waternsw-grabber/waternsw_grabber/waternsw_grabber.py

"""waternsw_grabber.py
Download bore records from the WaterNSW data portal.
"""

import os
import re
import time
import shutil
import logging
import warnings
import requests
import numpy as np
import pandas as pd
from tqdm import tqdm
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.support.ui import WebDriverWait, Select
from selenium.webdriver.support import expected_conditions as EC
from selenium.common.exceptions import (
    TimeoutException, StaleElementReferenceException, NoSuchElementException)


def has_admin():
    """Check if current user has admin rights.
    https://stackoverflow.com/questions/2946746
    """
    if os.name == 'nt':
        try:
            # Check if C:/Windows/temp is readable for current user
            os.listdir(os.path.join(os.environ.get('systemroot'), 'temp'))
        except PermissionError:
            return False
        else:
            return True
    else:
        if 'SUDO_USER' in os.environ and os.geteuid() == 0:
            return True
        else:
            return False


def wait_for_element(driver, by, x, timeout=180):
    """Wait for element on page to load.

    Args:
        driver:   selenium webdriver object
        by:       locator strategy (e.g. By.ID)
        x:        locator string
        timeout:  maximum wait time (seconds)

    Raises
        TimeoutException if element does not load within timeout period
    """
    element_present = EC.presence_of_element_located((by, x))
    WebDriverWait(driver, timeout).until(element_present)


def wait_for_body_text(driver):
    """Wait for body text element on page to load, and not be empty.

    Args:
        driver:   selenium webdriver object

    Returns
        Body text

    Raises
        TimeoutException if element does not load within timeout period
    """
    body_text = None
    while not body_text:
        try:
            # Get contents of body text
            body_text = driver.find_element_by_xpath('//*/body').text
        except (StaleElementReferenceException, NoSuchElementException):
            pass
        time.sleep(0.5)

    return body_text


def get_telemetered_bore(driver, bore_id, start_date, end_date):
    """Download single record from telemetered bore.

    Args:
        driver:      selenium webdriver object
        bore_id:     bore ID (string)
        start_date:  start date (string in YYYY-MM-DD format)
        end_date:    end date (string in YYYY-MM-DD format)
    """

    url = 'https://realtimedata.waternsw.com.au/water.stm'
    driver.get(url)

    driver.switch_to.default_content()
    webhyd = driver.find_element_by_id('webhyd')
    driver.switch_to.frame(webhyd)

    # Load site specific page
    driver.execute_script("go('{}','gw', 1)".format(bore_id))

    # Wait for results frame to load
    wait_for_element(driver, By.ID, 'gwgwlf_org')
    driver.switch_to.frame('gwgwlf_org')

    # Wait until body text of iframe has loaded
    body_text = wait_for_body_text(driver)

    # Detect if bore record does not exist
    if 'No SITE record found for site' in body_text:
        raise ValueError('No SITE record found for site {}'.format(bore_id))

    # Wait for navigation tabs
    wait_for_element(driver, By.XPATH, '//*[@id="tabstext"]')

    # Activate outputs tab
    driver.execute_script("menuloc.display_frame('gw', 'gwcf_org', '1')")
    driver.switch_to.parent_frame()
    wait_for_element(driver, By.ID, 'gwgwcf_org')
    driver.switch_to.frame('gwgwcf_org')

    # Wait until body text of iframe has loaded
    body_text = wait_for_body_text(driver)

    # Detect if no variables are available
    if 'No variables data found for this site.' in body_text:
        raise ValueError('No variables data found for site {}'.format(bore_id))

    # Wait for 'Get Output' button
    wait_for_element(driver, By.ID, 'submit')

    # Get output select controls
    selects = driver.find_elements_by_xpath('//*/select')
    for select in selects:
        s = Select(select)
        label = s.options[0].get_attribute('label')
        if label == 'All data':
            period = s
        elif label == 'Plot':
            output = s
        elif label == 'Annual':
            interval = s

    # Change period dropdown to 'Custom'
    period.select_by_visible_text('Custom')

    # Get date input fields
    fields = driver.find_elements_by_xpath('//*[starts-with(@id,"cdate")]')

    # Get available date ranges
    datestr = driver.find_elements_by_xpath('//*/tr/td[4]/span')
    dates = np.array([d.text.split(' to ') for d in datestr])

    if start_date is not None:
        start_date = pd.to_datetime(start_date)
    else:
        # Get date from page, if not provided
        start_date = pd.to_datetime(dates[:, 0], dayfirst=True).min()

    if end_date is not None:
        end_date = pd.to_datetime(end_date)
    else:
        # Get date from page, if not provided
        end_date = pd.to_datetime(dates[:, 1], dayfirst=True).max()

