Imports¶

In [1]:

import pyaurorax
import datetime
import pprint
import pandas as pd

aurorax = pyaurorax.PyAuroraX()

Search for conjunctions¶

In [3]:

# search for conjunctions between any THEMIS-ASI intrument and any Swarm instrument
start = datetime.datetime(2020, 1, 1, 0, 0, 0)
end = datetime.datetime(2020, 1, 1, 6, 59, 59)
ground = [{"programs": ["themis-asi"]}]
space = [{"programs": ["swarm"]}]
distance = 500

In [4]:

# perform search
s = aurorax.search.conjunctions.search(start, end, distance, ground=ground, space=space, verbose=True)

[2024-06-17 19:53:46.425510] Search object created
[2024-06-17 19:53:46.480896] Request submitted
[2024-06-17 19:53:46.480896] Request ID: 2e2027dd-ae9f-4e38-aba4-f6bdf5e30e61
[2024-06-17 19:53:46.480896] Request details available at: https://api.aurorax.space/api/v1/conjunctions/requests/2e2027dd-ae9f-4e38-aba4-f6bdf5e30e61
[2024-06-17 19:53:46.480896] Waiting for data ...
[2024-06-17 19:53:47.587181] Checking for data ...
[2024-06-17 19:53:47.674404] Data is now available
[2024-06-17 19:53:47.674404] Retrieving data ...
[2024-06-17 19:53:47.806341] Retrieved 376.7 kB of data containing 49 records

In [5]:

# output data as a pandas dataframe
conjunctions = [c.__dict__ for c in s.data]
df = pd.DataFrame(conjunctions)
df.sort_values("start")

Out[5]:

