"""
Cannabis Licenses | License Mao
Copyright (c) 2022 Cannlytics

Authors:
    Keegan Skeate <https://github.com/keeganskeate>
    Candace O'Sullivan-Sutherland <https://github.com/candy-o>
Created: 9/22/2022
Updated: 10/8/2022
License: <https://github.com/cannlytics/cannabis-data-science/blob/main/LICENSE>

Description: 

    Map the adult-use cannabis retailers permitted in the United States:

        ✓ Alaska
        ✓ Arizona
        ✓ California
        ✓ Colorado
        ✓ Connecticut
        ✓ Illinois
        ✓ Maine
        ✓ Massachusetts
        ✓ Michigan
        ✓ Montana
        ✓ Nevada
        ✓ New Jersey
        x New Mexico (FIXME)
        ✓ Oregon
        ✓ Rhode Island
        ✓ Vermont
        ✓ Washington

"""
# Standard imports.
from datetime import datetime
import json
import os

# External imports.
import folium
import pandas as pd


# Specify where your data lives.
DATA_DIR = '../'

# Read data subsets.
with open('../subsets.json', 'r') as f:
    SUBSETS = json.loads(f.read())


def aggregate_retailers(
        datafiles,
        index_col=0,
        lat='premise_latitude',
        long='premise_longitude',
    ):
    """Aggregate retailer license data files,
    keeping only those with latitude and longitude."""
    data = []
    for filename in datafiles:
        data.append(pd.read_csv(filename, index_col=index_col))
    data = pd.concat(data)
    return data.loc[(~data[lat].isnull()) & (~data[long].isnull())]


def create_retailer_map(
        df,
        color='crimson',
        filename=None,
        lat='premise_latitude',
        long='premise_longitude',
    ):
    """Create a map of licensed retailers."""
    m = folium.Map(
        location=[39.8283, -98.5795],
        zoom_start=3,
        control_scale=True,
    )
    for _, row in df.iterrows():
        folium.Circle(
            radius=5,
            location=[row[lat], row[long]],
            color=color,
        ).add_to(m)
    if filename:
        m.save(filename)
    return m


# === Test ===
if __name__ == '__main__':

    # Aggregate retailers.
    subsets = list(SUBSETS.values())
    datafiles = [DATA_DIR + x['data_url'] for x in subsets]
    retailers = aggregate_retailers(datafiles)

    # Create the retailers map.
    map_file = '../analysis/figures/cannabis-licenses-map.html'
    m = create_retailer_map(retailers, filename=map_file)

    # Save all of the retailers.
    timestamp = datetime.now().isoformat()[:19].replace(':', '-')
    retailers.to_csv(f'{DATA_DIR}/data/all/licenses-{timestamp}.csv', index=False)