/config/cache.php
--- a/dashboard/config/database.php
+++ b/dashboard/config/database.php
--- a/dashboard/config/filesystems.php
+++ b/dashboard/config/filesystems.php
--- a/dashboard/config/logging.php
+++ b/dashboard/config/logging.php
--- a/dashboard/config/mail.php
+++ b/dashboard/config/mail.php
@ -39,10 +39,10 @@ return [

        'smtp' => [
            'transport' => 'smtp',
+            'scheme' => env('MAIL_SCHEME'),
            'url' => env('MAIL_URL'),
            'host' => env('MAIL_HOST', '127.0.0.1'),
            'port' => env('MAIL_PORT', 2525),
-            'encryption' => env('MAIL_ENCRYPTION', 'tls'),
            'username' => env('MAIL_USERNAME'),
            'password' => env('MAIL_PASSWORD'),
            'timeout' => null,
--- a/dashboard/config/queue.php
+++ b/dashboard/config/queue.php
--- a/dashboard/config/services.php
+++ b/dashboard/config/services.php
--- a/dashboard/config/session.php
+++ b/dashboard/config/session.php
--- a/dashboard/database/.gitignore
+++ b/dashboard/database/.gitignore
--- a/dashboard/database/factories/UserFactory.php
+++ b/dashboard/database/factories/UserFactory.php
--- a/dashboard/database/migrations/0001_01_01_000000_create_users_table.php
+++ b/dashboard/database/migrations/0001_01_01_000000_create_users_table.php
--- a/dashboard/database/migrations/0001_01_01_000001_create_cache_table.php
+++ b/dashboard/database/migrations/0001_01_01_000001_create_cache_table.php
--- a/dashboard/database/migrations/0001_01_01_000002_create_jobs_table.php
+++ b/dashboard/database/migrations/0001_01_01_000002_create_jobs_table.php
--- a/dashboard/database/seeders/DatabaseSeeder.php
+++ b/dashboard/database/seeders/DatabaseSeeder.php
--- a/dashboard/package-lock.json
+++ b/dashboard/package-lock.json
@ -1,10 +1,11 @@
 {
-    "name": "frontend",
+    "name": "dashboard",
    "lockfileVersion": 3,
    "requires": true,
    "packages": {
        "": {
            "dependencies": {
+                "@patternfly/patternfly": "^6.0.0",
                "@picocss/pico": "^2.0.6",
                "echarts": "^5.5.1",
                "leaflet": "^1.9.4"
@ -442,9 +443,9 @@
            }
        },
        "node_modules/@jridgewell/gen-mapping": {
-            "version": "0.3.5",
-            "resolved": "https://registry.npmjs.org/@jridgewell/gen-mapping/-/gen-mapping-0.3.5.tgz",
-            "integrity": "sha512-IzL8ZoEDIBRWEzlCcRhOaCupYyN5gdIK+Q6fbFdPDg6HqX6jpkItn7DFIpW9LQzXG6Df9sA7+OKnq0qlz/GaQg==",
+            "version": "0.3.8",
+            "resolved": "https://registry.npmjs.org/@jridgewell/gen-mapping/-/gen-mapping-0.3.8.tgz",
+            "integrity": "sha512-imAbBGkb+ebQyxKgzv5Hu2nmROxoDOXHh80evxdoXNOrvAnVx7zimzc1Oo5h9RlfV4vPXaE2iM5pOFbvOCClWA==",
            "dev": true,
            "license": "MIT",
            "dependencies": {
@ -532,6 +533,12 @@
                "node": ">= 8"
            }
        },
+        "node_modules/@patternfly/patternfly": {
+            "version": "6.1.0",
+            "resolved": "https://registry.npmjs.org/@patternfly/patternfly/-/patternfly-6.1.0.tgz",
+            "integrity": "sha512-w+QazL8NHKkg5j01eotblsswKxQQSYB0CN3yBXQL9ScpHdp/fK8M6TqWbKZNRpf+NqhMxcH/om8eR0N/fDCJqw==",
+            "license": "MIT"
+        },
        "node_modules/@picocss/pico": {
            "version": "2.0.6",
            "resolved": "https://registry.npmjs.org/@picocss/pico/-/pico-2.0.6.tgz",
@ -553,9 +560,9 @@
            }
        },
        "node_modules/@rollup/rollup-android-arm-eabi": {
-            "version": "4.27.4",
-            "resolved": "https://registry.npmjs.org/@rollup/rollup-android-arm-eabi/-/rollup-android-arm-eabi-4.27.4.tgz",
-            "integrity": "sha512-2Y3JT6f5MrQkICUyRVCw4oa0sutfAsgaSsb0Lmmy1Wi2y7X5vT9Euqw4gOsCyy0YfKURBg35nhUKZS4mDcfULw==",
+            "version": "4.28.1",
+            "resolved": "https://registry.npmjs.org/@rollup/rollup-android-arm-eabi/-/rollup-android-arm-eabi-4.28.1.tgz",
+            "integrity": "sha512-2aZp8AES04KI2dy3Ss6/MDjXbwBzj+i0GqKtWXgw2/Ma6E4jJvujryO6gJAghIRVz7Vwr9Gtl/8na3nDUKpraQ==",
            "cpu": [
                "arm"
            ],
@ -567,9 +574,9 @@
            ]
        },
        "node_modules/@rollup/rollup-android-arm64": {
-            "version": "4.27.4",
-            "resolved": "https://registry.npmjs.org/@rollup/rollup-android-arm64/-/rollup-android-arm64-4.27.4.tgz",
-            "integrity": "sha512-wzKRQXISyi9UdCVRqEd0H4cMpzvHYt1f/C3CoIjES6cG++RHKhrBj2+29nPF0IB5kpy9MS71vs07fvrNGAl/iA==",
+            "version": "4.28.1",
+            "resolved": "https://registry.npmjs.org/@rollup/rollup-android-arm64/-/rollup-android-arm64-4.28.1.tgz",
+            "integrity": "sha512-EbkK285O+1YMrg57xVA+Dp0tDBRB93/BZKph9XhMjezf6F4TpYjaUSuPt5J0fZXlSag0LmZAsTmdGGqPp4pQFA==",
            "cpu": [
                "arm64"
            ],
@ -581,9 +588,9 @@
            ]
        },
        "node_modules/@rollup/rollup-darwin-arm64": {
-            "version": "4.27.4",
-            "resolved": "https://registry.npmjs.org/@rollup/rollup-darwin-arm64/-/rollup-darwin-arm64-4.27.4.tgz",
-            "integrity": "sha512-PlNiRQapift4LNS8DPUHuDX/IdXiLjf8mc5vdEmUR0fF/pyy2qWwzdLjB+iZquGr8LuN4LnUoSEvKRwjSVYz3Q==",
+            "version": "4.28.1",
+            "resolved": "https://registry.npmjs.org/@rollup/rollup-darwin-arm64/-/rollup-darwin-arm64-4.28.1.tgz",
+            "integrity": "sha512-prduvrMKU6NzMq6nxzQw445zXgaDBbMQvmKSJaxpaZ5R1QDM8w+eGxo6Y/jhT/cLoCvnZI42oEqf9KQNYz1fqQ==",
            "cpu": [
                "arm64"
            ],
@ -595,9 +602,9 @@
            ]
        },
        "node_modules/@rollup/rollup-darwin-x64": {
-            "version": "4.27.4",
-            "resolved": "https://registry.npmjs.org/@rollup/rollup-darwin-x64/-/rollup-darwin-x64-4.27.4.tgz",
-            "integrity": "sha512-o9bH2dbdgBDJaXWJCDTNDYa171ACUdzpxSZt+u/AAeQ20Nk5x+IhA+zsGmrQtpkLiumRJEYef68gcpn2ooXhSQ==",
+            "version": "4.28.1",
+            "resolved": "https://registry.npmjs.org/@rollup/rollup-darwin-x64/-/rollup-darwin-x64-4.28.1.tgz",
+            "integrity": "sha512-WsvbOunsUk0wccO/TV4o7IKgloJ942hVFK1CLatwv6TJspcCZb9umQkPdvB7FihmdxgaKR5JyxDjWpCOp4uZlQ==",
            "cpu": [
                "x64"
            ],
@ -609,9 +616,9 @@
            ]
        },
        "node_modules/@rollup/rollup-freebsd-arm64": {
-            "version": "4.27.4",
-            "resolved": "https://registry.npmjs.org/@rollup/rollup-freebsd-arm64/-/rollup-freebsd-arm64-4.27.4.tgz",
-            "integrity": "sha512-NBI2/i2hT9Q+HySSHTBh52da7isru4aAAo6qC3I7QFVsuhxi2gM8t/EI9EVcILiHLj1vfi+VGGPaLOUENn7pmw==",
+            "version": "4.28.1",
+            "resolved": "https://registry.npmjs.org/@rollup/rollup-freebsd-arm64/-/rollup-freebsd-arm64-4.28.1.tgz",
+            "integrity": "sha512-HTDPdY1caUcU4qK23FeeGxCdJF64cKkqajU0iBnTVxS8F7H/7BewvYoG+va1KPSL63kQ1PGNyiwKOfReavzvNA==",
            "cpu": [
                "arm64"
            ],
@ -623,9 +630,9 @@
            ]
        },
        "node_modules/@rollup/rollup-freebsd-x64": {
-            "version": "4.27.4",
-            "resolved": "https://registry.npmjs.org/@rollup/rollup-freebsd-x64/-/rollup-freebsd-x64-4.27.4.tgz",
-            "integrity": "sha512-wYcC5ycW2zvqtDYrE7deary2P2UFmSh85PUpAx+dwTCO9uw3sgzD6Gv9n5X4vLaQKsrfTSZZ7Z7uynQozPVvWA==",
+            "version": "4.28.1",
+            "resolved": "https://registry.npmjs.org/@rollup/rollup-freebsd-x64/-/rollup-freebsd-x64-4.28.1.tgz",
+            "integrity": "sha512-m/uYasxkUevcFTeRSM9TeLyPe2QDuqtjkeoTpP9SW0XxUWfcYrGDMkO/m2tTw+4NMAF9P2fU3Mw4ahNvo7QmsQ==",
            "cpu": [
                "x64"
            ],
@ -637,9 +644,9 @@
            ]
        },
        "node_modules/@rollup/rollup-linux-arm-gnueabihf": {
-            "version": "4.27.4",
-            "resolved": "https://registry.npmjs.org/@rollup/rollup-linux-arm-gnueabihf/-/rollup-linux-arm-gnueabihf-4.27.4.tgz",
-            "integrity": "sha512-9OwUnK/xKw6DyRlgx8UizeqRFOfi9mf5TYCw1uolDaJSbUmBxP85DE6T4ouCMoN6pXw8ZoTeZCSEfSaYo+/s1w==",
+            "version": "4.28.1",
+            "resolved": "https://registry.npmjs.org/@rollup/rollup-linux-arm-gnueabihf/-/rollup-linux-arm-gnueabihf-4.28.1.tgz",
+            "integrity": "sha512-QAg11ZIt6mcmzpNE6JZBpKfJaKkqTm1A9+y9O+frdZJEuhQxiugM05gnCWiANHj4RmbgeVJpTdmKRmH/a+0QbA==",
            "cpu": [
                "arm"
            ],
@ -651,9 +658,9 @@
            ]
        },
        "node_modules/@rollup/rollup-linux-arm-musleabihf": {
-            "version": "4.27.4",
-            "resolved": "https://registry.npmjs.org/@rollup/rollup-linux-arm-musleabihf/-/rollup-linux-arm-musleabihf-4.27.4.tgz",
-            "integrity": "sha512-Vgdo4fpuphS9V24WOV+KwkCVJ72u7idTgQaBoLRD0UxBAWTF9GWurJO9YD9yh00BzbkhpeXtm6na+MvJU7Z73A==",
+            "version": "4.28.1",
+            "resolved": "https://registry.npmjs.org/@rollup/rollup-linux-arm-musleabihf/-/rollup-linux-arm-musleabihf-4.28.1.tgz",
+            "integrity": "sha512-dRP9PEBfolq1dmMcFqbEPSd9VlRuVWEGSmbxVEfiq2cs2jlZAl0YNxFzAQS2OrQmsLBLAATDMb3Z6MFv5vOcXg==",
            "cpu": [
                "arm"
            ],
@ -665,9 +672,9 @@
            ]
        },
        "node_modules/@rollup/rollup-linux-arm64-gnu": {
-            "version": "4.27.4",
-            "resolved": "https://registry.npmjs.org/@rollup/rollup-linux-arm64-gnu/-/rollup-linux-arm64-gnu-4.27.4.tgz",
-            "integrity": "sha512-pleyNgyd1kkBkw2kOqlBx+0atfIIkkExOTiifoODo6qKDSpnc6WzUY5RhHdmTdIJXBdSnh6JknnYTtmQyobrVg==",
+            "version": "4.28.1",
+            "resolved": "https://registry.npmjs.org/@rollup/rollup-linux-arm64-gnu/-/rollup-linux-arm64-gnu-4.28.1.tgz",
+            "integrity": "sha512-uGr8khxO+CKT4XU8ZUH1TTEUtlktK6Kgtv0+6bIFSeiSlnGJHG1tSFSjm41uQ9sAO/5ULx9mWOz70jYLyv1QkA==",
            "cpu": [
                "arm64"
            ],
@ -679,9 +686,9 @@
            ]
        },
        "node_modules/@rollup/rollup-linux-arm64-musl": {
-            "version": "4.27.4",
-            "resolved": "https://registry.npmjs.org/@rollup/rollup-linux-arm64-musl/-/rollup-linux-arm64-musl-4.27.4.tgz",
-            "integrity": "sha512-caluiUXvUuVyCHr5DxL8ohaaFFzPGmgmMvwmqAITMpV/Q+tPoaHZ/PWa3t8B2WyoRcIIuu1hkaW5KkeTDNSnMA==",
+            "version": "4.28.1",
+            "resolved": "https://registry.npmjs.org/@rollup/rollup-linux-arm64-musl/-/rollup-linux-arm64-musl-4.28.1.tgz",
+            "integrity": "sha512-QF54q8MYGAqMLrX2t7tNpi01nvq5RI59UBNx+3+37zoKX5KViPo/gk2QLhsuqok05sSCRluj0D00LzCwBikb0A==",
            "cpu": [
                "arm64"
            ],
@ -692,10 +699,24 @@
                "linux"
            ]
        },
+        "node_modules/@rollup/rollup-linux-loongarch64-gnu": {
+            "version": "4.28.1",
+            "resolved": "https://registry.npmjs.org/@rollup/rollup-linux-loongarch64-gnu/-/rollup-linux-loongarch64-gnu-4.28.1.tgz",
+            "integrity": "sha512-vPul4uodvWvLhRco2w0GcyZcdyBfpfDRgNKU+p35AWEbJ/HPs1tOUrkSueVbBS0RQHAf/A+nNtDpvw95PeVKOA==",
+            "cpu": [
+                "loong64"
+            ],
+            "dev": true,
+            "license": "MIT",
+            "optional": true,
+            "os": [
+                "linux"
+            ]
+        },
        "node_modules/@rollup/rollup-linux-powerpc64le-gnu": {
-            "version": "4.27.4",
-            "resolved": "https://registry.npmjs.org/@rollup/rollup-linux-powerpc64le-gnu/-/rollup-linux-powerpc64le-gnu-4.27.4.tgz",
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+            "version": "3.4.17",
+            "resolved": "https://registry.npmjs.org/tailwindcss/-/tailwindcss-3.4.17.tgz",
+            "integrity": "sha512-w33E2aCvSDP0tW9RZuNXadXlkHXqFzSkQew/aIa2i/Sj8fThxwovwlXHSPXTbAHwEIhBFXAedUhP2tueAKP8Og==",
            "dev": true,
            "license": "MIT",
            "dependencies": {
@ -2547,7 +2559,7 @@
                "glob-parent": "^6.0.2",
                "is-glob": "^4.0.3",
                "jiti": "^1.21.6",
-                "lilconfig": "^2.1.0",
+                "lilconfig": "^3.1.3",
                "micromatch": "^4.0.8",
                "normalize-path": "^3.0.0",
                "object-hash": "^3.0.0",
--- a/dashboard/package.json
+++ b/dashboard/package.json
@ -15,6 +15,7 @@
        "vite": "^5.0"
    },
    "dependencies": {
+        "@patternfly/patternfly": "^6.0.0",
        "@picocss/pico": "^2.0.6",
        "echarts": "^5.5.1",
        "leaflet": "^1.9.4"
--- a/dashboard/phpunit.xml
+++ b/dashboard/phpunit.xml
--- a/dashboard/postcss.config.js
+++ b/dashboard/postcss.config.js
--- a/dashboard/public/.htaccess
+++ b/dashboard/public/.htaccess
--- a/dashboard/public/favicon.ico
+++ b/dashboard/public/favicon.ico
--- a/dashboard/public/index.php
+++ b/dashboard/public/index.php
--- a/dashboard/public/robots.txt
+++ b/dashboard/public/robots.txt
--- a/dashboard/resources/css/app.css
+++ b/dashboard/resources/css/app.css
@ -0,0 +1,330 @@
+/* 1. Use a more-intuitive box-sizing model */
+
+
+*, *::before, *::after {
+  box-sizing: border-box;
+}
+
+/* 2. Remove default margin */
+* {
+  margin: 0;
+  font-family: sans-serif;
+}
+
+body {
+  /* 3. Add accessible line-height */
+  line-height: 1.5;
+  /* 4. Improve text rendering */
+  -webkit-font-smoothing: antialiased;
+  padding: 0 1em;
+  height: 100vh;
+  background-image: radial-gradient(73% 147%, #EADFDF 59%, #ECE2DF 100%), radial-gradient(91% 146%, rgba(255,255,255,0.50) 47%, rgba(0,0,0,0.50) 100%);
+  background-blend-mode: screen;
+}
+
+/* 5. Improve media defaults */
+img, picture, video, canvas, svg {
+  display: block;
+  max-width: 100%;
+}
+
+/* 6. Inherit fonts for form controls */
+input, button, textarea, select {
+  font: inherit;
+}
+
+/* 7. Avoid text overflows */
+p, h1, h2, h3, h4, h5, h6 {
+  overflow-wrap: break-word;
+}
+
+/* 8. Improve line wrapping */
+p {
+  text-wrap: pretty;
+}
+h1, h2, h3, h4, h5, h6 {
+  text-wrap: balance;
+}
+
+dt{
+  font-weight: 600;
+}
+
+dd + dt{
+  margin-top: .2em;
+}
+
+nav + button,
+span + button{
+  margin-left: .5em;
+}
+
+ul{
+  padding-left: 1em;
+}
+
+p + ul{
+  margin-top: 1em;
+}
+
+button[popovertarget]{
+  background: no-repeat center / .3em #4d004b url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 192 512'%3E%3C!--!Font Awesome Free 6.7.2 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free Copyright 2025 Fonticons, Inc.--%3E%3Cpath fill='%23fff' d='M48 80a48 48 0 1 1 96 0A48 48 0 1 1 48 80zM0 224c0-17.7 14.3-32 32-32l64 0c17.7 0 32 14.3 32 32l0 224 32 0c17.7 0 32 14.3 32 32s-14.3 32-32 32L32 512c-17.7 0-32-14.3-32-32s14.3-32 32-32l32 0 0-192-32 0c-17.7 0-32-14.3-32-32z'/%3E%3C/svg%3E%0A");
+  cursor: pointer;
+  display: inline-block;
+  width: 1.5em;
+  height: 1.5em;
+  border-radius: 50%;
+  border: 1px solid #fff;
+}
+
+button[popovertarget]::before{
+  color: #fff;
+  font-weight: 700;
+}
+
+button[popovertarget]>span{
+  position: absolute;
+  left: -999em;
+  top: -999em;
+}
+
+[popover] {
+  border: none;
+  border-radius: 1em;
+  background: #fff;
+  padding: 1.5em;
+  border-radius: var(--small-border);
+  box-shadow: .0625em .0625em .625em rgba(0, 0, 0, 0.1);
+  max-width: 40em;
+  top: 4em;
+  margin: 0 auto;
+}
+
+[popover]::backdrop{
+  background-color: rgba(0,0,0,.5);
+}
+
+[popover] h2{
+  font-size: 1em;
+}
+
+[popover] h3{
+  font-size: .95em;
+  margin-top: 1em;
+}
+
+p.formula{
+  font-family: monospace;
+  border: 1px solid #aaa;
+  padding: .5em 1em;
+}
+
+p + p{
+  margin-top: 1em;
+}
+
+/*
+  9. Create a root stacking context
+*/
+#root, #__next {
+  isolation: isolate;
+}
+
+body>header{
+    position: fixed;
+    top: 0;
+    left: 0;
+    width: 100%;
+    height: 3em;
+    background: #ccc;
+    z-index: 99;
+    display: flex;
+    align-items: center;
+    padding: 0 1em;
+}
+
+body>header>nav{
+  text-align: center;
+  min-width: 10em;
+  background: #fff;
+  border-radius: .2em;
+  position: relative;
+  border: 1px solid #fff;
+}
+
+body>header>nav>ul{
+  position: absolute;
+  background: #fff;
+  width: calc(100% + 2px);
+  list-style: none;
+  padding: 0 0 1em;
+  top: -999em;
+  left: -999em;
+  border-radius: 0 0 .2em .2em;
+  border-left: 1px solid #aaa;
+  border-right: 1px solid #aaa;
+  border-bottom: 1px solid #aaa;
+}
+
+body>header>nav:hover{
+  border-radius: .2em .2em 0 0;
+  border: 1px solid #aaa;
+
+}
+
+body>header>nav:hover ul{
+  top: initial;
+  left: -1px;
+}
+
+body>header>nav>ul>li a,
+body>header>nav>strong{
+  display: inline-block;
+  padding: .2em .4em;
+}
+
+a{
+  color: #000;
+}
+
+a:hover,
+a:focus{
+  color: #aaa;
+}
+
+
+main{
+    width: 100%;
+    height: 100vh;
+    padding: 4em 0 1em;
+    display: grid;
+    gap: .5em;
+}
+
+body.overview main{
+  grid-template-columns: repeat(8, minmax(1%, 50%));
+  grid-template-rows: repeat(4, 1fr);
+  grid-template-areas:
+      "chart1 chart1 chart1 chart2 chart2 chart2 chart4 chart4"
+      "chart1 chart1 chart1 chart2 chart2 chart2 chart4 chart4"
+      "chart1 chart1 chart1 chart3 chart3 chart3 chart4 chart4"
+      "chart1 chart1 chart1 chart3 chart3 chart3 chart4 chart4"
+}
+
+body.region main{
+  grid-template-columns: repeat(4, minmax(10%, 50%));
+    grid-template-rows: repeat(6, 1fr) 4em;
+    grid-template-areas:
+      "chart1 chart1 chart2 chart2"
+      "chart1 chart1 chart2 chart2"
+      "chart1 chart1 chart3 chart4"
+      "chart1 chart1 chart3 chart4"
+      "chart1 chart1 chart6 chart6"
+      "chart1 chart1 chart6 chart6"
+      "chart1 chart1 timeline timeline";
+}
+
+body.property main{
+  grid-template-columns: repeat(4, minmax(10%, 50%));
+    grid-template-rows: repeat(4, 1fr) 4em;
+    grid-template-areas:
+      "chart1 chart1 chart2 chart2"
+      "chart1 chart1 chart2 chart2"
+      "chart5 chart5 chart3 chart4"
+      "chart5 chart5 chart3 chart4"
+      "chart5 chart5 timeline timeline";
+}
+
+article{
+    background: #f9f9f9;
+    border: .0625em solid #ccc;
+    box-shadow: 0 5px 10px rgba(154,160,185,.05), 0 15px 40px rgba(166,173,201,.2);
+    border-radius: .2em;
+    display: grid;
+}
+
+article.header{
+    grid-template-columns: 100%;
+    grid-template-rows: minmax(1%, 2em) 1fr;
+    padding: .5em 1em 1em .5em;
+}
+
+article.map{
+    padding: 0;
+}
+
+article.map>header{
+  padding: .5em 1em 1em .5em;
+}
+
+article>header{
+  display: grid;
+  grid-template-columns: 1fr 1em;
+  grid-template-rows: 1fr;
+}
+
+article>header>h2{
+    font-size: .8em;
+    font-weight: 600;
+}
+
+@media(max-width: 960px){
+
+  body{
+    height: auto;
+  }
+
+  body.overview main,
+  body.region main,
+  body.property main{
+    height: auto;
+    grid-template-columns: 100%;
+    grid-template-rows: repeat(5, minmax(20em, 25em)) 4em;
+    grid-template-areas: "chart1" "chart2" "chart3" "chart4" "chart5" "chart6" "timeline";
+  }
+
+  body.overview main{
+    grid-template-rows: minmax(20em, 40em) repeat(3, minmax(20em, 25em));
+    grid-template-areas: "chart1" "chart2" "chart3" "chart4";
+  }
+
+  body.region main{
+    grid-template-rows: minmax(20em, 40em) repeat(4, minmax(20em, 25em)) 4em;
+    grid-template-areas: "chart1" "chart2" "chart3" "chart4" "chart6" "timeline";
+  }
+
+  body.property main{
+    grid-template-rows: repeat(5, minmax(20em, 25em)) 4em;
+    grid-template-areas: "chart1" "chart2" "chart3" "chart4" "chart5" "timeline";
+  }
+}
+
+.leaflet-marker-icon span{
+  background: #4d004b;
+  width: 2rem;
+  height: 2rem;
+  display: block;
+  left: -1rem;
+  top: -1rem;
+  position: relative;
+  border-radius: 50% 50% 0;
+  transform: rotate(45deg);
+  border: 2px solid #fff
+}
+
+/*['#9ecae1','#6baed6','#4292c6','#2171b5','#084594'*/
+.leaflet-marker-icon.region1 span{
+  background: #8c96c6;
+}
+
+.leaflet-marker-icon.region2 span{
+  background: #88419d;
+}
+
+.leaflet-marker-icon.region3 span{
+  background: #810f7c;
+}
+
+.leaflet-marker-icon.region4 span{
+  background: #4d004b;
+}
--- a/dashboard/resources/css/pico.min.css
+++ b/dashboard/resources/css/pico.min.css
--- a/dashboard/resources/js/app.js
+++ b/dashboard/resources/js/app.js
@ -0,0 +1,4 @@
+import * as echarts from 'echarts';
+import 'leaflet'
+
+window.echarts = echarts;
--- a/dashboard/resources/js/bootstrap.js
+++ b/dashboard/resources/js/bootstrap.js
--- a/dashboard/resources/views/base.blade.php
+++ b/dashboard/resources/views/base.blade.php
@ -0,0 +1,17 @@
+<!DOCTYPE html>
+<html lang="de">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Dashboard</title>
+    @vite(['resources/css/app.css', 'resources/js/app.js', 'node_modules/leaflet/dist/leaflet.css'])
+</head>
+<body class="@yield('body-class')">
+    <header>
+        @yield('header')
+    </header>
+    <main>
+        @yield('main')
+    </main>
+</body>
+</html>
--- a/dashboard/resources/views/overview.blade.php
+++ b/dashboard/resources/views/overview.blade.php
@ -0,0 +1,365 @@
+@extends('base')
+@section('body-class', 'overview')
+@section('header')
+<nav>
+  <strong>Start</strong>
+  <ul>
+    @foreach($regions as $r)
+    <li><a href="/region/{{ $r['id'] }}">{{ $r['name'] }}</a></li>
+    @endforeach
+  </ul>
+</nav>
+@endsection
+@section('main')
+<article class="header" style="grid-area: chart1;">
+  <header>
+    <h2>Verfügbarkeit aller Mietobjekte über gesamten beobachteten Zeitraum</h2>
+    <button popovertarget="pop1">
+      <span>Erklärungen zum Diagramm</span>
+    </button>
+    <div popover id="pop1">
+      <h2>Verfügbarkeit aller Mietobjekte über gesamten beobachteten Zeitraum</h2>
+      <p>
+        Das Diagramm zeigt die Verfügbarkeit aller Mietobjekte zu allen beobachteten Zeitpunkten.
