complete files

detail/admin.py

+from django.contrib import admin
+
+# Register your models here.

detail/apps.py

+from django.apps import AppConfig
+
+
+class DetailConfig(AppConfig):
+    default_auto_field = 'django.db.models.BigAutoField'
+    name = 'detail'

detail/models.py

+from django.db import models
+
+# Create your models here.

detail/tests.py

+from django.test import TestCase
+
+# Create your tests here.

detail/views.py

+from django.shortcuts import render
+from load_data.models import StandortTransition
+from django_pandas.io import read_frame
+import json
+from django.http import JsonResponse
+from edm_tuwien.decorators import group_required
+
+
+@group_required('detail_group')
+def detail(request):
+    if request.method == 'POST':
+        # Handle data received from frontend
+        campus = request.POST.get('campus')
+        address = request.POST.get('address')
+
+        # Process the data as needed
+        # Example: Query database based on selected campus and address
+        # Example: Return JsonResponse with relevant data
+
+        # Dummy response (replace with actual data retrieval logic)
+        response_data = {
+            'message': 'Data received successfully',
+            'selected_campus': campus,
+            'selected_address': address,
+        }
+        return JsonResponse(response_data)
+
+    # For GET requests, continue rendering the initial page
+    dataquery = StandortTransition.objects.all()
+    standort_data = read_frame(dataquery)
+    campuses = sorted(standort_data['Campus'].unique())
+    addresses = standort_data[['Campus', 'Adresse']].to_dict(orient='records')
+    addresses_json = json.dumps(addresses)
+
+    context = {
+        'campuses': campuses,
+        'addresses_json': addresses_json,
+    }
+    return render(request, 'detail.html', context)

edm_tuwien/settings.py

@@ -50,14 +50,14 @@ INSTALLED_APPS = [
     "django.contrib.messages",
     "django.contrib.staticfiles",
     "bootstrap5",
+    "home.apps.HomeConfig",
     "load_data.apps.LoadDataConfig",
     "overview.apps.OverviewConfig",
     "test_app.apps.TestAppConfig",
     "map.apps.MapConfig",
+    "detail.apps.DetailConfig",
     "crispy_forms",
     "crispy_bootstrap5",
-    "home.apps.HomeConfig",
-    "detail.apps.DetailConfig",
     "captcha"
 ]
 

home/admin.py

+from django.contrib import admin
+
+# Register your models here.

home/apps.py

+from django.apps import AppConfig
+
+
+class HomeConfig(AppConfig):
+    default_auto_field = "django.db.models.BigAutoField"
+    name = "home"

home/models.py

+from django.db import models
+
+# Create your models here.

home/tests.py

+from django.test import TestCase
+
+# Create your tests here.

home/views.py

+from django.shortcuts import render
+
+def home(request):
+    return render(request, "home.html")
+
+# Create your views here.

load_data/migrations/0006_fernwaermerechnungendata.py

+# Generated by Django 4.2.11 on 2024-06-05 10:43
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ("load_data", "0005_wienernetzteportaldata"),
+    ]
+
+    operations = [
+        migrations.CreateModel(
+            name="FernwaermeRechnungenData",
+            fields=[
+                (
+                    "Zaehlerpunkt",
+                    models.CharField(max_length=255, primary_key=True, serialize=False),
+                ),
+                ("Address", models.CharField(max_length=255, null=True)),
+                ("Messstelle", models.IntegerField(null=True)),
+                ("Zeitraum", models.DateField(null=True)),
+                ("Verbrauch_MWh", models.FloatField(null=True)),
+                ("Kosten", models.FloatField(null=True)),
+                ("Zahlungsperiode", models.CharField(max_length=1, null=True)),
+                ("Campus", models.CharField(max_length=100, null=True)),
+            ],
+            options={
+                "db_table": "fernwaerme_rechnungen",
+                "managed": False,
+            },
+        ),
+    ]

load_data/migrations/0007_gaswienernetztedata.py

+# Generated by Django 4.2.11 on 2024-07-04 08:07
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('load_data', '0006_fernwaermerechnungendata'),
+    ]
+
+    operations = [
+        migrations.CreateModel(
+            name='GasWienerNetzteData',
+            fields=[
+                ('Zählpunkt', models.CharField(max_length=255, primary_key=True, serialize=False)),
+                ('Datum', models.DateField(null=True)),
+                ('Von', models.CharField(max_length=255, null=True)),
+                ('Bis', models.CharField(max_length=255, null=True)),
+                ('Verbrauch', models.FloatField(null=True)),
+            ],
+            options={
+                'db_table': 'data_gas',
+                'managed': False,
+            },
+        ),
+    ]

map/tests.py

-from django.test import TestCase
-
-# Create your tests here.
-import numpy as np
-import pandas as pd
-import datetime
-from sklearn.linear_model import LinearRegression
-import plotly.graph_objects as go
-
-# Predict missing values for the current year
-    predicted_values = []
-    months = np.array([m for m in range(1, 13)])  # Numeric months
-
-    for month in month_names:
-        # Check if data for the latest year is missing for a specific month
-        if df_grouped[(df_grouped['Year'] == current_year) & (df_grouped['Monat'] == month_names[month])].empty:
-            # Get the data from previous years for this month
-            previous_years_data = df_grouped[
-                (df_grouped['Monat'] == month_names[month]) & (df_grouped['Year'] < current_year)]
-
-            if len(previous_years_data) > 1:
-                # Perform linear regression to predict the value for the missing month
-                X = np.array(previous_years_data['Year']).reshape(-1, 1)
-                y = np.array(previous_years_data['Verbrauch_kWh'])
-                model = LinearRegression()
-                model.fit(X, y)
-                predicted_value = model.predict([[current_year]])[0]
-
-                predicted_values.append(
-                    {'Monat': month_names[month], 'Year': current_year, 'Verbrauch_kWh': predicted_value})
-
-    # Add predicted values as a new trace with black-bordered bubbles
-    if predicted_values:
-        df_predicted = pd.DataFrame(predicted_values)
-
-        fig.add_trace(go.Scatter(
-            x=df_predicted['Monat'],
-            y=df_predicted['Year'],
-            mode='markers',
-            marker=dict(
-                size=(df_predicted['Verbrauch_kWh'] * 30) / df_grouped['Verbrauch_kWh'].max(),
-                color=df_predicted['Verbrauch_kWh'],
-                colorscale='Jet',
-                opacity=0.8,
-                line=dict(width=2, color='black')  # Black border for predicted values
-            ),
-            text=df_predicted['Verbrauch_kWh'].astype(str).str.replace('.', ',', regex=True),
-            hovertemplate='%{text} GWh (Predicted)',
-            name="Predicted Verbrauch"
-        ))
\ No newline at end of file