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TECxTMS_2F (Total electron content)

TECxTMS_2F (Total electron content)#

Abstract: Access to the total electric contents (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 pandas as pd
import matplotlib.pyplot as plt

request = SwarmRequest()

TECxTMS_2F product information#

Derived total electron content (TEC)

Documentation:

Check what “TEC” data variables are available#

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

{'IPD': ['SW_OPER_IPDAIRR_2F', 'SW_OPER_IPDBIRR_2F', 'SW_OPER_IPDCIRR_2F']}

request.available_measurements("TEC")

['GPS_Position',
 'LEO_Position',
 'PRN',
 'L1',
 'L2',
 'P1',
 'P2',
 'S1',
 'S2',
 'Elevation_Angle',
 'Absolute_VTEC',
 'Absolute_STEC',
 'Relative_STEC',
 'Relative_STEC_RMS',
 'DCB',
 'DCB_Error']

Fetch one day of TEC data#

request.set_collection("SW_OPER_TECATMS_2F")
request.set_products(measurements=request.available_measurements("TEC"))
data = request.get_between(dt.datetime(2014,1,1),
                           dt.datetime(2014,1,2))

Loading as pandas#

df = data.as_dataframe()
df.head()
Relative_STEC S1 DCB_Error Radius Latitude GPS_Position Elevation_Angle Absolute_VTEC LEO_Position P2 Spacecraft DCB L2 S2 Absolute_STEC P1 PRN Longitude Relative_STEC_RMS L1
Timestamp
2014-01-01 00:00:04 24.041127 36.83 0.832346 6.878338e+06 -1.482419 [22448765.690377887, 5421379.431197803, -13409... 39.683333 10.894886 [6668214.552999999, -1677732.678, -177943.998] 2.182914e+07 A -11.446853 -6.261989e+06 36.83 16.429938 2.182913e+07 15 -14.122572 0.555675 -6.261986e+06
2014-01-01 00:00:04 22.754795 34.75 0.832346 6.878338e+06 -1.482419 [16113499.491062254, -16306172.004347403, -126... 37.478137 10.461554 [6668214.552999999, -1677732.678, -177943.998] 2.135749e+07 A -11.446853 -1.466568e+07 34.75 16.456409 2.135749e+07 18 -14.122572 0.488000 -1.466568e+07
2014-01-01 00:00:04 15.585271 30.90 0.832346 6.878338e+06 -1.482419 [10823457.339250825, -24014739.352816023, -248... 24.681787 9.529846 [6668214.552999999, -1677732.678, -177943.998] 2.277638e+07 A -11.446853 -5.455454e+06 30.90 20.434286 2.277638e+07 22 -14.122572 1.313984 -5.455452e+06
2014-01-01 00:00:04 52.291341 29.87 0.832346 6.878338e+06 -1.482419 [20631539.59055339, 13441368.439225309, 100505... 24.647445 9.357587 [6668214.552999999, -1677732.678, -177943.998] 2.298847e+07 A -11.446853 -3.402821e+06 29.87 20.085094 2.298846e+07 24 -14.122572 0.899036 -3.402816e+06
2014-01-01 00:00:04 52.672067 30.88 0.832346 6.878338e+06 -1.482419 [16637723.905075422, -10692759.977004562, 1761... 30.753636 8.597559 [6668214.552999999, -1677732.678, -177943.998] 2.229900e+07 A -11.446853 -2.285986e+06 30.88 15.676947 2.229899e+07 25 -14.122572 0.546729 -2.285981e+06

NB: The time interval is not always the same:

times = df.index
np.unique(np.sort(np.diff(times.to_pydatetime())))

array([datetime.timedelta(0), datetime.timedelta(seconds=10)],
      dtype=object)

len(df), 60*60*24

(49738, 86400)

Loading and plotting as xarray#

ds = data.as_xarray()
ds

<xarray.Dataset>
Dimensions:            (Timestamp: 49738, WGS84: 3)
Coordinates:
  * Timestamp          (Timestamp) datetime64[ns] 2014-01-01T00:00:04 ... 201...
  * WGS84              (WGS84) <U1 'X' 'Y' 'Z'
Data variables: (12/20)
    Spacecraft         (Timestamp) object 'A' 'A' 'A' 'A' ... 'A' 'A' 'A' 'A'
    Relative_STEC      (Timestamp) float64 24.04 22.75 15.59 ... 16.92 26.51
    S1                 (Timestamp) float64 36.83 34.75 30.9 ... 23.03 37.73 37.5
    DCB_Error          (Timestamp) float64 0.8323 0.8323 ... 0.8323 0.8323
    Radius             (Timestamp) float64 6.878e+06 6.878e+06 ... 6.88e+06
    Latitude           (Timestamp) float64 -1.482 -1.482 -1.482 ... -81.7 -81.7
    ...                 ...
    Absolute_STEC      (Timestamp) float64 16.43 16.46 20.43 ... 10.77 10.78
    P1                 (Timestamp) float64 2.183e+07 2.136e+07 ... 2.171e+07
    PRN                (Timestamp) uint16 15 18 22 24 25 29 ... 7 15 16 18 21 26
    Longitude          (Timestamp) float64 -14.12 -14.12 -14.12 ... 1.559 1.559
    Relative_STEC_RMS  (Timestamp) float64 0.5557 0.488 1.314 ... 0.6458 3.041
    L1                 (Timestamp) float64 -6.262e+06 -1.467e+07 ... -3.41e+06
Attributes:
    Sources:         ['SW_OPER_TECATMS_2F_20140101T000000_20140101T235959_0401']
    MagneticModels:  []
    AppliedFilters:  []
fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(15,5), sharex=True)
ds["Absolute_VTEC"].plot.line(x="Timestamp", ax=axes[0])
ds["Absolute_STEC"].plot.line(x="Timestamp", ax=axes[1]);
fig.subplots_adjust(hspace=0)
../_images/0cdfe7e36d437289467ed0bd4dd97b079d78579b58618d2370b5a81cb9c7ef04.png
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