ageswrtcompanies.py

from fastapi import FastAPI, Query, Body
from fastapi.responses import JSONResponse
import numpy as np

app = FastAPI()

# Function to create population density around each company and return individual densities
def create_company_population_density(company_x, company_y, width, height, max_radius=50):
    """
    Creates individual population densities for each company.
    :param company_x: List of x-coordinates for companies
    :param company_y: List of y-coordinates for companies
    :param width: Width of the grid
    :param height: Height of the grid
    :param max_radius: Radius of influence for each company point
    :return: List of density arrays for each company
    """
    # List to hold density arrays for each company
    densities = []
    
    # Loop through each company and create density map
    for x, y in zip(company_x, company_y):
        if 0 <= x < width and 0 <= y < height:  # Ensure each company is within bounds
            Y, X = np.mgrid[0:height, 0:width]
            distance = np.sqrt((X - x)**2 + (Y - y)**2)
            density = np.clip(1 - (distance / max_radius), 0, 1)  # Radial decay
            densities.append(density)  # Store individual company density
    
    return densities  # List of individual density arrays

# Endpoint to generate the heatmap based on company coordinates and grid size
@app.post("/get_company_heatmap")
async def get_company_heatmap(companies: list = Body(...), width: int = Query(...), height: int = Query(...), radius: int = Query(50)):
    """
    Generate and store density maps for each company within the specified grid.
    :param companies: List of company coordinates as [{"x": int, "y": int}, ...]
    :param width: Width of the heatmap grid
    :param height: Height of the heatmap grid
    :param radius: Radius of influence for each company point
    :return: JSON list of population densities for each company
    """
    # Extract company coordinates
    company_x = [company["x"] for company in companies]
    company_y = [company["y"] for company in companies]

    # Generate density map for each company
    densities = create_company_population_density(company_x, company_y, width, height, max_radius=radius)
    
    # Return the list of density arrays for each company
    return JSONResponse(content=[density.tolist() for density in densities])

# Endpoint to compute population density at a specific point, considering all company densities
@app.get("/get_company_population_density")
async def get_company_population_density(x: int = Query(...), y: int = Query(...), radius: int = Query(10), width: int = Query(...), height: int = Query(...)):
    """
    Calculate the combined population density at a specific point from all companies.
    :param x, y: Coordinates of the target point
    :param radius: Radius within which to measure density impact
    :param width, height: Dimensions of the grid for consistent scaling
    :return: Population density percentage at the specified point
    """
    # Placeholder density grids to simulate a response (use actual densities in practice)
    densities = create_company_population_density([10, 20, 30], [40, 50, 60], width, height)  # Example company locations
    
    # Calculate density impact from each company within the specified radius
    def calculate_density_at_point(density_grid, x, y, radius):
        Y, X = np.mgrid[0:height, 0:width]
        distance = np.sqrt((X - x)**2 + (Y - y)**2)
        mask = distance <= radius
        return density_grid[mask].mean() if mask.any() else 0

    # Aggregate densities from all companies at (x, y)
    total_density = sum(calculate_density_at_point(density, x, y, radius) for density in densities)
    density_percentage = (total_density / len(densities)) * 100 if densities else 0  # Averaged over all companies

    return JSONResponse(content={"x": x, "y": y, "population_density": density_percentage})