+      </p>
+      <ul>
+        <li>X-Achse: Zeitpunkt Beobachtung.</li>
+        <li>Y-Achse: Mietobjekte.</li>
+        <li>Kategorien: 0% = Das Mietobjekt ist komplett Ausgebucht; 100% = Das Mietobjekt kann zu allen Verfügbaren Daten gebucht werden.</li>
+      </ul>
+
+      <h3>Berrechnung Verfügbarkeit</h3>
+      <p>Die Verfügbarkeit eines Mietobjekt errechnet sich folgendermassen:</p>
+      <p class="formula">
+        Verfügbarkeit = (100 / (Anzahl Buchungsdaten * 2)) * Summe Status
+      </p>
+      <ul>
+        <li>Status: Jeder verfügbare Kalendertag kann den Status «Nicht Verfügbar» (0), «Verfügbar (kein Anreisetag)» (1) oder «Verfügbar» (2) aufweisen.</li>
+        <li>Anzahl Buchungsdaten: Die Summe aller angebotenen Buchungsdaten mit zwei multipliziert (= Alle Buchungdaten haben den Status «Verfügbar»)</li>
+      </ul>
+    </div>
+    <div>
+  </header>
+  <div id="chart-heatmap"></div>
+</article>
+<article class="header" style="grid-area: chart2;">
+  <header>
+    <h2>
+      Anzahl jemals gefundene Kurzzeitmietobjekte pro Region
+    </h2>
+    <button popovertarget="pop2">
+      <span>Erklärungen zum Diagramm</span>
+    </button>
+    <div popover id="pop2">
+      <h2>Anzahl jemals gefundener Mietobjekte pro Region</h2>
+      <p>
+        Das Balkendiagramm zeigt die Anzahl jemals gefundener Mietobjekte pro Region.
+      </p>
+      <ul>
+        <li>X-Achse: Region</li>
+        <li>Y-Achse: Anzahl Mietobjekte</li>
+      </ul>
+    </div>
+    <div>
+  </header>
+  <div id="chart-props-per-region"></div>
+</article>
+<article class="header" style="grid-area: chart3;">
+  <header>
+    <h2>
+      Entwicklung der Anzahl jemals gefunden Kurzzeitmietobjekte
+    </h2>
+    <button popovertarget="pop3">
+      <span>Erklärungen zum Diagramm</span>
+    </button>
+    <div popover id="pop3">
+      <h2>Entwicklung Anzahl jemals gefundener Mietobjekte pro Region</h2>
+      <p>
+        Das Liniendiagramm zeigt die Entwicklung aller jemals gefundener Mietobjekte pro Region.
+      </p>
+      <ul>
+        <li>X-Achse: Zeitpunkt Beobachtung</li>
+        <li>Y-Achse: Anzahl Mietobjekte</li>
+      </ul>
+    </div>
+    <div>
+  </header>
+  <div id="extractions"></div>
+</article>
+<article style="grid-area: chart4;">
+  <div id="leaflet"></div>
+</article>
+<script type="module">
+const sharedOptions = {
+  basic: {
+    color: {!! $diagramsOptions['shared']['colors'] !!},
+    grid: {
+      top: 30,
+      left: 70,
+      right: 0,
+      bottom: 45
+    },
+    name: (opt) => {
+      return {
+        name: opt.name,
+        nameLocation: opt.location,
+        nameGap: 50,
+        nameTextStyle: {
+          fontWeight: 'bold',
+        },
+      }
+    }
+  }
+}
+const extractionDates = {!! $diagramsOptions['shared']['extractionDates'] !!};
+
+const chartHeatmap = document.getElementById('chart-heatmap');
+const cHeatmap = echarts.init(chartHeatmap);
+const cHeatmapOptions = {
+  animation: false,
+  tooltip: {
+    position: 'top'
+  },
+  grid: {
+    show: true,
+    borderWidth: 1,
+    borderColor: '#aaa',
+    top: 30,
+    right: 45,
+    bottom: 70,
+    left: 30
+  },
+  dataZoom: [{
+      type: 'slider'
+    },
+    {
+      type: 'slider',
+      show: true,
+      yAxisIndex: 0,
+  }],
+  xAxis: {
+    show: true,
+    name: 'Zeitpunkt Beobachtung',
+    type: 'category',
+    data: extractionDates,
+    splitArea: {
+      show: false
+    },
+    splitArea: {
+      show: false
+    },
+    axisLabel: {
+      show: false,
+    },
+    axisTick: {
+        show: false,
+    },
+    axisLine: {
+        show: false,
+    },
+    nameLocation: 'center',
+    nameGap: 10,
+    nameTextStyle: {
+      fontWeight: 'bold',
+    }
+  },
+  yAxis: {
+    show: true,
+    type: 'category',
+    data: {!! $diagramsOptions['heatmap']['yAxis']['data'] !!},
+    splitArea: {
+      show: false
+    },
+    axisTick: {
+        show: false,
+    },
+    axisLine: {
+        show: false,
+    },
+    axisLabel: {
+      show: false,
+    },
+    name: 'Mietobjekte',
+    nameLocation: 'center',
+    nameGap: 10,
+    nameTextStyle: {
+      fontWeight: 'bold',
+    }
+  },
+  visualMap: {
+    type: 'piecewise',
+    min: 0,
+    max: 100,
+    calculable: true,
+    orient: 'horizontal',
+    left: 'center',
+    top: 0,
+    formatter: (v1, v2) => {
+      return `${v1} – ${v2} %`;
+    },
+    inRange: {
+      color: sharedOptions.basic.color,
+    },
+  },
+  series: [
+    {
+      name: 'Verfügbarkeit',
+      type: 'heatmap',
+      blurSize: 0,
+      data: {!! $diagramsOptions['heatmap']['series']['data'] !!},
+      label: {
+        show: false
+      },
+      tooltip: {
+        formatter: (data) => {
+          return `Kurzzeitmietobjekte-ID: ${data.data[1]}<br />Beobachtungszeitpunkt: ${data.data[0]}<br/>Verfügbarkeit: ${data.data[2].toFixed(2)} %`
+        },
+      },
+      emphasis: {
+        itemStyle: {
+          borderColor: '#000',
+          borderWidth: 2
+        }
+      }
+    }
+  ]
+}
+
+cHeatmap.setOption(cHeatmapOptions);
+
+const chartPropsPerRegion = document.getElementById('chart-props-per-region');
+const cPropsPerRegion = echarts.init(chartPropsPerRegion);
+const cPropsPerRegionOptions = {
+  grid: sharedOptions.basic.grid,
+  color: sharedOptions.basic.color,
+  xAxis: {
+    name: 'Region',
+    nameLocation: 'center',
+    nameGap: 30,
+    nameTextStyle: {
+      fontWeight: 'bold',
+    },
+    type: 'category',
+    data: {!! $diagramsOptions['propertiesPerRegion']['xAxis']['data'] !!}
+  },
+  yAxis: {
+    type: 'value',
+    name: 'Anzahl Mietobjekte',
+    nameLocation: 'middle',
+    nameGap: 50,
+    nameTextStyle: {
+      fontWeight: 'bold',
+    },
+  },
+  series: [
+    {
+      data: {!! $diagramsOptions['propertiesPerRegion']['yAxis']['data'] !!},
+      type: 'bar',
+      itemStyle: {
+        color: (e) => {
+          return sharedOptions.basic.color[e.dataIndex];
+        }
+      }
+    },
+  ]
+};
+
+cPropsPerRegion.setOption(cPropsPerRegionOptions);
+
+const chartExtractions = document.getElementById('extractions');
+const cExtractions = echarts.init(chartExtractions);
+
+const cExtractionsOptions = {
+  color: sharedOptions.basic.color,
+  tooltip: {
+    trigger: 'axis'
+  },
+  legend: {
+    show: true
+  },
+  grid: sharedOptions.basic.grid,
+  xAxis: {
+    name: 'Zeitpunkt Beobachtung',
+    nameLocation: 'center',
+    nameGap: 24,
+    nameTextStyle: {
+      fontWeight: 'bold',
+    },
+    type: 'category',
+    boundaryGap: false,
+    data: extractionDates
+  },
+  yAxis: {
+    name: 'Anzahl Mietobjekte',
+    nameLocation: 'center',
+    nameGap: 50,
+    nameTextStyle: {
+      fontWeight: 'bold',
+    },
+    type: 'value'
+  },
+  series: [
+    {
+      name: 'Alle',
+      type: 'line',
+      stack: 'Total',
+      data: {!! json_encode($diagramsOptions['extractions']['series']['total_all']) !!},
+    },
+    {
+      connectNulls: true,
+      name: 'Davos',
+      type: 'line',
+      data: {!! json_encode($diagramsOptions['extractions']['series']['total_davos']) !!}
+    },
+    {
+      connectNulls: true,
+      name: 'Engadin',
+      type: 'line',
+      data: {!! json_encode($diagramsOptions['extractions']['series']['total_engadin']) !!}
+    },
+    {
+      connectNulls: true,
+      name: 'Heidiland',
+      type: 'line',
+      data: {!! json_encode($diagramsOptions['extractions']['series']['total_heidiland']) !!}
+    },
+    {
+      connectNulls: true,
+      name: 'St. Moritz',
+      type: 'line',
+      data: {!! json_encode($diagramsOptions['extractions']['series']['total_stmoritz']) !!}
+    },
+  ]
+};
+
+cExtractions.setOption(cExtractionsOptions);
+
+const map = L.map('leaflet');
+
+L.tileLayer('https://tile.openstreetmap.org/{z}/{x}/{y}.png', {
+    maxZoom: 19,
+    attribution: '&copy; <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a>'
+}).addTo(map);
+
+function icon(id){
+  return L.divIcon({
+    className: "region"+id,
+    html: '<span></span>'
+  })
+}
+
+const markers = L.featureGroup([
+    @foreach($geo as $g)
+    L.marker([{{ $g['latlng'] }}], {icon: icon({{ $g['region_id'] }})}).bindPopup('<a href="/property/{{ $g['property_id'] }}">{{ $g['latlng'] }}</a>'),
+    @endforeach
+]).addTo(map);
+
+map.fitBounds(markers.getBounds(), {padding: [20,20]})
+
+cHeatmap.on('click', 'series', (e) => {
+  window.open(`/property/${e.value[1]}?date=${e.value[0]}`, '_self');
+})
+
+
+</script>
+@endsection
--- a/dashboard/resources/views/property-nodata.blade.php
+++ b/dashboard/resources/views/property-nodata.blade.php
@ -0,0 +1,27 @@
+@extends('base')
+@section('body-class', 'property')
+@section('header')
+<nav>
+  <strong>Property: {{ $base['check_data'] }} ({{ $base['region_name'] }})</strong>
+  <ul>
+    <li><a href="/">Start</a></li>
+    @foreach($regions as $r)
+    <li><a href="/region/{{ $r['id'] }}">{{ $r['name'] }}</a></li>
+    @endforeach
+  </ul>
+</nav>
+<button popovertarget="prop-details"></button>
+<div popover id="prop-details">
+    <dl>
+        <dt>Region</dt>
+        <dd>{{ $base['region_name'] }}</dd>
+        <dt>Zum ersten mal gefunden</dt>
+        <dd>{{ $base['first_found'] }}</dd>
+        <dt>Zum letzten mal gefunden</dt>
+        <dd>{{ $base['last_found'] }}</dd>
+    </dl>
+</div>
+@endsection
+@section('main')
+<p>Für dieses Mietobjekt sind keine Daten vorhanden.</p>
+@endsection
--- a/dashboard/resources/views/property.blade.php
+++ b/dashboard/resources/views/property.blade.php
@ -0,0 +1,463 @@
+@extends('base')
+@section('body-class', 'property')
+@section('header')
+<nav>
+  <strong>Mietobjekt: {{ $base['latlng'] }} ({{ $base['region_name'] }})</strong>
+  <ul>
+    <li><a href="/">Start</a></li>
+    @foreach($regions as $r)
+    <li><a href="/region/{{ $r['id'] }}">{{ $r['name'] }}</a></li>
+    @endforeach
+  </ul>
+</nav>
+<button popovertarget="prop-details"></button>
+<div popover id="prop-details">
+    <dl>
+        <dt>Region</dt>
+        <dd>{{ $base['region_name'] }}</dd>
+        <dt>Zum ersten mal gefunden</dt>
+        <dd>{{ $base['first_found'] }}</dd>
+        <dt>Zum letzten mal gefunden</dt>
+        <dd>{{ $base['last_found'] }}</dd>
+    </dl>
+</div>
+@endsection
+
+@section('main')
+<article style="grid-area: timeline;">
+    <div id="timeline"></div>
+</article>
+<article class="header" style="grid-area: chart2;">
+    <header>
+        <h2 id="belegung-title">
+            Kalenderansicht der Verfügbarkeit am <span class="date">{{ $startDate }}</span>
+        </h2><button popovertarget="popup-cal"></button>
+        <div popover id="popup-cal">
+            <p>
+                Das Kalenderdiagram zeigt die drei Verfügbarkeitskategorien des Mietobjekts.
