Parsing times with sunpy.time.parse_time

This is an example to show some possible usage of parse_time. parse_time is a function that can be useful to create Time objects from various other time objects and strings.

Import the required modules.

import time
from datetime import date, datetime

import numpy as np
import pandas

from sunpy.time import parse_time

Suppose you want to parse some strings, parse_time can do that.

t1 = parse_time('1995-12-31 23:59:60')

Of course you could do the same with Time. But sunpy parse_time can parse even more formats of time strings. And as you see from the examples, thanks to Time, parse_time can handle leap seconds too.

t2 = parse_time('1995-Dec-31 23:59:60')

You can mention the scale of the time as a keyword parameter if you need. Similar to scale you can pass in any astropy Time compatible keywords to parse_time. See all arguments here:

t3 = parse_time('2012:124:21:08:12', scale='tai')

Now that you are done with strings, let’s see other type parse_time handles, tuples. Time does not handle tuples but parse_time does.

t4 = parse_time((1998, 11, 14))
t5 = parse_time((2001, 1, 1, 12, 12, 12, 8899))

This also means that you can parse a time.struct_time.

t6 = parse_time(time.localtime())

parse_time also parses datetime and date objects.

t7 = parse_time(datetime.now())
t8 = parse_time(date.today())

parse_time can return astropy.time.Time objects for pandas.Timestamp, pandas.Series and pandas.DatetimeIndex.

t9 = parse_time(pandas.Timestamp(datetime(1966, 2, 3)))

t10 = parse_time(
    pandas.Series([[datetime(2012, 1, 1, 0, 0),
                    datetime(2012, 1, 2, 0, 0)],
                   [datetime(2012, 1, 3, 0, 0),
                    datetime(2012, 1, 4, 0, 0)]]))

t11 = parse_time(
    pandas.DatetimeIndex([
        datetime(2012, 1, 1, 0, 0),
        datetime(2012, 1, 2, 0, 0),
        datetime(2012, 1, 3, 0, 0),
        datetime(2012, 1, 4, 0, 0)
    ]))

parse_time can parse numpy.datetime64 objects.

t12 = parse_time(np.datetime64('2014-02-07T16:47:51.008288123'))
t13 = parse_time(
    np.array(
        ['2014-02-07T16:47:51.008288123', '2014-02-07T18:47:51.008288123'],
        dtype='datetime64'))

Parse time returns Time object for every parsable input that you give to it. parse_time can handle all formats that Time can handle. That is, [‘jd’, ‘mjd’, ‘decimalyear’, ‘unix’, ‘cxcsec’, ‘gps’, ‘plot_date’, ‘datetime’, ‘iso’, ‘isot’, ‘yday’, ‘fits’, ‘byear’, ‘jyear’, ‘byear_str’, ‘jyear_str’] at the time of writing. This can be used by passing format keyword argument to parse_time.

parse_time(1234.0, format='jd')
parse_time('B1950.0', format='byear_str')

<Time object: scale='tt' format='byear_str' value=B1950.000>