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A package to handle the SLR(Satellite Laser Ranging) data

Project description

Welcome to the SLRfield package

This package is an archive of scientific routines for data processing related to SLR(Satellite Laser Ranging). Currently, operations on SLR data include:

  1. Automatically download the CPF(Consolidated Prediction Format) ephemeris file
  2. Parse the CPF ephemeris file
  3. Predict the azimuth, altitude, distance of the target, and the time of flight for laser pulse etc. given the coordinates of a station
  4. Automatically download TLE/3LE data from SPACETRACK
  5. Pick out space targets that meets specific demands from DISCOS(Database and Information System Characterising Objects in Space) and CELESTRAK database by setting a series of parameters, such as mass, shape, RCS(Radar Cross Section), and orbit altitude etc.
  6. Calculate one-day prediction and multiple-day visible passes for space targets based on TLE/3LE data

How to Install

SLRfield can be installed with pip install slrfield.

How to Upgrade

SLRfield can be updated to the latest version with pip install slrfield --upgrade.

How to use

Download the latest CPF ephemeris files at the current moment

In this package, for prediction data centers, only CDDIS(Crustal Dynamics Data Information System) and EDC(EUROLAS Data Center) are available. If the prediction data center is not provided, then it is set to CDDIS by default.

Download all available targets

The data storage directory(such as CPF/CDDIS/2020-10-02) will be automatically created or emptied(if exist) by default ahead of requesting and downloading CPF files .

>>> from slrfield import cpf_download
>>> cpf_files_all = cpf_download() # From CDDIS by default
>>> # cpf_files_all = cpf_download(source = 'EDC') # From EDC

By setting append = True, the data storage directory will not be emptied(if exist) ahead of requesting and downloading CPF files .

>>> cpf_files_append = cpf_download(['beaconc','lageos1'],append=True)

Download a set of specified targets

>>> sat_lists = ['ajisai','lageos1','hy2a','etalon2','jason3']
>>> cpf_files_cddis = cpf_download(sat_lists) # From CDDIS
>>> cpf_files_edc = cpf_download(sat_lists,source = 'EDC') # From EDC

Download the latest CPF ephemeris files before a specific date and time

>>> sat_name = 'lageos1'
>>> date = '2007-06-01 11:30:00'
>>> cpf_file_cddis = cpf_download(sat_name,date) # From CDDIS
>>> sat_lists = ['starlette','lageos1']
>>> date = '2017-01-01 11:30:00'
>>> cpf_files_edc = cpf_download(sat_lists,date,'EDC') # From EDC

Parse the CPF ephemeris files and read the data

Information from the parsed CPF ephemeris files includes the following contents:

  • Format
  • Format Version
  • Ephemeris Source
  • Date and time of ephemeris production
  • Ephemeris Sequence number
  • Target name
  • COSPAR ID
  • SIC
  • NORAD ID
  • Starting date and time of ephemeris
  • Ending date and time of ephemeris
  • Time between table entries (UTC seconds)
  • Target type
  • Reference frame
  • Rotational angle type
  • Center of mass correction
  • Direction type
  • Modified Julian Date
  • Second of Day
  • Leap Second
  • Time moment in UTC
  • Target positions in meters

Parse a single CPF ephemeris file

>>> from slrfield import read_cpfs
>>> cpf_data_cddis = read_cpfs(cpf_file_cddis)
>>> print(cpf_data_cddis.info)
[{'MJD': array([54252, 54252, ..., 54254, 54254]),'SoD': array([0., 300., ..., 85800., 86100.]),'positions[m]': array([[ 6033709.581,  6786287.416, 8199639.624], ..., [ 3434366.77 , -2533996.246, 11511370.917]]),'Leap_Second': array([0, 0, ..., 0, 0]),'Format': 'CPF', 'Format Version': '1', 'Ephemeris Source': 'SGF', 'Time of Ephemeris Production': '2007-06-01 02:00:00.000', 'Ephemeris Sequence Number': '6521', 'Target Name': 'Lageos1', 'COSPAR ID': '7603901', 'SIC': '1155', 'NORAD ID': '08820', 'Start': '2007-06-01 00:00:00.000', 'End': '2007-06-03 23:54:00.000', 'Time Interval[sec]': '300', 'Target Type': 'passive(retro-reflector) artificial satellite', 'Reference Frame': 'ITRF(default)', 'Rotational Angle': 'Not Applicable', 'Center of Mass Correction': 'None applied. Prediction is for center of mass of target', 'Direction': 'instantaneous vector from geocenter to target, without light-time iteration', 'ts_utc': array(['2007-06-01 00:00:00.000', '2007-06-01 00:05:00.000', ..., '2007-06-03 23:50:00.000', '2007-06-03 23:55:00.000'], dtype='<U23')}]   

Parse a set of CPF ephemeris files

>>> cpf_data_edc = read_cpfs(cpf_files_edc)
>>> print(cpf_data_edc.target_name)
['starlette', 'lageos1']
>>> print(cpf_data_edc.ephemeris_source)
['HTS', 'HTS']
>>> print(cpf_data_edc.norad_id)
['7646', '8820']
>>> print(cpf_data_edc.cospar_id)
['7501001', '7603901']
>>> print(cpf_data_edc.time_ephemeris) # Date and time of ephemeris production
['2016-12-31 12:00:00.000', '2016-12-31 12:00:00.000']
>>> print(cpf_data_edc.version) # Format Version
['1', '1']

Make predictions

The azimuth, altitude, distance of a target w.r.t. a given station, and the time of flight for laser pulse etc. can be easily predicted by calling a method pred. The output prediction files named with target names are stored in directory pred by default.