	conjunction_type	start	end	data_sources	min_distance	max_distance	events	closest_epoch	farthest_epoch
43	nbtrace	2020-01-01T00:08:00	2020-01-01T00:09:00	[DataSource(identifier=53, program='themis-asi...	206.588207	260.819127	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:09:00	2020-01-01T00:08:00
15	nbtrace	2020-01-01T00:08:00	2020-01-01T00:09:00	[DataSource(identifier=53, program='themis-asi...	192.029390	336.631805	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:09:00	2020-01-01T00:08:00
39	nbtrace	2020-01-01T00:10:00	2020-01-01T00:11:00	[DataSource(identifier=51, program='themis-asi...	195.554240	298.419001	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:10:00	2020-01-01T00:11:00
12	nbtrace	2020-01-01T00:10:00	2020-01-01T00:11:00	[DataSource(identifier=51, program='themis-asi...	282.877438	285.744881	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:10:00	2020-01-01T00:11:00
29	nbtrace	2020-01-01T00:11:00	2020-01-01T00:13:00	[DataSource(identifier=46, program='themis-asi...	53.391843	441.814398	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:12:00	2020-01-01T00:11:00
4	nbtrace	2020-01-01T00:12:00	2020-01-01T00:13:00	[DataSource(identifier=46, program='themis-asi...	89.436436	332.998863	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:12:00	2020-01-01T00:13:00
9	nbtrace	2020-01-01T00:13:00	2020-01-01T00:15:00	[DataSource(identifier=50, program='themis-asi...	105.345328	488.349867	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:14:00	2020-01-01T00:15:00
36	nbtrace	2020-01-01T00:13:00	2020-01-01T00:14:00	[DataSource(identifier=50, program='themis-asi...	215.337413	330.343161	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:14:00	2020-01-01T00:13:00
16	nbtrace	2020-01-01T00:13:00	2020-01-01T00:13:00	[DataSource(identifier=54, program='themis-asi...	401.883399	401.883399	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:13:00	2020-01-01T00:13:00
33	nbtrace	2020-01-01T00:44:00	2020-01-01T00:45:00	[DataSource(identifier=48, program='themis-asi...	176.558673	365.537448	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:45:00	2020-01-01T00:44:00
7	nbtrace	2020-01-01T00:44:00	2020-01-01T00:46:00	[DataSource(identifier=48, program='themis-asi...	114.890751	488.203200	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:45:00	2020-01-01T00:46:00
40	nbtrace	2020-01-01T00:46:00	2020-01-01T00:47:00	[DataSource(identifier=52, program='themis-asi...	275.851379	345.489291	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:47:00	2020-01-01T00:46:00
14	nbtrace	2020-01-01T00:46:00	2020-01-01T00:47:00	[DataSource(identifier=52, program='themis-asi...	216.790371	385.043153	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:47:00	2020-01-01T00:46:00
8	nbtrace	2020-01-01T00:47:00	2020-01-01T00:48:00	[DataSource(identifier=49, program='themis-asi...	332.998445	461.144528	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:48:00	2020-01-01T00:47:00
34	nbtrace	2020-01-01T00:47:00	2020-01-01T00:48:00	[DataSource(identifier=49, program='themis-asi...	355.233113	396.151714	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:48:00	2020-01-01T00:47:00
35	nbtrace	2020-01-01T00:50:00	2020-01-01T00:51:00	[DataSource(identifier=51, program='themis-asi...	117.379897	241.904346	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:51:00	2020-01-01T00:50:00
24	nbtrace	2020-01-01T00:52:00	2020-01-01T00:52:00	[DataSource(identifier=46, program='themis-asi...	460.151231	460.151231	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:52:00	2020-01-01T00:52:00
42	nbtrace	2020-01-01T00:53:00	2020-01-01T00:53:00	[DataSource(identifier=54, program='themis-asi...	430.171520	430.171520	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:53:00	2020-01-01T00:53:00
21	nbtrace	2020-01-01T00:54:00	2020-01-01T00:55:00	[DataSource(identifier=44, program='themis-asi...	265.640223	333.568278	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:54:00	2020-01-01T00:55:00
28	nbtrace	2020-01-01T01:29:00	2020-01-01T01:29:00	[DataSource(identifier=47, program='themis-asi...	493.180071	493.180071	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T01:29:00	2020-01-01T01:29:00
2	nbtrace	2020-01-01T01:43:00	2020-01-01T01:45:00	[DataSource(identifier=45, program='themis-asi...	131.293608	465.392815	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T01:44:00	2020-01-01T01:43:00
25	nbtrace	2020-01-01T01:43:00	2020-01-01T01:45:00	[DataSource(identifier=45, program='themis-asi...	198.022033	490.128034	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T01:44:00	2020-01-01T01:45:00
23	nbtrace	2020-01-01T01:45:00	2020-01-01T01:46:00	[DataSource(identifier=44, program='themis-asi...	205.885002	420.474697	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T01:46:00	2020-01-01T01:45:00
0	nbtrace	2020-01-01T01:46:00	2020-01-01T01:47:00	[DataSource(identifier=44, program='themis-asi...	284.420850	493.197476	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T01:46:00	2020-01-01T01:47:00
10	nbtrace	2020-01-01T02:17:00	2020-01-01T02:18:00	[DataSource(identifier=50, program='themis-asi...	347.656216	390.934039	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T02:17:00	2020-01-01T02:18:00
37	nbtrace	2020-01-01T02:17:00	2020-01-01T02:18:00	[DataSource(identifier=50, program='themis-asi...	375.455878	459.284383	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T02:17:00	2020-01-01T02:18:00
41	nbtrace	2020-01-01T02:19:00	2020-01-01T02:20:00	[DataSource(identifier=52, program='themis-asi...	197.889134	494.591201	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T02:20:00	2020-01-01T02:19:00
13	nbtrace	2020-01-01T02:20:00	2020-01-01T02:21:00	[DataSource(identifier=52, program='themis-asi...	228.831208	480.716179	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T02:20:00	2020-01-01T02:21:00
22	nbtrace	2020-01-01T02:28:00	2020-01-01T02:29:00	[DataSource(identifier=45, program='themis-asi...	156.167998	288.063655	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T02:29:00	2020-01-01T02:28:00
6	nbtrace	2020-01-01T03:15:00	2020-01-01T03:16:00	[DataSource(identifier=47, program='themis-asi...	412.812856	489.362899	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T03:16:00	2020-01-01T03:15:00
32	nbtrace	2020-01-01T03:15:00	2020-01-01T03:16:00	[DataSource(identifier=47, program='themis-asi...	385.355069	414.231784	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T03:16:00	2020-01-01T03:15:00
47	nbtrace	2020-01-01T03:17:00	2020-01-01T03:18:00	[DataSource(identifier=64, program='themis-asi...	224.424121	433.727070	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T03:17:00	2020-01-01T03:18:00
18	nbtrace	2020-01-01T03:17:00	2020-01-01T03:18:00	[DataSource(identifier=64, program='themis-asi...	186.992979	344.140279	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T03:17:00	2020-01-01T03:18:00
17	nbtrace	2020-01-01T03:50:00	2020-01-01T03:51:00	[DataSource(identifier=54, program='themis-asi...	106.219997	351.707007	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T03:51:00	2020-01-01T03:50:00
44	nbtrace	2020-01-01T03:50:00	2020-01-01T03:51:00	[DataSource(identifier=54, program='themis-asi...	127.208169	287.958853	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T03:51:00	2020-01-01T03:50:00
30	nbtrace	2020-01-01T03:51:00	2020-01-01T03:52:00	[DataSource(identifier=46, program='themis-asi...	139.462865	365.173735	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T03:52:00	2020-01-01T03:51:00
3	nbtrace	2020-01-01T03:51:00	2020-01-01T03:52:00	[DataSource(identifier=46, program='themis-asi...	142.425348	433.674543	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T03:52:00	2020-01-01T03:51:00
11	nbtrace	2020-01-01T03:53:00	2020-01-01T03:53:00	[DataSource(identifier=51, program='themis-asi...	219.412264	219.412264	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T03:53:00	2020-01-01T03:53:00
38	nbtrace	2020-01-01T03:53:00	2020-01-01T03:53:00	[DataSource(identifier=51, program='themis-asi...	190.663238	190.663238	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T03:53:00	2020-01-01T03:53:00
27	nbtrace	2020-01-01T04:04:00	2020-01-01T04:04:00	[DataSource(identifier=47, program='themis-asi...	368.898466	368.898466	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T04:04:00	2020-01-01T04:04:00
45	nbtrace	2020-01-01T04:05:00	2020-01-01T04:06:00	[DataSource(identifier=65, program='themis-asi...	236.205457	341.436305	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T04:06:00	2020-01-01T04:05:00
48	nbtrace	2020-01-01T04:49:00	2020-01-01T04:50:00	[DataSource(identifier=65, program='themis-asi...	244.886114	355.591219	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T04:49:00	2020-01-01T04:50:00
20	nbtrace	2020-01-01T04:49:00	2020-01-01T04:50:00	[DataSource(identifier=65, program='themis-asi...	323.719101	352.655457	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T04:49:00	2020-01-01T04:50:00
1	nbtrace	2020-01-01T05:23:00	2020-01-01T05:23:00	[DataSource(identifier=45, program='themis-asi...	336.102854	336.102854	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T05:23:00	2020-01-01T05:23:00
26	nbtrace	2020-01-01T05:23:00	2020-01-01T05:23:00	[DataSource(identifier=45, program='themis-asi...	389.724805	389.724805	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T05:23:00	2020-01-01T05:23:00
19	nbtrace	2020-01-01T06:54:00	2020-01-01T06:55:00	[DataSource(identifier=64, program='themis-asi...	256.732039	421.977224	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T06:55:00	2020-01-01T06:54:00
46	nbtrace	2020-01-01T06:54:00	2020-01-01T06:55:00	[DataSource(identifier=64, program='themis-asi...	253.959112	302.213217	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T06:55:00	2020-01-01T06:54:00
31	nbtrace	2020-01-01T06:56:00	2020-01-01T06:56:00	[DataSource(identifier=47, program='themis-asi...	196.531937	196.531937	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T06:56:00	2020-01-01T06:56:00
5	nbtrace	2020-01-01T06:56:00	2020-01-01T06:56:00	[DataSource(identifier=47, program='themis-asi...	212.741402	212.741402	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T06:56:00	2020-01-01T06:56:00

