*** Wartungsfenster jeden ersten Mittwoch vormittag im Monat ***

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Commit ad85b1a0 authored by Sharma, Sukrit's avatar Sharma, Sukrit
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comment map views

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map/views.py
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......@@ -11,15 +11,24 @@ import plotly.graph_objects as go
import math
import branca
@group_required('map_group')
@group_required('map_group') # Restricts access to users in the 'map_group' group
def show_map(request):
"""
This view renders a map using Folium, displaying markers with custom popups
for location data and KPIs. The popups contain visualizations and consumption data.
"""
# Fetch data from LocationData model
dataquery = LocationData.objects.all()
# Convert data from the database into a pandas DataFrame
marker_data = read_frame(dataquery)
# Fetch KPI data using the custom kpi_map_marker function
kpi_data = kpi_map_marker()
# Replace NaN values in KPI data with 0
kpi_data = kpi_data.fillna(0)
# Define columns to check (all columns except 'zaehlpunkt')
......@@ -28,44 +37,32 @@ def show_map(request):
# Remove rows where all values in the specified columns are less than 0.1
kpi_data = kpi_data[~(kpi_data[cols_to_check] < 1).all(axis=1)]
# Merge the marker data with the KPI data based on 'Zaehlpunkt'
marker_data = pd.merge(marker_data, kpi_data, left_on='Zaehlpunkt', right_on='zaehlpunkt', how='right')
# Replace NaN values in the merged DataFrame with 0
marker_data = marker_data.fillna(0)
# Replace the substring in Zaehlpunkt with '...'
# Replace a specific substring in 'Zaehlpunkt' with '...'
marker_data['Zaehlpunkt'] = marker_data['Zaehlpunkt'].str.replace('000000000000001000', '...')
# Define the base URL for the basemap.at service
# Define the base URL for the basemap.at service to use as the map background
base_url = "http://maps.wien.gv.at/basemap/bmapgrau/normal/google3857/{z}/{y}/{x}.png"
# Define the available layers and their names
# layers = {
# "bmapgrau": "Gray",
# "geolandbasemap": "Basemap",
# "bmapoverlay": "Overlay",
# "bmaporthofoto30cm": "Orthophoto",
# "bmapgelaende": "Terrain",
# "bmapoberflaeche": "Surface"
# }
# Create a new map with Folium
m = folium.Map(width="100%", height="75%", location=[48.2082, 16.3738], zoom_start=12, control_scale=False, tiles = None)
# Create a new Folium map object, centered on Vienna with a specific zoom level
m = folium.Map(width="100%", height="75%", location=[48.2082, 16.3738], zoom_start=12, control_scale=False,
tiles=None)
# Add the basemap layer to the map
folium.TileLayer(tiles=base_url, attr="basemap.at", name="Gray").add_to(m)
# # Add the TileLayers for each layer
# for layer, name in layers.items():
# url = base_url.format(layer)
# folium.TileLayer(tiles=url, attr="basemap.at", name=name).add_to(m)
# Initialize MarkerCluster to group nearby markers
# Initialize a MarkerCluster to group nearby markers together on the map
marker_cluster = MarkerCluster().add_to(m)
# Dictionary to keep track of marker counts at each location
# Dictionary to track marker counts at each location (not used in this code)
marker_counts = {}
# Loop through DataFrame rows using iterrows()
# Loop through each row in the marker data DataFrame
for index, row in marker_data.iterrows():
Zaehlpunkt = row['Zaehlpunkt']
lat = row['Latitude']
......@@ -73,80 +70,63 @@ def show_map(request):
location = (lat, lon)
Address = row['Address']
Code = row['Code']
font_size = 2.5 * 30 // (len(Code)+1) # Adjust the range (8 to 12) as needed
###############
font_size = 2.5 * 30 // (len(Code) + 1) # Adjust font size based on Code length
# Prepare the data for consumption visualization
data = row.to_dict()
max_verbrauch = max([data['durchschnittliche_verbrauch_monatlich_max'],
data['aktueller_monatsverbrauch']])
# Calculate the maximum consumption for the current location
max_verbrauch = max([data['durchschnittliche_verbrauch_monatlich_max'], data['aktueller_monatsverbrauch']])
if max_verbrauch!=0 :
# Determine the logarithmic scale for consumption values
if max_verbrauch != 0:
exponent = math.floor(math.log10(abs(max_verbrauch)))
else:
exponent = 1
data['verbrauch_monatlich_max'] = (int(str(max_verbrauch)[0])+1)*10**exponent
# Update the consumption data
data['verbrauch_monatlich_max'] = (int(str(max_verbrauch)[0]) + 1) * 10 ** exponent
#data = {
# 'aktueller_monatsverbrauch': 500,
# 'durchschnittliche_verbrauch_monatlich_min': 200,
# 'durchschnittliche_verbrauch_monatlich_max': 800,
# 'verbrauch_monatlich_max': 1000
#}