    # Update fields with specified dates
    for field, date in zip(fields, [start_date, end_date]):
        field.clear()
        field.send_keys(pd.datetime.strftime(date, '%H:%M_%d/%m/%Y'))

    # Set output dropdown to 'Download'
    output.select_by_visible_text('Download')

    # Set interval dropdown to 'All points'
    interval.select_by_visible_text('All points')

    # Make sure 'Groundwater Level - AHD' is selected as an output
    try:
        checkbox = driver.find_element_by_xpath(
            '//*/input[contains(@name, "sel__110.00_115.00")]')
        if not checkbox.get_attribute('selected'):
            checkbox.click()
    except NoSuchElementException:
        pass

    # Download data
    driver.execute_script("get_output()")
    driver.execute_script("hide_object('confirm');co(level,tab,1)")

    # Close popup
    wait_for_element(
        driver,
        By.XPATH,
        "//div[contains(@class, 'lity-container')]",
        timeout=60)
    webdriver.ActionChains(driver).send_keys(Keys.ESCAPE).perform()


def open_browser(download_dir):
    """Opens an automated Firefox browser instance.

    Args:
        download_dir:  path to where downloaded files will be saved

    Returns:
        A selenium web browser object
    """

    # Make download directory absolute
    download_dir = os.path.abspath(download_dir)

    # Set up Firefox to silently download files to specified folder
    profile = webdriver.FirefoxProfile()
    profile.set_preference('browser.download.folderList', 2)
    profile.set_preference('browser.download.manager.showWhenStarting', False)
    profile.set_preference('browser.download.dir', download_dir)
    profile.set_preference('browser.helperApps.neverAsk.saveToDisk',
                           ('application/zip,'
                            'application/octet-stream,'
                            'application/x-zip-compressed,'
                            'multipart/x-zip'))

    # Create download folder if it does not exist
    os.makedirs(download_dir, exist_ok=True)

    # Open browser
    driver = webdriver.Firefox(firefox_profile=profile)

    return driver


def telemetered_bore_downloader(basin,
                                download_dir=None,
                                start_date=None,
                                end_date=None):
    """Download multiple records from telemetered bore.

    Args:
        basin:         basin name or code (string)
        download_dir:  path to where downloaded files will be saved
        start_date:    start date (string YYYY-MM-DD format)
        end_date:      end date (string YYYY-MM-DD format)

    Raises:
        ValueError when bore ID is invalid
    """

    # Get full name of basin
    basins = get_basins()

    # Check if full basin name was provided
    if basin in basins['Basin name'].values:
        basin_name = basin
    else:
        try:
            # Check ift two-letter basin code was provided
            basin_name = basins.groupby('Basin code').first().loc[basin][
                'Basin name']
        except KeyError:
            raise KeyError('Basin {} not found'.format(basin))

    # Get list of bore IDs from selected basin
    bore_ids = basins[basins['Basin name'] == basin_name].index.values

    # Add basin name to root download directory
    download_dir = os.path.join(download_dir, basin_name)

    # Open browser
    driver = open_browser(download_dir)

    # Set up log File
    log_name = os.path.join(download_dir, 'errors.log')
    logging.basicConfig(filename=log_name, level=logging.ERROR)

    # Download bore logs
    pbar = tqdm(bore_ids)
    for bore_id in pbar:
        pbar.set_description(bore_id)
        try:
            get_telemetered_bore(driver, bore_id, start_date, end_date)
        except ValueError as e:
            logging.error(e)
        except TimeoutException:
            e = 'Request timed out on {}. Try again later?'.format(bore_id)
            logging.error(e)

    # Tidy up console after tqdm
    print('\n')

    # Stop logging
    logging.shutdown()

    if os.path.isfile(log_name):
        with open(log_name, 'r') as f:
            log_data = f.read()

        # Check contents of log file
        if log_data:
            warnings.warn(
                'Some files failed to download. See log for details.',
                stacklevel=2)
        else:
            os.remove(log_name)

    # Close browser
    driver.quit()


def get_basins():
    """Load basin definitions file."""

    # Get basin info for telemetered site data
    csv_name = os.path.join(
        os.path.dirname(__file__), 'data', 'telemetered-sites.csv')
    basins = pd.read_csv(csv_name, index_col=0)

    return basins


def extract_definitions(input_dir, output_dir):
    """Extract variable and quality metadata from bore records.

