Conjunctions (TOLEOS)

%load_ext watermark
%watermark -i -v -p viresclient,pandas,xarray,matplotlib,cartopy

Python implementation: CPython
Python version       : 3.11.6
IPython version      : 8.18.0

viresclient: 0.15.2
pandas     : 2.1.3
xarray     : 2023.12.0
matplotlib : 3.8.2
cartopy    : 0.22.0

import datetime as dt

import matplotlib.pyplot as plt
import numpy as np
from viresclient import SwarmRequest

Product information

The MM_OPER_CON_EPH_2_ product contains conjunction information between Swarm (A, B, C), CHAMP, GOCE, GRACE (1, 2), and GRACE-FO (1, 2).

The product is implemented in VirES as two collections, each available as a single flat time series.

MM_OPER_CON_EPH_2_:crossover contains the list of times where satellite ground-tracks overlap within a ~7 hour window.

MM_OPER_CON_EPH_2_:plane_alignment contains much rarer events, where the planes of different spacecraft are aligned

request = SwarmRequest()
for collection in (
    "MM_OPER_CON_EPH_2_:crossover",
    "MM_OPER_CON_EPH_2_:plane_alignment",
):
    print(f"{collection}:\n{request.available_measurements(collection)}\n")

MM_OPER_CON_EPH_2_:crossover:
['time_1', 'time_2', 'time_difference', 'satellite_1', 'satellite_2', 'latitude', 'longitude', 'altitude_1', 'altitude_2', 'magnetic_latitude', 'magnetic_longitude', 'local_solar_time_1', 'local_solar_time_2']

MM_OPER_CON_EPH_2_:plane_alignment:
['time', 'altitude_1', 'altitude_2', 'ltan_1', 'ltan_2', 'ltan_rate_1', 'ltan_rate_2', 'satellite_1', 'satellite_2']

Fetching data

Crossovers

Let’s fetch all the available conjunctions for a given day.

Note that the start_time and end_time specified are used for a full interval query over both time_1 and time_2 given in the outputs.

request = SwarmRequest()
request.set_collection("MM_OPER_CON_EPH_2_:crossover")
request.set_products(request.available_measurements("MM_OPER_CON_EPH_2_:crossover"))
data = request.get_between(
    dt.datetime(2020, 1, 1),
    dt.datetime(2020, 1, 2),
)
df = data.as_dataframe()
df

	altitude_1	Spacecraft	local_solar_time_1	time_2	longitude	magnetic_latitude	local_solar_time_2	satellite_1	magnetic_longitude	altitude_2	latitude	time_difference	satellite_2
time_1
2019-12-31 16:45:34.507031296	508386.037694	-	12.813727	2020-01-01 00:03:20.865398528	-168.631028	79.949318	5.517517	GF1	-164.456295	445802.184339	87.321728	26266.358365	SWC
2019-12-31 16:45:34.666273280	508384.174138	-	12.822272	2020-01-01 00:03:29.977781248	-168.540829	79.956628	5.523574	GF1	-164.497178	445817.275119	87.330964	26275.311505	SWA
2019-12-31 16:45:58.498234368	508285.501449	-	12.808170	2020-01-01 00:03:20.801171712	-168.714116	79.946058	5.518642	GF2	-164.469981	445802.184832	87.322763	26242.302943	SWC
2019-12-31 16:45:58.642820352	508283.850034	-	12.815265	2020-01-01 00:03:29.901976576	-168.645621	79.952162	5.523248	GF2	-164.509020	445817.254541	87.331274	26251.259161	SWA
2019-12-31 17:32:44.852499968	522002.918063	-	0.806052	2020-01-01 00:50:00.517710848	-0.411375	-79.455668	17.518367	GF1	14.152966	464173.845073	-87.324172	26235.665210	SWC
...	...	...	...	...	...	...	...	...	...	...	...	...	...
2020-01-01 23:42:29.863429888	511607.216872	-	4.383929	2020-01-02 01:49:40.551429632	38.339964	75.124965	2.264293	SWB	146.831766	508318.284479	81.399341	7630.688001	GF1
2020-01-01 23:43:26.628039168	511293.312109	-	6.837217	2020-01-02 03:59:58.525515520	42.564400	71.743591	2.561690	SWB	142.500846	445127.722214	77.902092	15391.897473	SWA
2020-01-01 23:43:39.701632768	511208.234498	-	6.874009	2020-01-02 03:59:37.480210944	43.203973	70.954621	2.607960	SWB	141.708566	445042.331540	77.089844	15357.778578	SWC
2020-01-01 23:59:39.406749952	507422.300094	-	6.175955	2020-01-02 02:11:45.781679616	59.698567	14.544932	3.974184	GF2	134.574062	435473.994296	21.148917	7926.374931	SWC
2020-01-01 23:59:59.915086080	507614.183522	-	6.186112	2020-01-02 02:12:29.624203264	59.668240	17.345313	3.977859	GF1	134.913629	435703.475318	23.980608	7949.709113	SWC