+            </p>
+        </div>
+    </header>
+    <div id="chart-calendar"></div>
+</article>
+<article class="header map" style="grid-area: chart5;">
+    <header>
+        <h2 id="belegung-title">
+            Kurzzeitmietobjekte in der Nähe
+        </h2>
+    </header>
+    <div id="chart-map"></div>
+</article>
+<article class="header" style="grid-area: chart3;">
+    <header>
+        <h2>
+            Verfügbarkeit Mietobjekt Monate am <span class="date">{{ $startDate }}</span>
+        </h2>
+    </header>
+    <div id="chart-capacity-monthly">
+    </div>
+</article>
+<article class="header" style="grid-area: chart1;">
+    <header>
+        <h2>
+            Entwicklung der Verfügbarkeit
+        </h2>
+        <button popovertarget="chart-capacity-popover"></button>
+        <div id="chart-capacity-popover" popover>
+            <h2>Erkläung zum Diagramm</h2>
+            <p>Das Liniendiagramm zeigt, wie sich die insgesamte Verfügbarkeit des Kurzzeitmietobjekts entwickelt hat.</p>
+        </div>
+    </header>
+    <div id="chart-capacity"></div>
+</article>
+<article class="header" style="grid-area: chart4;">
+    <header>
+        <h2>
+            Verfügbarkeit Mietobjekt Tage am <span class="date">{{ $startDate }}</span>
+        </h2>
+    </header>
+    <div id="chart-capacity-daily">
+</article>
+<script type="module">
+
+const sharedOptions = {
+        extractiondates: {!! $diagramsOptions['shared']['extractiondates']!!},
+        basic: {
+            color: {!!$diagramsOptions['shared']['colors']!!},
+        grid: {
+            top: 20,
+            left: 60,
+            right: 0,
+            bottom: 50
+        },
+        tooltip: {
+            show: true,
+            trigger: 'axis',
+            valueFormatter: (value) => value.toFixed(2) + ' %'
+        },
+        name: (opt) => {
+            return {
+                name: opt.name,
+                nameLocation: opt.location,
+                nameGap: 24,
+                nameTextStyle: {
+                    fontWeight: 'bold',
+                }
+            }
+        }
+    }
+}
+
+const chartTimeline = document.getElementById('timeline');
+const cTimeline = echarts.init(chartTimeline);
+const cTimelineOptions = {
+    grid: {
+        show: false,
+    },
+    timeline: {
+        data: sharedOptions.extractiondates,
+        playInterval: 1000,
+        axisType: 'time',
+        left: 8,
+        right: 8,
+        bottom: 0,
+        label: {
+            show: false
+        }
+    },
+};
+
+cTimeline.setOption(cTimelineOptions);
+
+const chartCapacityMonthly = document.getElementById('chart-capacity-monthly');
+const cCapacityMonthly = echarts.init(chartCapacityMonthly);
+
+const cCapacityMonthlyOptions = {
+    tooltip: sharedOptions.basic.tooltip,
+    timeline: {
+        show: false,
+        data: sharedOptions.extractiondates,
+        axisType: 'time',
+    },
+    grid: {
+        top: 5,
+        bottom: 40,
+        left: 70,
+        right: 10
+    },
+    xAxis: {
+        type: 'value',
+        max: 100,
+        name: 'Verfügbarkeit in %',
+        nameLocation: 'center',
+        nameGap: 25,
+            nameTextStyle: {
+          fontWeight: 'bold',
+        }
+    },
+    yAxis: {
+        type: 'category',
+    },
+    options: [
+        @foreach ($diagramsOptions['capacityMonthly']['options'] as $cM)
+        {
+        yAxis: {
+            data: {!! json_encode($cM['months']) !!}
+        },
+        series: [{
+            type: 'bar',
+            itemStyle: {
+                color: sharedOptions.basic.color[3]
+            },
+            data: {!! json_encode($cM['capacities']) !!}
+        }]
+    },
+    @endforeach
+    ]
+};
+
+cCapacityMonthly.setOption(cCapacityMonthlyOptions);
+
+const chartCapacityDaily = document.getElementById('chart-capacity-daily');
+const cCapacityDaily = echarts.init(chartCapacityDaily);
+
+const cCapacityDailyOptions = {
+    tooltip: sharedOptions.basic.tooltip,
+    timeline: {
+        show: false,
+        data: sharedOptions.extractiondates,
+        axisType: 'time',
+    },
+    grid: {
+        top: 5,
+        bottom: 40,
+        left: 70,
+        right: 10
+    },
+    xAxis: {
+        type: 'value',
+        max: 100,
+        name: 'Verfügbarkeit in %',
+        nameLocation: 'center',
+        nameGap: 25,
+            nameTextStyle: {
+          fontWeight: 'bold',
+        }
+    },
+    yAxis: {
+        type: 'category',
+    },
+    options: [
+        @foreach ($diagramsOptions['capacityDaily']['options'] as $cD)
+        {
+        yAxis: {
+            data: {!! json_encode($cD['weekdays']) !!}
+        },
+        series: [{
+            type: 'bar',
+            itemStyle: {
+                color: sharedOptions.basic.color[3]
+            },
+            data: {!! json_encode($cD['capacities']) !!}
+        }]
+    },
+    @endforeach
+    ]
+};
+
+cCapacityDaily.setOption(cCapacityDailyOptions);
+
+const chartCapacity = document.getElementById('chart-capacity');
+const cCapacity = echarts.init(chartCapacity);
+
+const cCapacityOptions = {
+    color: sharedOptions.basic.color,
+    legend: {
+        show: true
+    },
+  tooltip: {
+    trigger: 'axis',
+    valueFormatter: (value) => value.toFixed(2)+' %'
+  },
+  grid: {
+    top: 40,
+    left: 25,
+    right: 10,
+    bottom: 20,
+    containLabel: true
+  },
+  xAxis: {
+    type: 'category',
+    boundaryGap: false,
+    data: {!! $diagramsOptions['capacities']['xAxis']['data'] !!},
+    name: 'Zeitpunkt Beobachtung',
+    nameLocation: 'center',
+    nameGap: 24,
+    nameTextStyle: {
+      fontWeight: 'bold',
+    }
+  },
+  yAxis: {
+    type: 'value',
+    min: 0,
+    max: 100,
+    name: 'Verfügbarkeit in %',
+    nameLocation: 'center',
+    nameGap: 38,
+    nameTextStyle: {
+      fontWeight: 'bold',
+    }
+  },
+  series: [
+    {
+          name: 'Verfügbarkeit Mietobjekt',
+          type: 'line',
+          symbolSize: 7,
+          data: {!! $diagramsOptions['capacities']["series"][0]["data"] !!}
+        },
+        {
+            name: 'Verfügbarkeit {{ $base['region_name'] }}',
+            type: 'line',
+            symbolSize: 7,
+            data: {!! $diagramsOptions['capacities']["series"][1]["data"] !!}
+        },
+        {
+            name: 'Verfügbarkeit alle Regionen',
+            type: 'line',
+            symbolSize: 7,
+            data: {!! $diagramsOptions['capacities']["series"][2]["data"] !!}
+        }
+    ]
+};
+
+cCapacity.setOption(cCapacityOptions);
+
+const chartCalendar = document.getElementById('chart-calendar');
+const cCalendar = echarts.init(chartCalendar);
+const h2Belegung = document.getElementById('belegung-title');
+
+const cCalendarOptions = {
+    timeline: {
+        show: false,
+        data: sharedOptions.extractiondates,
+        axisType: 'time',
+    },
+    visualMap: {
+        categories: [0,1,2],
+        inRange: {
+            color: ['#ca0020', '#92c5de', '#0571b0']
+        },
+        formatter: (cat) => {
+            switch (cat) {
+                case 0:
+                return 'Ausgebucht';
+                case 1:
+                    return 'Verfügbar (kein Anreisetag)';
+                case 2:
+                    return 'Verfügbar';
+            }
+        },
+        type: 'piecewise',
+        orient: 'horizontal',
+        left: 'center',
+        top: 0
+    },
+    calendar:[
+        {
+          orient: 'horizontal',
+          range: '2024',
+          top: '15%',
+          right: 10,
+          bottom: '65%',
+          left: 50,
+          dayLabel: {
+            fontSize: 10
+          }
+        },
+        {
+          orient: 'horizontal',
+          range: '2025',
+          top: '47%',
+          right: 10,
+          bottom: '33%',
+          left: 50,
+          dayLabel: {
+            fontSize: 10
+          }
+        },
+        {
+          orient: 'horizontal',
+          range: '2026',
+          top: '79%',
+          right: 10,
+          bottom: '1%',
+          left: 50,
+          dayLabel: {
+            fontSize: 10
+          }
+        }
+    ],
+    options: [
+        @foreach ($diagramsOptions['calendar']['series'] as $c)
+        {
+            series: [{
+                type: 'heatmap',
+                coordinateSystem: 'calendar',
+                calendarIndex: 0,
+                data: {!! json_encode($c) !!}
+            },
+            {
+                type: 'heatmap',
+                coordinateSystem: 'calendar',
+                calendarIndex: 1,
+                data: {!! json_encode($c) !!}
+            },
+            {
+                type: 'heatmap',
+                coordinateSystem: 'calendar',
+                calendarIndex: 2,
+                data: {!! json_encode($c) !!}
+            }]
+        },
+        @endforeach
+    ]
+};
+
+cCalendar.setOption(cCalendarOptions);
+
+
+cTimeline.on('timelinechanged', (e) => {
+
+    let dateTitles = document.querySelectorAll('span.date');
+    dateTitles.forEach(el => {
+        el.innerText = cTimelineOptions.timeline.data[e.currentIndex];
+    });
+
+    // Set markpoint on linechart
+    let x = cCapacityOptions.xAxis.data[e.currentIndex];
+    let y = cCapacityOptions.series[0].data[e.currentIndex];
+
+    cCapacityMonthly.dispatchAction({
+        type: 'timelineChange',
+        currentIndex: e.currentIndex
+    });
+
+    cCapacityDaily.dispatchAction({
+        type: 'timelineChange',
+        currentIndex: e.currentIndex
+    });
+
+     cCalendar.dispatchAction({
+        type: 'timelineChange',
+        currentIndex: e.currentIndex
+    });
+
+    cCapacity.setOption({
+        series: {
+            markPoint: {
+            data: [{
+                    coord: [x, y]
+                }]
+            }
+        }
+    });
+
+})
+
+/* Map w/ neighbours*/
+const map = L.map('chart-map');
+
+L.tileLayer('https://tile.openstreetmap.org/{z}/{x}/{y}.png', {
+    maxZoom: 19,
+    attribution: '&copy; <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a>'
+}).addTo(map);
+
+function icon(id = 0){
+  return L.divIcon({
+    className: "region"+id,
+    html: '<span></span>'
+  })
+}
+
+const markers = L.featureGroup([
+    L.marker([{{ $base['latlng'] }}], {icon: icon(1)}),
+    @foreach($neighbours as $n)
+    L.marker([{{ $n['lat'] }}, {{ $n['lon'] }}], {icon: icon()}).bindPopup('<a href="/property/{{ $n['id'] }}">{{ $n['lat'] }}, {{ $n['lon'] }}</a>'),
+    @endforeach
+]).addTo(map);
+
+map.fitBounds(markers.getBounds(), {padding: [20,20]})
+
+cCapacity.on('click', 'series', (e) => {
+
+    // Switch to correct calendar in the timeline
+    cTimeline.dispatchAction({
+        type: 'timelineChange',
+        currentIndex: e.dataIndex
+    });
+
+});
+
+
+</script>
+@endsection
--- a/dashboard/resources/views/region.blade.php
+++ b/dashboard/resources/views/region.blade.php
@ -0,0 +1,583 @@
+@extends('base')
+@section('body-class', 'region')
+@section('header')
+<nav>
+  <strong>{{ $region['name'] }}</strong>
+  <ul>
+    <li><a href="/">Start</a></li>
+    @foreach($regions as $r)
+    @if($r['id'] != $regionId)
+    <li><a href="/region/{{ $r['id'] }}">{{ $r['name'] }}</a></li>
+    @endif
+    @endforeach
+  </ul>
+</nav>
+@endsection
+@section('main')
+<article style="grid-area: timeline;">
+    <div id="timeline"></div>
+</article>
+<article class="header" style="grid-area: chart6;">
+    <header>
+        <h2 id="prediction-title">Gleitender Mittelwert für die Verfügbarkeit der Region</h2>
+        <button popovertarget="chart-prediction-popover"></button>
+        <div id="chart-prediction-popover" popover>
+            <h2>Gleitender Mittelwert für die Verfügbarkeit der Region</h2>
+            <p>Das Diagramm...</p>
+            <ul>
+                <li>X-Achse: Zeitpunkt der Beobachtung</li>
+                <li>Y-Achse: Verfügbarkeit einer Region. 0% = Alle Mietobjekte der Region sind komplett ausgebucht; 100% = Alle Mietobjekte der Region können zu allen verfügbaren Daten gebucht werden. </li>
+            </ul>
+        </div>
+    </header>
+    <div id="chart-prediction"></div>
+</article>
+<article class="header" style="grid-area: chart1;">
+    <header>
+        <h2 id="belegung-title">Verfügbarkeit aller Mietobjekte der Region über gesamten beobachteten Zeitraum</h2>
+        <button popovertarget="popup-heat"></button><div popover id="popup-heat">
+      <h2>Verfügbarkeit aller Mietobjekte der Region über gesamten beobachteten Zeitraum</h2>
+      <p>
+        Das Diagramm zeigt die Verfügbarkeit aller Mietobjekte der Region zu allen beobachteten Zeitpunkten.
+      </p>
+      <ul>
+        <li>X-Achse: Zeitpunkt Beobachtung.</li>
+        <li>Y-Achse: Mietobjekte.</li>
+        <li>Kategorien: 0% = Das Mietobjekt ist komplett Ausgebucht; 100% = Das Mietobjekt kann zu allen Verfügbaren Daten gebucht werden.</li>
+      </ul>
+
+      <h3>Berrechnung Verfügbarkeit</h3>
+      <p>Die Verfügbarkeit eines Mietobjekt errechnet sich folgendermassen:</p>
+      <p class="formula">
+        Verfügbarkeit = (100 / (Anzahl Buchungsdaten * 2)) * Summe Status
+      </p>
+      <ul>
+        <li>Status: Jeder verfügbare Kalendertag kann den Status «Nicht Verfügbar» (0), «Verfügbar (kein Anreisetag)» (1) oder «Verfügbar» (2) aufweisen.</li>
+        <li>Anzahl Buchungsdaten: Die Summe aller angebotenen Buchungsdaten mit zwei multipliziert (= Alle Buchungdaten haben den Status «Verfügbar»)</li>
+      </ul>
+    </div>
+    <div>
+    </header>
+    <div id="chart-heatmap"></div>
+</article>
+<article class="header" style="grid-area: chart3;">
+    <header>
+        <h2>
+            Verfügbarkeit nach Monat am <span class="date">{{ $startDate }}</span>
+        </h2>
+    </header>
+    <div id="chart-capacity-monthly">
+    </div>
+</article>
+<article class="header" style="grid-area: chart2;">
+    <header>
+        <h2>
+            Entwicklung der Verfügbarkeit
+        </h2>
+        <button popovertarget="chart-capacity-popover"></button>
+        <div id="chart-capacity-popover" popover>
+            <h2>Entwicklung der Verfügbarkeit</h2>
+            <p>Das Liniendiagramm zeigt die Entwicklung Verfügbarkeit der Region im Vergleich zu allen Regionen an.</p>
+            <ul>
+                <li>X-Achse: Zeitpunkt der Beobachtung</li>
+                <li>Y-Achse: Verfügbarkeit einer Region. 0% = Alle Mietobjekte der Region sind komplett ausgebucht; 100% = Alle Mietobjekte der Region können zu allen verfügbaren Daten gebucht werden. </li>
+            </ul>
+        </div>
+    </header>
+    <div id="chart-capacity"></div>
+</article>
+<article class="header" style="grid-area: chart4;">
+    <header>
+        <h2>
+            Verfügbarkeit nach Wochentage am <span class="date">{{ $startDate }}</span>
+        </h2>
+    </header>
+    <div id="chart-capacity-daily">
+</article>
+
+<script type="module">
+
+const sharedOptions = {
+  basic: {
+    color: {!! $diagramsOptions['shared']['colors'] !!},
+    grid: {
+      top: 20,
+      left: 60,
+      right: 0,
+      bottom: 50
+    },
+    tooltip: {
+        show: true,
+        trigger: 'axis',
+        valueFormatter: (value) => value == null ? 'N/A' : value.toFixed(2)+' %'
+    },
+    name: (opt) => {
+      return {
+        name: opt.name,
+        nameLocation: opt.location,
+        nameGap: 24,
+        nameTextStyle: {
+          fontWeight: 'bold',
+        },
+      }
+    }
+  }
+}
+
+const chartCapacity = document.getElementById('chart-capacity');
+const cCapacity = echarts.init(chartCapacity);
+
+const cCapacityOptions = {
+  legend: {
+    show: true
+  },
+  tooltip: sharedOptions.basic.tooltip,
+  color: sharedOptions.basic.color,
+  grid: {
+    top: 20,
+    left: 25,
+    right: 10,
+    bottom: 20,
+    containLabel: true
+  },
+  xAxis: {
+    type: 'category',
+    boundaryGap: false,
+    data: {!! $diagramsOptions['capacity']['xAxis']['data'] !!},
+    name: 'Zeitpunkt Beobachtung',
+    nameLocation: 'center',
+    nameGap: 24,
+    nameTextStyle: {
+      fontWeight: 'bold',
+    }
+  },
+  yAxis: {
+    type: 'value',
+    min: 0,
+    max: 100,
+    name: 'Verfügbarkeit in %',
+    nameLocation: 'center',
+    nameGap: 38,
+    nameTextStyle: {
+      fontWeight: 'bold',
+    }
+  },
+  series: [{
+      name: 'Verfügbarkeit alle Regionen',
+      type: 'line',
+      symbolSize: 7,
+      data: {!! $diagramsOptions['capacity']['series']['all']['data'] !!}
+    },
+    {
+        name: 'Verfügbarkeit Region',
+        type: 'line',
+        symbolSize: 7,
+        data: {!! $diagramsOptions['capacity']['series']['region']['data'] !!}
+    }]
+};
+
+cCapacity.setOption(cCapacityOptions);
+
+const chartCapacityMonthly = document.getElementById('chart-capacity-monthly');
+const cCapacityMonthly = echarts.init(chartCapacityMonthly);
+
+const cCapacityMonthlyOptions = {
+    timeline: {
+        show: false,
+        data: {!! $diagramsOptions['capacity']['xAxis']['data'] !!},
+        axisType: 'time',
+    },
+    grid: {
+        top: 5,
+        bottom: 40,
+        left: 70,
+        right: 10
+    },
+    xAxis: {
+        type: 'value',
+        max: 100,
+        name: 'Verfügbarkeit in %',
+        nameLocation: 'center',
+        nameGap: 25,
+            nameTextStyle: {
+          fontWeight: 'bold',
+        }
+    },
+    yAxis: {
+        type: 'category',
+    },
+    tooltip: sharedOptions.basic.tooltip,
+    options: [
+        @foreach ($diagramsOptions['capacityMonthly']['options'] as $m)
+        {
+        yAxis: {
+            data: {!! json_encode($m['months']) !!}
+        },
+        series: [{
+            type: 'bar',
+            itemStyle: {
+                color: sharedOptions.basic.color[3]
+            },
+            data: {!! json_encode($m['capacities']) !!}
+        }]
+    },
+    @endforeach
+    ]
+};
+
+cCapacityMonthly.setOption(cCapacityMonthlyOptions);
+
+const chartCapacityDaily = document.getElementById('chart-capacity-daily');
+const cCapacityDaily = echarts.init(chartCapacityDaily);
+
+const cCapacityDailyOptions = {
+    timeline: {
+        show: false,
+        data: {!! $diagramsOptions['capacity']['xAxis']['data'] !!},
+        axisType: 'time',
+    },
+    tooltip: sharedOptions.basic.tooltip,
+    grid: {
+        top: 5,
+        bottom: 40,
+        left: 70,
+        right: 10
+    },
+    xAxis: {
+        type: 'value',
+        max: 100,
+        name: 'Verfügbarkeit in %',
+        nameLocation: 'center',
+        nameGap: 25,
+        nameTextStyle: {
+          fontWeight: 'bold',
+        }
+    },
+    yAxis: {
+        type: 'category',
+    },
+    options: [
+        @foreach ($diagramsOptions['capacityDaily']['options'] as $d)
+        {
+        yAxis: {
+            data: {!! json_encode($d['weekdays']) !!}
+        },
+        series: [{
+            type: 'bar',
+            itemStyle: {
+                color: sharedOptions.basic.color[3]
+            },
+            data: {!! json_encode($d['capacities']) !!}
+        }]
+    },
+    @endforeach
+    ]
+};
+
+cCapacityDaily.setOption(cCapacityDailyOptions);
+
+const chartPrediction = document.getElementById('chart-prediction');
+const cPrediction = echarts.init(chartPrediction);
+
+const cPredictionOptions = {
+  color: [sharedOptions.basic.color[0], sharedOptions.basic.color[4], sharedOptions.basic.color[5]],
+  timeline: {
+      show: false,
+      data: {!! $diagramsOptions['capacity']['xAxis']['data'] !!},
+      axisType: 'time',
+      replaceMerge: ['graphic', 'series']
+  },
+  legend: {
+    show: true
+  },
+  tooltip: sharedOptions.basic.tooltip,
+  grid: {
+    top: 20,
+    left: 25,
+    right: 10,
+    bottom: 20,
+    containLabel: true
+  },
+  xAxis: {
+    type: 'category',
+    boundaryGap: false,
+    name: 'Zeitpunkt Beobachtung',
+    nameLocation: 'center',
+    nameGap: 24,
+    nameTextStyle: {
+      fontWeight: 'bold',
+    },
+  },
+  yAxis: {
+    type: 'value',
+    min: 0,
+    max: 100,
+    name: 'Verfügbarkeit in %',
+    nameLocation: 'center',
+    nameGap: 38,
+    nameTextStyle: {
+      fontWeight: 'bold',
+    }
+  },
+  options: [
+    @foreach ($diagramsOptions['predictions']['options'] as $p)
+    @if($p === null)
+    {
+      graphic: {
+        elements: [
+          {
+            type: 'text',
+            left: 'center',
+            top: 'center',
+            style: {
+              text: 'Keine Daten für Zeitspanne',
+              fontSize: 44,
+              fontWeight: 'bold',
+            }
