"""Download lateset time series data from MHL website and update local copy.
D. Howe
2019-08-30
"""
import os
import pandas as pd

# Set output directory
output_dir = 'csv'

# Define data URLs
url = 'http://www.mhl.nsw.gov.au/data/realtime/timeseries/'
txt_names = [
    'SYDDOW.WaveHeight.csv.dat.txt',
    'SYDDOW.WavePeriod.csv.dat.txt',
]

for txt_name in txt_names:
    # Read text file from MHL website
    df = pd.read_csv(url + txt_name, index_col=0, parse_dates=True)

    # Create name for exported data
    csv_name = os.path.join(input_dir, txt_name.replace('.dat.txt', ''))
    try:
        # Load local master table
        master = pd.read_csv(csv_name, index_col=0, parse_dates=True)

        # Only include timestamps that do not already exist
        df = df[~df.index.isin(master.index)]

        # Update master
        master = master.append(df)

    except FileNotFoundError:
        # Create new master table if none exists
        master = df

    # Export master table
    master.to_csv(csv_name)