5408 rows × 13 columns

Pairs of conjunctioning spacecraft are defined with short designations in the satellite_1 and satellite_2 variables:

df["satellite_1"].unique()

array(['GF1', 'GF2', 'SWB', 'SWC', 'SWA'], dtype=object)

df["satellite_2"].unique()

array(['SWC', 'SWA', 'SWB', 'GF2', 'GF1'], dtype=object)

Each conjunction has a start and end time defined with the time_1 and time_2 variables:

df.iloc[0:5][["time_2", "satellite_1", "satellite_2"]]

	time_2	satellite_1	satellite_2
time_1
2019-12-31 16:45:34.507031296	2020-01-01 00:03:20.865398528	GF1	SWC
2019-12-31 16:45:34.666273280	2020-01-01 00:03:29.977781248	GF1	SWA
2019-12-31 16:45:58.498234368	2020-01-01 00:03:20.801171712	GF2	SWC
2019-12-31 16:45:58.642820352	2020-01-01 00:03:29.901976576	GF2	SWA
2019-12-31 17:32:44.852499968	2020-01-01 00:50:00.517710848	GF1	SWC

We can select all the conjunctions containing a given satellite:

df_SWA = df.where((df["satellite_1"] == "SWA") | (df["satellite_2"] == "SWA")).dropna()
df_SWA

	altitude_1	Spacecraft	local_solar_time_1	time_2	longitude	magnetic_latitude	local_solar_time_2	satellite_1	magnetic_longitude	altitude_2	latitude	time_difference	satellite_2
time_1
2019-12-31 16:45:34.666273280	508384.174138	-	12.822272	2020-01-01 00:03:29.977781248	-168.540829	79.956628	5.523574	GF1	-164.497178	445817.275119	87.330964	26275.311505	SWA
2019-12-31 16:45:58.642820352	508283.850034	-	12.815265	2020-01-01 00:03:29.901976576	-168.645621	79.952162	5.523248	GF2	-164.509020	445817.254541	87.331274	26251.259161	SWA
2019-12-31 17:32:44.934890496	522004.159533	-	0.811638	2020-01-01 00:50:09.627804672	-0.365544	-79.460015	17.521446	GF1	14.136825	464161.845406	-87.328937	26244.692912	SWA
2019-12-31 17:33:08.975226624	522106.188666	-	0.802755	2020-01-01 00:50:09.531093760	-0.498389	-79.454945	17.519267	GF2	14.116901	464161.901037	-87.329375	26220.555867	SWA
2019-12-31 17:42:18.655843840	511774.213962	-	8.064305	2020-01-01 00:02:09.084413952	120.426718	75.269785	1.733630	SWB	-175.176848	445697.680298	84.623616	22790.428566	SWA
...	...	...	...	...	...	...	...	...	...	...	...	...	...
2020-01-01 23:31:37.199038976	524464.926293	-	3.789605	2020-01-02 00:10:47.848054528	54.144711	-82.426605	3.136647	GF2	30.386096	463967.092769	-85.252281	2350.649018	SWA
2020-01-01 23:36:50.826312448	511401.685124	-	17.280079	2020-01-02 00:59:48.513515776	-115.750956	80.903810	15.897388	SWB	-87.255342	445355.973591	76.710613	4977.687203	SWA
2020-01-01 23:41:14.676492288	511872.512023	-	8.571359	2020-01-02 07:08:49.337390592	21.364816	79.420521	1.111731	SWB	156.361396	445591.642678	85.793936	26854.660897	SWA
2020-01-01 23:41:47.813570304	511777.132307	-	7.710203	2020-01-02 05:34:56.224296704	31.918781	77.564465	1.824534	SWB	151.398185	445578.775959	83.922645	21188.410732	SWA
2020-01-01 23:43:26.628039168	511293.312109	-	6.837217	2020-01-02 03:59:58.525515520	42.564400	71.743591	2.561690	SWB	142.500846	445127.722214	77.902092	15391.897473	SWA