+          }
+        ]
+      }
+    },
+    @else
+    {
+      color: sharedOptions.basic.color,
+      graphic: {
+        elements: []
+      },
+      xAxis: {
+        data: {!! json_encode($p['dates']) !!}
+      },
+      series: [
+        {
+          name: 'Gleitender Mittelwert',
+          showSymbol: false,
+          connectNulls: true,
+          type: 'line',
+          symbolSize: 7,
+          data: {!! json_encode($p['capacities_moving_average']) !!}
+        },
+        {
+            name: 'Ausgangsdaten',
+            showSymbol: false,
+            connectNulls: true,
+            type: 'line',
+            symbolSize: 7,
+            data: {!! json_encode($p['capacities_timeframe_before']) !!}
+        },
+        {
+            name: 'Vergleichsdaten',
+            showSymbol: false,
+            connectNulls: true,
+            type: 'line',
+            symbolSize: 7,
+            data: {!! json_encode($p['capacities_timeframe_after']) !!}
+        }
+      ]
+    },
+    @endif
+    @endforeach
+  ]
+};
+
+cPrediction.setOption(cPredictionOptions);
+
+const chartHeatmap = document.getElementById('chart-heatmap');
+const cHeatmap = echarts.init(chartHeatmap);
+const cHeatmapOptions = {
+  animation: false,
+  tooltip: {
+    position: 'top'
+  },
+  grid: {
+    show: true,
+    borderWidth: 1,
+    borderColor: '#aaa',
+    top: 30,
+    right: 45,
+    bottom: 70,
+    left: 30
+  },
+  dataZoom: [{
+      type: 'slider'
+    },
+    {
+      type: 'slider',
+      show: true,
+      yAxisIndex: 0,
+  }],
+  xAxis: {
+    show: true,
+    name: 'Zeitpunkt Beobachtung',
+    type: 'category',
+    data: {!! $diagramsOptions['heatmap']['xAxis']['data'] !!},
+    splitArea: {
+      show: false
+    },
+    splitArea: {
+      show: false
+    },
+    axisLabel: {
+      show: false,
+    },
+    axisTick: {
+        show: false,
+    },
+    axisLine: {
+        show: false,
+    },
+    nameLocation: 'center',
+    nameGap: 10,
+    nameTextStyle: {
+      fontWeight: 'bold',
+    }
+  },
+  yAxis: {
+    show: true,
+    type: 'category',
+    data: {!! $diagramsOptions['heatmap']['yAxis']['data'] !!},
+    splitArea: {
+      show: false
+    },
+    axisTick: {
+        show: false,
+    },
+    axisLine: {
+        show: false,
+    },
+    axisLabel: {
+      show: false,
+    },
+    name: 'Mietobjekte',
+    nameLocation: 'center',
+    nameGap: 10,
+    nameTextStyle: {
+      fontWeight: 'bold',
+    }
+  },
+  visualMap: {
+    type: 'piecewise',
+    min: 0,
+    max: 100,
+    calculable: true,
+    orient: 'horizontal',
+    left: 'center',
+    top: 0,
+    formatter: (v1, v2) => {
+      return `${v1} – ${v2} %`;
+    },
+    inRange: {
+      color: sharedOptions.basic.color,
+    },
+  },
+  series: [
+    {
+      name: 'Verfügbarkeit',
+      type: 'heatmap',
+      blurSize: 0,
+      data: {!! $diagramsOptions['heatmap']['series']['data'] !!},
+      label: {
+        show: false
+      },
+      tooltip: {
+        formatter: (data) => {
+          return `Kurzzeitmietobjekte-ID: ${data.data[1]}<br />Beobachtungszeitpunkt: ${data.data[0]}<br/>Verfügbarkeit: ${data.data[2].toFixed(2)} %`
+        },
+      },
+      emphasis: {
+        itemStyle: {
+          borderColor: '#000',
+          borderWidth: 2
+        }
+      }
+    }
+  ]
+}
+
+cHeatmap.setOption(cHeatmapOptions);
+
+const chartTimeline = document.getElementById('timeline');
+const cTimeline = echarts.init(chartTimeline);
+
+const cTimelineOptions = {
+    grid: {
+        show: false,
+    },
+    timeline: {
+        data: {!! $diagramsOptions['capacity']['xAxis']['data'] !!},
+        playInterval: 2000,
+        axisType: 'time',
+        left: 8,
+        right: 8,
+        bottom: 0,
+        label: {
+            show: false
+        }
+    },
+};
+
+cTimeline.setOption(cTimelineOptions);
+
+cTimeline.on('timelinechanged', (e) => {
+
+    let dateTitles = document.querySelectorAll('span.date');
+    dateTitles.forEach(el => {
+        el.innerText = cTimelineOptions.timeline.data[e.currentIndex];
+    });
+
+    // Set markpoint on linechart
+    let x = cCapacityOptions.xAxis.data[e.currentIndex];
+    let y = cCapacityOptions.series[0].data[e.currentIndex];
+
+    cCapacityMonthly.dispatchAction({
+        type: 'timelineChange',
+        currentIndex: e.currentIndex
+    });
+
+    cCapacityDaily.dispatchAction({
+        type: 'timelineChange',
+        currentIndex: e.currentIndex
+    });
+
+    cPrediction.dispatchAction({
+        type: 'timelineChange',
+        currentIndex: e.currentIndex
+    });
+
+    cCapacity.setOption({
+        series: {
+            markPoint: {
+            data: [{
+                    coord: [x, y]
+                }]
+            }
+        }
+    });
+
+})
+
+
+document.querySelector('header').addEventListener('click', () => {
+    console.log('test');
+    cCapacityMonthly.dispatchAction({
+        type: 'timelineChange',
+        currentIndex: 10
+    });
+})
+
+
+cCapacity.on('click', 'series', (e) => {
+
+    // Switch to correct calendar in the timeline
+    cTimeline.dispatchAction({
+        type: 'timelineChange',
+        currentIndex: e.dataIndex
+    });
+
+});
+
+cHeatmap.on('click', 'series', (e) => {
+  window.open(`/property/${e.value[1]}?date=${e.value[0]}`, '_self');
+})
+
+</script>
+@endsection
--- a/dashboard/routes/web.php
+++ b/dashboard/routes/web.php
@ -0,0 +1,228 @@
+<?php
+
+use Illuminate\Support\Facades\Route;
+use App\Api;
+use App\Chart;
+
+Route::get('/', function () {
+
+    $regionBase = Api::regionBase(-1);
+    $regionPropertiesCapacities = Api::regionPropertiesCapacities(-1);
+    $propertiesGrowth = Api::propertiesGrowth();
+    $regions = Api::regions()['regions'];
+    $propertiesPerRegion = $regions;
+    $regions[] = ['name' => 'Alle Regionen', 'id' => -1];
+    $propertiesGeo = Api::propertiesGeo()['properties'];
+
+    $heatmapValues = [];
+
+    foreach ($regionPropertiesCapacities['values'] as $el) {
+        $heatmapValues[] = array_values($el);
+    }
+
+    $diagramsOptions = [
+        "shared" => [
+            "extractionDates" => json_encode($regionPropertiesCapacities['dates']),
+            "colors" => Chart::colors()
+        ],
+        "heatmap" => [
+            "yAxis" => [
+                "data" => json_encode($regionPropertiesCapacities['property_ids'])
+            ],
+            "series" => [
+                "data" => json_encode($heatmapValues)
+            ]
+        ],
+        "propertiesPerRegion" => [
+            "yAxis" => [
+                "data" => json_encode(array_column($propertiesPerRegion, 'count_properties'))
+            ],
+            "xAxis" => [
+                "data" => json_encode(array_column($propertiesPerRegion, 'name'))
+            ]
+        ],
+        "extractions" => [
+            "series" => $propertiesGrowth,
+        ]
+    ];
+
+    return view('overview', [
+        "regions" => $regions,
+        "region" => $regionBase,
+        "diagramsOptions" => $diagramsOptions,
+        "geo" => $propertiesGeo,
+
+    ]);
+});
+
+Route::get('/region/{id}', function (int $id) {
+
+    $regions = Api::regions()['regions'];
+    $regions[] = ['name' => 'Alle Regionen', 'id' => -1];
+    $region = $id >= 0 ? Api::regionBase($id) : ['name' => 'Alle Regionen'];
+    $regionPropertiesCapacities = Api::regionPropertiesCapacities($id);
+    $regionCapacitiesRegion = Api::regionCapacities($id);
+    $regionCapacitiesAll = Api::regionCapacities(-1);
+    $regionCapacitiesMonthly = [];
+    $regionCapacitiesDaily = [];
+    $regionPredictions = [];
+
+    $heatmapValues = [];
+
+    foreach ($regionPropertiesCapacities['values'] as $el) {
+        $heatmapValues[] = array_values($el);
+    }
+
+    foreach ($regionCapacitiesRegion['dates'] as $date) {
+        $regionCapacitiesMonthly[] = Api::regionCapacitiesMonthly($id, $date);
+        $regionCapacitiesDaily[] = Api::regionCapacitiesDaily($id, $date);
+        $regionPredictions[] = Api::regionMovingAverage($id, $date);
+    }
+
+    $diagramsOptions = [
+        "shared" => [
+            "extractionDates" => json_encode($regionPropertiesCapacities['dates']),
+            "colors" => Chart::colors()
+        ],
+        "heatmap" => [
+            "xAxis" => [
+                "data" => json_encode($regionPropertiesCapacities['dates'])
+            ],
+            "yAxis" => [
+                "data" => json_encode($regionPropertiesCapacities['property_ids'])
+            ],
+            "series" => [
+                "data" => json_encode($heatmapValues)
+            ]
+        ],
+        "predictions" => [
+            "options" => $regionPredictions,
+        ],
+        "capacityMonthly" => [
+            "options" => $regionCapacitiesMonthly,
+        ],
+        "capacityDaily" => [
+            "options" => $regionCapacitiesDaily,
+        ],
+        "capacity" => [
+            "xAxis" => [
+                "data" => json_encode($regionCapacitiesRegion['dates'])
+            ],
+            "series" => [
+                "all" => [
+                    "data" => json_encode($regionCapacitiesAll['capacities'])
+                ],
+                "region" => [
+                    "data" =>  json_encode($regionCapacitiesRegion['capacities'])
+                ]
+            ]
+        ]
+    ];
+
+    return view('region', [
+        'diagramsOptions' => $diagramsOptions,
+        'startDate' => $regionCapacitiesRegion['dates'][0],
+        'regions' => $regions,
+        'region' => $region,
+        'regionId' => $id,
+        'regionPropertiesCapacities' => $regionPropertiesCapacities,
+        'predictions' => $regionPredictions]);
+
+
+});
+
+Route::get('/property/{id}', function (int $id) {
+
+
+    $regions = Api::regions()['regions'];
+    $regions[] = ['name' => 'Alle Regionen', 'id' => -1];
+
+    $base = Api::propertyBase($id);
+    $calendars = Api::propertyExtractions($id)['extractions'];
+    $propertyCapacities = Api::propertyCapacities($id);
+    $propertyNeighbours = Api::propertyNeighbours($id)['neighbours'];
+    $regionCapacitiesRegion = Api::regionCapacities($base['region_id']);
+    $regionCapacitiesAll = Api::regionCapacities(-1);
+    $regionCapacities = [[],[]];
+
+    $propertyCapacitiesMonthly = [];
+    $propertyCapacitiesDaily = [];
+
+    if($propertyCapacities){
+        foreach ($propertyCapacities['dates'] as $date) {
+            $propertyCapacitiesMonthly[] = Api::propertyCapacitiesMonthly($id, $date);
+            $propertyCapacitiesDaily[] = Api::propertyCapacitiesDaily($id, $date);
+        }
+
+        // filter out all date, which were not scraped for the property
+        foreach ($regionCapacitiesAll['dates'] as $index => $date) {
+            if(in_array($date, $propertyCapacities['dates'])){
+                $regionCapacities[0][] = $regionCapacitiesAll['capacities'][$index];
+            }
+        }
+
+        foreach ($regionCapacitiesRegion['dates'] as $index => $date) {
+            if(in_array($date, $propertyCapacities['dates'])){
+                $regionCapacities[1][] = $regionCapacitiesRegion['capacities'][$index];
+            }
+        }
+    }else{
+        return view('property-nodata', [
+            'base' => $base,
+            'regions' => $regions,
+        ]);
+    }
+
+    // prepare data for calendar chart
+    $calendarData = [];
+
+    foreach ($calendars as $el) {
+
+        $series = [];
+        $calendar = json_decode($el['calendar'], 1);
+
+        foreach ($calendar as $date => $status) {
+            $series[] = [$date, $status];
+        }
+
+        $calendarData[] = $series;
+
+    }
+
+    $diagramsOptions = [
+        "shared" => [
+            "colors" => Chart::colors(),
+            "extractiondates" => json_encode($propertyCapacities['dates'])
+        ],
+        "calendar" => [
+            "series" => $calendarData
+        ],
+        "capacities" => [
+            "xAxis" => [
+                "data" => json_encode($propertyCapacities['dates'])
+            ],
+            "series" => [
+                ["data" => json_encode($propertyCapacities['capacities'])],
+                ["data" => json_encode($regionCapacities[0])],
+                ["data" => json_encode($regionCapacities[1])],
+            ]
+        ],
+        "capacityMonthly" => [
+            "options" => $propertyCapacitiesMonthly,
+        ],
+        "capacityDaily" => [
+            "options" => $propertyCapacitiesDaily,
+        ],
+    ];
+
+    return view('property', [
+        'diagramsOptions' => $diagramsOptions,
+        'startDate' => $propertyCapacities['dates'][0],
+        'base' => $base,
+        'regions' => $regions,
+        'neighbours' => $propertyNeighbours
+    ]);
+
+});
+
+
--- a/dashboard/storage/app/.gitignore
+++ b/dashboard/storage/app/.gitignore
--- a/dashboard/storage/app/private/.gitignore
+++ b/dashboard/storage/app/private/.gitignore
--- a/dashboard/storage/app/public/.gitignore
+++ b/dashboard/storage/app/public/.gitignore
--- a/dashboard/storage/framework/.gitignore
+++ b/dashboard/storage/framework/.gitignore
--- a/dashboard/storage/framework/cache/.gitignore
+++ b/dashboard/storage/framework/cache/.gitignore
--- a/dashboard/storage/framework/cache/data/.gitignore
+++ b/dashboard/storage/framework/cache/data/.gitignore
--- a/dashboard/storage/framework/sessions/.gitignore
+++ b/dashboard/storage/framework/sessions/.gitignore
--- a/dashboard/storage/framework/testing/.gitignore
+++ b/dashboard/storage/framework/testing/.gitignore
--- a/dashboard/storage/framework/views/.gitignore
+++ b/dashboard/storage/framework/views/.gitignore
--- a/dashboard/storage/logs/.gitignore
+++ b/dashboard/storage/logs/.gitignore
--- a/dashboard/tailwind.config.js
+++ b/dashboard/tailwind.config.js
--- a/dashboard/tests/Feature/ExampleTest.php
+++ b/dashboard/tests/Feature/ExampleTest.php
--- a/dashboard/tests/TestCase.php
+++ b/dashboard/tests/TestCase.php
--- a/dashboard/tests/Unit/ExampleTest.php
+++ b/dashboard/tests/Unit/ExampleTest.php
--- a/dashboard/vite.config.js
+++ b/dashboard/vite.config.js
--- a/documentation/diagrams/ETL_C4.drawio
+++ b/documentation/diagrams/ETL_C4.drawio
@ -0,0 +1,142 @@
+<mxfile host="app.diagrams.net" agent="Mozilla/5.0 (X11; Linux x86_64; rv:134.0) Gecko/20100101 Firefox/134.0" version="26.0.6">
+  <diagram name="Seite-1" id="WNMV8rePnVf-2Vz_xhjt">
+    <mxGraphModel dx="1937" dy="1185" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="827" pageHeight="1169" math="0" shadow="0">
+      <root>
+        <mxCell id="0" />
+        <mxCell id="1" parent="0" />
+        <mxCell id="e6qn9whkbaCBCFCjUvdY-7" value="" style="rounded=0;whiteSpace=wrap;html=1;strokeColor=none;fillColor=#F5F5F5;" vertex="1" parent="1">
+          <mxGeometry x="10" y="420" width="1070" height="690" as="geometry" />
+        </mxCell>
+        <object placeholders="1" c4Name="ETL" c4Type="ContainerScopeBoundary" c4Application="Component" label="&lt;font style=&quot;font-size: 16px&quot;&gt;&lt;b&gt;&lt;div style=&quot;text-align: left&quot;&gt;%c4Name%&lt;/div&gt;&lt;/b&gt;&lt;/font&gt;&lt;div style=&quot;text-align: left&quot;&gt;[%c4Application%]&lt;/div&gt;" id="0Mexl9jQAquWokRCgHYt-11">
+          <mxCell style="rounded=1;fontSize=11;whiteSpace=wrap;html=1;dashed=1;arcSize=20;fillColor=default;strokeColor=#666666;fontColor=#333333;labelBackgroundColor=none;align=left;verticalAlign=bottom;labelBorderColor=none;spacingTop=0;spacing=10;dashPattern=8 4;metaEdit=1;rotatable=0;perimeter=rectanglePerimeter;noLabel=0;labelPadding=0;allowArrows=0;connectable=0;expand=0;recursiveResize=0;editable=1;pointerEvents=0;absoluteArcSize=1;points=[[0.25,0,0],[0.5,0,0],[0.75,0,0],[1,0.25,0],[1,0.5,0],[1,0.75,0],[0.75,1,0],[0.5,1,0],[0.25,1,0],[0,0.75,0],[0,0.5,0],[0,0.25,0]];" parent="1" vertex="1">
+            <mxGeometry x="30" y="440" width="1030" height="500" as="geometry" />
+          </mxCell>
+        </object>
+        <object placeholders="1" c4Name="Datenbank Aggregation" c4Type="Container" c4Technology="MySQL" c4Description="Datenbank welche während Aggregation verwendet wurde." label="&lt;font style=&quot;font-size: 16px&quot;&gt;&lt;b&gt;%c4Name%&lt;/b&gt;&lt;/font&gt;&lt;div&gt;[%c4Type%:&amp;nbsp;%c4Technology%]&lt;/div&gt;&lt;br&gt;&lt;div&gt;&lt;font style=&quot;font-size: 11px&quot;&gt;&lt;font color=&quot;#E6E6E6&quot;&gt;%c4Description%&lt;/font&gt;&lt;/div&gt;" id="0Mexl9jQAquWokRCgHYt-1">
+          <mxCell style="shape=cylinder3;size=15;whiteSpace=wrap;html=1;boundedLbl=1;rounded=0;labelBackgroundColor=none;fillColor=#23A2D9;fontSize=12;fontColor=#ffffff;align=center;strokeColor=#0E7DAD;metaEdit=1;points=[[0.5,0,0],[1,0.25,0],[1,0.5,0],[1,0.75,0],[0.5,1,0],[0,0.75,0],[0,0.5,0],[0,0.25,0]];resizable=0;" parent="1" vertex="1">
+            <mxGeometry x="40" y="100" width="240" height="120" as="geometry" />
+          </mxCell>
+        </object>
+        <object placeholders="1" c4Name="Datenbank Analyse" c4Type="Container" c4Technology="DuckDB" c4Description="Datenbank, welcher für die Analysen&lt;br&gt; verwendet wurden." label="&lt;font style=&quot;font-size: 16px&quot;&gt;&lt;b&gt;%c4Name%&lt;/b&gt;&lt;/font&gt;&lt;div&gt;[%c4Type%:&amp;nbsp;%c4Technology%]&lt;/div&gt;&lt;br&gt;&lt;div&gt;&lt;font style=&quot;font-size: 11px&quot;&gt;&lt;font color=&quot;#E6E6E6&quot;&gt;%c4Description%&lt;/font&gt;&lt;/div&gt;" id="0Mexl9jQAquWokRCgHYt-2">
+          <mxCell style="shape=cylinder3;size=15;whiteSpace=wrap;html=1;boundedLbl=1;rounded=0;labelBackgroundColor=none;fillColor=#23A2D9;fontSize=12;fontColor=#ffffff;align=center;strokeColor=#0E7DAD;metaEdit=1;points=[[0.5,0,0],[1,0.25,0],[1,0.5,0],[1,0.75,0],[0.5,1,0],[0,0.75,0],[0,0.5,0],[0,0.25,0]];resizable=0;" parent="1" vertex="1">
+            <mxGeometry x="790" y="100" width="240" height="120" as="geometry" />
+          </mxCell>
+        </object>
+        <mxCell id="0Mexl9jQAquWokRCgHYt-5" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;exitX=0;exitY=0.5;exitDx=0;exitDy=0;exitPerimeter=0;dashed=1;dashPattern=8 8;" parent="1" source="0Mexl9jQAquWokRCgHYt-3" target="0Mexl9jQAquWokRCgHYt-1" edge="1">
+          <mxGeometry relative="1" as="geometry" />
+        </mxCell>
+        <mxCell id="0Mexl9jQAquWokRCgHYt-7" value="liest Datenbank" style="edgeLabel;html=1;align=center;verticalAlign=middle;resizable=0;points=[];" parent="0Mexl9jQAquWokRCgHYt-5" vertex="1" connectable="0">
+          <mxGeometry x="-0.2497" y="-1" relative="1" as="geometry">
+            <mxPoint x="-10" y="1" as="offset" />
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+          <mxGeometry relative="1" as="geometry" />
+        </mxCell>
+        <mxCell id="Xmw1x83A06H2_JC6hK8s-18" value="&lt;div&gt;Liefert Inhalte zum Webbrowser&amp;nbsp;&lt;/div&gt;&lt;div&gt;von Benutzer:innen&lt;/div&gt;" style="edgeLabel;html=1;align=center;verticalAlign=middle;resizable=0;points=[];" vertex="1" connectable="0" parent="Xmw1x83A06H2_JC6hK8s-17">
+          <mxGeometry x="-0.0888" y="-2" relative="1" as="geometry">
+            <mxPoint x="5" as="offset" />
+          </mxGeometry>
+        </mxCell>
+        <object placeholders="1" c4Name="Visual Analytics Tool" c4Type="SystemScopeBoundary" c4Application="Software System" label="&lt;font style=&quot;font-size: 16px&quot;&gt;&lt;b&gt;&lt;div style=&quot;text-align: left&quot;&gt;%c4Name%&lt;/div&gt;&lt;/b&gt;&lt;/font&gt;&lt;div style=&quot;text-align: left&quot;&gt;[%c4Application%]&lt;/div&gt;" id="Xmw1x83A06H2_JC6hK8s-19">
+          <mxCell style="rounded=1;fontSize=11;whiteSpace=wrap;html=1;dashed=1;arcSize=20;fillColor=none;strokeColor=#666666;fontColor=#333333;labelBackgroundColor=none;align=left;verticalAlign=bottom;labelBorderColor=none;spacingTop=0;spacing=10;dashPattern=8 4;metaEdit=1;rotatable=0;perimeter=rectanglePerimeter;noLabel=0;labelPadding=0;allowArrows=0;connectable=0;expand=0;recursiveResize=0;editable=1;pointerEvents=0;absoluteArcSize=1;points=[[0.25,0,0],[0.5,0,0],[0.75,0,0],[1,0.25,0],[1,0.5,0],[1,0.75,0],[0.75,1,0],[0.5,1,0],[0.25,1,0],[0,0.75,0],[0,0.5,0],[0,0.25,0]];" vertex="1" parent="1">
+            <mxGeometry x="20" y="210" width="1080" height="460" as="geometry" />
+          </mxCell>
+        </object>
+      </root>
+    </mxGraphModel>
+  </diagram>
+</mxfile>
--- a/etl/README.md
+++ b/etl/README.md
@ -0,0 +1,25 @@
+## Installation
+Folgende Schritte zur Installation vornehmen
+
+### Abhängigkeiten installieren
+Zur Verwaltung der Abhängigkeiten wird [pixi](https://pixi.sh/) verwendet.
