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EEFxTMS_2F (Equatorial electric field)

EEFxTMS_2F (Equatorial electric field)#

Abstract: Access to the equatorial electric field (level 2 product).

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

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

viresclient: 0.12.3
pandas     : 2.1.3
xarray     : 2023.12.0
matplotlib : 3.8.2

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

request = SwarmRequest()

EEFxTMS_2F product information#

Dayside equatorial electric field, sampled at every dayside equator crossing +- 20mins

Documentation:

Check what “EEF” data variables are available#

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

{'EEF': ['SW_OPER_EEFATMS_2F', 'SW_OPER_EEFBTMS_2F', 'SW_OPER_EEFCTMS_2F']}

request.available_measurements("EEF")

['EEF', 'EEJ', 'RelErr', 'Flags']

Fetch all the EEF and EEJ values from Bravo during 2016#

request.set_collection("SW_OPER_EEFBTMS_2F")
request.set_products(measurements=["EEF", "EEJ", "Flags"])
data = request.get_between(
    dt.datetime(2016,1,1),
    dt.datetime(2017,1,1)
)
Processing: 100%
[ Elapsed: 00:04, Remaining: 00:00 ] [1/1]
Downloading: 100%
[ Elapsed: 00:00, Remaining: 00:00 ] (3.808MB)
# The first three and last three source (daily) files
data.sources[:3], data.sources[-3:]

(['SW_OPER_EEFBTMS_2F_20160101T000000_20160101T235959_0305',
  'SW_OPER_EEFBTMS_2F_20160102T000000_20160102T235959_0305',
  'SW_OPER_EEFBTMS_2F_20160103T000000_20160103T235959_0305'],
 ['SW_OPER_EEFBTMS_2F_20161229T000000_20161229T235959_0305',
  'SW_OPER_EEFBTMS_2F_20161230T000000_20161230T235959_0305',
  'SW_OPER_EEFBTMS_2F_20161231T000000_20161231T235959_0305'])

df = data.as_dataframe()
df.head()
EEF Flags Latitude EEJ Spacecraft Longitude
Timestamp
2016-01-01 00:54:18.582250240 -1.003437 0 7.236889 [-7.035791632757655, -10.284245548823328, -1.6... B 113.752507
2016-01-01 02:29:05.999242240 -5.886811 0 7.519415 [7.69339913827803, 7.32175144542921, 4.9947298... B 89.978253
2016-01-01 04:03:53.439624960 -0.402845 0 6.901753 [0.21498716587741507, 11.145579690064755, 10.6... B 66.180580
2016-01-01 05:38:40.856788992 -0.184914 0 7.373078 [1.5063256962779412, 5.265722206556244, 4.6157... B 42.411229
2016-01-01 07:13:28.198109440 -0.342591 0 10.032836 [-3.2989852027098365, -0.9353105754037763, 1.7... B 18.698123 
ax = df.plot(y="EEF", figsize=(20,10))
ax.set_ylim((-2, 2));
ax.set_ylabel("EEF [mV/m]");
../_images/1caa471274ae330e3df739779b784162a5ab179788d3a2d746f040b995ab29db.png

Take a look at the time jumps between entries… Nominally the product should produce one measurement “every dayside equator crossing ±20 minutes”

times = df.index
delta_t_minutes = [t.seconds/60 for t in np.diff(times.to_pydatetime())]
print("Range of time gaps (in minutes) between successive measurements:")
np.unique(np.sort(delta_t_minutes))

Range of time gaps (in minutes) between successive measurements:

array([ 47.3       ,  47.46666667,  94.73333333,  94.75      ,
        94.76666667,  94.78333333, 142.2       , 189.48333333,
       189.5       , 189.51666667, 189.53333333, 189.55      ,
       189.56666667, 852.86666667])

Access the EEJ estimate via xarray instead of pandas#

Since the EEJ estimate has both time and latitude dimensions, it is not suited to pandas. Here we load the data as a xarray.Dataset which better handles n-dimensional data.

ds = data.as_xarray()
ds

<xarray.Dataset>
Dimensions:     (Timestamp: 5468, EEJ_QDLat: 81)
Coordinates:
  * Timestamp   (Timestamp) datetime64[ns] 2016-01-01T00:54:18.582250240 ... ...
  * EEJ_QDLat   (EEJ_QDLat) float64 -20.0 -19.5 -19.0 -18.5 ... 19.0 19.5 20.0
Data variables:
    Spacecraft  (Timestamp) object 'B' 'B' 'B' 'B' 'B' ... 'B' 'B' 'B' 'B' 'B'
    Longitude   (Timestamp) float64 113.8 89.98 66.18 ... -105.3 -129.1 -153.0
    EEF         (Timestamp) float64 -1.003 -5.887 -0.4028 ... 0.4697 0.5593
    Flags       (Timestamp) uint16 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
    Latitude    (Timestamp) float64 7.237 7.519 6.902 ... -7.72 -4.03 -0.77
    EEJ         (Timestamp, EEJ_QDLat) float64 -7.036 -10.28 ... -5.67 -7.144
Attributes:
    Sources:         ['SW_OPER_EEFBTMS_2F_20160101T000000_20160101T235959_030...
    MagneticModels:  []
    AppliedFilters:  []

Let’s select a subset (one month) and visualise it:

_ds = ds.sel({"Timestamp": "2016-01"})

fig, ax1 = plt.subplots(nrows=1, figsize=(15,3), sharex=True)
_ds.plot.scatter(x="Timestamp", y="EEJ_QDLat", hue="EEJ", vmax=10, s=1, linewidths=0, ax=ax1)

<matplotlib.collections.PathCollection at 0x7f60985b7b90>
../_images/96eebd9774a339fa26f6122be02b3a815838897a4e0f708123e6836bd1e4339a.png
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