Demo IBIxTMS_2F (ionospheric bubble index)¶
Authors: Ashley Smith
Abstract: Access to the ionospheric bubble index (level 2 product).
%load_ext watermark
%watermark -i -v -p viresclient,pandas,xarray,matplotlib
2021-01-24T15:47:39+00:00
CPython 3.7.6
IPython 7.11.1
viresclient 0.7.1
pandas 0.25.3
xarray 0.15.0
matplotlib 3.1.2
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¶
request.available_collections("IBI", details=False)
{'IBI': ['SW_OPER_IBIATMS_2F', 'SW_OPER_IBIBTMS_2F', 'SW_OPER_IBICTMS_2F']}
request.available_measurements("IBI")
['Bubble_Index',
'Bubble_Probability',
'Flags_Bubble',
'Flags_F',
'Flags_B',
'Flags_q']
Fetch 3 minutes of IBI data¶
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)
data.sources
['SW_OPER_IBIATMS_2F_20160101T000000_20160101T235959_0301']
df = data.as_dataframe()
df.head()
| Spacecraft | Flags_q | Longitude | Radius | Bubble_Probability | Flags_Bubble | Latitude | Flags_B | Flags_F | Bubble_Index | |
|---|---|---|---|---|---|---|---|---|---|---|
| Timestamp | ||||||||||
| 2016-01-01 09:07:00 | A | 5 | -52.119907 | 6830079.62 | 0.0 | 0 | -16.923659 | 0 | 1 | 0 |
| 2016-01-01 09:07:01 | A | 5 | -52.120848 | 6830087.28 | 0.0 | 0 | -16.987667 | 0 | 1 | 0 |
| 2016-01-01 09:07:02 | A | 5 | -52.121786 | 6830094.93 | 0.0 | 0 | -17.051675 | 0 | 1 | 0 |
| 2016-01-01 09:07:03 | A | 5 | -52.122722 | 6830102.57 | 0.0 | 0 | -17.115682 | 0 | 1 | 0 |
| 2016-01-01 09:07:04 | A | 5 | -52.123656 | 6830110.21 | 0.0 | 0 | -17.179689 | 0 | 1 | 0 |
df.plot(y=["Bubble_Index", "Bubble_Probability"], figsize=(20,5));
ds = data.as_xarray()
ds
<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'
Flags_q (Timestamp) uint8 5 5 5 5 5 5 5 5 5 ... 5 5 5 5 5 5 5 5
Longitude (Timestamp) float64 -52.12 -52.12 ... -52.24 -52.24
Radius (Timestamp) float64 6.83e+06 6.83e+06 ... 6.831e+06
Bubble_Probability (Timestamp) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
Flags_Bubble (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
Flags_B (Timestamp) uint8 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0
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: []