+```bash
+pixi install
+```
+
+### Datenbankverbindung konfigurieren
+Enviroment File erstellen:
+```bash
+cp src/.env.example .env
+```
+
+Im erstellten .env File die Datei anpassen:
+```
+DATABASE="/path/to/db.duckdb"
+```
+
+# FastAPI starten
+FastAPI auf einem anderen Port ausführen als das Dashboard.
+```bash
+fastapi dev api/main.py --port 8080
+```
--- a/etl/pixi.lock
+++ b/etl/pixi.lock
--- a/etl/pyproject.toml
+++ b/etl/pyproject.toml
@ -1,8 +1,7 @@
 [project]
-authors = [{name = "Giò Diani", email = "mail@gionathandiani.name"}]
-dependencies = []
-description = "Add a short description here"
-name = "consultancy_2"
+authors = [{name = "Giò Diani", email = "mail@gionathandiani.name"}, {name = "Mauro Stoffel", email = "mauro.stoffel@stud.fhgr.ch"}, {name = "Colin Bolli", email = "colin.bolli@stud.fhgr.ch"}, {name = "Charles Winkler", email = "charles.winkler@stud.fhgr.ch"}]
+description = "Datenauferbeitung"
+name = "ETL"
 requires-python = ">= 3.11"
 version = "0.1.0"

@ -15,7 +14,7 @@ channels = ["conda-forge"]
 platforms = ["win-64", "linux-64", "osx-64", "osx-arm64"]

 [tool.pixi.pypi-dependencies]
-consultancy_2 = { path = ".", editable = true }
+etl = { path = ".", editable = true }

 [tool.pixi.tasks]

@ -25,5 +24,6 @@ pandas = ">=2.2.3,<3"
 plotly = ">=5.24.1,<6"
 duckdb = ">=1.1.2,<2"
 python-dotenv = ">=1.0.1,<2"
+fastapi = ">=0.115.4,<0.116"
 polars = ">=0.20.26,<2"
 pyarrow = ">=18.0.0,<19"
--- a/etl/src/api/main.py
+++ b/etl/src/api/main.py
@ -0,0 +1,268 @@
+import datetime
+from typing import List, Union
+
+import data
+import polars as pl
+from data import etl_property_capacities as etl_pc
+from data import etl_property_capacities_daily as etl_pcd
+from data import etl_property_capacities_monthly as etl_pcm
+from data import etl_property_neighbours as etl_pn
+from data import etl_region_capacities as etl_rc
+from data import etl_region_capacities_daily as etl_rcd
+from data import etl_region_capacities_monthly as etl_rcm
+from data import etl_region_movAverage as etl_rmA
+from data import etl_region_properties_capacities as etl_rpc
+from fastapi import FastAPI
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel
+
+
+class RegionsItems(BaseModel):
+    name: str
+    id: str
+    count_properties: int
+
+class Regions(BaseModel):
+    regions: List[RegionsItems]
+
+class RegionBase(BaseModel):
+    name: str
+    id: str
+
+class RegionPropertiesCapacitiesValues(BaseModel):
+	date: str
+	property_id: str
+	capacity: float
+
+class RegionCapacities(BaseModel):
+    capacities: List[float]
+    dates: List
+
+class RegionCapacitiesMonthly(BaseModel):
+    months: List[str]
+    capacities: List[float]
+
+class RegionCapacitiesDaily(BaseModel):
+    weekdays: List[str]
+    capacities: List[float]
+
+class RegionPropertiesCapacities(BaseModel):
+    dates: List
+    property_ids: List
+    values: List[RegionPropertiesCapacitiesValues]
+
+class RegionMovingAverage(BaseModel):
+    dates: List
+    capacities_timeframe_before: List[Union[float, None]]
+    capacities_timeframe_after: List[Union[float, None]]
+    capacities_moving_average: List[Union[float, None]]
+
+class PropertiesGrowth(BaseModel):
+    dates: List
+    total_all: List[Union[int, None]]
+    total_heidiland: List[Union[int, None]]
+    total_engadin: List[Union[int, None]]
+    total_stmoritz: List[Union[int, None]]
+    total_davos: List[Union[int, None]]
+
+class PropertiesGeoList(BaseModel):
+    property_id: str
+    latlng: str
+    region_id: str
+
+class PropertiesGeo(BaseModel):
+    properties: List[PropertiesGeoList]
+
+class PropertyNeighboursList(BaseModel):
+    id: str
+    lat: float
+    lon: float
+
+class PropertyNeighbours(BaseModel):
+    neighbours: List[PropertyNeighboursList]
+
+class PropertyNeighboursList(BaseModel):
+    id: str
+    lat: float
+    lon: float
+
+class PropertyExtractionsList(BaseModel):
+    calendar: str
+    date: str
+
+class PropertyExtractions(BaseModel):
+    extractions: List[PropertyExtractionsList]
+
+class PropertyCapacities(BaseModel):
+    capacities: List[float]
+    dates: List[str]
+
+class PropertyCapacitiesMonthly(BaseModel):
+    months: List[str]
+    capacities: List[float]
+
+class PropertyCapacitiesDaily(BaseModel):
+    weekdays: List[str]
+    capacities: List[float]
+
+class PropertyBaseDetail(BaseModel):
+    property_platform_id: str
+    first_found: str
+    last_found: str
+    latlng: str
+    region_id: str
+    region_name: str
+
+class PropertyBase(BaseModel):
+    property_platform_id: str
+    first_found: str
+    last_found: str
+    latlng: str
+    region_id: str
+    region_name: str
+
+
+d = data.load()
+
+tags_metadata = [
+    {
+        "name": "regions",
+        "description": "Get data for regions.",
+    },
+    {
+        "name": "properties",
+        "description": "Get data for properties",
+    },
+]
+
+app = FastAPI(openapi_tags=tags_metadata)
+
+@app.get("/")
+def read_root():
+    return {"Hi there!"}
+
+@app.get("/regions", response_model=Regions, tags=['region'])
+def regions():
+	"""
+	Returns a list of all available regions.
+	"""
+	return {"regions" : d.properties_per_region().pl().to_dicts()}
+
+@app.get("/regions/{id}/base", response_model=RegionBase, tags=['region'])
+def region_base(id: int):
+	"""
+	Returns basic information about a region.
+	"""
+	base = d.region_base_data(id).pl().to_dicts()
+	return {"id": base[0]["id"], "name": base[0]["name"]}
+
+@app.get("/regions/{id}/capacities", response_model=RegionCapacities, tags=['region'])
+def region_capacities(id: int):
+	"""
+	Returs the capacities of a region, for every scraping. Set id to -1 to obtain data for all regions.
+	"""
+	capacities = etl_rc.region_capacities(id)
+	return capacities
+
+@app.get("/regions/{id}/capacities/monthly/{date}", response_model=RegionCapacitiesMonthly, tags=['region'])
+def region_capacities_monthly(id: int, date: datetime.date):
+	"""
+	Returns the capacities of a region for specified date by months. set id to -1 to obtain data for all regions.
+	"""
+	capacities = etl_rcm.region_capacities_monthly(id, date)
+	return capacities
+
+@app.get("/regions/{id}/capacities/daily/{date}", response_model=RegionCapacitiesDaily, tags=['region'])
+def region_capacities_daily(id: int, date: datetime.date):
+	"""
+	Returns the capacities of a region for specified date by days. set id to -1 to obtain data for all regions.
+	"""
+	capacities = etl_rcd.region_capacities_daily(id, date)
+	return capacities
+
+@app.get("/regions/{id}/moving-average/{date}",  response_model=RegionMovingAverage, tags=['region'])
+def region_capacities_data(id: int, date: datetime.date):
+	"""
+	Returns the moving average of a region for specified date. set id to -1 to obtain data for all regions.
+	"""
+	result = etl_rmA.region_movingAverage(id, date)
+	return result
+
+@app.get("/regions/{id}/properties/capacities", response_model=RegionPropertiesCapacities, tags=['region'])
+def region_property_capacities(id: int):
+	"""
+	Returns the capacities of properties in region, for every scraping. set id to -1 to obtain data for all regions.
+	"""
+	capacities = etl_rpc.region_properties_capacities(id)
+	return capacities
+
+@app.get("/properties/growth", response_model=PropertiesGrowth, tags=['property'])
+def properties_growth():
+	"""
+	Returns the growth rate of found properties
+	"""
+	options = {"dates" : d.properties_growth().pl()['date'].to_list(), "total_all" : d.properties_growth().pl()['total_all'].to_list(),  "total_heidiland" : d.properties_growth().pl()['total_heidiland'].to_list(), "total_engadin" : d.properties_growth().pl()['total_engadin'].to_list(), "total_davos" : d.properties_growth().pl()['total_davos'].to_list(), "total_stmoritz" : d.properties_growth().pl()['total_stmoritz'].to_list()}
+	return options
+
+@app.get("/properties/geo", response_model=PropertiesGeo, tags=['property'])
+def properties_geo():
+	"""
+	Returns the geocoordinates of properties
+	"""
+	return {"properties": d.properties_geo().pl().to_dicts()}
+
+@app.get("/properties/{id}/base", response_model=PropertyBase, tags=['property'])
+def property_base_data(id: int):
+	"""
+	Returns basic information about a property.
+	"""
+	base = d.property_base_data(id).pl().to_dicts()
+	return {
+		"property_platform_id": base[0]['property_platform_id'], 
+		"first_found": str(base[0]['first_found']), 
+		"last_found": str(base[0]['last_found']), 
+		"latlng": base[0]['latlng'], 
+		"region_id": base[0]['region_id'], 
+		"region_name": base[0]['region_name']}
+
+@app.get("/properties/{id}/neighbours", response_model=PropertyNeighbours, tags=['property'])
+def property_neighbours(id: int):
+	"""
+	Returns the 10 nearest properties from given property.
+	"""
+	return {"neighbours" : etl_pn.property_neighbours(id)}
+
+@app.get("/properties/{id}/extractions", response_model=PropertyExtractions, tags=['property'])
+def property_extractions(id: int):
+	"""
+	Returns extracted data from given property.
+	"""
+	return {"extractions" : d.extractions_for(property_id = id).pl().cast({"date": pl.String}).to_dicts()}
+
+@app.get("/properties/{id}/capacities", response_model=PropertyCapacities, tags=['property'])
+def property_capacities_data(id: int):
+	"""
+	Returns capacities for given property.
+	"""
+	capacities = etl_pc.property_capacities(id)
+	return capacities
+
+@app.get("/properties/{id}/capacities/monthly/{date}", response_model=PropertyCapacitiesMonthly, tags=['property'])
+def property_capacities_data_monthly(id: int, date: datetime.date):
+	"""
+	Returns capacities for given property and date by month.
+	"""
+	capacities = etl_pcm.property_capacities_monthly(id, date)
+	return capacities
+
+@app.get("/properties/{id}/capacities/daily/{date}", response_model=PropertyCapacitiesDaily, tags=['property'])
+def property_capacities_data_daily(id: int, date: datetime.date):
+	"""
+	Returns capacities for given property and date by day.
+	"""
+	capacities = etl_pcd.property_capacities_daily(id, date)
+	return capacities
+
+
+
+
--- a/etl/src/dashboard/main.py
+++ b/etl/src/dashboard/main.py
@ -1,22 +0,0 @@
-from typing import Union
-
-import polars as pl
-from fastapi import FastAPI, Response
-
-import data
-
-d = data.load()
-
-app = FastAPI()
-
-
-@app.get("/")
-def read_root():
-    return {"Hello": "World"}
-
-@app.get("/items/{item_id}")
-def read_item(item_id: int):
-	ext = d.extractions_for(item_id).pl()
-	out = ext.with_columns(pl.col("calendar").str.extract_all(r"([0-9]{4}-[0-9]{2}-[0-9]{2})|[0-2]").alias("calendar_data"))
-	out = out.drop(['calendar', 'property_id'])
-	return Response(content=out.write_json(), media_type="application/json")
--- a/etl/src/data/database.py
+++ b/etl/src/data/database.py
@ -28,8 +28,6 @@ class Database:
 		if(spatial_installed and not spatial_installed[0]):
 			self.connection.sql("INSTALL spatial")

-
-
 	def db_overview(self):
 		return self.connection.sql("DESCRIBE;").show()

@ -46,19 +44,100 @@ class Database:
 		
 	def properties_growth(self):
 		return self.connection.sql("""
+			WITH PropertiesALL AS (
+				SELECT 
+					strftime(created_at, '%Y-%m-%d') AS date, 
+					COUNT(*) as properties_count,
+					SUM(properties_count) OVER (ORDER BY date) AS total
+				FROM
+					consultancy_d.properties p
+				GROUP BY 
+					date
+				ORDER BY
+					date
+			),
+			PropertiesR1 AS (
+				SELECT 
+					strftime(created_at, '%Y-%m-%d') AS date, 
+					COUNT(*) as properties_count,
+					SUM(properties_count) OVER (ORDER BY date) AS total
+				FROM
+					consultancy_d.properties p
+				WHERE
+					p.seed_id = 1
+				GROUP BY 
+					date
+				ORDER BY
+					date
+			),
+			PropertiesR2 AS (
+				SELECT 
+					strftime(created_at, '%Y-%m-%d') AS date, 
+					COUNT(*) as properties_count,
+					SUM(properties_count) OVER (ORDER BY date) AS total
+				FROM
+					consultancy_d.properties p
+				WHERE
+					p.seed_id = 2
+				GROUP BY 
+					date
+				ORDER BY
+					date
+			),
+			PropertiesR3 AS (
+				SELECT 
+					strftime(created_at, '%Y-%m-%d') AS date, 
+					COUNT(*) as properties_count,
+					SUM(properties_count) OVER (ORDER BY date) AS total
+				FROM
+					consultancy_d.properties p
+				WHERE
+					p.seed_id = 3
+				GROUP BY 
+					date
+				ORDER BY
+					date
+			),
+			PropertiesR4 AS (
+				SELECT 
+					strftime(created_at, '%Y-%m-%d') AS date, 
+					COUNT(*) as properties_count,
+					SUM(properties_count) OVER (ORDER BY date) AS total
+				FROM
+					consultancy_d.properties p
+				WHERE
+					p.seed_id = 4
+				GROUP BY 
+					date
+				ORDER BY
+					date
+			)
 			SELECT 
-				strftime(created_at, '%Y-%m-%d') AS date, 
-				COUNT(*) as properties_count 
+				p.date, 
+				p.total AS total_all, 
+				pR1.total as total_heidiland, 
+				pR2.total AS total_davos, 
+				pR3.total AS total_engadin, 
+				pR4.total AS total_stmoritz  
 			FROM 
-				consultancy_d.properties 
-			GROUP BY 
-				date;
+				PropertiesAll p
+			LEFT JOIN 
+				PropertiesR1 pR1 ON p.date = pR1.date
+			LEFT JOIN 
+				PropertiesR2 pR2 ON p.date = pR2.date
+			LEFT JOIN 
+				PropertiesR3 pR3 ON p.date = pR3.date
+			LEFT JOIN 
+				PropertiesR4 pR4 ON p.date = pR4.date
+			ORDER BY 
+				p.date
 			""")
 	
 	def properties_per_region(self):
 		return self.connection.sql("""
 			SELECT 
 				regions.name, 
+				regions.id,
 				COUNT(*) AS count_properties 
 			FROM 
 				consultancy_d.properties 
@ -68,7 +147,22 @@ class Database:
 				consultancy_d.regions ON regions.id = seeds.region_id 
 			GROUP BY 
 				properties.seed_id, 
-				regions.name
+				regions.name,
+				regions.id
+			ORDER BY
+				count_properties ASC
+			""")
+
+	def propIds_with_region(self):
+		return self.connection.sql("""
+			SELECT 
+				properties.id, seed_id, regions.name
+			FROM 
+				consultancy_d.properties 
+			LEFT JOIN 
+				consultancy_d.seeds ON seeds.id = properties.seed_id 
+			LEFT JOIN 
+				consultancy_d.regions ON regions.id = seeds.region_id 
 			""")

 	def properties_unreachable(self):
@ -196,21 +290,7 @@ class Database:
 		""")

 	def extractions(self):
-		return self.connection.sql(f"""
-			SELECT 
-				JSON_EXTRACT(body, '$.content.days') as calendar,
-				property_id,
-				created_at 
-			FROM 
-				consultancy_d.extractions
-			WHERE
-				type == 'calendar'
-			ORDER BY
-				property_id
-		""")
-
-	def extractions_for(self, property_id):
-		return self.connection.sql(f"""
+		return self.connection.sql("""
 			SELECT 
 				JSON_EXTRACT(body, '$.content.days') as calendar,
 				property_id,
@ -219,11 +299,63 @@ class Database:
 				consultancy_d.extractions
 			WHERE
 				type == 'calendar' AND
-				property_id = {property_id}
+				calendar NOT NULL
 			ORDER BY
 				property_id
 		""")

+	def extractions_with_region(self):
+		return self.connection.sql("""
+			SELECT 
+				JSON_EXTRACT(body, '$.content.days') as calendar, 
+				extractions.property_id, 
+				extractions.created_at,
+				properties.seed_id, 
+				regions.name
+			FROM 
+				consultancy_d.extractions 
+			LEFT JOIN 
+				consultancy_d.properties ON properties.id = extractions.property_id 
+			LEFT JOIN 
+				consultancy_d.seeds ON seeds.id = properties.seed_id 
+			LEFT JOIN 
+				consultancy_d.regions ON regions.id = seeds.region_id 
+			WHERE
+				calendar NOT NULL
+			""")
+
+	def extractions_for(self, property_id):
+		return self.connection.sql(f"""
+			SELECT 
+				JSON_EXTRACT(body, '$.content.days') as calendar,
+				created_at as date
+			FROM 
+				consultancy_d.extractions
+			WHERE
+				type == 'calendar' AND
+				property_id = {property_id} AND
+				calendar NOT NULL
+			ORDER BY
+				created_at
+		""")
+
+	def extractions_propId_scrapeDate(self, property_id: int, scrape_date: str):
+		return self.connection.sql(f"""
+			SELECT
+				JSON_EXTRACT(body, '$.content.days') as calendar,
+				created_at 
+			FROM 
+				consultancy_d.extractions
+			WHERE
+				type == 'calendar' AND
+				property_id = {property_id} AND
+				calendar NOT NULL AND
+				created_at >= '{scrape_date}'
+			ORDER BY
+				created_at
+			LIMIT 1
+		""")
+
 	# Anzahl der extrahierten properties pro Exktraktionsvorgang
 	def properties_per_extraction(self, property_id):
 		return self.connection.sql("""
@ -267,3 +399,180 @@ class Database:
 			ORDER BY property_id
 		""")

+	def property_base_data(self, id):
+		return self.connection.sql(f"""
+			SELECT
+				p.property_platform_id,
+				p.created_at as first_found,
+				p.last_found,
+				p.check_data as latlng,
+				r.id as region_id,
+				r.name as region_name 
+			FROM
+				consultancy_d.properties p
+			INNER JOIN consultancy_d.seeds s ON s.id = p.seed_id 
+			INNER JOIN consultancy_d.regions r ON s.region_id = r.id
+			WHERE
+				p.id = {id}
+			""")
+
+	def region_base_data(self, id):
+		if id == -1:
+			where = ''
+		else:
+			where = f"WHERE r.id = {id}"
+		return self.connection.sql(f"""
+			SELECT
+				r.id as id,
+				r.name as name
+			FROM
+				consultancy_d.regions r
+			{where}
+			""")
+
+	def properties_geo(self):
+		return self.connection.sql("""
+			SELECT
+				p.id as property_id,
+				p.check_data as latlng,
+				r.id as region_id
+			FROM
+				consultancy_d.properties p
+			LEFT JOIN 
+				consultancy_d.seeds s ON s.id = p.seed_id
+			LEFT JOIN 
+				consultancy_d.regions r ON r.id = s.region_id
+		""")
+
+	def properties_geo_seeds(self):
+		return self.connection.sql("""
+			SELECT
+				p.id,
+				p.seed_id,
+				p.check_data as coordinates
+			FROM
+				consultancy_d.properties p
+		""")
+
+	def capacity_of_region(self, region_id):
+		return self.connection.sql(f"""
+			SELECT 
+				JSON_EXTRACT(body, '$.content.days') as calendarBody, 
+				strftime(extractions.created_at, '%Y-%m-%d') AS ScrapeDate,
+				extractions.property_id, 
+			FROM 
+				consultancy_d.extractions 
+			LEFT JOIN 
+				consultancy_d.properties ON properties.id = extractions.property_id 
+			WHERE
+				type == 'calendar' AND
+				properties.seed_id = {region_id} AND
+				calendarBody NOT NULL		
+			""")
+
+	def singleScrape_of_region(self, region_id: int, scrape_date_min: str, scrape_date_max: str):
+		return self.connection.sql(f"""
+			SELECT 
+				JSON_EXTRACT(body, '$.content.days') as calendarBody, 
+			FROM 
+				consultancy_d.extractions 
+			LEFT JOIN 
+				consultancy_d.properties ON properties.id = extractions.property_id 
+			WHERE
+				type == 'calendar' AND
+				properties.seed_id = {region_id} AND 
+				extractions.created_at >= '{scrape_date_min}' AND
+				extractions.created_at < '{scrape_date_max}' AND 
+				calendarBody NOT NULL		
+			""")
+
+	def singleScrape_of_global(self, scrape_date_min: str, scrape_date_max: str):
+		return self.connection.sql(f"""
+			SELECT 
+				JSON_EXTRACT(body, '$.content.days') as calendarBody, 
+			FROM 
+				consultancy_d.extractions 
+			LEFT JOIN 
+				consultancy_d.properties ON properties.id = extractions.property_id 
+			WHERE
+				type == 'calendar' AND
+				extractions.created_at >= '{scrape_date_min}' AND
+				extractions.created_at < '{scrape_date_max}' AND 
+				calendarBody NOT NULL		
+			""")
+
+	def singleScrape_of_region_scrapDate(self, region_id: int, scrape_date_min: str, scrape_date_max: str):
+		return self.connection.sql(f"""
+			SELECT 
+				JSON_EXTRACT(body, '$.content.days') as calendarBody, 
+				extractions.created_at 
+			FROM 
+				consultancy_d.extractions 
+			LEFT JOIN 
+				consultancy_d.properties ON properties.id = extractions.property_id 
+			WHERE
+				type == 'calendar' AND
+				properties.seed_id = {region_id} AND 
+				extractions.created_at >= '{scrape_date_min}' AND
+				extractions.created_at < '{scrape_date_max}' AND
+				calendarBody NOT NULL		
+			""")
+
+	def singleScrape_of_global_scrapDate(self, scrape_date_min: str, scrape_date_max: str):
+		return self.connection.sql(f"""
+			SELECT 
+				JSON_EXTRACT(body, '$.content.days') as calendarBody, 
+				extractions.created_at 
+			FROM 
+				consultancy_d.extractions 
+			LEFT JOIN 
+				consultancy_d.properties ON properties.id = extractions.property_id 
+			WHERE
+				type == 'calendar' AND
+				extractions.created_at >= '{scrape_date_min}' AND
+				extractions.created_at < '{scrape_date_max}' AND
+				calendarBody NOT NULL	
+			""")
+
+	def capacity_global(self):
+		return self.connection.sql(f"""
+			SELECT 
+				JSON_EXTRACT(body, '$.content.days') as calendarBody, 
+				strftime(extractions.created_at, '%Y-%m-%d') AS ScrapeDate,
+				extractions.property_id, 
+			FROM 
+				consultancy_d.extractions 
+			LEFT JOIN 
+				consultancy_d.properties ON properties.id = extractions.property_id 
+			WHERE
+				type == 'calendar'
+			AND
+				calendarBody NOT NULL
+			""")
+
+	def capacity_comparison_of_region(self, region_id_1, region_id_2):
+		return self.connection.sql(f"""
+			SELECT 
+				JSON_EXTRACT(body, '$.content.days') as calendarBody, 
+				strftime(extractions.created_at, '%Y-%m-%d') AS ScrapeDate,
+				extractions.property_id, 
+				properties.seed_id
+			FROM 
+				consultancy_d.extractions 
+			LEFT JOIN 
+				consultancy_d.properties ON properties.id = extractions.property_id 
+			WHERE
+				type == 'calendar' AND
+				(properties.seed_id = {region_id_1} OR 
+				properties.seed_id = {region_id_2})	AND
+				calendarBody NOT NULL		
+			""")
+
+	def unique_scrapeDates(self):
+		return self.connection.sql(f"""
+			SELECT DISTINCT 
+				strftime(extractions.created_at, '%Y-%m-%d') AS ScrapeDate,
+			FROM 
+				consultancy_d.extractions 
+			""")
+
--- a/etl/src/data/etl_cache.py
+++ b/etl/src/data/etl_cache.py
@ -0,0 +1,18 @@
+from pathlib import Path
+from pickle import dump, load
+
+Path('cache').mkdir(parents=True, exist_ok=True)
+
+# load pickle obj
+def openObj(file):
+	filepath = Path(f"cache/{file}")
+	if filepath.is_file():
+		with open(filepath, 'rb') as f:
+			return load(f)
+	return False
+
+# save pickle obj
+def saveObj(file, result):
+	filepath = Path(f"cache/{file}")
+	with open(filepath, 'wb') as f:
+		dump(result, f)
--- a/etl/src/data/etl_pipelines.py
+++ b/etl/src/data/etl_pipelines.py
@ -1,47 +0,0 @@
-import polars as pl
-import json
-from datetime import datetime, timedelta
-def expansion_Pipeline(df):
-    '''
-    Rearranges a given extractions Dataframe into an expanded Dataframe.