Search for conjunctions with metadata filters¶

In [6]:

# set up a search for conjunctions between any THEMIS-ASI or REGO instrument, and
# any Swarm instrument with north B trace region = "north polar cap"
start = datetime.datetime(2019, 2, 1, 0, 0, 0)
end = datetime.datetime(2019, 2, 10, 23, 59, 59)
ground = [{"programs": ["themis-asi", "rego"]}]
space = [{
    "programs": ["swarm"],
    "ephemeris_metadata_filters": {
        "logical_operator": "AND",
        "expressions": [{
            "key": "nbtrace_region",
            "operator": "=",
            "values": ["north polar cap"]
        }]
    }
}]

In [7]:

# perform search
s = aurorax.search.conjunctions.search(start, end, distance, ground=ground, space=space, verbose=True)

[2024-06-17 19:54:16.140261] Search object created
[2024-06-17 19:54:16.159847] Request submitted
[2024-06-17 19:54:16.159847] Request ID: 09794659-3f19-42c7-9073-244a55a2b84a
[2024-06-17 19:54:16.159847] Request details available at: https://api.aurorax.space/api/v1/conjunctions/requests/09794659-3f19-42c7-9073-244a55a2b84a
[2024-06-17 19:54:16.159847] Waiting for data ...
[2024-06-17 19:54:17.244664] Checking for data ...
[2024-06-17 19:54:18.317411] Checking for data ...
[2024-06-17 19:54:19.410627] Checking for data ...
[2024-06-17 19:54:19.482882] Data is now available
[2024-06-17 19:54:19.482882] Retrieving data ...
[2024-06-17 19:54:19.806243] Retrieved 2.1 MB of data containing 273 records

In [9]:

# output data as a pandas dataframe
conjunctions = [c.__dict__ for c in s.data]
df = pd.DataFrame(conjunctions)
df.sort_values("start")

Out[9]:

	conjunction_type	start	end	data_sources	min_distance	max_distance	events	closest_epoch	farthest_epoch
95	nbtrace	2019-02-01T02:20:00	2019-02-01T02:21:00	[DataSource(identifier=53, program='themis-asi...	153.987890	371.020379	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2019-02-01T02:20:00	2019-02-01T02:21:00
197	nbtrace	2019-02-01T02:20:00	2019-02-01T02:21:00	[DataSource(identifier=80, program='rego', pla...	153.987890	371.020379	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2019-02-01T02:20:00	2019-02-01T02:21:00
232	nbtrace	2019-02-01T02:21:00	2019-02-01T02:22:00	[DataSource(identifier=83, program='rego', pla...	267.685472	300.984658	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2019-02-01T02:22:00	2019-02-01T02:21:00
166	nbtrace	2019-02-01T03:55:00	2019-02-01T03:56:00	[DataSource(identifier=78, program='rego', pla...	471.096543	490.131048	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2019-02-01T03:56:00	2019-02-01T03:55:00
225	nbtrace	2019-02-01T03:57:00	2019-02-01T03:57:00	[DataSource(identifier=83, program='rego', pla...	496.774661	496.774661	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2019-02-01T03:57:00	2019-02-01T03:57:00
...	...	...	...	...	...	...	...	...	...
22	nbtrace	2019-02-10T10:31:00	2019-02-10T10:31:00	[DataSource(identifier=51, program='themis-asi...	306.385115	306.385115	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2019-02-10T10:31:00	2019-02-10T10:31:00
70	nbtrace	2019-02-10T10:31:00	2019-02-10T10:31:00	[DataSource(identifier=51, program='themis-asi...	284.073933	284.073933	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2019-02-10T10:31:00	2019-02-10T10:31:00
233	nbtrace	2019-02-10T12:13:00	2019-02-10T12:13:00	[DataSource(identifier=83, program='rego', pla...	291.186735	291.186735	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2019-02-10T12:13:00	2019-02-10T12:13:00
192	nbtrace	2019-02-10T12:14:00	2019-02-10T12:15:00	[DataSource(identifier=80, program='rego', pla...	441.735730	481.820741	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2019-02-10T12:14:00	2019-02-10T12:15:00
97	nbtrace	2019-02-10T12:14:00	2019-02-10T12:15:00	[DataSource(identifier=53, program='themis-asi...	441.735730	481.820741	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2019-02-10T12:14:00	2019-02-10T12:15:00

273 rows × 9 columns

Search for conjunctions with multiple ground and space instruments, and advanced distances¶

In [10]:

# search for conjunctions between any REGO instrument, any TREx instrument,
# any Swarm spacecraft, and any THEMIS spacecraft
#
# we call this a search for "quadruple conjunctions"
start = datetime.datetime(2020, 1, 1, 0, 0, 0)
end = datetime.datetime(2020, 1, 4, 23, 59, 59)
ground = [{"programs": ["rego"]}, {"programs": ["trex"]}]
space = [{"programs": ["swarm"]}, {"programs": ["themis"]}]
advanced_distances = {
    "ground1-ground2": None,
    "ground1-space1": 500,
    "ground1-space2": 500,
    "ground2-space1": 500,
    "ground2-space2": 500,
    "space1-space2": None
}

In [11]:

# perform search
s = aurorax.search.conjunctions.search(start, end, advanced_distances, ground=ground, space=space, verbose=True)

[2024-06-17 19:54:32.651104] Search object created
[2024-06-17 19:54:32.679067] Request submitted
[2024-06-17 19:54:32.679067] Request ID: 0ff5a494-1897-4bf3-a2d5-e755f20afb7f
[2024-06-17 19:54:32.679067] Request details available at: https://api.aurorax.space/api/v1/conjunctions/requests/0ff5a494-1897-4bf3-a2d5-e755f20afb7f
[2024-06-17 19:54:32.679067] Waiting for data ...
[2024-06-17 19:54:33.762481] Checking for data ...
[2024-06-17 19:54:34.845200] Checking for data ...
[2024-06-17 19:54:35.929597] Checking for data ...
[2024-06-17 19:54:37.014398] Checking for data ...
[2024-06-17 19:54:38.102336] Checking for data ...
[2024-06-17 19:54:39.186688] Checking for data ...
[2024-06-17 19:54:40.270129] Checking for data ...
[2024-06-17 19:54:41.344924] Checking for data ...
[2024-06-17 19:54:42.433446] Checking for data ...
[2024-06-17 19:54:42.520000] Data is now available
[2024-06-17 19:54:42.520000] Retrieving data ...
[2024-06-17 19:54:42.627556] Retrieved 87.8 kB of data containing 8 records

In [12]:

# output data as a pandas dataframe
conjunctions = [c.__dict__ for c in s.data]
df = pd.DataFrame(conjunctions)
df.sort_values("start")

Out[12]:

	conjunction_type	start	end	data_sources	min_distance	max_distance	events	closest_epoch	farthest_epoch
4	nbtrace	2020-01-02T13:00:00	2020-01-02T13:01:00	[DataSource(identifier=81, program='rego', pla...	384.347496	440.461162	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-02T13:01:00	2020-01-02T13:00:00
7	nbtrace	2020-01-02T13:00:00	2020-01-02T13:01:00	[DataSource(identifier=81, program='rego', pla...	384.347496	440.461162	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-02T13:01:00	2020-01-02T13:00:00
0	nbtrace	2020-01-03T12:33:00	2020-01-03T12:35:00	[DataSource(identifier=81, program='rego', pla...	111.324353	485.848555	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-03T12:34:00	2020-01-03T12:35:00
1	nbtrace	2020-01-03T12:33:00	2020-01-03T12:35:00	[DataSource(identifier=81, program='rego', pla...	111.324353	485.848555	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-03T12:34:00	2020-01-03T12:35:00
3	nbtrace	2020-01-03T12:33:00	2020-01-03T12:34:00	[DataSource(identifier=81, program='rego', pla...	122.118464	294.065540	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-03T12:34:00	2020-01-03T12:33:00
6	nbtrace	2020-01-03T12:33:00	2020-01-03T12:34:00	[DataSource(identifier=81, program='rego', pla...	122.118464	294.065540	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-03T12:34:00	2020-01-03T12:33:00
2	nbtrace	2020-01-03T12:40:00	2020-01-03T12:42:00	[DataSource(identifier=81, program='rego', pla...	255.883772	476.507384	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-03T12:41:00	2020-01-03T12:40:00
5	nbtrace	2020-01-03T12:40:00	2020-01-03T12:42:00	[DataSource(identifier=81, program='rego', pla...	255.883772	476.507384	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-03T12:41:00	2020-01-03T12:40:00

Search for conjunctions between spacecrafts only¶

In [13]:

# search for conjunctions between Swarm A or Swarm B, and
# any THEMIS spacecraft with the south B trace region = "south polar cap"
start = datetime.datetime(2019, 1, 1, 0, 0, 0)
end = datetime.datetime(2019, 1, 1, 23, 59, 59)
space = [{
    "programs": ["themis"]
}, {
    "programs": ["swarm"],
    "platforms": ["swarma", "swarmb"],
    "hemisphere": ["southern"],
    "ephemeris_metadata_filters": {
        "logical_operator": "AND",
        "expressions": [{
            "key": "sbtrace_region",
            "operator": "=",
            "values": ["south polar cap"]
        }]
    }
}]
distance = 500

In [14]:

#perform search
s = aurorax.search.conjunctions.search(start, end, distance, space=space, verbose=True)

[2024-06-17 19:54:55.358357] Search object created
[2024-06-17 19:54:55.395953] Request submitted
[2024-06-17 19:54:55.395953] Request ID: 2b07ff59-0b40-4948-affd-7387fc858152
[2024-06-17 19:54:55.395953] Request details available at: https://api.aurorax.space/api/v1/conjunctions/requests/2b07ff59-0b40-4948-affd-7387fc858152
[2024-06-17 19:54:55.395953] Waiting for data ...
[2024-06-17 19:54:56.480359] Checking for data ...
[2024-06-17 19:54:56.566325] Data is now available
[2024-06-17 19:54:56.566325] Retrieving data ...
[2024-06-17 19:54:56.652588] Retrieved 17.3 kB of data containing 2 records