  • There are two modes for the prediction. If the mode is set to geometric, then the transmitting direction of the laser will coincide with the receiving direction at a certain moment. In this case, the output prediction file will not contain the difference between the receiving direction and the transmitting direction. If the mode is set to apparent, then the transmitting direction of the laser is inconsistent with the receiving direction at a certain moment. In this case, the output prediction file will contain the difference between the receiving direction and the transmitting direction. The default mode is set to apparent.
  • The 10-point(degree 9) Lagrange polynomial interpolation method is used to interpolate the CPF ephemeris.
  • Effects of leap second have been considered in the prediction generation.

Coordinates of station can either be geocentric(x, y, z) in meters or geodetic(lon, lat, height) in degrees and meters. The default coordinates type is set to geodetic.

Note: The first use of slrfield will call astropy to automatically download the Earth Orientation Parameters file, so it may take a long time to output the prediction file.

For geodetic(lon, lat, height) station coordinates

t_start = '2007-06-01 17:06:40'
t_end = '2007-06-02 09:06:40'
t_increment = 0.5 # second

station = [46.877230,7.465222,951.33] # geodetic(lon, lat, height) coordinates in degrees and meters by default
cpf_data_cddis.pred(station,t_start,t_end,t_increment)

For geocentric(x, y, z) station coordinates

t_start = '2017-01-01 22:06:40'
t_end = '2017-01-02 09:06:40'
t_increment = 2 # second

station = [4331283.557, 567549.902,4633140.353] # geocentric(x, y, z) coordinates in meters
cpf_data_edc.pred(station,t_start,t_end,t_increment,coord_type = 'geocentric',mode='geometric')

Pick out space targets

Pick out space targets that meets specific physical characteristics from the DISCOS(Database and Information System Characterising Objects in Space) database. The DISCOS database mainly provides the geometric information of the spatial target, such as shape, size and mass, etc. Before using this package to access the DISCOS database, you need to register an account and get a token.

from slrfield import discos_query
result = discos_query(Shape=['Box','Pan','+'],Length=[0.5,5],Mass=[300,500],sort='Mass')

A pandas dataframe and a csv-formatted file discos_catalog.csv will be returned.

Pick out space targets that meets specific orbital demands from the CELESTRAK database. The CELESTRAK database mainly provides information such as the orbit inclination, period, and altitude, etc.

from slrfield import celestrak_query
result = celestrak_query(Payload=False,Decayed=False,MeanAlt=None=[400,1000],sort='-Inclination')

A pandas dataframe and a csv-formatted file celestrak_catalog.csv will be returned.

Pick out space targets from the combination of DISCOS and CELESTRAK.

from slrfield import target_query
targets_df = target_query(Payload=False,Decayed=False,RCSAvg=[5,100],MeanAlt=[300,800])

A pandas dataframe and a csv-formatted file target_catalog.csv will be returned.

Download TLE/3LE data

Download the TLE/3LE data from SPACE-TRACK; before using this package to access the TLE/3LE database, you need to register an account.

from slrfield import tle_download
noradids = list(targets_df['NORADID'])
#noradids = 'satno.txt'
tle_download(noradids)

A text file satcat_3le.txt will be created in the directory TLE.

Calculate the visible passes for target transit

t_start = '2020-06-08 23:00:00' # starting date and time 
t_end = '2020-06-11 00:00:00' # ending date and time
timezone = 8 # timezone
site = ['25.03 N','102.80 E','1987.05'] # station position in lat[deg], lon[deg], and height[m] above the WGS84 ellipsoid
visible_pass(t_start,t_end,site,timezone)

csv-formatted file VisiblePasses_bysat.csv and VisiblePasses_bydate.csv, as well as a set of text file xxxx.txt will be created in the directory prediction, where 'xxxx' represents the NORADID of the target.

Change log

  • 0.1.11 — Oct 03, 2020

    The CDDIS will discontinue anonymous ftp access to its archive in October 2020, therefore, this package implements the transition from ftp to EARTHDATA for downloading CPF files.

  • 0.1.9 — Jul 26, 2020

    Added progress bar for downloading data

  • 0.1.5 — Jun 9, 2020

    Expanded the following functions:

    • Automatically download TLE/3LE data from SPACETRACK
    • Pick out space targets that meets specific demands from DISCOS(Database and Information System Characterising Objects in Space) and CELESTRAK database by setting a series of parameters, such as mass, shape, RCS(Radar Cross Section), and orbit altitude etc.
    • Calculate one-day prediction and multiple-day visible passes for space targets based on TLE/3LE data
  • 0.0.2 — Apr 21, 2020

    • The slrfield package was released.

Next release

  • Improve the code structure to make it easier to read
  • Add functions to download, parse, and handle the CRD(Consolidated Laser Ranging Data Format) observations
  • Add functions to estimate the apparent magnitude of the target

Reference

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