# Create a DataFrame from the data
# Create a DataFrame for consumption data
df = pd.DataFrame(data, index=[0])
# Calculate values for stacked bars
# Prepare the data for stacked bar chart visualization
df['1.Durchschnittliche Min'] = df['durchschnittliche_verbrauch_monatlich_min']
df['2.Durchschnittliche Max'] = df['durchschnittliche_verbrauch_monatlich_max'] - df[
'1.Durchschnittliche Min']
df['2.Durchschnittliche Max'] = df['durchschnittliche_verbrauch_monatlich_max'] - df['1.Durchschnittliche Min']
df['3.Maximum'] = df['verbrauch_monatlich_max'] - df['2.Durchschnittliche Max']
# Melt the DataFrame to transform it into long format for plotting
df_melted = df.melt(value_vars=['1.Durchschnittliche Min', '2.Durchschnittliche Max','3.Maximum'], var_name='category', value_name='kWh')
# Transform the DataFrame into long format for use in Altair chart
df_melted = df.melt(value_vars=['1.Durchschnittliche Min', '2.Durchschnittliche Max', '3.Maximum'],
var_name='category', value_name='kWh')
#leg_order = ['segment_1', 'segment_2', 'segment_3']
# Define the base stacked bar chart with inverted stacking order
# Create the base stacked bar chart in Altair
base = alt.Chart(df_melted, title='Verbrauch dieser Monat').mark_bar().encode(
alt.X('kWh:Q', stack='zero'), # Invert stacking order based on color
color=alt.Color('category:N', #sort=leg_order,
scale=alt.Scale(
#domain=leg_order,
#domain=['Durchschnittliche Min', 'Durchschnittliche Max', 'Maximum'],
range=['green', 'orange', 'red']
),
legend=alt.Legend(title='',
orient='none',
legendY=55,
direction='horizontal'
))
alt.X('kWh:Q', stack='zero'),
color=alt.Color('category:N', scale=alt.Scale(range=['green', 'orange', 'red']),
legend=alt.Legend(title='', orient='none', legendY=55, direction='horizontal'))
)
# Overlay the stacked bar chart with the aktueller_monatsverbrauch
# Overlay the current month's consumption on top of the stacked bars
df_melt = df.melt(value_vars=['aktueller_monatsverbrauch'], var_name='category', value_name='kWh')
overlay = alt.Chart(df_melt).mark_bar(size=10).encode(
alt.X('kWh:Q', stack='zero'),
color=alt.value('blue'), # Set the color directly to blue
color=alt.value('blue'),
opacity=alt.value(1)
)
# Combine the base and overlay
# Combine the base chart with the overlay
chart = (base + overlay)
# Create an iframe with the Altair chart
iframe = folium.IFrame(chart.to_html(), width=300, height=300)
data = alt.Data(values=[{'x': 'A'}])
# Replace periods with commas in the relevant fields
# Prepare footer text with consumption values, formatted with commas
last_month = str(row.verbrauch_taeglich_last_month).replace('.', ',')
last_quarter = str(row.verbrauch_taeglich_last_quarter).replace('.', ',')
last_year = str(row.verbrauch_taeglich_last_year).replace('.', ',')
yesterday = str(row.verbrauch_taeglich_yesterday).replace('.', ',')
# Update the footer text with formatted values
# Construct the footer text to display in the popup
footer = [
f'Zaehlpunkt: {Zaehlpunkt}',
f'Address: {Address}',
......@@ -158,25 +138,18 @@ def show_map(request):
f'Gestern: {yesterday}'
]
text = (
alt.Chart(data)
.mark_text(text=footer, x=0, y='height', dx=0, dy=70, align='left', fontSize=18)
)
# Create Altair chart for footer text
text = alt.Chart(alt.Data(values=[{'x': 'A'}])).mark_text(text=footer, x=0, y='height', dx=0, dy=70,
align='left', fontSize=18)
# Combine the main chart with the footer text
chart = alt.layer(chart, text)
vega_lite = folium.VegaLite(
chart,
width="100%",
height="100%",
)
# Embed the chart into a VegaLite object to display in Folium popup
vega_lite = folium.VegaLite(chart, width="100%", height="100%")
popup = folium.Popup().add_child(vega_lite)
# Define custom marker UI (SVG icon)
# Define the custom marker UI (SVG with circle and text)
marker_ui = f"""
<div>
<svg width="100" height="100">
......@@ -187,11 +160,9 @@ def show_map(request):
</div>
"""
# Create marker with custom popup and UI, and add to marker cluster
folium.Marker(location=location, popup=popup, icon=folium.DivIcon(html=marker_ui), lazy=True).add_to(marker_cluster)
# Create a marker with the custom popup and UI, and add it to the marker cluster
folium.Marker(location=location, popup=popup, icon=folium.DivIcon(html=marker_ui), lazy=True).add_to(
marker_cluster)
# Render the map directly into the template and return it as a response
# Render the map to the template and return it as an HTML response
return render(request, 'map.html', {'map': m._repr_html_()})
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