In [15]:

# output data as a pandas dataframe
conjunctions = [c.__dict__ for c in s.data]
df = pd.DataFrame(conjunctions)
df.sort_values("start")

Out[15]:

	conjunction_type	start	end	data_sources	min_distance	max_distance	events	closest_epoch	farthest_epoch
1	nbtrace	2019-01-01T10:01:00	2019-01-01T10:01:00	[DataSource(identifier=33, program='themis', p...	311.183877	311.183877	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2019-01-01T10:01:00	2019-01-01T10:01:00
0	nbtrace	2019-01-01T12:08:00	2019-01-01T12:08:00	[DataSource(identifier=33, program='themis', p...	215.817544	215.817544	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2019-01-01T12:08:00	2019-01-01T12:08:00

Do the search step-by-step¶

Under the hood, the AuroraX API performs a conjunction search asynchronously. Note that this does not mean that it can be done using a Python async method; it means that PyAuroraX does more than just a single HTTP request against the AuroraX API when doing a search. With the API operating this way, it adds some more complexity within PyAuroraX but also opens the search up to some very important capabilities. The main capability enabled by this architecture is the ablity to perform queries for a large timeframe, and/or between a large number of data sources. Queries like this can easily take several minutes. A conventional HTTP request would normally timeout because of this, ultimately failing to complete the search.

Instead of using the pyaurorax.conjunctions.search method which wraps all logic into an easy function, you can also perform a conjunction search step-by-step if you want more control over the process. Below, we do a search in this manner.

In [16]:

# set up the search parameters
start = datetime.datetime(2020, 1, 1, 0, 0, 0)
end = datetime.datetime(2020, 1, 1, 6, 59, 59)
ground = [{"programs": ["themis-asi"]}]
space = [{"programs": ["swarm"]}]
distance = 500

In [18]:

# create a Search object
s = pyaurorax.search.ConjunctionSearch(aurorax, start, end, distance, ground=ground, space=space)
print(s)

ConjunctionSearch(executed=False, completed=False, request_id='')

In [19]:

# execute the search
s.execute()
print(s)

ConjunctionSearch(executed=True, completed=False, request_id='86b5e7b0-fe87-424a-a894-dd8c80f7beaa')

In [20]:

# get request status
s.update_status()
pprint.pprint(s.status)

{'logs': [{'level': 'debug',
           'summary': 'Search request arrived and saved',
           'timestamp': '2024-06-18T01:56:33.399473'},
          {'level': 'info',
           'summary': 'Starting conjunction search for query '
                      '86b5e7b0-fe87-424a-a894-dd8c80f7beaa',
           'timestamp': '2024-06-18T01:56:33.405771'},
          {'level': 'debug',
           'summary': 'Start search for nbtrace conjunctions using query: Find '
                      'conjunctions of type (nbtrace) with epoch precision of '
                      '60 seconds between data sources of ground1=(program in '
                      '(themis-asi)) AND space1=(program in (swarm)) WHERE '
                      'epochs are between 2020-01-01T00:00:00 AND '
                      '2020-01-01T06:59:59 UTC HAVING max distances between '
                      'location points of ground1-space1=500 km.',
           'timestamp': '2024-06-18T01:56:33.408107'},
          {'level': 'debug',
           'summary': 'Querying the database 1 times for this overall '
                      'combination of criteria blocks and conjunction types. '
                      'This log message will repeat 1 times during this '
                      'search.',
           'timestamp': '2024-06-18T01:56:33.41452'},
          {'level': 'debug',
           'summary': 'Querying database for criteria combination [(program in '
                      '(themis-asi)), (program in (swarm))]',
           'timestamp': '2024-06-18T01:56:33.416734'},
          {'level': 'debug',
           'summary': 'Found 92 single epoch events',
           'timestamp': '2024-06-18T01:56:33.498374'},
          {'level': 'debug',
           'summary': 'Reduced single events into 49 continuous intervals',
           'timestamp': '2024-06-18T01:56:33.500306'},
          {'level': 'debug',
           'summary': 'Searching for super events across all data source '
                      'combinations: [[64, 3], [64, 29], [64, 30], [65, 3], '
                      '[65, 29], [65, 30], [66, 3], [66, 29], [66, 30], [71, '
                      '3], [71, 29], [71, 30], [73, 3], [73, 29], [73, 30], '
                      '[44, 3], [44, 29], [44, 30], [45, 3], [45, 29], [45, '
                      '30], [46, 3], [46, 29], [46, 30], [47, 3], [47, 29], '
                      '[47, 30], [48, 3], [48, 29], [48, 30], [49, 3], [49, '
                      '29], [49, 30], [50, 3], [50, 29], [50, 30], [51, 3], '
                      '[51, 29], [51, 30], [52, 3], [52, 29], [52, 30], [53, '
                      '3], [53, 29], [53, 30], [54, 3], [54, 29], [54, 30], '
                      '[55, 3], [55, 29], [55, 30], [56, 3], [56, 29], [56, '
                      '30], [57, 3], [57, 29], [57, 30], [58, 3], [58, 29], '
                      '[58, 30], [59, 3], [59, 29], [59, 30], [60, 3], [60, '
                      '29], [60, 30], [61, 3], [61, 29], [61, 30], [62, 3], '
                      '[62, 29], [62, 30]]',
           'timestamp': '2024-06-18T01:56:33.503857'},
          {'level': 'debug',
           'summary': 'Found 49 super events',
           'timestamp': '2024-06-18T01:56:33.507299'},
          {'level': 'debug',
           'summary': 'Saving results',
           'timestamp': '2024-06-18T01:56:33.696636'},
          {'level': 'info',
           'summary': 'Search completed',
           'timestamp': '2024-06-18T01:56:33.726311'},
          {'level': 'debug',
           'summary': 'File size is 376747 bytes.',
           'timestamp': '2024-06-18T01:56:33.728624'},
          {'level': 'info',
           'summary': 'Finished search in 0.33 seconds and found 49 records.',
           'timestamp': '2024-06-18T01:56:33.733203'}],
 'search_request': {'query': {'adhoc': [],
                              'conjunction_types': ['nbtrace'],
                              'end': '2020-01-01T06:59:59',
                              'epoch_search_precision': 60,
                              'events': [],
                              'ground': [{'ephemeris_metadata_filters': None,
                                          'instrument_types': [],
                                          'platforms': [],
                                          'programs': ['themis-asi']}],
                              'max_distances': {'ground1-space1': 500},
                              'request_id': None,
                              'space': [{'ephemeris_metadata_filters': None,
                                         'hemisphere': [],
                                         'instrument_types': [],
                                         'platforms': [],
                                         'programs': ['swarm']}],
                              'start': '2020-01-01T00:00:00'},
                    'request_id': '86b5e7b0-fe87-424a-a894-dd8c80f7beaa',
                    'request_type': 'conjunction',
                    'requested': '2024-06-18T01:56:33.390293'},
 'search_result': {'completed_timestamp': '2024-06-18T01:56:33.730679',
                   'data_uri': '/api/v1/conjunctions/requests/86b5e7b0-fe87-424a-a894-dd8c80f7beaa/data',
                   'error_condition': False,
                   'file_size': 376747,
                   'query_duration': 325,
                   'result_count': 49,
                   'result_file_deleted_timestamp': None}}

