visualisation.py

###deployument package needed
import pandas as pd
import plotly.express as px
import plotly.io as pio
###
from typing import List, Dict, Any
from constants import get_s3_bucket


def plot_item(items: List[Dict[str, Any]], param: str) -> None:
    filtered_items = [item for item in items if 'average' in item and 'latitude' in item and 'longitude' in item]
    if not filtered_items:
        return
    
    latitudes: List[float] = [item['latitude'] for item in filtered_items]
    longitudes: List[float] = [item['longitude'] for item in filtered_items]
    readings: List[float] = [item['average'] for item in filtered_items]
    
    min_reading: float = min(readings)
    max_reading: float = max(readings)
    reading_range: float = max_reading - min_reading
    
    if reading_range == 0:
        normalized_radius = [0] * len(readings)
    else:
        normalized_radius = [(float(reading) - float(min_reading)) / float(reading_range) for reading in readings]
        
    radius: List[float] = [float(r) * float(reading_range) for r in normalized_radius]
    


    if len(latitudes) == len(longitudes) == len(radius):
        fig = px.density_mapbox(lat=latitudes, lon=longitudes, z=radius, radius=40, opacity=0.7, labels={'z': param})
        fig.update_layout(mapbox_style='open-street-map', mapbox_center_lon=4.4699, mapbox_center_lat=50.5039, mapbox_zoom=6)

        image_bytes = pio.to_image(fig, format='png')
        parameterName = param + ".png"
        
        bucket=get_s3_bucket()
        bucket.put_object(Body=image_bytes, Key=parameterName)