-    New Columns :propId, created_at calendar_date, calendar_value
-    :param df: Inputs from database.py/extractions or database.py/extractions_for functions
-    :return: expanded dataframe
-    '''
-    data = []
-
-    for row in df.iter_rows():
-        propId = row[1]
-        createdAt = row[2]
-        if row[0]:
-            temp = json.loads(row[0])
-            keys = temp.keys()
-            for key in keys:
-                out = [propId, createdAt.date(), datetime.strptime(key, '%Y-%m-%d').date(), temp[key]]
-                data.append(out)
-
-    df = pl.DataFrame(data, schema=["property_id", "created_at", "calendar_date", "calendar_value"])
-    return df
-
-
-def liveDates_Pipeline(df):
-    '''
-    Returns the expanded Dataframe with only the live data and no future data
-    :param df: Inputs from database.py/extractions or database.py/extractions_for functions
-    :return: expanded and filtered dataframe
-    '''
-    df = expansion_Pipeline(df)
-    print(df)
-    df = df.filter(pl.col("calendar_date") == pl.col("created_at")+timedelta(days=2))
-    return df
-
-def liveDates_PipelineFromExpanded(df):
-    '''
-    Filters an already expanded df and returns only the live data and no future data
-    NOTE: The actual live date and the next is always 0. The reason is most likely that it is forbidden to
-    book on the current or next day. Workaround: Compare with the day after tomorrow
-    :param df: Inputs from expansion_Pipeline
-    :return: expanded and filtered dataframe
-    '''
-    df = df.filter(pl.col("calendar_date") == pl.col("created_at")+timedelta(days=2))
-    return df
--- a/etl/src/data/etl_property_capacities.py
+++ b/etl/src/data/etl_property_capacities.py
@ -0,0 +1,46 @@
+from io import StringIO
+
+import polars as pl
+
+import data
+from data import etl_cache
+
+d = data.load()
+
+def property_capacities(id: int):
+
+	file = f"etl_property_capacities_{id}.obj"
+	obj = etl_cache.openObj(file)
+	if obj:
+		return obj
+
+	extractions = d.extractions_for(id).pl()
+	df_dates = pl.DataFrame()
+
+	for row in extractions.rows(named=True):
+		df_calendar = pl.read_json(StringIO(row['calendar']))
+		#df_calendar.insert_column(0, pl.Series("created_at", [row['created_at']]))
+		df_dates = pl.concat([df_calendar, df_dates], how="diagonal")
+	
+	# order = sorted(df_dates.columns)
+	# df_dates = df_dates.select(order)
+	sum_hor = df_dates.sum_horizontal()
+	#print(sum_hor)
+	# Get the available dates per extraction
+	count_days = []
+	for dates in df_dates.rows():
+		# Remove all None values
+		liste = [x for x in dates if x is not None]
+		count_days.append(len(liste))
+
+	counts = pl.DataFrame({"count_days" : count_days, "sum" : sum_hor})
+	result = {"capacities": [], "dates": extractions['date'].cast(pl.Date).cast(pl.String).to_list() }
+	
+	for row in counts.rows(named=True):
+		max_capacity = row['count_days'] * 2
+		max_capacity_perc = 100 / max_capacity
+		result['capacities'].append(round(max_capacity_perc * row['sum'], 2))
+	result['capacities'].reverse()
+
+	etl_cache.saveObj(file, result)
+	return result
--- a/etl/src/data/etl_property_capacities_daily.py
+++ b/etl/src/data/etl_property_capacities_daily.py
@ -0,0 +1,41 @@
+from io import StringIO
+
+import polars as pl
+
+import data
+from data import etl_cache
+
+d = data.load()
+
+def property_capacities_daily(id: int, scrapeDate: str):
+	
+	file = f"etl_property_capacities_weekdays_{id}_{scrapeDate}.obj"
+	obj = etl_cache.openObj(file)
+	if obj:
+		return obj
+
+	extractions = d.extractions_propId_scrapeDate(id, scrapeDate).pl()
+	weekdays = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
+	df_calendar = pl.DataFrame()
+	numWeeks = 0
+
+	for row in extractions.rows(named=True):
+		scrapeDate = row['created_at']
+		df_calendar = pl.read_json(StringIO(row['calendar']))
+		columnTitles = df_calendar.columns
+		df_calendar = df_calendar.transpose()
+		df_calendar = df_calendar.with_columns(pl.Series(name="dates", values=columnTitles))
+		df_calendar = df_calendar.with_columns((pl.col("dates").str.to_date()))
+		numWeeks = round((df_calendar.get_column("dates").max() - df_calendar.get_column("dates").min()).days / 7, 0)
+		df_calendar = df_calendar.with_columns(pl.col("dates").dt.weekday().alias("weekday_num"))
+		df_calendar = df_calendar.with_columns(pl.col("dates").dt.strftime("%A").alias("weekday"))
+		df_calendar = df_calendar.drop("dates")
+
+	df_calendar = df_calendar.group_by(["weekday", "weekday_num"]).agg(pl.col("column_0").sum())
+	df_calendar = df_calendar.with_columns((pl.col("column_0") / numWeeks / 2 * 100).alias("column_0"))
+	df_calendar = df_calendar.sort('weekday_num')
+	df_calendar = df_calendar.drop('weekday_num')
+
+	result = {"date": scrapeDate, "weekdays": df_calendar['weekday'].to_list(), 'capacities': df_calendar['column_0'].to_list()}
+	etl_cache.saveObj(file, result)
+	return result
--- a/etl/src/data/etl_property_capacities_monthly.py
+++ b/etl/src/data/etl_property_capacities_monthly.py
@ -0,0 +1,38 @@
+from io import StringIO
+
+import polars as pl
+
+import data
+from data import etl_cache
+
+d = data.load()
+
+def property_capacities_monthly(id: int, scrapeDate: str):
+
+	file = f"etl_property_capacities_monthly_{id}_{scrapeDate}.obj"
+	obj = etl_cache.openObj(file)
+	if obj:
+		return obj
+
+	extractions = d.extractions_propId_scrapeDate(id, scrapeDate).pl()
+	df_calendar = pl.DataFrame()
+
+	for row in extractions.rows(named=True):
+		scrapeDate = row['created_at']
+		df_calendar = pl.read_json(StringIO(row['calendar']))
+		columnTitles = df_calendar.columns
+		df_calendar = df_calendar.transpose()
+		df_calendar = df_calendar.with_columns(pl.Series(name="dates", values=columnTitles))
+		df_calendar = df_calendar.with_columns((pl.col("dates").str.to_date()))
+		df_calendar = df_calendar.with_columns((pl.col("dates").dt.month_end().dt.day().alias('numDays')))
+		df_calendar = df_calendar.with_columns((pl.col("dates").dt.strftime("%b") + " " + (pl.col("dates").dt.strftime("%Y"))).alias('date_short'))
+		df_calendar = df_calendar.with_columns((pl.col("dates").dt.strftime("%Y") + " " + (pl.col("dates").dt.strftime("%m"))).alias('dates'))
+
+
+
+	df_calendar = df_calendar.group_by(['dates', 'date_short', 'numDays']).agg(pl.col("column_0").sum())
+	df_calendar = df_calendar.with_columns((pl.col("column_0") / pl.col("numDays") / 2 * 100).alias("column_0"))
+	df_calendar = df_calendar.sort('dates')
+	result = {"months": df_calendar['date_short'].to_list(), 'capacities': df_calendar['column_0'].to_list()}
+	etl_cache.saveObj(file, result)
+	return result
--- a/etl/src/data/etl_property_neighbours.py
+++ b/etl/src/data/etl_property_neighbours.py
@ -0,0 +1,73 @@
+from math import asin, atan2, cos, degrees, radians, sin, sqrt
+
+import polars as pl
+
+import data
+from data import etl_cache
+
+d = data.load()
+
+
+def calcHaversinDistance(latMain, lonMain, lat, lon):
+    R = 6371
+
+    # convert decimal degrees to radians
+    latMain, lonMain, lat, lon = map(radians, [latMain, lonMain, lat, lon])
+
+    # haversine formula
+    dlon = lonMain - lon
+    dlat = latMain - lat
+
+    a = sin(dlat / 2) ** 2 + cos(lat) * cos(latMain) * sin(dlon / 2) ** 2
+    c = 2 * atan2(sqrt(a), sqrt(1-a))
+    d = R * c
+
+    return d
+
+
+def property_neighbours(id: int):
+
+    file = f"etl_property_neighbours_{id}.obj"
+    obj = etl_cache.openObj(file)
+    if obj:
+        return obj
+
+    extractions = d.properties_geo_seeds().pl()
+
+    # Get lat, long and region from main property
+    latMain, lonMain = extractions.filter(pl.col('id') == str(id))['coordinates'][0].split(',')
+    latMain, lonMain = map(float, [latMain, lonMain])
+    region = extractions.filter(pl.col('id') == str(id))['seed_id'][0]
+
+    # Prefilter the dataframe to only the correct region
+    extractions = extractions.filter(pl.col('seed_id') == str(region))
+    extractions = extractions.drop('seed_id')
+
+    # Remove main property from DF
+    extractions = extractions.filter(pl.col('id') != str(id))
+
+    # Split coordinate into lat and lon
+    extractions = extractions.with_columns(pl.col("coordinates").str.split_exact(",", 1).struct.rename_fields(["lat", "lon"]).alias("lat/lon")).unnest("lat/lon")
+    extractions = extractions.drop('coordinates')
+    extractions = extractions.with_columns(pl.col("lat").cast(pl.Float32))
+    extractions = extractions.with_columns(pl.col("lon").cast(pl.Float32))
+
+    # Calculate distances
+    distances = []
+    for row in extractions.rows(named=True):
+        lat = row['lat']
+        lon = row['lon']
+        dist = calcHaversinDistance(latMain, lonMain, lat, lon)
+        distances.append(dist)
+
+    # Add distance to DF
+    extractions = extractions.with_columns(pl.Series(name="distances", values=distances))
+
+    # Sort for distance and give only first 10
+    extractions = extractions.sort("distances").head(10)
+    extractions = extractions.drop('distances')
+
+    result = extractions.to_dicts()
+    etl_cache.saveObj(file, result)
+
+    return result
--- a/etl/src/data/etl_region_capacities.py
+++ b/etl/src/data/etl_region_capacities.py
@ -0,0 +1,58 @@
+from datetime import date
+from io import StringIO
+
+import polars as pl
+
+import data
+from data import etl_cache
+
+d = data.load()
+
+def region_capacities(id: int):
+
+	file = f"etl_region_capacities_{id}.obj"
+	obj = etl_cache.openObj(file)
+	if obj:
+		return obj
+
+	# Get Data
+	if id == -1:
+		extractions = d.capacity_global().pl()
+	else:
+		extractions = d.capacity_of_region(id).pl()
+	# turn PropertyIDs to ints for sorting
+	extractions = extractions.cast({"property_id": int})
+
+	extractions.drop('property_id')
+	df_dates = pl.DataFrame()
+
+	# Get Data from JSON
+	gridData = pl.DataFrame(schema=[("scrape_date", pl.String), ("sum_hor", pl.Int64), ("calendar_width", pl.Int64)])
+	dayCounts = []
+	for row in extractions.rows(named=True):
+		# Return 0 for sum if calendar is null
+		if row['calendarBody']:
+			calDF = pl.read_json(StringIO(row['calendarBody']))
+			sum_hor = calDF.sum_horizontal()[0]
+		else:
+			sum_hor = 0
+		gridData = gridData.vstack(pl.DataFrame({"scrape_date" : row['ScrapeDate'], "sum_hor": sum_hor, "calendar_width": calDF.width}))
+
+	# Create Aggregates of values
+	df_count = gridData.group_by("scrape_date").agg(pl.col("sum_hor").count())
+	df_sum = gridData.group_by("scrape_date").agg(pl.col("sum_hor").sum())
+	df_numDays = gridData.group_by("scrape_date").agg(pl.col("calendar_width").max())
+
+	# Join and rename DF's
+	df = df_sum.join(df_count, on= 'scrape_date').join(df_numDays, on= 'scrape_date')
+
+	# Calculate normed capacities for each scrapeDate
+	df = df.with_columns((pl.col("sum_hor") / pl.col("sum_hor_right") / (pl.col("calendar_width")*2) * 100).alias("capacity"))
+
+	# Sort the date column
+	df = df.cast({"scrape_date": date}).sort('scrape_date')
+
+	result = {"capacities": df['capacity'].to_list(), "dates": df['scrape_date'].to_list()}
+
+	etl_cache.saveObj(file, result)
+	return result
--- a/etl/src/data/etl_region_capacities_daily.py
+++ b/etl/src/data/etl_region_capacities_daily.py
@ -0,0 +1,64 @@
+from datetime import datetime, timedelta
+from io import StringIO
+
+import polars as pl
+
+import data
+from data import etl_cache
+
+d = data.load()
+
+def region_capacities_daily(id: int, scrapeDate_start: str):
+
+    file = f"etl_region_capacities_weekdays_{id}_{scrapeDate_start}.obj"
+    obj = etl_cache.openObj(file)
+    if obj:
+        return obj
+
+    # Get end date of start search-window
+    scrapeDate_end = scrapeDate_start + timedelta(days=1)
+
+    # Get Data
+    if id == -1:
+        extractions = d.singleScrape_of_global_scrapDate(scrapeDate_start, scrapeDate_end).pl()
+    else:
+        extractions = d.singleScrape_of_region_scrapDate(id, scrapeDate_start, scrapeDate_end).pl()
+
+    df_calendar = pl.DataFrame()
+    numWeeks = 0
+    firstExe = True
+    counter = 0
+    for row in extractions.rows(named=True):
+        scrapeDate = row['created_at']
+        if row['calendarBody']:
+            counter += 1
+            df_calendar = pl.read_json(StringIO(row['calendarBody']))
+            columnTitles = df_calendar.columns
+            df_calendar = df_calendar.transpose()
+            df_calendar = df_calendar.with_columns(pl.Series(name="dates", values=columnTitles))
+            df_calendar = df_calendar.with_columns((pl.col("dates").str.to_date()))
+            numWeeks = round((df_calendar.get_column("dates").max() - df_calendar.get_column("dates").min()).days / 7, 0)
+            df_calendar = df_calendar.with_columns(pl.col("dates").dt.weekday().alias("weekday_num"))
+            df_calendar = df_calendar.with_columns(pl.col("dates").dt.strftime("%A").alias("weekday"))
+            df_calendar = df_calendar.drop("dates")
+
+            df_calendar = df_calendar.group_by(["weekday", "weekday_num"]).agg(pl.col("column_0").sum())
+            df_calendar = df_calendar.with_columns((pl.col("column_0") / numWeeks / 2 * 100).alias("column_0"))
+            df_calendar = df_calendar.sort('weekday_num')
+            df_calendar = df_calendar.drop('weekday_num')
+            df_calendar = df_calendar.rename({'column_0': str(counter)})
+
+            if firstExe:
+                outDf = df_calendar
+                firstExe = False
+            else:
+                outDf = outDf.join(df_calendar, on='weekday')
+
+    # Calculate horizontal Mean
+    means = outDf.mean_horizontal()
+    outDf = outDf.insert_column(1, means)
+    outDf = outDf[['weekday', 'mean']]
+
+    result = {"weekdays": outDf['weekday'].to_list(),'capacities': outDf['mean'].to_list()}
+    etl_cache.saveObj(file, result)
+    return result
--- a/etl/src/data/etl_region_capacities_monthly.py
+++ b/etl/src/data/etl_region_capacities_monthly.py
@ -0,0 +1,65 @@
+from datetime import datetime, timedelta
+from io import StringIO
+
+import polars as pl
+
+import data
+from data import etl_cache
+
+d = data.load()
+
+
+def region_capacities_monthly(id: int, scrapeDate_start: str):
+
+    file = f"etl_region_capacities_monthly_{id}_{scrapeDate_start}.obj"
+    obj = etl_cache.openObj(file)
+    if obj:
+        return obj
+
+    # Get end date of start search-window
+    scrapeDate_end = scrapeDate_start + timedelta(days=1)
+
+    # Get Data
+    if id == -1:
+        extractions = d.singleScrape_of_global_scrapDate(scrapeDate_start, scrapeDate_end).pl()
+    else:
+        extractions = d.singleScrape_of_region_scrapDate(id, scrapeDate_start, scrapeDate_end).pl()
+
+    df_calendar = pl.DataFrame()
+    numWeeks = 0
+    firstExe = True
+    counter = 0
+    for row in extractions.rows(named=True):
+        scrapeDate = row['created_at']
+        if row['calendarBody']:
+            counter += 1
+            df_calendar = pl.read_json(StringIO(row['calendarBody']))
+            columnTitles = df_calendar.columns
+            df_calendar = df_calendar.transpose()
+            df_calendar = df_calendar.with_columns(pl.Series(name="dates", values=columnTitles))
+            df_calendar = df_calendar.with_columns((pl.col("dates").str.to_date()))
+            df_calendar = df_calendar.with_columns((pl.col("dates").dt.month_end().dt.day().alias('numDays')))
+            df_calendar = df_calendar.with_columns((pl.col("dates").dt.strftime("%b") + " " + (pl.col("dates").dt.strftime("%Y"))).alias('date_short'))
+            df_calendar = df_calendar.with_columns((pl.col("dates").dt.strftime("%Y") + " " + (pl.col("dates").dt.strftime("%m"))).alias('dates'))
+            df_calendar = df_calendar.group_by(['dates', 'date_short','numDays']).agg(pl.col("column_0").sum())
+            df_calendar = df_calendar.with_columns((pl.col("column_0") / pl.col("numDays") / 2 * 100).alias("column_0"))
+            df_calendar = df_calendar.sort('dates')
+            df_calendar = df_calendar.drop('dates')
+            df_calendar = df_calendar.drop('numDays')
+
+            df_calendar = df_calendar.rename({'column_0': str(counter)})
+
+            if firstExe:
+                outDf = df_calendar
+                firstExe = False
+            else:
+                outDf = outDf.join(df_calendar, on='date_short')
+
+    # Calculate horizontal Mean
+    means = outDf.mean_horizontal()
+    outDf = outDf.insert_column(1, means)
+    outDf = outDf[['date_short', 'mean']]
+
+    result = {"date": scrapeDate, "months": outDf['date_short'].to_list(),'capacities': outDf['mean'].to_list()}
+    etl_cache.saveObj(file, result)
+    return result
--- a/etl/src/data/etl_region_movAverage.py
+++ b/etl/src/data/etl_region_movAverage.py
@ -0,0 +1,136 @@
+from datetime import date, datetime, timedelta
+from io import StringIO
+
+import polars as pl
+
+import data
+from data import etl_cache
+
+d = data.load()
+
+def region_movingAverage(id: int, scrape_date_start_min: datetime.date):
+
+    file = f"etl_region_movingAverage_{id}_{scrape_date_start_min}.obj"
+    obj = etl_cache.openObj(file)
+    if obj:
+        return obj
+
+    # Settings
+    # Offset between actual and predict ScrapeDate
+    timeOffset = 30
+
+    # Calculation Frame
+    calcFrame = 180
+
+    # Filter Setting
+    windowSize = 7
+
+
+    # Get unique ScrapeDates
+    uniqueScrapeDates = d.unique_scrapeDates().pl()
+    uniqueScrapeDates = uniqueScrapeDates.get_column('ScrapeDate').str.to_date()
+    uniqueScrapeDates = uniqueScrapeDates.sort().to_list()