In [21]:

# view just the logs for the request (update the status beforehand, but we do that in the above cell)
pprint.pprint(s.logs)

[{'level': 'debug',
  'summary': 'Search request arrived and saved',
  'timestamp': '2024-06-18T01:56:33.399473'},
 {'level': 'info',
  'summary': 'Starting conjunction search for query '
             '86b5e7b0-fe87-424a-a894-dd8c80f7beaa',
  'timestamp': '2024-06-18T01:56:33.405771'},
 {'level': 'debug',
  'summary': 'Start search for nbtrace conjunctions using query: Find '
             'conjunctions of type (nbtrace) with epoch precision of 60 '
             'seconds between data sources of ground1=(program in '
             '(themis-asi)) AND space1=(program in (swarm)) WHERE epochs are '
             'between 2020-01-01T00:00:00 AND 2020-01-01T06:59:59 UTC HAVING '
             'max distances between location points of ground1-space1=500 km.',
  'timestamp': '2024-06-18T01:56:33.408107'},
 {'level': 'debug',
  'summary': 'Querying the database 1 times for this overall combination of '
             'criteria blocks and conjunction types. This log message will '
             'repeat 1 times during this search.',
  'timestamp': '2024-06-18T01:56:33.41452'},
 {'level': 'debug',
  'summary': 'Querying database for criteria combination [(program in '
             '(themis-asi)), (program in (swarm))]',
  'timestamp': '2024-06-18T01:56:33.416734'},
 {'level': 'debug',
  'summary': 'Found 92 single epoch events',
  'timestamp': '2024-06-18T01:56:33.498374'},
 {'level': 'debug',
  'summary': 'Reduced single events into 49 continuous intervals',
  'timestamp': '2024-06-18T01:56:33.500306'},
 {'level': 'debug',
  'summary': 'Searching for super events across all data source combinations: '
             '[[64, 3], [64, 29], [64, 30], [65, 3], [65, 29], [65, 30], [66, '
             '3], [66, 29], [66, 30], [71, 3], [71, 29], [71, 30], [73, 3], '
             '[73, 29], [73, 30], [44, 3], [44, 29], [44, 30], [45, 3], [45, '
             '29], [45, 30], [46, 3], [46, 29], [46, 30], [47, 3], [47, 29], '
             '[47, 30], [48, 3], [48, 29], [48, 30], [49, 3], [49, 29], [49, '
             '30], [50, 3], [50, 29], [50, 30], [51, 3], [51, 29], [51, 30], '
             '[52, 3], [52, 29], [52, 30], [53, 3], [53, 29], [53, 30], [54, '
             '3], [54, 29], [54, 30], [55, 3], [55, 29], [55, 30], [56, 3], '
             '[56, 29], [56, 30], [57, 3], [57, 29], [57, 30], [58, 3], [58, '
             '29], [58, 30], [59, 3], [59, 29], [59, 30], [60, 3], [60, 29], '
             '[60, 30], [61, 3], [61, 29], [61, 30], [62, 3], [62, 29], [62, '
             '30]]',
  'timestamp': '2024-06-18T01:56:33.503857'},
 {'level': 'debug',
  'summary': 'Found 49 super events',
  'timestamp': '2024-06-18T01:56:33.507299'},
 {'level': 'debug',
  'summary': 'Saving results',
  'timestamp': '2024-06-18T01:56:33.696636'},
 {'level': 'info',
  'summary': 'Search completed',
  'timestamp': '2024-06-18T01:56:33.726311'},
 {'level': 'debug',
  'summary': 'File size is 376747 bytes.',
  'timestamp': '2024-06-18T01:56:33.728624'},
 {'level': 'info',
  'summary': 'Finished search in 0.33 seconds and found 49 records.',
  'timestamp': '2024-06-18T01:56:33.733203'}]

