EFIx_LP_1B (Langmuir probe 2Hz)#
Abstract: Access to the electric field instrument langmuir probe data (2Hz) (level 1b 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 matplotlib.pyplot as plt
request = SwarmRequest()
EFIx_LP_1B product information#
Measurements from the Langmuir Probe (LP) of the Electric Field Instrument (EFI) at 2Hz, for each Swarm spacecraft.
Documentation:
Check what “EFI” data variables are available#
request.available_collections("EFI", details=False)
{'EFI': ['SW_OPER_EFIA_LP_1B',
'SW_OPER_EFIB_LP_1B',
'SW_OPER_EFIC_LP_1B',
'SW_FAST_EFIA_LP_1B',
'SW_FAST_EFIB_LP_1B',
'SW_FAST_EFIC_LP_1B']}
request.available_measurements("EFI")
['U_orbit',
'Ne',
'Ne_error',
'Te',
'Te_error',
'Vs',
'Vs_error',
'Flags_LP',
'Flags_Ne',
'Flags_Te',
'Flags_Vs']
Fetch one day of EFI data#
request.set_collection("SW_OPER_EFIA_LP_1B")
request.set_products(
measurements=['U_orbit',
'Ne',
'Ne_error',
'Te',
'Te_error',
'Vs',
'Vs_error',
'Flags_LP',
'Flags_Ne',
'Flags_Te',
'Flags_Vs']
)
data = request.get_between(
dt.datetime(2016,1,1),
dt.datetime(2016,1,2)
)
data.sources
['SW_OPER_EFIA_LP_1B_20160101T000000_20160101T235959_0602_MDR_EFI_LP']
Load and plot using pandas/matplotlib#
df = data.as_dataframe()
df.head()
| Flags_Ne | Vs_error | Ne | Flags_Te | U_orbit | Vs | Te | Ne_error | Te_error | Flags_Vs | Flags_LP | Radius | Latitude | Spacecraft | Longitude | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Timestamp | |||||||||||||||
| 2016-01-01 00:00:00.196999936 | 10 | 9.999990e+09 | 126188.4 | 10 | 7604.407 | -2.201 | 2945.204213 | 28365.234486 | -245.895850 | 20 | 1 | 6833853.41 | -72.511716 | A | 92.799630 |
| 2016-01-01 00:00:00.696000000 | 10 | 9.999990e+09 | 127792.9 | 10 | 7604.397 | -2.193 | 2891.969742 | 28725.923124 | -259.711123 | 20 | 1 | 6833854.00 | -72.543238 | A | 92.813943 |
| 2016-01-01 00:00:01.196999936 | 10 | 9.999990e+09 | 132515.3 | 10 | 7604.388 | -2.200 | 2921.955198 | 29787.432673 | -247.826218 | 20 | 1 | 6833854.59 | -72.574886 | A | 92.828370 |
| 2016-01-01 00:00:01.696000000 | 10 | 9.999990e+09 | 137933.0 | 10 | 7604.379 | -2.194 | 2936.736557 | 31005.243307 | -239.714158 | 20 | 1 | 6833855.18 | -72.606406 | A | 92.842799 |
| 2016-01-01 00:00:02.196999936 | 10 | 9.999990e+09 | 138913.8 | 10 | 7604.370 | -2.190 | 2870.090847 | 31225.718904 | -257.759606 | 20 | 1 | 6833855.77 | -72.638051 | A | 92.857342 |
df.plot(y=["Ne", "Te", "Vs"], subplots=True, figsize=(20,5));
df.plot(x="Latitude", y="Ne");
Load as xarray#
ds = data.as_xarray()
ds
<xarray.Dataset>
Dimensions: (Timestamp: 172776)
Coordinates:
* Timestamp (Timestamp) datetime64[ns] 2016-01-01T00:00:00.196999936 ... ...
Data variables: (12/15)
Spacecraft (Timestamp) object 'A' 'A' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A' 'A'
Flags_Ne (Timestamp) uint8 10 10 10 10 10 10 10 ... 10 10 10 10 10 10 10
Vs_error (Timestamp) float64 1e+10 1e+10 1e+10 ... 1e+10 1e+10 1e+10
Ne_error (Timestamp) float64 2.837e+04 2.873e+04 ... 1.458e+04 1.445e+04
Flags_LP (Timestamp) uint8 1 1 1 1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1 1 1
Latitude (Timestamp) float64 -72.51 -72.54 -72.57 ... 31.65 31.69 31.72
... ...
U_orbit (Timestamp) float64 7.604e+03 7.604e+03 ... 7.634e+03 7.634e+03
Vs (Timestamp) float64 -2.201 -2.193 -2.2 ... -2.244 -2.239 -2.243
Te (Timestamp) float64 2.945e+03 2.892e+03 ... 2.527e+03 2.545e+03
Flags_Vs (Timestamp) uint8 20 20 20 20 20 20 20 ... 20 20 20 20 20 20 20
Te_error (Timestamp) float64 -245.9 -259.7 -247.8 ... -408.5 -403.6
Radius (Timestamp) float64 6.834e+06 6.834e+06 ... 6.823e+06 6.823e+06
Attributes:
Sources: ['SW_OPER_EFIA_LP_1B_20160101T000000_20160101T235959_060...
MagneticModels: []
AppliedFilters: []fig, (ax1, ax2) = plt.subplots(figsize=(10, 5), nrows=2, sharex=True)
def subplot(_ax, da):
"""Plot a given DataArray on a given axes"""
_ax.plot(da)
_ax.set_ylabel(f"{da.name} [{da.units}]")
for var, ax in zip(("Ne", "Te"), (ax1, ax2)):
subplot(ax, ds[var])