+
+    # Get end date of start search-window
+    scrape_date_start_max = scrape_date_start_min + timedelta(days=1)
+
+    # Get start and end date of End search-window
+    scrape_date_end_min = scrape_date_start_min + timedelta(days=timeOffset)
+
+    # Get closest ScrapeDate
+    scrape_date_end_min = min(uniqueScrapeDates, key=lambda x: abs(x - scrape_date_end_min))
+    scrape_date_end_max = scrape_date_end_min + timedelta(days=1)
+
+    final_end_date = scrape_date_end_min + timedelta(days=calcFrame)
+
+    # Get Data
+    if id == -1:
+        ex_start = d.singleScrape_of_global(scrape_date_start_min, scrape_date_start_max)
+        ex_start_count = ex_start.shape[0]
+
+        ex_end = d.singleScrape_of_global(scrape_date_end_min, scrape_date_end_max)
+        ex_end_count = ex_end.shape[0]
+    else:
+        ex_start = d.singleScrape_of_region(id, scrape_date_start_min, scrape_date_start_max)
+        ex_start_count = ex_start.shape[0]
+
+        ex_end = d.singleScrape_of_region(id, scrape_date_end_min, scrape_date_end_max)
+        ex_end_count = ex_end.shape[0]
+
+    num_properties = [ex_start_count, ex_end_count]
+    start_end = [ex_start, ex_end]
+    outDFList = []
+
+    for df in start_end:
+        df = df.pl()
+        firstExe = True
+        counter = 1
+        outDF = pl.DataFrame(schema={"0": int, "dates": date})
+        for row in df.rows(named=True):
+            if row['calendarBody']:
+                calDF = pl.read_json(StringIO(row['calendarBody']))
+                columnTitles = calDF.columns
+                calDF = calDF.transpose()
+                calDF = calDF.with_columns(pl.Series(name="dates", values=columnTitles))
+                calDF = calDF.with_columns((pl.col("dates").str.to_date()))
+
+                # Filter out all Data that's in the calculation frame
+                calDF = calDF.filter((pl.col("dates") >= (scrape_date_start_min + timedelta(days=1))))
+                calDF = calDF.filter((pl.col("dates") < final_end_date))
+
+                # Join all information into one Dataframe
+                if firstExe:
+                    outDF = calDF
+                    firstExe = False
+                else:
+                    outDF = outDF.join(calDF, on='dates')
+                    outDF = outDF.rename({'column_0': str(counter)})
+                    counter += 1
+
+        outDF = outDF.sort('dates')
+        outDFList.append(outDF)
+
+
+    # Calculate the horizontal Sum for all Dates
+    arrayCunter = 0
+    tempDFList = []
+    for df in outDFList:
+        dates = df.select(pl.col("dates"))
+        values = df.select(pl.exclude("dates"))
+        sum_hor = values.sum_horizontal()
+
+        sum_hor = sum_hor / num_properties[arrayCunter] / 2 * 100
+        arrayCunter += 1
+
+        newDF = dates.with_columns(sum_hor=pl.Series(sum_hor))
+        tempDFList.append(newDF)
+
+    # Join actual and predict Values
+    outDF = tempDFList[0].join(tempDFList[1], on='dates', how='outer')
+
+    # Rename Columns for clarity
+    outDF = outDF.drop('dates_right')
+
+    # sum_hor_predict is the data from the earlier ScrapeDate
+    outDF = outDF.rename({'sum_hor_right': 'sum_hor_actual', 'sum_hor': 'sum_hor_predict'})
+
+    # Calculate Moving average from Start
+    baseValues = outDF.get_column('sum_hor_predict').to_list()
+    i = 0
+    moving_averages = []
+    while i < len(baseValues) - windowSize + 1:
+        window = baseValues[i: i + windowSize]
+        window_average = sum(window) / windowSize
+        moving_averages.append(window_average)
+        i += 1
+
+    # Add empty values back to the front and end of moving_averages
+    num_empty = int(windowSize / 2)
+    moving_averages = [None] *num_empty + moving_averages + [None] * num_empty
+
+    # Add moving_averages to df
+    outDF = outDF.with_columns(moving_averages=pl.Series(moving_averages))
+
+    result = {'dates': outDF.get_column('dates').to_list(), 'capacities_timeframe_before': outDF.get_column('sum_hor_predict').to_list(), 'capacities_timeframe_after':outDF.get_column('sum_hor_actual').to_list(), 'capacities_moving_average':outDF.get_column('moving_averages').to_list(),}
+    etl_cache.saveObj(file, result)
+    return result
--- a/etl/src/data/etl_region_properties_capacities.py
+++ b/etl/src/data/etl_region_properties_capacities.py
@ -0,0 +1,64 @@
+from io import StringIO
+
+import polars as pl
+
+import data
+from data import etl_cache
+
+d = data.load()
+
+def region_properties_capacities(id: int):
+
+    file = f"etl_region_properties_capacities_{id}.obj"
+    obj = etl_cache.openObj(file)
+    if obj:
+        return obj
+
+    # Get Data
+    if id == -1:
+        df = d.capacity_global().pl()
+    else:
+        df = d.capacity_of_region(id).pl()
+    # turn PropertyIDs to ints for sorting
+    df = df.cast({"property_id": int})
+
+    # Get uniques for dates and propIDs and sort them
+    listOfDates = df.get_column("ScrapeDate").unique().sort()
+    listOfPropertyIDs = df.get_column("property_id").unique().sort()
+
+    # Create DFs from lists to merge later
+    datesDF = pl.DataFrame(listOfDates).with_row_index("date_index")
+    propIdDF = pl.DataFrame(listOfPropertyIDs).with_row_index("prop_index")
+
+    # Merge Dataframe to generate indices
+    df = df.join(datesDF, on='ScrapeDate')
+    df = df.join(propIdDF, on='property_id')
+
+    # Calculate grid values
+    gridData = pl.DataFrame(schema=[("scrape_date", pl.String), ("property_id", pl.String), ("sum_hor", pl.Int64)])
+    for row in df.rows(named=True):
+        # Return 0 for sum if calendar is null
+        if row['calendarBody']:
+            calDF = pl.read_json(StringIO(row['calendarBody']))
+            sum_hor = calDF.sum_horizontal()[0]
+        else:
+            sum_hor = 0
+
+        gridData = gridData.vstack(pl.DataFrame({"scrape_date" : row['ScrapeDate'], "property_id": str(row['property_id']), "sum_hor": sum_hor}))
+
+    # get the overall maximum sum
+    maxValue = gridData['sum_hor'].max()
+    values = []
+
+    for row in gridData.rows(named=True):
+        capacity = (row['sum_hor']*100)/maxValue
+        values.append({"date" : row['scrape_date'], "property_id": row['property_id'], "capacity": capacity})
+
+    # Cast listOfDates to datetime
+    listOfDates = listOfDates.cast(pl.Date).to_list()
+    listOfPropertyIDs = listOfPropertyIDs.cast(pl.String).to_list()
+
+    # Create JSON
+    outDict = {'dates': listOfDates, 'property_ids': listOfPropertyIDs, 'values': values}
+    etl_cache.saveObj(file, outDict)
+    return outDict
--- a/etl/src/consultancy_2/init.py
+++ b/etl/src/consultancy_2/init.py
--- a/etl/src/gio/index.html
+++ b/etl/src/gio/index.html
--- a/etl/src/gio/test_duckdb.py
+++ b/etl/src/gio/test_duckdb.py
@ -1,22 +0,0 @@
-import polars as pl
-
-import data
-
-inst = data.load()
-test = inst.extractions_for(1).pl()
-
-out = test.with_columns(
-    pl.col("calendar").str.extract_all(r"([0-9]{4}-[0-9]{2}-[0-9]{2})|[0-2]").alias("extracted_nrs"),
-)
-out.drop(['calendar', 'property_id'])
-ll = out.get_column("extracted_nrs").explode().gather_every(2)
-llo = out.get_column("extracted_nrs").explode().gather_every(2, offset=1)
-lli = ll.list.concat(llo)
-
-
-print(ll)
-print(lli)
-
-
-
-
--- a/etl/src/mauro/Data_Analysis.py
+++ b/etl/src/mauro/Data_Analysis.py
@ -1,199 +0,0 @@
-import MySQLdb
-import json
-from datetime import datetime, timedelta
-import numpy as np
-
-
-def getPropertyDataFromDB():
-    db = MySQLdb.connect(host="localhost",user="root",passwd="admin",db="consultancy")
-    cur = db.cursor()
-
-    cur.execute("SELECT id, seed_id, check_data "
-                "FROM properties ")
-    propData = cur.fetchall()
-    db.close()
-
-    return propData
-
-def getDataFromDB(propId):
-    '''
-    Function to get data from MySQL database filter with the given propId
-    :return: scrapeDates and calendarData
-    '''
-    db = MySQLdb.connect(host="localhost",user="root",passwd="admin",db="consultancy")
-    cur = db.cursor()
-
-    cur.execute("SELECT JSON_EXTRACT(header, '$.Date') "
-                "FROM extractions "
-                f"WHERE type='calendar' AND property_id = {propId};")
-    scrapeDates = cur.fetchall()
-
-    cur.execute("SELECT JSON_EXTRACT(body, '$.content.days') "
-                "FROM extractions "
-                f"WHERE type='calendar' AND property_id = {propId};")
-    calendarData = cur.fetchall()
-    db.close()
-
-    return scrapeDates, calendarData
-
-def getUniqueScrapeDates():
-    db = MySQLdb.connect(host="localhost",user="root",passwd="admin",db="consultancy")
-    cur = db.cursor()
-
-    cur.execute("SELECT JSON_EXTRACT(header, '$.Date') "
-                "FROM extractions "
-                f"WHERE type='calendar'")
-    uniqueScrapeDates = cur.fetchall()
-    db.close()
-
-    return uniqueScrapeDates
-
-def getPropsPerScrape(scrapeDate):
-    date = datetime.strptime(scrapeDate, '%Y-%m-%d')
-    end_date = date + timedelta(days=1)
-
-    db = MySQLdb.connect(host="localhost",user="root",passwd="admin",db="consultancy")
-    cur = db.cursor()
-
-    cur.execute("SELECT property_id "
-                "FROM extractions "
-                f"WHERE type='calendar' AND created_at > '{scrapeDate}' AND created_at < '{str(end_date)}'")
-    uniqueScrapeDates = cur.fetchall()
-    db.close()
-
-    return uniqueScrapeDates
-
-def getuniquePropIdFromDB():
-    '''
-    Function to get unique propId from MySQL database
-    :return: propList
-    '''
-    db = MySQLdb.connect(host="localhost",user="root",passwd="admin",db="consultancy")
-    cur = db.cursor()
-    cur.execute("SELECT DISTINCT property_id "
-                "FROM extractions;")
-    propIds = cur.fetchall()
-    db.close()
-
-    propList = []
-    for propId in propIds:
-        propList.append(propId[0])
-
-    return propList
-
-def reformatScrapeDates(scrapeDatesIn):
-    '''
-    Reformats the scrapeDates column to a shortened datetime format
-    :param scrapeDatesIn:
-    :return:
-    '''
-    scrapeDates = []
-    for row in scrapeDatesIn:
-        date = datetime.strptime(json.loads(row[0])[0], '%a, %d %b %Y %H:%M:%S %Z').date()
-        str = date.strftime('%Y-%m-%d')
-        scrapeDates.append(str)
-
-    return scrapeDates
-
-def checkForLostProprty(calendarData):
-    '''
-    Checks if there are "None" Entries in the calendarData meaning they were no longer found
-    :param calendarData:
-    :return: Boolean indicating if there are "None" Entries in the calendarData
-    '''
-    for row in calendarData:
-        if None in row:
-            return True
-    return False
-
-
-def getMinMaxDate(calendarData):
-    '''
-    Gets the min and max values from a calendar data
-    :param calendarData: get all calendar data from querry
-    :return: the minimal and maximal date
-    '''
-    #minimales und maximales Datum ermitteln
-    fullDateList = []
-    for row in calendarData:
-        tempJson = json.loads(row[0]).keys()
-        for key in tempJson:
-            #print(key)
-            fullDateList.append(datetime.strptime(key, '%Y-%m-%d').date())
-
-    end_dt = max(fullDateList)
-    start_dt = min(fullDateList)
-    delta = timedelta(days=1)
-    HeaderDates = []
-
-    while start_dt <= end_dt:
-        HeaderDates.append(start_dt)
-        start_dt += delta
-
-    return HeaderDates
-
-
-def creatDataMatrix(HeaderDates, calendarData):
-    '''
-    Creates the data matrix from a calendar data
-    :param HeaderDates: The list of all possible Dates in the dataset is used as the headers
-    :param calendarData: the main information from the sql querry
-    :return: data Matrix with all the dates in the dataset
-    '''
-    data = []
-    for row in calendarData:
-        tempList = [-1] * len(HeaderDates)
-        tempJson = json.loads(row[0])
-        for key in tempJson:
-            date = datetime.strptime(key, '%Y-%m-%d').date()
-            content = tempJson[key]
-            index = [i for i, x in enumerate(HeaderDates) if x == date]
-            tempList[index[0]] = content
-        data.append(tempList)
-
-    return data
-
-
-def getAccuracy(df, baseLine, compLine):
-    '''
-    Calculates the accuracy of a given dataframe with a given baseLine and compLine
-    :param df:
-    :param baseLine:
-    :param compLine:
-    :return: Accuracy: The percentage of dates that had the same information in both baseLine and compLine
-    '''
-    try:
-        df = df.iloc[[baseLine,compLine]]
-    except IndexError:
-        return -1
-    total = 0
-    noChange = 0
-    first = True
-    for series_name, series in df.items():
-        if first:
-            first = False
-        else:
-            total += 1
-            #print(series_name)
-            if series[baseLine] != -1:
-                if series[compLine] != -1:
-                    if series[baseLine] == series[compLine]:
-                        noChange += 1
-
-    accuracy = noChange / total
-    return accuracy
-
-def getMeanAccuracy(accList):
-    '''
-    Get the mean Accuracy of the entire timedelay of one property
-    :param accList: List of accuracy Values of a comparison
-    :return: Average of the accuracy values while ignoring the '-1' values
-    '''
-    out = []
-    for row in accList:
-        row = [x for x in row if x != -1]
-        out.append(np.average(row))
-    return out
-
-
-
--- a/etl/src/mauro/OLD_ConsultProj1/CalendarVisualization.py
+++ b/etl/src/mauro/OLD_ConsultProj1/CalendarVisualization.py
@ -1,83 +0,0 @@
-from datetime import datetime, timedelta
-import json
-
-import MySQLdb                                  #Version 2.2.4
-import pandas as pd                             #Version 2.2.3
-import plotly.express as px                     #Version 5.24.1
-
-db = MySQLdb.connect(host="localhost",user="root",passwd="admin",db="consultancy")
-cur = db.cursor()
-
-cur.execute("SELECT JSON_EXTRACT(header, '$.Date') "
-            "FROM extractions "
-            "WHERE type='calendar' AND property_id = 200;")
-dateoutput = cur.fetchall()
-
-
-cur.execute("SELECT JSON_EXTRACT(body, '$.content.days') "
-            "FROM extractions "
-            "WHERE type='calendar' AND property_id = 200;")
-
-output = cur.fetchall()
-db.close()
-
-#createScrapedate Liste
-ytickVals = list(range(0, 30, 5))
-scrapeDates = []
-#print(dateoutput)
-for row in dateoutput:
-    date = datetime.strptime(json.loads(row[0])[0], '%a, %d %b %Y %H:%M:%S %Z').date()
-    str = date.strftime('%d/%m/%Y')
-    scrapeDates.append(str)
-
-#minimales und maximales Datum ermitteln
-fullDateList = []
-for row in output:
-    tempJson = json.loads(row[0]).keys()
-    for key in tempJson:
-        #print(key)
-        fullDateList.append(datetime.strptime(key, '%Y-%m-%d').date())
-
-end_dt = max(fullDateList)
-start_dt = min(fullDateList)
-delta = timedelta(days=1)
-HeaderDates = []
-
-while start_dt <= end_dt:
-    HeaderDates.append(start_dt)
-    start_dt += delta
-
-#Create data-Matrix
-data = []
-for row in output:
-    tempList = [-1] * len(HeaderDates)
-    tempJson = json.loads(row[0])
-    for key in tempJson:
-        date = datetime.strptime(key, '%Y-%m-%d').date()
-        content = tempJson[key]
-        index = [i for i, x in enumerate(HeaderDates) if x == date]
-        tempList[index[0]] = content
-    data.append(tempList)
-
-#Transform to Dataframe for Plotly
-df = pd.DataFrame(data, columns=HeaderDates)
-
-#Generate Plotly Diagramm
-colScale = [[0, 'rgb(0, 0, 0)'], [0.33, 'rgb(204, 16, 16)'], [0.66, 'rgb(10, 102, 15)'], [1, 'rgb(17, 184, 26)']]
-fig = px.imshow(df, color_continuous_scale= colScale)
-lines = list(range(0,30,1))
-for i in lines:
-    #fig.add_hline(y=i+0.5, line_color="white")
-    fig.add_hline(y=i+0.5)
-
-fig.update_layout(yaxis = dict(tickfont = dict(size=50))),
-fig.update_layout(xaxis = dict(tickfont = dict(size=50)))
-fig.update_layout(xaxis_title="Verfügbarkeitsdaten Mietobjekt", yaxis_title="Scrapingvorgang")
-fig.update_xaxes(title_font_size=100, title_font_weight="bold")
-fig.update_yaxes(title_font_size=100, title_font_weight="bold")
-fig.update_layout(yaxis = dict(tickmode = 'array',tickvals = ytickVals, ticktext = scrapeDates))
-fig.update_xaxes(title_standoff = 80)
-fig.update_yaxes(title_standoff = 80)
-fig.update_layout(xaxis={'side': 'top'})
-fig.show()
-
--- a/etl/src/mauro/accuracyPerLocation.py
+++ b/etl/src/mauro/accuracyPerLocation.py
@ -1,58 +0,0 @@
-import Data_Analysis as DA
-import pandas as pd
-
-accuracy = pd.read_csv(f'results/accMeanDf.csv')
-
-propData = DA.getPropertyDataFromDB()
-propData = pd.DataFrame(propData, columns =['property_id', 'region', 'geoLocation'])
-propData = propData.drop(columns=['geoLocation'])
-#print(propData)
-
-merge = pd.merge(propData, accuracy, on="property_id")
-#print(merge)
-
-#1 = Heidiland, 2 = Davos, 3 = Engadin 4 = St.Moritz
-
-heidiAcc = merge[merge['region'] == 1]
-davosAcc = merge[merge['region'] == 2]
-EngadAcc = merge[merge['region'] == 3]
-StMorAcc = merge[merge['region'] == 4]
-
-
-heidiMean = heidiAcc.mean(axis=0)
-davosMean = davosAcc.mean(axis=0)
-EngadMean = EngadAcc.mean(axis=0)
-StMorMean = StMorAcc.mean(axis=0)
-
-heidiSDev = heidiAcc.std(axis=0)
-davosSDev = davosAcc.std(axis=0)
-EngadSDev = EngadAcc.std(axis=0)
-StMorSDev = StMorAcc.std(axis=0)
-
-
-accuracyOverview = pd.DataFrame()
-
-accuracyOverview.insert(0, "St. Moritz StdDev", StMorSDev, True)