In [22]:

# wait for the data
s.wait()
s.update_status()

In [23]:

# get data
s.get_data()

# show data as pandas dataframe
data_products = [d.__dict__ for d in s.data]
df = pd.DataFrame(data_products)
df.sort_values("start")

Out[23]:

	conjunction_type	start	end	data_sources	min_distance	max_distance	events	closest_epoch	farthest_epoch
43	nbtrace	2020-01-01T00:08:00	2020-01-01T00:09:00	[DataSource(identifier=53, program='themis-asi...	206.588207	260.819127	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:09:00	2020-01-01T00:08:00
15	nbtrace	2020-01-01T00:08:00	2020-01-01T00:09:00	[DataSource(identifier=53, program='themis-asi...	192.029390	336.631805	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:09:00	2020-01-01T00:08:00
39	nbtrace	2020-01-01T00:10:00	2020-01-01T00:11:00	[DataSource(identifier=51, program='themis-asi...	195.554240	298.419001	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:10:00	2020-01-01T00:11:00
12	nbtrace	2020-01-01T00:10:00	2020-01-01T00:11:00	[DataSource(identifier=51, program='themis-asi...	282.877438	285.744881	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:10:00	2020-01-01T00:11:00
29	nbtrace	2020-01-01T00:11:00	2020-01-01T00:13:00	[DataSource(identifier=46, program='themis-asi...	53.391843	441.814398	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:12:00	2020-01-01T00:11:00
4	nbtrace	2020-01-01T00:12:00	2020-01-01T00:13:00	[DataSource(identifier=46, program='themis-asi...	89.436436	332.998863	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:12:00	2020-01-01T00:13:00
9	nbtrace	2020-01-01T00:13:00	2020-01-01T00:15:00	[DataSource(identifier=50, program='themis-asi...	105.345328	488.349867	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:14:00	2020-01-01T00:15:00
36	nbtrace	2020-01-01T00:13:00	2020-01-01T00:14:00	[DataSource(identifier=50, program='themis-asi...	215.337413	330.343161	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:14:00	2020-01-01T00:13:00
16	nbtrace	2020-01-01T00:13:00	2020-01-01T00:13:00	[DataSource(identifier=54, program='themis-asi...	401.883399	401.883399	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:13:00	2020-01-01T00:13:00
33	nbtrace	2020-01-01T00:44:00	2020-01-01T00:45:00	[DataSource(identifier=48, program='themis-asi...	176.558673	365.537448	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:45:00	2020-01-01T00:44:00
7	nbtrace	2020-01-01T00:44:00	2020-01-01T00:46:00	[DataSource(identifier=48, program='themis-asi...	114.890751	488.203200	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:45:00	2020-01-01T00:46:00
40	nbtrace	2020-01-01T00:46:00	2020-01-01T00:47:00	[DataSource(identifier=52, program='themis-asi...	275.851379	345.489291	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:47:00	2020-01-01T00:46:00
13	nbtrace	2020-01-01T00:46:00	2020-01-01T00:47:00	[DataSource(identifier=52, program='themis-asi...	216.790371	385.043153	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:47:00	2020-01-01T00:46:00
8	nbtrace	2020-01-01T00:47:00	2020-01-01T00:48:00	[DataSource(identifier=49, program='themis-asi...	332.998445	461.144528	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:48:00	2020-01-01T00:47:00
34	nbtrace	2020-01-01T00:47:00	2020-01-01T00:48:00	[DataSource(identifier=49, program='themis-asi...	355.233113	396.151714	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:48:00	2020-01-01T00:47:00
35	nbtrace	2020-01-01T00:50:00	2020-01-01T00:51:00	[DataSource(identifier=51, program='themis-asi...	117.379897	241.904346	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:51:00	2020-01-01T00:50:00
24	nbtrace	2020-01-01T00:52:00	2020-01-01T00:52:00	[DataSource(identifier=46, program='themis-asi...	460.151231	460.151231	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:52:00	2020-01-01T00:52:00
42	nbtrace	2020-01-01T00:53:00	2020-01-01T00:53:00	[DataSource(identifier=54, program='themis-asi...	430.171520	430.171520	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:53:00	2020-01-01T00:53:00
21	nbtrace	2020-01-01T00:54:00	2020-01-01T00:55:00	[DataSource(identifier=44, program='themis-asi...	265.640223	333.568278	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T00:54:00	2020-01-01T00:55:00
28	nbtrace	2020-01-01T01:29:00	2020-01-01T01:29:00	[DataSource(identifier=47, program='themis-asi...	493.180071	493.180071	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T01:29:00	2020-01-01T01:29:00
2	nbtrace	2020-01-01T01:43:00	2020-01-01T01:45:00	[DataSource(identifier=45, program='themis-asi...	131.293608	465.392815	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T01:44:00	2020-01-01T01:43:00
25	nbtrace	2020-01-01T01:43:00	2020-01-01T01:45:00	[DataSource(identifier=45, program='themis-asi...	198.022033	490.128034	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T01:44:00	2020-01-01T01:45:00
23	nbtrace	2020-01-01T01:45:00	2020-01-01T01:46:00	[DataSource(identifier=44, program='themis-asi...	205.885002	420.474697	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T01:46:00	2020-01-01T01:45:00
0	nbtrace	2020-01-01T01:46:00	2020-01-01T01:47:00	[DataSource(identifier=44, program='themis-asi...	284.420850	493.197476	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T01:46:00	2020-01-01T01:47:00