    Args:
        input_dir:   path to downloaded zip archives
        output_dir:  path to save csv files
    """

    # Get basin info for telemetered site data
    basins = get_basins()

    # Find zip files
    zip_names = [f for f in os.listdir(input_dir) if f.endswith('.zip')]

    # Prepare output directory
    os.makedirs(output_dir, exist_ok=True)

    for zip_name in zip_names:
        # Skip duplicate downloads
        if re.search(r'\([0-9]+\)', zip_name):
            continue

        # Rename '.part' file if zip was not correctly downloaded
        if os.path.getsize(os.path.join(input_dir, zip_name)) == 0:
            shutil.move(
                os.path.join(input_dir, zip_name) + '.part',
                os.path.join(input_dir, zip_name))

        # Read csv file inside zip archive
        df = pd.read_csv(
            os.path.join(input_dir, zip_name),
            header=2,
            skiprows=[3],
            parse_dates=['Date'],
            compression='zip',
            dayfirst=True,
            nrows=100)

        # Extract metadata from last column
        keys = ['Sites:', 'Variables:', 'Qualities:']
        meta = {k: [] for k in keys}
        for i, row in df.iterrows():
            line = row.values[-1]
            if line in keys:
                header = True
                var = line
            elif line == ' ':
                continue
            else:
                meta[var].append(line)

        # Get bore specifics
        site_data = meta['Sites:'][0]
        lat = float(re.search(r'(?<=Lat:)\S+', site_data).group())
        lon = float(re.search(r'(?<=Long:)\S+', site_data).group())
        try:
            elev = float(re.search(r'(?<=Elev:).+(?=m)', site_data).group())
        except AttributeError:
            elev = np.nan
        address = re.search(r'(?<=\d\.\d\.\d - ).+(?=\sLat)',
                            site_data).group()
        bore_id = re.search(r'^\S+', site_data).group()
        site, hole, pipe = bore_id.split('.')

        sites = pd.DataFrame()
        sites['ID'] = [bore_id]
        sites['Site'] = [site]
        sites['Hole'] = [hole]
        sites['Pipe'] = [pipe]
        sites['Lat'] = [lat]
        sites['Lon'] = [lon]
        sites['Elev'] = [elev]
        sites['Address'] = [address]
        sites = sites.set_index('ID')

        # Get basin from master site dataframe
        try:
            sites['Basin name'] = basins.loc[sites.index, 'Basin name']
            sites['Basin code'] = basins.loc[sites.index, 'Basin code']
        except ValueError:
            # FIXME: Some bores have duplicate IDs!
            # Get basin name from input directory
            sites['Basin name'] = input_dir
            basin_idx = basins['Basin name'] == input_dir
            sites['Basin code'] = basins.loc[basin_idx, 'Basin code'].values[0]

        # Save variable definitions
        variables = pd.DataFrame(
            [v.split(' - ', 1) for v in meta['Variables:']])
        variables.columns = ['Code', 'Description']
        variables['Code'] = variables['Code'].astype(int)
        variables = variables.set_index('Code')

        # Save quality definitions
        qualities = pd.DataFrame(
            [q.split(' - ', 1) for q in meta['Qualities:']])
        qualities.columns = ['Code', 'Description']
        qualities['Code'] = qualities['Code'].astype(int)
        qualities = qualities.set_index('Code')

        # Update existing values
        csv_name_s = os.path.join(output_dir, 'sites.csv')
        csv_name_v = os.path.join(output_dir, 'variables.csv')
        csv_name_q = os.path.join(output_dir, 'qualities.csv')

        try:
            sites = sites.append(pd.read_csv(csv_name_s, index_col=0))
            sites = sites[~sites.index.duplicated(keep='first')]
            sites = sites.sort_index()
        except FileNotFoundError:
            pass

        try:
            variables = variables.append(pd.read_csv(csv_name_v, index_col=0))
            variables = variables[~variables.index.duplicated(keep='first')]
            variables = variables.sort_index()
        except FileNotFoundError:
            pass

        try:
            qualities = qualities.append(pd.read_csv(csv_name_q, index_col=0))
            qualities = qualities[~qualities.index.duplicated(keep='first')]
            qualities = qualities.sort_index()
        except FileNotFoundError:
            pass

        # Export updated tables
        sites.to_csv(csv_name_s)
        variables.to_csv(csv_name_v)
        qualities.to_csv(csv_name_q)

    sites = sites[~sites.index.duplicated(keep='first')]
    return sites


def extract_records(input_dir, output_dir, clean_up=False):
    """Extract downloaded bore records.

