import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import folium
from branca.colormap import linear
from branca.colormap import LinearColormap
import numpy as np



# Load the data from the file
file_path = "./1352-1433UTC_Aug1_2024_CH4_lat_lon.txt"
figure_path = "./"

data = pd.read_csv(file_path, delim_whitespace=True, header=0)

# Filter out invalid methane concentration values
data = data[data['CH4LicormatchIphonePPB'] > 0]


# plot figure
m = folium.Map(location=[np.mean(data['lat']), np.mean(data['lon'])], zoom_start=15, tiles=None)

# Add a tile layer (try different ones if 'OpenStreetMap' doesn't work)
folium.TileLayer('OpenStreetMap',name='Street View').add_to(m)
folium.TileLayer('Esri.WorldImagery',name='Satellite View').add_to(m)

vmin = 2000
vmax = 2600
color_scale = LinearColormap(['blue', 'cyan', 'yellow', 'red'], vmin=vmin, vmax=vmax)

# Adding a color bar
color_scale.caption = 'Methane Concentration (ppb)'
color_scale.add_to(m)

# Add markers
for lat, lon, ch4 in zip(data['lat'], data['lon'], data['CH4LicormatchIphonePPB']):
    if ch4 > 0:  # Assuming negative values are invalid
        folium.CircleMarker(
            location=[lat, lon],
            radius=0.2,
            color=color_scale(ch4),
            fill=True,
            fill_color=color_scale(ch4),
            fill_opacity=0.7,
            popup=f'{ch4:.2f} ppb'
        ).add_to(m)
folium.LayerControl().add_to(m)
# Save and show the map
m.save(figure_path+'mathane_map.html')