Tools


Satellite Name/NORAD ID Lookup

Enter the name or NORAD ID of a satellite to search for the opposite field in the database. For example, if you enter a satellite name, the corresponding NORAD ID will be returned, and vice versa. For satellites with history of more than one name or NORAD ID, the most recent information will be returned.




Satellite Position Lookup

Use this tool to find the position of a satellite in the sky (azimuth, altitude, right ascension, and declination). Enter the location of the observer, date/time, and satellite identifier to search for the satellite's position. The observer location should be entered in decimal degrees for latitude and longitude, and in meters for altitude.

* denotes required field

Observer Location

Decimal degrees - positive is north, negative is south.

Decimal degrees - positive is east, negative is west.

Meters above sea level.

Date/Time (UTC)
Satellite Identifier *
-- or --




CSV from Pandas DataFrame

Below is an example Python script that demonstrates how to convert a Pandas DataFrame to a CSV file that is compatible with the SCORE upload format. The script renames the DataFrame columns to match the SCORE column names, reorders the columns, and replaces NaN values with an empty string. The script then uses the to_csv method to create a CSV file named score_upload.csv. 


                    import numpy as np
                    import pandas as pd

                    # Assuming `df` is your DataFrame
                    df = pd.DataFrame({
                        'name': ['satellite1', 'satellite2'],
                        'id': ['12345', '23456'],
                        'time': ['2021-01-01T12:00:00Z', '2021-01-02T12:00:00Z'],
                        'uncertainty': [1, 2],
                        'magnitude': [1, 2],
                        'magnitude_uncertainty': [0.1, 0.2],
                        'latitude': [33, 33],
                        'longitude': [-110, -110],
                        'observer_altitude_m': [1000, 1000],
                        'limiting_magnitude': [6, 6],
                        'instrument': ['instrument1', 'instrument2'],
                        'observing_mode': ['CCD', 'CCD'],
                        'observing_filter': ['R', 'V'],
                        'observer_email': ['sample@user.email', 'sample@user.email'],
                        'observer_orcid': ['0000-0000-0000-0000', '0000-0000-0000-0000'],
                    })

                    # Rename the DataFrame columns to match the SCORE column names
                    df_score = df.rename(columns={
                        'name': 'satellite_name',
                        'id': 'norad_cat_id',
                        'time': 'observation_time_utc',
                        'uncertainty': 'observation_time_uncertainty_sec',
                        'magnitude': 'apparent_magnitude',
                        'magnitude_uncertainty': 'apparent_magnitude_uncertainty',
                        'latitude': 'observer_latitude_deg',
                        'longitude': 'observer_longitude_deg'
                    })

                    # All SCORE related columns in order
                    columns_order = [
                        "satellite_name","norad_cat_id","observation_time_utc","observation_time_uncertainty_sec",
                        "apparent_magnitude","apparent_magnitude_uncertainty","observer_latitude_deg",
                        "observer_longitude_deg","observer_altitude_m","limiting_magnitude","instrument",
                        "observing_mode","observing_filter","observer_email","observer_orcid",
                        "satellite_right_ascension_deg","satellite_declination_deg","sigma_2_ra",
                        "sigma_ra_sigma_dec","sigma_2_dec","range_to_satellite_km","range_to_satellite_uncertainty_km",
                        "range_rate_of_satellite_km_per_sec","range_rate_of_satellite_uncertainty_km_per_sec",
                        "comments","data_archive_link","mpc_code"
                    ]

                    # Reindex the DataFrame with the desired columns
                    df_score = df_score.reindex(columns=columns_order)

                    # Replace NaN values with an empty string
                    df_score = df_score.replace(np.nan, '', regex=True)

                    # Use the `to_csv` method to create a CSV file
                    df_score.to_csv('score_upload.csv', index=False, columns=columns_order)