pymgp.satorb.ut2gmst#

pymgp.satorb.ut2gmst(ut1, model='IAU-82')[source]#

Compute Greenwich Mean Siderial Time from UT1.

Parameters:

ut1array_like, float64 or datetime64 or str: Universal time datetime64 object, numpy parsable datetime string, or matplotlib sequential datenumber (days since ‘01-Jan-1970’)
model{‘IAU-82’, ‘APPROXIMATE’}, optional: Model for ut to gmst conversion. Default is ‘IAU-82’ model.

Returns:

gmstarray_like: Greenwich Mean Siderial Time GMST [0-2pi rad] for UT1 (radians)
omegaefloat: Rotation rate of the Earth (rev/day)

See also

ecef2eci, eci2ecef

Examples

>>> ut2gmst('2012-01-04 00:00')
(1.7979884328663978, 1.0027379093)

>>> ut2gmst(['2012-01-04 15:00:03.13', '2012-01-04 16:00:03'])
(array([5.73595924, 5.99846593]), 1.0027379093)

Use matplotlib datenumbers as input (days since 1-jan-1970)

>>> datenum = np.datetime64('2012-01-04 15:00:03.13','ns').astype(np.int64)*1e-9/86400
>>> print(datenum)
15343.625036226851
>>> ut2gmst(datenum)
(5.7359592379770525, 1.0027379093)

>>> ut2gmst(np.arange(datenum,datenum+1,.1))
(array([5.73595924, 0.08281274, 0.71285155, 1.34289036, 1.97292917,
       2.60296798, 3.23300679, 3.8630456 , 4.49308441, 5.12312322]), 1.0027379093)
>>> gmst0, omegae = ut2gmst(datenum)
>>> gmst = (gmst0 + 2*np.pi*omegae*np.arange(0,1,.1) ) % ( 2*np.pi ) 
>>> gmst
array([5.73595924, 0.08281274, 0.71285155, 1.34289036, 1.97292917,
       2.60296798, 3.23300679, 3.8630456 , 4.49308441, 5.12312322])