Note

Notebook source repo: https://github.com/Swarm-DISC/Swarm_notebooks
Notebook name: 03g__Demo-IBIxTMS_2F.ipynb
(download .ipynb)
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Demo IBIxTMS_2F (ionospheric bubble index)¶

Authors: Ashley Smith

Abstract: Access to the ionospheric bubble index (level 2 product).

[1]:

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

2020-03-12T14:47:23+00:00

CPython 3.7.6
IPython 7.11.1

viresclient 0.6.0
pandas 0.25.3
xarray 0.15.0
matplotlib 3.1.2

[2]:

from viresclient import SwarmRequest
import datetime as dt
import numpy as np

request = SwarmRequest()

IBIxTMS_2F product information¶

Ionospheric bubble index at 1Hz

Documentation: - https://earth.esa.int/web/guest/missions/esa-eo-missions/swarm/data-handbook/level-2-product-definitions#IBIxTMS_2F

Check what “IBI” data variables are available¶

[3]:

request.available_collections("IBI", details=False)

[3]:

{'IBI': ['SW_OPER_IBIATMS_2F', 'SW_OPER_IBIBTMS_2F', 'SW_OPER_IBICTMS_2F']}

[4]:

request.available_measurements("IBI")

[4]:

['Bubble_Index',
 'Bubble_Probability',
 'Flags_Bubble',
 'Flags_F',
 'Flags_B',
 'Flags_q']

Fetch 3 minutes of IBI data¶

[5]:

request = SwarmRequest()
request.set_collection("SW_OPER_IBIATMS_2F")
request.set_products(measurements=request.available_measurements("IBI"))
data = request.get_between(
    dt.datetime(2016,1,1,9,7),
    dt.datetime(2016,1,1,9,10)
)

[1/1] Processing:  100%|███████████████████████████████████████████████████|  [ Elapsed: 00:01, Remaining: 00:00 ]
      Downloading: 100%|█████████████████████████████████████████|  [ Elapsed: 00:00, Remaining: 00:00 ] (0.096MB)

[6]:

data.sources

[6]:

['SW_OPER_IBIATMS_2F_20160101T000000_20160101T235959_0301']

[7]:

df = data.as_dataframe()
df.head()

[7]:

	Radius	Flags_Bubble	Flags_B	Latitude	Bubble_Probability	Longitude	Spacecraft	Flags_q	Flags_F	Bubble_Index
Timestamp
2016-01-01 09:07:00	6830079.62	0	0	-16.923659	0.0	-52.119907	A	5	1	0
2016-01-01 09:07:01	6830087.28	0	0	-16.987667	0.0	-52.120848	A	5	1	0
2016-01-01 09:07:02	6830094.93	0	0	-17.051675	0.0	-52.121786	A	5	1	0
2016-01-01 09:07:03	6830102.57	0	0	-17.115682	0.0	-52.122722	A	5	1	0
2016-01-01 09:07:04	6830110.21	0	0	-17.179689	0.0	-52.123656	A	5	1	0

[8]:

df.plot(y=["Bubble_Index", "Bubble_Probability"], figsize=(20,5));

../_images/Swarm_notebooks_03g__Demo-IBIxTMS_2F_11_0.png

[9]:

ds = data.as_xarray()
ds

[9]:

<xarray.Dataset>
Dimensions:             (Timestamp: 180)
Coordinates:
  * Timestamp           (Timestamp) datetime64[ns] 2016-01-01T09:07:00 ... 2016-01-01T09:09:59
Data variables:
    Spacecraft          (Timestamp) object 'A' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A'
    Radius              (Timestamp) float64 6.83e+06 6.83e+06 ... 6.831e+06
    Flags_Bubble        (Timestamp) uint8 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0
    Flags_B             (Timestamp) uint8 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0
    Latitude            (Timestamp) float64 -16.92 -16.99 ... -28.31 -28.38
    Bubble_Probability  (Timestamp) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
    Longitude           (Timestamp) float64 -52.12 -52.12 ... -52.24 -52.24
    Flags_q             (Timestamp) uint8 5 5 5 5 5 5 5 5 5 ... 5 5 5 5 5 5 5 5
    Flags_F             (Timestamp) uint8 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1
    Bubble_Index        (Timestamp) int8 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0
Attributes:
    Sources:         ['SW_OPER_IBIATMS_2F_20160101T000000_20160101T235959_0301']
    MagneticModels:  []
    RangeFilters:    []