def morrison_and_stephenson_2004_table(): """Table of smoothed Delta T values from Morrison and Stephenson, 2004.""" import pandas as pd f = load('http://eclipse.gsfc.nasa.gov/SEcat5/deltat.html') tables = pd.read_html(f.read()) df = tables[0] return pd.DataFrame({'year': df[0], 'delta_t': df[1]})
def usno_monthly_delta_t(): import pandas as pd f = load('http://maia.usno.navy.mil/ser7/deltat.data') return pd.read_table(f, sep=r' +', engine='python', names=['year', 'month', 'day', 'delta_t'])
def usno_predicted_delta_t(): import pandas as pd f = load('http://maia.usno.navy.mil/ser7/deltat.preds') df = pd.read_table(f, sep=r' +', engine='python') return pd.DataFrame({ 'year': df['YEAR'], 'delta_t': df['TT-UT PREDICTION'] })
def usno_historic_delta_t(): import pandas as pd f = load('http://maia.usno.navy.mil/ser7/historic_deltat.data') df = pd.read_table(f, sep=r' +', engine='python', skiprows=[1]) return pd.DataFrame({'year': df['Year'], 'delta_t': df['TDT-UT1']})
def usno_predicted_delta_t(): import pandas as pd f = load('http://maia.usno.navy.mil/ser7/deltat.preds') df = pd.read_table(f, sep=r' +', engine='python') return pd.DataFrame({'year': df['YEAR'], 'delta_t': df['TT-UT PREDICTION']})
def load(match_function): """Yield the Hipparcos stars for which `match_function(line)` is true.""" with iokit.load(url) as f: for line in gzip.GzipFile(fileobj=f): if match_function(line): yield parse(line)