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87 lines
2.1 KiB
Python
87 lines
2.1 KiB
Python
"""Download latest time series data from BOM website and update local copy.
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BOM does not allow direct http requests to data files, so a requests session
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is opened on the product page before accessing the resource.
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D. Howe
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2019-09-04
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"""
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import os
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import json
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import requests
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import pandas as pd
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from lxml import html
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# Set base URL
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bom_url = 'http://www.bom.gov.au/'
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# Set product URL for North Head
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product_url = 'products/IDN60901/IDN60901.95768.shtml'
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# Set output directory
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output_dir = 'bom'
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# Set output column names
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cols = [
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'wind_dir',
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'wind_spd_kmh',
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'gust_kmh',
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]
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def update_master(output_dir, csv_name, df):
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"""Update master csv time series.
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Args:
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output_dir (str): path to time series directory
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csv_name (str): name of time series file
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df (dataframe): dataframe with datetime index
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Returns:
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None
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"""
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try:
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# Load local master table if it exists
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master = pd.read_csv(os.path.join(output_dir, csv_name),
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index_col=0,
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parse_dates=True)
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# Only include timestamps that do not already exist
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df = df[~df.index.isin(master.index)]
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# Update master
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master = master.append(df)
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except FileNotFoundError:
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# Create new master table if none exists
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master = df
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# Export master table
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master.to_csv(os.path.join(output_dir, csv_name))
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# Open new session with BOM website
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with requests.session() as session:
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# Load product page
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page = session.get(bom_url + product_url)
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tree = html.fromstring(page.content)
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# Find and download JSON data
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json_url = tree.xpath('//*[@id="content"]/p[4]/a')[0].get('href')
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json_data = json.loads(session.get(bom_url + json_url).content)
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# Extract file base name
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csv_name = json_url.split('/')[-1].replace('.json', '.csv')
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# Create dataframe
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df = pd.DataFrame(json_data['observations']['data'])
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# Set local time as index
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df.index = pd.to_datetime(df['local_date_time_full'])
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# Extract columns of interest
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df = df[cols]
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# Update master table
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update_master(output_dir, csv_name, df)
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