1657 rows × 13 columns

Plane alignments

request = SwarmRequest()
request.set_collection("MM_OPER_CON_EPH_2_:plane_alignment")
request.set_products(
    request.available_measurements("MM_OPER_CON_EPH_2_:plane_alignment")
)
data = request.get_between(
    dt.datetime(2000, 1, 1),
    dt.datetime(2022, 1, 1),
)
df = data.as_dataframe()
df

	altitude_1	Spacecraft	ltan_rate_1	satellite_1	ltan_2	ltan_1	altitude_2	ltan_rate_2	satellite_2
time
2003-05-11 10:10:03.145992192	400547.265815	-	-0.091268	CH	4.684153	16.684153	488291.762308	-0.074543	GR2
2003-05-11 10:21:36.896242176	400527.975912	-	-0.091268	CH	4.683406	16.683406	487617.059444	-0.074549	GR1
2005-04-08 16:07:15.667320192	361914.544690	-	-0.091834	CH	0.629215	0.629215	469993.893995	-0.074332	GR2
2005-04-08 16:12:54.103234304	361913.083670	-	-0.091834	CH	0.628855	0.628855	470666.606845	-0.074331	GR1
2007-02-14 05:07:14.704273408	350444.851771	-	-0.092207	CH	22.255276	10.255276	481552.511743	-0.074526	GR2
...	...	...	...	...	...	...	...	...	...
2020-05-21 19:46:01.686648320	436300.307971	-	-0.089979	SWC	17.434235	17.434235	485113.048217	-0.074638	GF2
2020-05-21 20:03:47.512773632	436330.337154	-	-0.089979	SWC	17.433122	17.433122	485000.585092	-0.074633	GF1
2021-09-30 18:39:55.482117120	430877.890053	-	-0.089902	SWA	20.642289	20.642289	430878.908792	-0.090034	SWC
2021-10-03 13:41:11.625304576	503359.670523	-	-0.085605	SWB	20.390555	8.390555	431177.812782	-0.090089	SWC
2021-10-03 15:03:35.963726592	431054.450419	-	-0.089954	SWA	8.385697	20.385697	503289.814384	-0.085605	SWB

82 rows × 9 columns

def alignments(df, sat="SWA"):
    return df.where((df["satellite_1"] == sat) | (df["satellite_2"] == sat)).dropna()


sats = ("CH", "GO", "GR1", "GR2", "GF1", "GF2", "SWA", "SWB", "SWC")
fig, axes = plt.subplots(len(sats), 1, figsize=(10, 5), sharex=True)
empty = np.empty(df.index.shape)
empty[:] = np.nan
axes[0].plot(df.index, empty)
for sat, ax in zip(sats, axes):
    _df = alignments(df, sat=sat)
    for date in _df.index:
        ax.axvline(date)
    ax.set_yticks([])
    ax.set_ylabel(sat)
fig.subplots_adjust(hspace=0)
fig.suptitle("Plane alignments");