10	nbtrace	2020-01-01T02:17:00	2020-01-01T02:18:00	[DataSource(identifier=50, program='themis-asi...	347.656216	390.934039	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T02:17:00	2020-01-01T02:18:00
37	nbtrace	2020-01-01T02:17:00	2020-01-01T02:18:00	[DataSource(identifier=50, program='themis-asi...	375.455878	459.284383	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T02:17:00	2020-01-01T02:18:00
41	nbtrace	2020-01-01T02:19:00	2020-01-01T02:20:00	[DataSource(identifier=52, program='themis-asi...	197.889134	494.591201	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T02:20:00	2020-01-01T02:19:00
14	nbtrace	2020-01-01T02:20:00	2020-01-01T02:21:00	[DataSource(identifier=52, program='themis-asi...	228.831208	480.716179	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T02:20:00	2020-01-01T02:21:00
22	nbtrace	2020-01-01T02:28:00	2020-01-01T02:29:00	[DataSource(identifier=45, program='themis-asi...	156.167998	288.063655	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T02:29:00	2020-01-01T02:28:00
6	nbtrace	2020-01-01T03:15:00	2020-01-01T03:16:00	[DataSource(identifier=47, program='themis-asi...	412.812856	489.362899	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T03:16:00	2020-01-01T03:15:00
32	nbtrace	2020-01-01T03:15:00	2020-01-01T03:16:00	[DataSource(identifier=47, program='themis-asi...	385.355069	414.231784	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T03:16:00	2020-01-01T03:15:00
47	nbtrace	2020-01-01T03:17:00	2020-01-01T03:18:00	[DataSource(identifier=64, program='themis-asi...	224.424121	433.727070	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T03:17:00	2020-01-01T03:18:00
18	nbtrace	2020-01-01T03:17:00	2020-01-01T03:18:00	[DataSource(identifier=64, program='themis-asi...	186.992979	344.140279	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T03:17:00	2020-01-01T03:18:00
17	nbtrace	2020-01-01T03:50:00	2020-01-01T03:51:00	[DataSource(identifier=54, program='themis-asi...	106.219997	351.707007	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T03:51:00	2020-01-01T03:50:00
44	nbtrace	2020-01-01T03:50:00	2020-01-01T03:51:00	[DataSource(identifier=54, program='themis-asi...	127.208169	287.958853	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T03:51:00	2020-01-01T03:50:00
30	nbtrace	2020-01-01T03:51:00	2020-01-01T03:52:00	[DataSource(identifier=46, program='themis-asi...	139.462865	365.173735	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T03:52:00	2020-01-01T03:51:00
3	nbtrace	2020-01-01T03:51:00	2020-01-01T03:52:00	[DataSource(identifier=46, program='themis-asi...	142.425348	433.674543	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T03:52:00	2020-01-01T03:51:00
11	nbtrace	2020-01-01T03:53:00	2020-01-01T03:53:00	[DataSource(identifier=51, program='themis-asi...	219.412264	219.412264	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T03:53:00	2020-01-01T03:53:00
38	nbtrace	2020-01-01T03:53:00	2020-01-01T03:53:00	[DataSource(identifier=51, program='themis-asi...	190.663238	190.663238	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T03:53:00	2020-01-01T03:53:00
27	nbtrace	2020-01-01T04:04:00	2020-01-01T04:04:00	[DataSource(identifier=47, program='themis-asi...	368.898466	368.898466	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T04:04:00	2020-01-01T04:04:00
45	nbtrace	2020-01-01T04:05:00	2020-01-01T04:06:00	[DataSource(identifier=65, program='themis-asi...	236.205457	341.436305	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T04:06:00	2020-01-01T04:05:00
48	nbtrace	2020-01-01T04:49:00	2020-01-01T04:50:00	[DataSource(identifier=65, program='themis-asi...	244.886114	355.591219	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T04:49:00	2020-01-01T04:50:00
20	nbtrace	2020-01-01T04:49:00	2020-01-01T04:50:00	[DataSource(identifier=65, program='themis-asi...	323.719101	352.655457	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T04:49:00	2020-01-01T04:50:00
1	nbtrace	2020-01-01T05:23:00	2020-01-01T05:23:00	[DataSource(identifier=45, program='themis-asi...	336.102854	336.102854	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T05:23:00	2020-01-01T05:23:00
26	nbtrace	2020-01-01T05:23:00	2020-01-01T05:23:00	[DataSource(identifier=45, program='themis-asi...	389.724805	389.724805	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T05:23:00	2020-01-01T05:23:00
19	nbtrace	2020-01-01T06:54:00	2020-01-01T06:55:00	[DataSource(identifier=64, program='themis-asi...	256.732039	421.977224	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T06:55:00	2020-01-01T06:54:00
46	nbtrace	2020-01-01T06:54:00	2020-01-01T06:55:00	[DataSource(identifier=64, program='themis-asi...	253.959112	302.213217	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T06:55:00	2020-01-01T06:54:00
31	nbtrace	2020-01-01T06:56:00	2020-01-01T06:56:00	[DataSource(identifier=47, program='themis-asi...	196.531937	196.531937	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T06:56:00	2020-01-01T06:56:00
5	nbtrace	2020-01-01T06:56:00	2020-01-01T06:56:00	[DataSource(identifier=47, program='themis-asi...	212.741402	212.741402	[{'conjunction_type': 'nbtrace', 'e1_source': ...	2020-01-01T06:56:00	2020-01-01T06:56:00

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