    Args:
        input_dir:   path to downloaded zip archives
        output_dir:  path to save csv files
        clean_up:    delete original zip archive after extracting it
    """

    # Update definition tables
    sites = extract_definitions(input_dir, output_dir)

    # Keep unique basin codes
    basin_codes = sites['Basin code'].unique()

    # Find zip files
    zip_names = [f for f in os.listdir(input_dir) if f.endswith('.zip')]

    # Prepare output directory
    os.makedirs(output_dir, exist_ok=True)

    # Create master dataframe
    periods = ['all', 'daily', 'weekly']
    master = {}
    for basin_code in basin_codes:
        master[basin_code] = {}
        for period in periods:
            master[basin_code][period] = pd.DataFrame()

    for zip_name in tqdm(zip_names):
        # Skip duplicate downloads
        if re.search(r'\([0-9]+\)', zip_name):
            continue

        # Rename '.part' file if zip was not correctly downloaded
        if os.path.getsize(os.path.join(input_dir, zip_name)) == 0:
            shutil.move(
                os.path.join(input_dir, zip_name) + '.part',
                os.path.join(input_dir, zip_name))

        # Read header
        header = pd.read_csv(
            os.path.join(input_dir, zip_name), compression='zip', nrows=3)

        # Remove comments
        header = header.iloc[:, 1:-1].T

        # Apply product codes to all columns
        header.iloc[1::2, 0] = header.iloc[::2, 0].values
        header[0] = header[0].astype(float).astype(int).astype(str)

        # Move quality label
        header.iloc[1::2, 1] = header.iloc[1::2, 2]

        # Combine labels
        columns = [' '.join(c) for c in header.iloc[:, :-1].values]

        # Read csv file inside zip archive
        df = pd.read_csv(
            os.path.join(input_dir, zip_name),
            header=2,
            skiprows=[3],
            parse_dates=['Date'],
            index_col=['Date'],
            compression='zip',
            dayfirst=True)

        # Convert quality codes to integers
        for col in df.columns:
            if 'Quality' in col:
                df[col] = df[col].astype(int)

        # Update column names
        df.columns = columns + ['Metadata']

        # Get bore specifics
        meta = df['Metadata'].iloc[1]
        bore_id = re.search(r'^\S+', meta).group()
        site, hole, pipe = bore_id.split('.')
        df = df.drop(columns='Metadata')

        # Get basin ID
        basin_code = sites.loc[bore_id, 'Basin code']

        # Make copy of original dataframe
        df_all = df.copy()

        # Get quality columns
        q_idx = ['Quality' in col for col in df.columns]

        # Resample if necessary
        for period in periods:
            if period == 'daily':
                # Resample to daily timestamps
                df = df_all.resample('1d').mean()
                # Get first quality code for each period, as mean doesn't work
                q_val = df_all.loc[:, q_idx].resample('1d').first()
                df.loc[:, q_idx] = q_val

            elif period == 'weekly':
                # Resample to weekly timestamps
                df = df_all.resample('1w').mean()
                # Get first quality code for each period, as mean doesn't work
                q_val = df_all.loc[:, q_idx].resample('1w').first()
                df.loc[:, q_idx] = q_val

            # Add specific borehole details
            df['Site'] = sites.loc[bore_id, 'Site']
            df['Hole'] = sites.loc[bore_id, 'Hole']
            df['Pipe'] = sites.loc[bore_id, 'Pipe']
            df['Basin'] = sites.loc[bore_id, 'Basin code']
            df = df[['Site', 'Hole', 'Pipe', 'Basin'] + columns]

            # Remove empty rows
            df = df.dropna()

            # Add to master dataframe
            master[basin_code][period] = pd.concat(
                [master[basin_code][period], df])

        if clean_up:
            # Remove original zip archive
            os.remove(os.path.join(input_dir, zip_name))

    for basin_code in basin_codes:
        for period in periods:
            # Ignore empty dataframes
            if len(master[basin_code][period]) == 0:
                continue

            # Get latest date from dataframe
            latest_date = master[basin_code][period].index[-1].strftime(
                '%Y-%m-%d')
            csv_name = os.path.join(
                output_dir, '{}-{}-{}.csv'.format(basin_code, latest_date,
                                                  period))

            # Export to csv
            master[basin_code][period].to_csv(
                csv_name, index=True, float_format='%0.3f')