"""Get latest observations from Port Authority of NSW and update local copy.

Station IDs are below:

02: Offshore (Directional) Wave
03: Bombora (Directional) Wave
04: Captain Cook Channel (SG) Wave
05: Kurnell (SG) Wave
06: Molineaux Point Wind
07: Sydney Airport (Main Runway BOM) Wind
08: Brotherson Emergency Response Jetty Tide
09: Caltex (Directional) Current
12: Western Wedding Cake Wind
13: Fort Denison (Sth end BOM) Wind
14: Overseas Passenger Terminal Wind
15: Glebe Island Wind
16: Fort Denison-Primary (Nth end) Tide
17: Fort Denison-Secondary (Vegapuls64) Tide
18: Circular Quay ADCP Current
19: Balls Head Current
22: Twofold Bay - Munganno Point Wave
23: Twofold Bay - Multipurpose Wharf Wind
24: Breakwater Wharf Wind
27: Middle Wall (Vegapulse WL61) Tide
28: Goodwood (Vegapulse WL61) Tide
"""

import os
import re
import datetime
import requests
import pandas as pd
from lxml import html

# Set station as Fort Denison tide
stn_id = 16

output_dir = 'csv'


def update_master(output_dir, csv_name, df):
    """Update master csv time series.

    Args:
        output_dir (str): path to time series directory
        csv_name (str):   name of time series file
        df (dataframe):   dataframe with datetime index

    Returns:
        None
    """
    try:
        # Load local master table if it exists
        master = pd.read_csv(os.path.join(output_dir, 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(os.path.join(output_dir, csv_name))


# Get main page
url = 'http://wavewindtide.portauthoritynsw.com.au/'
page = requests.get(url)
tree = html.fromstring(page.content)

# Get elements from selected station
t_raw = tree.get_element_by_id(f'MainContent_ctl{stn_id:02}_lblRecordDate')
meas = tree.get_element_by_id(f'MainContent_ctl{stn_id:02}_lblSummary')
description = tree.get_element_by_id(f'MainContent_ctl{stn_id:02}_lblTitle')

# Parse column names
text = re.split(':\s|(m|s|knots|deg)\s', meas.text + ' ')
parts = [t for t in text if t]
parameters = [p.strip() for p in parts[::3]]
values = [float(p) for p in parts[1::3]]
units = parts[2::3]
columns = [f'{p} ({u})' for p, u in zip(parameters, units)]

# Parse time
time = re.search('at ([0-9]{4})', t_raw.text).group(1)
date = t_raw.text.split(',')[1].strip()
t = datetime.datetime.strptime(date + time, '%d %b %Y%H%M')

# Create dataframe
df = pd.DataFrame({c: v for c, v in zip(columns, values)}, index=[t])
df.index.name = 'datetime'

# Update master dataframe
csv_name = description.text + '.csv'
update_master(output_dir, csv_name, df)