-accuracyOverview.insert(0, "St. Moritz Mean", StMorMean, True)
-accuracyOverview.insert(0, "Engadin StdDev", EngadSDev, True)
-accuracyOverview.insert(0, "Engadin Mean", EngadMean, True)
-accuracyOverview.insert(0, "Davos StdDev", davosSDev, True)
-accuracyOverview.insert(0, "Davos Mean", davosMean, True)
-accuracyOverview.insert(0, "Heidi StdDev", heidiSDev, True)
-accuracyOverview.insert(0, "Heidi Mean", heidiMean, True)
-
-
-accuracyOverview.drop(index=accuracyOverview.index[0], axis=0, inplace=True)
-accuracyOverview.drop(index=accuracyOverview.index[0], axis=0, inplace=True)
-accuracyOverview.to_csv('results/accuracyOverview.csv', index=True)
-
-#delete unused DF's
-del merge, accuracy, propData
-del heidiAcc, davosAcc, EngadAcc, StMorAcc
-del heidiMean, davosMean, EngadMean, StMorMean
-del heidiSDev, davosSDev, EngadSDev, StMorSDev
-
-
-print(accuracyOverview)
-
-
--- a/etl/src/mauro/createAccuracyValues.py
+++ b/etl/src/mauro/createAccuracyValues.py
@ -1,73 +0,0 @@
-import pandas as pd
-import os
-import re
-import numpy as np
-
-def getAccuracy(df, baseLine, compLine):
-    try:
-        df = df.iloc[[baseLine,compLine]]
-    except IndexError:
-        return -1
-    total = 0
-    noChange = 0
-    first = True
-    for series_name, series in df.items():
-        if first:
-            first = False
-        else:
-            total += 1
-            #print(series_name)
-            if series[baseLine] != -1:
-                if series[compLine] != -1:
-                    if series[baseLine] == series[compLine]:
-                        noChange += 1
-
-    accuracy = noChange / total
-    return accuracy
-
-def getMeanAccuracy(accList):
-    out = []
-    for row in accList:
-        row = [x for x in row if x != -1]
-        out.append(np.average(row))
-    return out
-
-deltaList = [1, 2, 10, 20]
-#1 = 1 Scrape Interval
-#2 = ca. 1 Woche
-#10 = 1 Monat (30Tage)
-#20 = 2 Monate
-
-
-directory = os.fsencode("dok")
-
-columnNames = ['property_id', 'timedelay_1', 'timedelay_2','timedelay_10','timedelay_20']
-accListDf = pd.DataFrame(columns = columnNames)
-accMeanDf = pd.DataFrame(columns = columnNames)
-
-
-for file in os.listdir(directory):
-    filename = os.fsdecode(file)
-    if filename.endswith(".csv"):
-        propId = re.findall("\d+", filename)[0]
-        print(propId)
-        df = pd.read_csv(f'dok/{filename}')
-        fullList = []
-        accList = []
-        #Loop though all deltas in the deltaList
-        for delta in deltaList:
-            accList = []
-            #Loop through all Dates as Baseline date
-            for i in range(df.shape[0]):
-                acc = getAccuracy(df, i, i+delta)
-                accList.append(acc)
-            fullList.append(accList)
-
-
-        meanList = getMeanAccuracy(fullList)
-        accListDf = accListDf._append({'property_id': propId, 'timedelay_1': fullList[0], 'timedelay_2': fullList[1], 'timedelay_10': fullList[2], 'timedelay_20': fullList[3]}, ignore_index=True)
-        accMeanDf = accMeanDf._append({'property_id': propId, 'timedelay_1': meanList[0], 'timedelay_2': meanList[1], 'timedelay_10': meanList[2], 'timedelay_20': meanList[3]}, ignore_index=True)
-
-
-accListDf.to_csv('results/accListDf.csv',  index=False)
-accMeanDf.to_csv('results/accMeanDf.csv',  index=False)
--- a/etl/src/mauro/createLostPropertyList.py
+++ b/etl/src/mauro/createLostPropertyList.py
@ -1,20 +0,0 @@
-import Data_Analysis as DA
-import csv
-
-propIds = DA.getuniquePropIdFromDB()
-
-lostProperties = []
-
-for propId in propIds:
-    print(propId)
-    scrapeDates, calendarData = DA.getDataFromDB(propId)
-    if DA.checkForLostProprty(calendarData):
-        lostProperties.append(propId)
-
-print(f"{len(lostProperties)} of {len(propIds)} properties are lost")
-
-with open('results/allLostProperties', 'w') as f:
-    write = csv.writer(f)
-    write.writerow(lostProperties)
-
-#Output: 221 of 1552 properties were lost at some point
--- a/etl/src/mauro/createPropCSV.py
+++ b/etl/src/mauro/createPropCSV.py
@ -1,28 +0,0 @@
-import Data_Analysis as DA
-import pandas as pd
-import os
-
-
-
-propIds = DA.getuniquePropIdFromDB()
-
-for propId in propIds:
-    name = f"dok/calendarData_prop{propId}.csv"
-    if not os.path.exists(name):
-        print(propId)
-        scrapeDates, calendarData = DA.getDataFromDB(propId)
-        if DA.checkForLostProprty(calendarData):
-            print(f"Lost Proprty: {propId}")
-        else:
-            scrapeDates = DA.reformatScrapeDates(scrapeDates)
-            HeaderDates = DA.getMinMaxDate(calendarData)
-            data = DA.creatDataMatrix(HeaderDates, calendarData)
-
-            # Transform to Dataframe for Plotly
-            df = pd.DataFrame(data, columns=HeaderDates)
-            df.insert(0, "ScrapeDate", scrapeDates, True)
-
-            df = df.drop(index=0)  # Irregulärer Abstand in den Scraping Zeiten (nur 2 Tage)
-            df = df.drop(df.columns[[1, 2]], axis=1)
-            df.to_csv(name, index=False)
-
--- a/etl/src/mauro/createPropertiesPerScrape.py
+++ b/etl/src/mauro/createPropertiesPerScrape.py
@ -1,32 +0,0 @@
-import Data_Analysis as DA
-import pandas as pd
-
-#Alle Scrape Dates auslesen, umformatieren und doppelte Löschen
-uniqueScrapeDates = DA.getUniqueScrapeDates()
-uniqueScrapeDates = DA.reformatScrapeDates(uniqueScrapeDates)
-uniqueScrapeDates= list(dict.fromkeys(uniqueScrapeDates))
-#print(uniqueScrapeDates)
-
-#Liste der Listen der properties pro Scrape Datum erstellen
-fullPropList = []
-for date in uniqueScrapeDates:
-    propList = []
-    strDate = date
-    properties = DA.getPropsPerScrape(strDate)
-    for prop in properties:
-        propList.append(prop[0])
-        propList = list(dict.fromkeys(propList))
-    fullPropList.append(propList)
-    #print(propList)
-print(fullPropList)
-
-#zu DF umwandeln, mit Property ID's in the Spaltennamen und One-Hot-Encoding
-all_property_ids = sorted(set([item for sublist in fullPropList for item in sublist]))
-print(all_property_ids)
-df = pd.DataFrame(0, index=range(len(fullPropList)), columns=all_property_ids)
-for i, property_list in enumerate(fullPropList):
-    df.loc[i, property_list] = 1
-
-df.to_csv('results/PropertiesPerScrape.csv', index=True)
-
-print(df)
--- a/etl/src/mauro/dok/calendarData_prop1.csv
+++ b/etl/src/mauro/dok/calendarData_prop1.csv
--- a/etl/src/mauro/dok/calendarData_prop10.csv
+++ b/etl/src/mauro/dok/calendarData_prop10.csv
--- a/etl/src/mauro/dok/calendarData_prop100.csv
+++ b/etl/src/mauro/dok/calendarData_prop100.csv
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Giò Diani	43fe5b7931	typo	2025-01-19 14:23:02 +01:00
Giò Diani	bbb3c71d79	remove deleted library from API	2025-01-19 14:17:57 +01:00
Giò Diani	b616c27173	Merge remote-tracking branch 'origin/main'	2025-01-19 14:12:59 +01:00
Giò Diani	75bd923038	Vereinheitlichung API	2025-01-19 14:12:36 +01:00
mmaurostoffel	e758e064d0	Unbenutzer ETL Prozess gelöscht	2025-01-19 13:14:35 +01:00
Giò Diani	e67636dbd6	Beschriftung	2025-01-19 12:44:52 +01:00
Giò Diani	acf1989576	Predition charts symbols ausblenden, Popovers überarbeitet.	2025-01-19 12:42:04 +01:00
mmaurostoffel	cdb92ac50f	Aufräumen des etl_property_neighbours Kommentare gelöscht und haversineFormel angepasst, dass sie gleich wie in der Quelle ist	2025-01-19 11:54:22 +01:00
Giò Diani	41f8c89178	really basic mobile view	2025-01-19 11:27:21 +01:00
Giò Diani	5664e5130e	reactivate cache of webapp	2025-01-18 23:59:19 +01:00
Giò Diani	107f8c46b9	Navi Beschriftung	2025-01-18 23:46:07 +01:00
Giò Diani	bc27afe05a	prettier navi	2025-01-18 23:10:53 +01:00
mmaurostoffel	7663791f33	ungenutzte Datei etl_pipelines.py gelöscht	2025-01-18 22:43:49 +01:00
Giò Diani	3d0d45e6bc	aktualisierte diagramme	2025-01-18 22:09:12 +01:00
Giò Diani	c7988e77b8	fix missing name	2025-01-18 18:41:21 +01:00
Giò Diani	5ad31709a9	refactor dashboard	2025-01-18 17:31:31 +01:00
Giò Diani	cd2d211259	documentation for api	2025-01-18 15:39:29 +01:00
Giò Diani	e4e05b4788	refactoring; more consistent naming for API endpoint and variables.	2025-01-17 23:46:22 +01:00
Giò Diani	e0c8b3eb1b	Beschriftungen	2025-01-17 19:09:16 +01:00
mmaurostoffel	2560d43c3f	fixed calculation of monthly data, closes #18	2025-01-17 17:51:27 +01:00
Giò Diani	4e233da745	fix error in tooltip of moving average chart	2025-01-17 17:37:01 +01:00
Giò Diani	7497271ac8	remove unused modul causing error	2025-01-17 17:30:30 +01:00
mmaurostoffel	5ffc222430	movAverage überarbeitet, closes #17 erster Monat mit wird nun auch angezeigt "Nullstelle" zu Beginn entfernt	2025-01-17 17:01:29 +01:00
mmaurostoffel	468ad94430	Mauro Ordner gelöscht closes #16	2025-01-17 15:27:25 +01:00
mmaurostoffel	5b97c7ead2	Merge branch 'main' of https://gitea.fhgr.ch/stoffelmauro/ConsultancyProject_2_ETL	2025-01-17 15:25:28 +01:00
mmaurostoffel	9fb7fb2f82	Für alle weiteren SQL Queries die Bedingung das calendar not null sein darf eingefügt	2025-01-17 15:25:22 +01:00
Giò Diani	d598f9d861	Lücken in linechart verbinden	2025-01-17 15:25:03 +01:00
Giò Diani	e4462b0cfa	Fehlermeldung, wenn keine Daten für Property vorhanden.	2025-01-17 15:13:41 +01:00
Giò Diani	0d3bc7acd3	don't include null values	2025-01-17 14:54:25 +01:00
Giò Diani	23bec87af6	Achsenbeschriftungen	2025-01-17 13:49:43 +01:00
Giò Diani	142046b1c6	Achsenbeschriftungen und grid Heatmap	2025-01-17 13:22:05 +01:00
Giò Diani	639533dda0	Rahmen um Heatmap	2025-01-17 13:02:54 +01:00
Giò Diani	4b10406eb7	Aufräumen etl modul; pixi install erneut ausführen, damit die Pfade angepasst werden!	2025-01-17 12:53:44 +01:00
Giò Diani	968f706218	Update README	2025-01-17 12:52:31 +01:00
Giò Diani	c66f7c476d	Beschriftung MovAvg Chart, Region bei Property im Menu anzeigen.	2025-01-17 12:51:45 +01:00
mmaurostoffel	c3ab7d8e2f	global for movingAverage implemented	2025-01-15 21:27:53 +01:00
mmaurostoffel	7e3862a578	global Extractions für region capacities monthly and weekdays eingefügt, closes #15	2025-01-15 20:44:16 +01:00
Giò Diani	0733709366	diagramm etl	2025-01-15 17:48:12 +01:00
Giò Diani	3290c1cce3	some polishing	2025-01-15 16:57:18 +01:00
Giò Diani	959b84d1e1	my fault fixes #14	2025-01-15 14:31:07 +01:00
Giò Diani	a5a21fb925	Überarbeitung Dashboard	2025-01-14 22:11:31 +01:00
Giò Diani	8bef4b9621	added simple caching for etl	2025-01-14 19:56:15 +01:00
mmaurostoffel	d436c5d892	added missing logic to etl_region_movAverage	2025-01-13 23:06:42 +01:00
Giò Diani	cd66207bc7	Prediction Charts	2025-01-13 22:50:03 +01:00
Giò Diani	18a672a5de	bugfix unnecessary parentheses broke api	2025-01-13 19:22:54 +01:00
mmaurostoffel	3d7d5bbbe3	etl_region_capacities_monthly eingefügt closes #10	2025-01-13 18:02:19 +01:00
mmaurostoffel	d8d2d1e757	Werte von 0-1 zu 0-100 angepasst closes #12	2025-01-13 17:17:46 +01:00
mmaurostoffel	4487932f1b	Merge branch 'main' of https://gitea.fhgr.ch/stoffelmauro/ConsultancyProject_2_ETL	2025-01-13 17:12:15 +01:00
mmaurostoffel	fcd7ca34ad	closes #11	2025-01-13 17:08:37 +01:00
Giò Diani	ebcd647a2f	Einige Anpassungen am Dashboard. Region Ansicht.	2025-01-13 17:06:54 +01:00
Giò Diani	a3121bf58e	navigation, regions	2025-01-12 20:55:46 +01:00
Giò Diani	50ea3f1bd8	fix module not found error (matplotlib not needed)	2025-01-12 20:44:05 +01:00
mmaurostoffel	af1c2301a9	Added Enpoint for movAvg	2025-01-12 20:38:16 +01:00
mmaurostoffel	a571c8c40f	Merge branch 'main' of https://gitea.fhgr.ch/stoffelmauro/ConsultancyProject_2_ETL	2025-01-12 20:30:03 +01:00
mmaurostoffel	b23879b6d3	First Version of etl_region_movAverage.py eingefügt	2025-01-12 20:27:33 +01:00
Giò Diani	0250221d96	implements regions base endpoint #9	2025-01-12 20:16:20 +01:00
mmaurostoffel	f31c23ea51	Merge branch 'main' of https://gitea.fhgr.ch/stoffelmauro/ConsultancyProject_2_ETL	2025-01-12 16:53:33 +01:00
mmaurostoffel	c059890ba7	singleScrape_of_region eingefügt	2025-01-12 16:53:31 +01:00
Giò Diani	992e299829	Überarbeitung Property	2025-01-12 16:49:29 +01:00
Giò Diani	67c0d85213	Betrifft #7 . Möglicher fix, bitte Resultat kontrollieren. Das Problem lag m.E. darin, dass durch das hin und her zwischen Listen und DataFrame die Typisierungen der Werte verloren gehen, weshalb es dann auch entsprechenden Fehler schmeisste.	2025-01-12 11:56:33 +01:00
mmaurostoffel	e176d1e73f	Bugfix: Testfunktion aus etl_region_comparison gelöscht sry	2025-01-12 11:28:53 +01:00
mmaurostoffel	f114eb7f5a	strict=False hinzugefügt wie in der Fehlermeldung vorgeschlagen	2025-01-12 11:20:39 +01:00
Giò Diani	99a112df24	longer timeouts	2025-01-11 21:54:26 +01:00
Giò Diani	2013d2b440	Wiederinstandsetzung Heatmap @stoffelmauro musste dazu etwas in der API anpassen.	2025-01-11 20:52:02 +01:00
mmaurostoffel	67382003ca	closes #7 : etl_region_capacities erstellt !! Wie im Issue beschrieben wurde etl_region_capacities zu etl_region_properties_capacities angepasst und die Endpoints ebenfalls.!! !!Die Abfrage der globalen Daten ist implementiert und funktioniert, braucht aber recht lange!!	2025-01-11 17:33:50 +01:00
Giò Diani	774e30c945	fix daily chart	2025-01-09 18:49:09 +01:00
Giò Diani	3b935a4d20	Nachbaren in Popup	2025-01-09 18:34:20 +01:00
mmaurostoffel	638d835d3b	closes #5 Test to try out the closing feature	2025-01-09 18:26:01 +01:00
mmaurostoffel	cb6935b60c	updated the output of the etl_property_neighbour.py closes issue #5	2025-01-09 18:22:55 +01:00
mmaurostoffel	60a3d7d9b3	Little fixes for weekdays #6	2025-01-09 17:40:58 +01:00
mmaurostoffel	65b63d1326	Sortierung für etl_property_capacities_monthly eingefügt	2025-01-09 15:59:16 +01:00
mmaurostoffel	a6cbe3bc29	update des etl_capacities_weekdays.py	2025-01-09 15:07:38 +01:00
mmaurostoffel	2508b34ceb	bugfix: falscher name	2025-01-09 14:49:03 +01:00
mmaurostoffel	cc71cbba2d	Endpoint für etl_property_neighbours hinzugefügt	2025-01-09 14:48:33 +01:00
Giò Diani	258f1e4df6	Anzeige Auslastung p. Monat bei Properties im Dashboard.	2025-01-08 22:02:33 +01:00
mmaurostoffel	7884febe53	issue 5 resolved #3 Ausgabeforma: {ids: [84, 43...44], lat:[...], lon[...]}	2025-01-07 20:20:48 +01:00
mmaurostoffel	42dc14021f	etl_Property_capacities_weekdays.py eingefügt Abfragemöglichkeit für die Wochentage eingefügt	2025-01-06 19:42:49 +01:00
Giò Diani	f5a2b16721	Fehlende Anführungszeichen hinzugefügt resolves #3	2025-01-05 21:45:23 +01:00
mmaurostoffel	d9cae3d0ab	Issue 3 fast fertig #3 Der Issue ist soweit bereit, es gibt aber noch das Problem, dass das ScrapeDate nicht als Datum sondern asl Integer interpretiert wird im database.py. Deshalb ist es im Moment als konstante implementiert	2025-01-05 21:23:10 +01:00
mmaurostoffel	8bcc1c57b5	Gitea Issue 1 Beispiel 2 #1 etl_region_capacities_comparison eingefügt	2025-01-05 17:25:29 +01:00
mmaurostoffel	03e78a4105	Issue 1 Beispiel 1 resolved #1 Globale region capacities eingefügt: Vorsicht! lange Ladezeit	2025-01-05 16:12:16 +01:00
mmaurostoffel	2a9ef9d991	Merge branch 'main' of https://gitea.fhgr.ch/stoffelmauro/ConsultancyProject_2_ETL	2025-01-05 15:51:27 +01:00
mmaurostoffel	8fcaf2a6f7	Gitea Issue 2 resolved #2 etl_region_capacities.py: neues Output Format = [datum, prop_id, capacity]	2025-01-05 15:51:19 +01:00
Giò Diani	8655255782	info button, dashboard startseite	2025-01-05 13:26:51 +01:00
mmaurostoffel	281d9d3f5a	Merge branch 'main' of https://gitea.fhgr.ch/stoffelmauro/ConsultancyProject_2_ETL	2025-01-05 13:19:45 +01:00
mmaurostoffel	c68e6f54bd	cleanup commit	2025-01-05 13:19:43 +01:00
Giò Diani	32d162c7c5	Überarbeitung Detailansicht.	2025-01-04 18:16:12 +01:00
Giò Diani	466d3168c4	Added caching (adjust setting in .env accordingly; CACHE_STORE=file)	2025-01-03 16:44:17 +01:00
Giò Diani	5a2cc96a95	Achsenbeschrftungen, Farben	2025-01-03 16:25:30 +01:00
Giò Diani	640a5b2f9e	Implement region capacity as test	2024-12-20 21:46:54 +01:00
Giò Diani	f585a7a2aa	fix fehlender import	2024-12-20 21:36:47 +01:00
mmaurostoffel	818d6fb5ec	Merge branch 'main' of https://gitea.fhgr.ch/stoffelmauro/ConsultancyProject_2_ETL	2024-12-20 20:57:20 +01:00
mmaurostoffel	a8b856b714	etl_region_capacities erstellt + database und api/main Anpassungen dafür	2024-12-20 20:57:10 +01:00
Giò Diani	0aa0f2345c	documentation folder, c4 diagram	2024-12-20 17:38:08 +01:00
Giò Diani	eb362d78ad	Vorbereitung heatmap	2024-12-20 15:25:33 +01:00
Giò Diani	5f61911a69	More sensible default in env, more documentation how to install and run.	2024-12-20 15:03:22 +01:00
Giò Diani	66d048c70e	Enhance coordinated view of property.	2024-12-20 12:20:49 +01:00
Giò Diani	63590d69ab	fix wrong import path.	2024-12-20 10:56:10 +01:00
Giò Diani	47a5035787	dashboard einzelansicht trend auslastung.	2024-12-19 19:22:09 +01:00
mmaurostoffel	4b7067fb63	Merge branch 'main' of https://gitea.fhgr.ch/stoffelmauro/ConsultancyProject_2_ETL	2024-12-19 18:11:17 +01:00
mmaurostoffel	eba2f0a265	Data Quality updated to include Regions and more information	2024-12-19 18:11:15 +01:00
Giò Diani	ce46655003	Add some more description.	2024-12-18 20:10:11 +01:00
Giò Diani	233f3c475a	Added README	2024-12-18 19:55:42 +01:00
Giò Diani	a8543d619f	Further dashboard development.	2024-12-18 19:52:06 +01:00
Giò Diani	1574edea88	First steps Dashboard.	2024-12-18 15:14:13 +01:00
mmaurostoffel	a03ce3d647	Änderungen von bevor Monorepo übernommen	2024-12-18 15:11:23 +01:00
Giò Diani	f4a927e125	refactor to monorepo, install laravel.	2024-12-18 10:14:56 +01:00
mmaurostoffel	125250a665	data_quality.py erstellt zur Visualisierung der Datenqualität	2024-12-11 01:01:52 +01:00
mmaurostoffel	338d3e9cc2	Untersuchung Vorbuchungszeit abgeschlossen	2024-11-28 16:10:53 +01:00