#!/usr/bin/env python
# coding: utf-8

# # Wildfire Live Map
# 
# This code shows how to generate a map of wildfire hotspots in the United States. The map uses data from the NASA Fire Information for Resource Management System (FIRMS) API and updates every minute.
# 
# To get a free API key ("MAP_KEY"), sign up here: https://firms.modaps.eosdis.nasa.gov/api/map_key
# 
# MAP_KEY limit is 5000 transactions / 10-minute interval. Put the key in the `.env` file.

# In[1]:


import os
import pandas as pd
import geopandas as gpd
import atexit

from dotenv import load_dotenv
from ipyleaflet import Map, GeoData, CircleMarker, MarkerCluster
from apscheduler.schedulers.background import BackgroundScheduler
from IPython.display import display, clear_output

# Load environment variables from .env file
load_dotenv()

# Get the API key from the environment variable
API_KEY = os.getenv('FIRMS_API_KEY')

# Define the URL for the FIRMS API
FIRMS_URL = f"https://firms.modaps.eosdis.nasa.gov/api/country/csv/{API_KEY}/VIIRS_NOAA21_NRT/USA/2"


# In[2]:


def get_color(frp):
    """
    Returns a color based on the Fire Radiative Power (FRP) value
    Low FRP: Green/Yellow
    Medium FRP: Orange
    High FRP: Red
    Very High FRP: Dark Red
    """
    if frp < 1.64:
        return '#ffff00'  # Yellow
    elif frp < 3.77:
        return '#ffa500'  # Orange
    elif frp < 11.77:
        return '#ff4500'  # Red-Orange
    else:
        return '#ff0000'  # Red


# ## Function to fetch and process data into a DataFrame

# In[3]:


def fetch_and_process_fire_data():
    """Fetch and process fire data from the FIRMS API and return a GeoDataFrame with the data and color information."""
    try:
        df = pd.read_csv(FIRMS_URL)
        df_subset = df[['latitude', 'longitude', 'bright_ti4', 'acq_date', 'acq_time', 'confidence', 'frp']]
        
        # Filter out low confidence data
        filtered_df = df_subset[df_subset['confidence'] != 'l'].copy()
        
        # Filter out rows with NaN values in critical columns
        filtered_df = filtered_df.dropna(subset=['latitude', 'longitude', 'frp'])

        # Add color column based on FRP
        filtered_df.loc[:, 'color'] = filtered_df['frp'].apply(get_color)

        # Create GeoDataFrame with style information
        geodf = gpd.GeoDataFrame(
            filtered_df, 
            geometry=gpd.points_from_xy(filtered_df.longitude, filtered_df.latitude)
        )
        
        return geodf
    except Exception as e:
        print(f"Error fetching data: {e}")
        return None


# ## Display Wildfire Map

# In[4]:


# Create a map centered around the USA
wildfire_map = Map(center=(37.0902, -95.7129), zoom=4)

def scheduled_task():
    """Defined a scheduled task to update the layers of the map"""

    # Clear existing layers (retain only the base layer)
    wildfire_map.layers = wildfire_map.layers[:1]  # Keep only the base layer

    # Fetch and process fire data
    gdf = fetch_and_process_fire_data()
    if gdf is not None:
        markers = []
        
        # Iterate over each point in the GeoDataFrame
        for idx, row in gdf.iterrows():
            # Create a CircleMarker with the specified color
            marker = CircleMarker(
                location=(row['latitude'], row['longitude']),
                radius=8,
                color=row['color'],
                fill_color=row['color'],
                fill_opacity=0.7,
                stroke=True,
                weight=1
            )

            # Add the marker to the cluster
            markers.append(marker)

        cluster = MarkerCluster(markers=markers)
        wildfire_map.add_layer(cluster)


# In[5]:


# Set up a background scheduler to update the map every minute
scheduler = BackgroundScheduler()
scheduler.add_job(scheduled_task, 'interval', minutes=1)  # Fetch every minute
scheduler.start()

# Display the initial map
scheduled_task()


# In[6]:


# To prevent orphaned background processes when the notebook is closed, ensure that the scheduler shuts down properly.
atexit.register(lambda: scheduler.shutdown())

# Display the map (it will be updated every minute)
display(wildfire_map)


# In[ ]: