pygwtf.response.orbits#

Functions#

`Orbit`(→ numpy.ndarray)	Function to calculate the position of the spacecrafts in the constellation as a function of time.
`get_analytic_orbits`(→ numpy.ndarray)
`read_in_mojito_orbit`(→ tuple[numpy.ndarray, ...)	Plugin for reading in Mojito orbit files for LISA spacecraft positions.
`read_in_mojito_ltts`(→ tuple[numpy.ndarray, ...)	Read in mojito LTT file.
`generate_mojito_orbit_splines_resample`(...)	Generates orbit splines from a mojito orbit file. Then evaluate them on SFT time grid.
`get_analytic_ltts`(→ numpy.ndarray)	Get analytic link lengths for each arm from spacecraft positions.

Module Contents#

pygwtf.response.orbits.Orbit(t: numpy.ndarray, initial_ecliptic_longitude: float, e: float, n: int, initial_orientation_of_constellation: float = 0) → numpy.ndarray#

Function to calculate the position of the spacecrafts in the constellation as a function of time.

Reference: https://arxiv.org/pdf/gr-qc/0311069

Args:: t (jax.numpy.array): Time array initial_ecliptic_longitude (float): Initial ecliptic longitude of the constellation e (float): Eccentricity of the orbit n (int): Spacecraft number initial_orientation_of_constellation (float): Initial orientation of the constellation
Returns:: pos (jax.numpy.array): Position of the spacecraft as a function of time

pygwtf.response.orbits.get_analytic_orbits(t_tranche: numpy.ndarray) → numpy.ndarray#

‘ Get analytic orbits for the 3 spacecrafts in the constellation as a function of time.

Args:: t_tranche (numpy.array): Time array for the tranche of data being analyzed. Shape (nT,)
Returns:: orbits (numpy.array): Array of shape (nT, 3, 3) containing the positions of the 3 spacecraft as a function of time.

pygwtf.response.orbits.read_in_mojito_orbit(filepath, t0_data=97729939.827664, tend_data=160846137.32766402) → tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray]#

Plugin for reading in Mojito orbit files for LISA spacecraft positions.

To obtain t0_data and tend_data, the easiest method:

Use MojitoProcessor to download the TDI-data for the sampling rate required
Look at the t_tdi array in the processed data, this will give you the start (t0_data) and end times (tend_data) of the data collection.

For context on timings and time-shifts see the CD1 CU-SIM document: TODO: Add link to CD1 document here.

Args:: filepath (str): Path to the Mojito orbit file t0_data (float): Beginning of the data collection in seconds, default is 97729939.827664 (0.4Hz downsampled data) tend_data (float): End of the data collection in seconds, default is 160846137.32766402 (0.4Hz downsampled data)
Returns:: pos_sc1 (numpy.array): Array of shape (3,N_times_orbital) containing the positions of spacecraft 1 as a function of time. pos_sc2 (numpy.array): Array of shape (3,N_times_orbital) containing the positions of spacecraft 2 as a function of time. pos_sc3 (numpy.array): Array of shape (3,N_times_orbital) containing the positions of spacecraft 3 as a function of time. orbital_times_shifted (numpy.array): Array of shape (N_times_orbital,) containing the shifted orbital times corresponding to the positions.

pygwtf.response.orbits.read_in_mojito_ltts(filepath, t0_data=97729939.827664, tend_data=160846137.32766402) → tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray]#

Read in mojito LTT file.

To obtain t0_data and tend_data, the easiest method:

Use MojitoProcessor to download the TDI-data for the sampling rate required
Look at the t_tdi array in the processed data, this will give you the start (t0_data) and end times (tend_data) of the data collection.

For context on timings and time-shifts see the CD1 CU-SIM document: TODO: Add link to CD1 document here.

Only extracts: {12, 23, 31} links for now.

NOTE/WARNING: In seconds not metres, need to be converted to metres to match transfer function conventions!!!

Args:: filepath (str): Path to the Mojito LTT file
Returns:: L12 (numpy.array): Array of shape (N_times_LTT,) containing the LTTs for link 12 as a function of time. L23 (numpy.array): Array of shape (N_times_LTT,) containing the LTTs for link 23 as a function of time. L31 (numpy.array): Array of shape (N_times_LTT,) containing the LTTs for link 31 as a function of time. LTT_times_shifted (numpy.array): Array of shape (N_times_LTT,) containing the shifted LTT times corresponding to the LTTs.

pygwtf.response.orbits.generate_mojito_orbit_splines_resample(mojito_orbit_filepath: str, mojito_ltt_filepath: str, t_tranches: numpy.ndarray) → tuple[numpy.ndarray, numpy.ndarray]#

Generates orbit splines from a mojito orbit file. Then evaluate them on SFT time grid.

Args:: mojito_orbit_filepath (str): Path to the Mojito orbit file mojito_ltt_filepath (str): Path to the Mojito LTT file (assumes npz file) t_tranches (numpy.array): Time array for the SFT midpoints, shape (nT,)
Returns:: p_fine (numpy.array): Array of shape (nT, 3, 3) containing the positions of the 3 spacecraft as a function of time. LTTs_fine (numpy.array): Array of shape (nT, 3) containing the LTTs for the 3 links (12, 23, 31) as a function of time.

pygwtf.response.orbits.get_analytic_ltts(spacecraft_orbits: numpy.ndarray) → numpy.ndarray#

Get analytic link lengths for each arm from spacecraft positions.

Despite the historical function name, the transfer-function code expects arm lengths in meters, not light-travel times in seconds.

Args:

spacecraft_orbits (numpy.array): Array of shape (nT, 3, 3) containing the positions of the 3 spacecraft as a function of time.

Returns:

ltts (numpy.array): Array of shape (nT, 3) containing the link: lengths in meters for links (12, 23, 31) as a function of time.